From 6092e064592ea0d8a7615a5bdd92ac60ec694928 Mon Sep 17 00:00:00 2001 From: Roger Labbe Date: Sat, 18 Jul 2015 08:43:24 -0700 Subject: [PATCH] Better code formatting with ``python There is more than that here. I had a bad commit of a bunch of temporary files, I had to reset back in time, and now I am doing a massive commit. Sorry. --- 01-g-h-filter.ipynb | 56 +- 02-Discrete-Bayes.ipynb | 23 +- 03-Gaussians.ipynb | 24 +- 04-One-Dimensional-Kalman-Filters.ipynb | 179 ++--- 05-Multivariate-Gaussians.ipynb | 4 +- 06-Multivariate-Kalman-Filters.ipynb | 62 +- 07-Kalman-Filter-Math.ipynb | 36 +- 08-Designing-Kalman-Filters.ipynb | 14 +- 09-Nonlinear-Filtering.ipynb | 4 +- 10-Unscented-Kalman-Filter.ipynb | 817 +++++++++++------------ 11-Extended-Kalman-Filters.ipynb | 41 +- 12-Particle-Filters.ipynb | 341 +++++----- 13-Smoothing.ipynb | 27 +- 14-Adaptive-Filtering.ipynb | 33 +- Appendix-E-Ensemble-Kalman-Filters.ipynb | 89 +-- Interactions.ipynb | 8 +- code/nonlinear_plots.py | 2 +- code/ukf_internal.py | 129 +++- pdf/clean_book | 2 + pdf/clean_book.bat | 6 +- pdf/make_chapter.bat | 5 + pdf/to_pdf.py | 10 +- 22 files changed, 1022 insertions(+), 890 deletions(-) create mode 100644 pdf/make_chapter.bat diff --git a/01-g-h-filter.ipynb b/01-g-h-filter.ipynb index 58ef0ee..06ff13f 100644 --- a/01-g-h-filter.ipynb +++ b/01-g-h-filter.ipynb @@ -750,16 +750,19 @@ "source": [ "Now let's try a randomly chosen number to scale our estimate: $\\frac{4}{10}$. Our estimate will be four tenths the measurement and the rest will be from the prediction. In other words, we are expressing a belief here, a belief that the prediction is somewhat more likely to be correct than the measurement. We compute that as\n", "\n", - "$$ new\\_estimate = prediction + \\frac{4}{10}(measurement - prediction)\n", - "$$\n", + "$$\\mathtt{new\\_estimate} = \\mathtt{prediction} + \\frac{4}{10}(\\mathtt{measurement} - \\mathtt{prediction})$$\n", "\n", "The difference between the measurement and prediction is called the *residual*, which is depicted by the black vertical line in the plot above. This will become an important value to use later on, as it is an exact computation of the difference between measurements and the filter's output. Smaller residuals imply better performance.\n", "\n", + "$$\\begin{cases}\n", + "x=0 & \\text{hi there} \\\\\n", + "x = 1 & \\text{good bye}\n", + "\\end{cases}$$\n", "Let's code that and see the results when we test it against the series of weights from above. We have to take into account one other factor. Weight gain has units of lbs/time, so to be general we will need to add a time step $t$, which we will set to 1 (day). \n", "\n", "I hand generated the weight data to correspond to a true starting weight of 160 lbs, and a weight gain of 1 lb per day. In other words on the first day (day zero) the true weight is 160lbs, on the second day (day one, the first day of weighing) the true weight is 161 lbs, and so on. \n", "\n", - "We need to make a guess for the initial weight to feed into the filter. It is too early to talk about strategies for making that initial guess/estimate, so for now I will assume 159 lbs." + "We need to make a guess for the initial weight. It is too early to talk about initialization strategies, so for now I will assume 159 lbs." ] }, { @@ -895,7 +898,7 @@ "\n", "So, should we set the new gain/day to 4.4 lbs? Yesterday we though the weight gain was 1 lb, today we think it is 4.4 lbs. We have two numbers, and want to combine them somehow. Hmm, sounds like our same problem again. Let's use our same tool, and the only tool we have so far - pick a value part way between the two. This time I will use another arbitrarily chosen number, $\\frac{1}{3}$. The equation is identical as for the weight estimate except we have to incorporate time because this is a rate (gain/day):\n", "\n", - "$$new gain = old gain + \\frac{1}{3}\\frac{measurement - predicted~weight}{1~ day}\n", + "$$\\text{new gain} = \\text{old gain} + \\frac{1}{3}\\frac{\\text{measurement - predicted weight}}{1 \\text{ day}}\n", "$$" ] }, @@ -964,7 +967,8 @@ "\n", "One final point before we go on. In the prediction step I wrote the line\n", "\n", - " gain_rate = gain_rate\n", + "```python\n", + "gain_rate = gain_rate```\n", " \n", "This obviously has no effect, and can be removed. I wrote this to emphasize that in the prediction step you need to predict next value for **all** variables, both *weight* and *gain_rate*. In this case we are assuming that the the gain does not vary, but when we generalize this algorithm we will remove that assumption. " ] @@ -1385,17 +1389,18 @@ "source": [ "In the example above, I explicitly coded this to solve the weighing problem that we've been discussing throughout the chapter. For example, the variables are named \"weight_scale\", \"gain\", and so on. I did this to make the algorithm easy to follow - you can easily see that we correctly implemented each step. But, that is code written for exactly one problem, and the algorithm is the same for any problem. So let's rewrite the code to be generic - to work with any problem. Use this function signature:\n", "\n", - " def g_h_filter(data, x0, dx, g, h, dt):\n", - " \"\"\"\n", - " Performs g-h filter on 1 state variable with a fixed g and h.\n", + "```python\n", + "def g_h_filter(data, x0, dx, g, h, dt):\n", + " \"\"\"\n", + " Performs g-h filter on 1 state variable with a fixed g and h.\n", "\n", - " 'data' contains the data to be filtered.\n", - " 'x0' is the initial value for our state variable\n", - " 'dx' is the initial change rate for our state variable\n", - " 'g' is the g-h's g scale factor\n", - " 'h' is the g-h's h scale factor\n", - " 'dt' is the length of the time step \n", - " \"\"\"\n", + " 'data' contains the data to be filtered.\n", + " 'x0' is the initial value for our state variable\n", + " 'dx' is the initial change rate for our state variable\n", + " 'g' is the g-h's g scale factor\n", + " 'h' is the g-h's h scale factor\n", + " 'dt' is the length of the time step \n", + " \"\"\"```\n", "\n", "Return the data as a NumPy array, not a list. Test it by passing in the same weight data as before, plot the results, and visually determine that it works." ] @@ -2074,18 +2079,21 @@ "\n", "First, let's simulate the situation without a filter. We will assume that the train is currently at kilometer 23, and moving at 15 m/s. We can code this as \n", "\n", - " pos = 23*1000\n", - " vel = 15\n", - " \n", + "```python\n", + "pos = 23*1000\n", + "vel = 15```\n", + "\n", "Now we can compute the position of the train at some future time, *assuming* no change in velocity, with\n", "\n", - " def compute_new_position(pos, vel, dt=1):\n", - " return pos + (vel * dt)\n", - " \n", + "```python\n", + "def compute_new_position(pos, vel, dt=1):\n", + " return pos + (vel * dt)```\n", + "\n", "We can simulate the measurement by adding in some random noise to the position. Here our error is 500m, so the code might look like:\n", "\n", - " def measure_position(pos):\n", - " return pos + random.randn()*500\n", + "```python\n", + "def measure_position(pos):\n", + " return pos + random.randn()*500```\n", " \n", "Let's put that in a cell and plot the results of 100 seconds of simulation. I will use NumPy's `asarray` function to convert the data into an NumPy array. This will allow me to divide all of the elements of the array at once by using the '/' operator." ] @@ -2564,7 +2572,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.3" + "version": "3.4.1" } }, "nbformat": 4, diff --git a/02-Discrete-Bayes.ipynb b/02-Discrete-Bayes.ipynb index 404e30c..886c3f0 100644 --- a/02-Discrete-Bayes.ipynb +++ b/02-Discrete-Bayes.ipynb @@ -1761,13 +1761,13 @@ "$$P(x_i|Z) = \\frac{P(Z|x_i) P(x_i)}{P(Z)}$$\n", "\n", "That looks ugly, but it is actually quite simple. Let's figure out what each term on the right means. First is $P(Z|x_i)$. This is the probability for the measurement at every cell $x_i$. $P(x_i)$ is the *prior* - our belief before incorporating the measurements. We multiply those together. This is just the unnormalized multiplication in the `update()` function, where `belief` is our prior $P(x_i)$.\n", - " \n", "\n", - " for i, val in enumerate(map_):\n", - " if val == z:\n", - " belief[i] *= correct_scale\n", - " else:\n", - " belief[i] *= 1.\n", + "```python\n", + "for i, val in enumerate(map_):\n", + " if val == z:\n", + " belief[i] *= correct_scale\n", + " else:\n", + " belief[i] *= 1.```\n", "\n", "I added the `else` here, which has no mathematical effect, to point out that every element in $x$ (called `belief` in the code) is multiplied by a probability. You may object that I am multiplying by a scale factor, which I am, but this scale factor is derived from the probability of the measurement being correct vs the probability being incorrect.\n", "\n", @@ -1792,10 +1792,11 @@ "\n", "That equation is called the *total probability theorem*. Quoting from Wikipedia [6] \"It expresses the total probability of an outcome which can be realized via several distinct events\". I could have given you that equation and implemented `predict()`, but your chances of understanding why the equation works would be slim. As a reminder, here is the code that computes this equation\n", "\n", - " for i in range(N):\n", - " for k in range (kN):\n", - " index = (i + (width-k) - offset) % N\n", - " result[i] += prob_dist[index] * kernel[k]" + "```python\n", + "for i in range(N):\n", + " for k in range (kN):\n", + " index = (i + (width-k) - offset) % N\n", + " result[i] += prob_dist[index] * kernel[k]```" ] }, { @@ -1882,7 +1883,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.3" + "version": "3.4.1" } }, "nbformat": 4, diff --git a/03-Gaussians.ipynb b/03-Gaussians.ipynb index 588e0c5..a2fcc8c 100644 --- a/03-Gaussians.ipynb +++ b/03-Gaussians.ipynb @@ -907,14 +907,15 @@ "\n", "> **Optional:** Let's remind ourselves how to look at a function stored in a file by using the *%load* magic. If you type *%load -s gaussian stats.py* into a code cell and then press CTRL-Enter, the notebook will create a new input cell and load the function into it.\n", "\n", - " %load -s gaussian stats.py\n", - " \n", - " def gaussian(x, mean, var):\n", - " \"\"\"returns normal distribution for x given a \n", - " gaussian with the specified mean and variance. \n", - " \"\"\"\n", - " return np.exp((-0.5*(x-mean)**2)/var) / \\\n", - " np.sqrt(_two_pi*var)" + "```python\n", + "%load -s gaussian stats.py\n", + "\n", + "def gaussian(x, mean, var):\n", + " \"\"\"returns normal distribution for x given a \n", + " gaussian with the specified mean and variance. \n", + " \"\"\"\n", + " return np.exp((-0.5*(x-mean)**2)/var) / \\\n", + " np.sqrt(_two_pi*var)```" ] }, { @@ -1005,7 +1006,7 @@ "\n", "The standard notation for a normal distribution for a random variable $X$ is $X \\sim\\ \\mathcal{N}(\\mu,\\sigma^2)$ where $\\sim$ means *distributed according to*. This means I can express the temperature reading of our thermometer as\n", "\n", - "$$temp \\sim \\mathcal{N}(22,4)$$\n", + "$$\\text{temp} \\sim \\mathcal{N}(22,4)$$\n", "\n", "This is an **extremely important** result. Gaussians allow me to capture an infinite number of possible values with only two numbers! With the values $\\mu=22$ and $\\sigma^2=4$ I can compute the distribution of measurements for over any range." ] @@ -1538,7 +1539,8 @@ "\n", "The code for rand_student_t is included in `filterpy.stats`. You may use it with\n", "\n", - " from filterpy.stats import rand_student_t" + "```python\n", + "from filterpy.stats import rand_student_t```" ] }, { @@ -1599,7 +1601,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.3" + "version": "3.4.1" } }, "nbformat": 4, diff --git a/04-One-Dimensional-Kalman-Filters.ipynb b/04-One-Dimensional-Kalman-Filters.ipynb index 710592e..62f1fcb 100644 --- a/04-One-Dimensional-Kalman-Filters.ipynb +++ b/04-One-Dimensional-Kalman-Filters.ipynb @@ -271,15 +271,6 @@ "load_style()" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -360,13 +351,15 @@ "\n", "Our model assumes that the velocity of our dog is constant, but we acknowledged that things like hills and obstacles make that very unlikely to be true. We can simulate this by adding a randomly generated value to the velocity each time we compute the dog's position.\n", "\n", - " noisy_vel = vel + randn()*velocity_std\n", - " x = x + noisy_vel*dt\n", + "```python\n", + "noisy_vel = vel + randn()*velocity_std\n", + "x = x + noisy_vel*dt```\n", " \n", "\n", "To model the sensor we need to take the current position of the dog and then add some noise to it proportional to the Gaussian of the measurement noise. This is simply\n", "\n", - " z = x + randn()*measurement_std\n", + "```python\n", + "z = x + randn()*measurement_std```\n", " \n", "I don't like the bookkeeping required to keep track of the various values for position, velocity, and standard deviations so I will implement the simulation as a class. " ] @@ -732,25 +725,28 @@ "\n", "Recall the discrete Bayes code for adding a measurement to a preexisting belief:\n", "\n", - " def update(pos, measure, p_hit, p_miss):\n", - " q = array(pos, dtype=float)\n", - " for i in range(len(hallway)):\n", - " if hallway[i] == measure:\n", - " q[i] = pos[i] * p_hit\n", - " else:\n", - " q[i] = pos[i] * p_miss\n", - " normalize(q)\n", - " return q\n", + "```python\n", + "def update(pos, measure, p_hit, p_miss):\n", + " q = array(pos, dtype=float)\n", + " for i in range(len(hallway)):\n", + " if hallway[i] == measure:\n", + " q[i] = pos[i] * p_hit\n", + " else:\n", + " q[i] = pos[i] * p_miss\n", + " normalize(q)\n", + " return q```\n", " \n", "Note that the algorithm is essentially computing:\n", "\n", - " new_belief = prior_belief * measurement * sensor_error\n", + "```python\n", + "new_belief = prior_belief * measurement * sensor_error```\n", " \n", "The measurement term might not be obvious, but recall that measurement in this case was always 1 or 0, and so it was left out for convenience. Here *prior* carries the both the colloquial sense of *previoius*, but also the Bayesian meaning. In Bayesian terms the *prior* is the probability after the prediction and before the measurements have been incorporated.\n", " \n", "If we are implementing this with Gaussians, we might expect it to be implemented as:\n", "\n", - " new_gaussian = measurement * old_gaussian\n", + "```python\n", + "new_gaussian = measurement * old_gaussian```\n", " \n", "where measurement is a Gaussian returned from the sensor. But does that make sense? Can we multiply Gaussians? If we multiply a Gaussian with a Gaussian is the result another Gaussian, or something else?" ] @@ -1067,9 +1063,10 @@ "source": [ "Perhaps this would be clearer if we used more specific names:\n", "\n", - " def update_dog(dog_pos, dog_variance, measurement, measurement_variance):\n", - " return multiply(dog_pos, dog_variance, \n", - " measurement, measurement_variance)" + "```python\n", + "def update_dog(dog_pos, dog_variance, measurement, measurement_variance):\n", + " return multiply(dog_pos, dog_variance, \n", + " measurement, measurement_variance)```" ] }, { @@ -1155,17 +1152,17 @@ "\n", "How do we perform the predict function with Gaussians? Recall the discrete Bayes method:\n", "\n", - " def predict(pos, move, p_correct, p_under, p_over):\n", - " n = len(pos)\n", - " result = array(pos, dtype=float)\n", - " for i in range(n):\n", - " result[i] = \\\n", - " pos[(i-move) % n] * p_correct + \\\n", - " pos[(i-move-1) % n] * p_over + \\\n", - " pos[(i-move+1) % n] * p_under \n", - " return result\n", - " \n", - " \n", + "```python\n", + "def predict(pos, move, p_correct, p_under, p_over):\n", + " n = len(pos)\n", + " result = array(pos, dtype=float)\n", + " for i in range(n):\n", + " result[i] = \\\n", + " pos[(i-move) % n] * p_correct + \\\n", + " pos[(i-move-1) % n] * p_over + \\\n", + " pos[(i-move+1) % n] * p_under \n", + " return result```\n", + "\n", "In a nutshell, we shift the probability vector by the amount we believe the animal moved, and adjust the probability. How do we do that with Gaussians?\n", "\n", "It turns out that we add them. Think of the case without Gaussians. I think my dog is at 7.3 meters, and he moves 2.6 meters to right, where is he now? Obviously, 7.3 + 2.6 = 9.9. He is at 9.9 meters. Abstractly, the algorithm is `new_pos = old_pos + dist_moved`. It does not matter if we use floating point numbers or Gaussians for these values, the algorithm must be the same. \n", @@ -1329,17 +1326,19 @@ "source": [ "Now let's walk through the code and output.\n", "\n", - " pos = (0., 400.) # gaussian N(0, 400)\n", - "\n", + "```python\n", + "pos = (0., 400.) # gaussian N(0, 400)```\n", " \n", "Here we set the initial position at 0 m. We set the variance to 400 m$^2$, which is a standard deviation of 20 meters. You can think of this as saying \"I believe with 99% accuracy the position is 0 plus or minus 60 meters\". This is because with Gaussians ~99% of values fall within 3$\\sigma$ of the mean.\n", "\n", - " velocity = 1.\n", + "```python\n", + "velocity = 1.```\n", " \n", "So how do I know the velocity? Magic? Consider it a prediction, or perhaps we have a secondary velocity sensor. If this is a robot then this would be a control input to the robot. In the next chapter we will learn how to handle situations where you don't have a velocity sensor or input, so please accept this simplification for now.\n", "\n", - " process_variance = 1.\n", - " sensor_variance = 2.\n", + "```python\n", + "process_variance = 1.\n", + "sensor_variance = 2.```\n", " \n", "Here the variance for the predict step is `process_variance` and the variance for the sensor is `sensor_variance`. The meaning of sensor variance should be clear - it is how much variance there is in each sensor reading. The process variance is how much error there is in the prediction model. We are predicting that at each time step the dog moves forward one meter. Dogs rarely do what we expect, and of course things like hills or the whiff of a squirrel will change his progress. If this was a robot responding to digital commands the performance would be better, but not perfect. Perhaps a hand made robot would have a variance of $\\sigma^2=.1$, and an industrial robot might have $\\sigma^2=0.02$. These are not 'magic' numbers; the square root of the express the distance error in meters. It is easy to get a Kalman filter working by just plugging in numbers, but if the numbers do not reflect reality the performance of the filter will be poor." ] @@ -1350,12 +1349,13 @@ "source": [ "Next we initialize the simulator and run it with\n", "\n", - " dog = DogSimulation(pos[0], velocity=velocity, \n", - " measurement_variance=sensor_variance, \n", - " process_variance=process_variance)\n", + "```python\n", + "dog = DogSimulation(pos[0], velocity=velocity, \n", + " measurement_variance=sensor_variance, \n", + " process_variance=process_variance)\n", "\n", - " # simulate dog and get measurements\n", - " zs = [dog.move_and_sense() for _ in range(N)]\n", + "# simulate dog and get measurements\n", + "zs = [dog.move_and_sense() for _ in range(N)]```\n", " \n", "It may seem very 'convenient' to set the simulator to the same position as our guess, and it is. Do not fret. In the next example we will see the effect of a wildly inaccurate guess for the dog's initial position. We move the dog for 10 steps forward and store the measurements in `zs` via a list comprehension.\n", "\n", @@ -1363,7 +1363,8 @@ "\n", "The next code allocates an array to store the output of the filtered positions. \n", "\n", - " positions = np.zeros(N)" + "```python\n", + "positions = np.zeros(N)```" ] }, { @@ -1372,9 +1373,10 @@ "source": [ "Now we enter our `predict() ... update()` loop.\n", "\n", - " for i in range(N):\n", - " pos = predict(pos=pos[0], variance=pos[1], \n", - " movement=vel, movement_variance=process_variance)\n", + "```python\n", + "for i in range(N):\n", + " pos = predict(pos=pos[0], variance=pos[1], \n", + " movement=vel, movement_variance=process_variance)```\n", "\n", "We call the `predict()` function with the Gaussian for the dog's position, and another Gaussian for its movement. `pos[0], pos[1]` is the mean and variance for the position, and 'vel, process_variance' is the mean and variance for the movement. The first time through the loop we get\n", "\n", @@ -1384,8 +1386,9 @@ "\n", "Then we call the update function of our filter and save the result in our `positions` array.\n", "\n", - " pos = update(pos[0], pos[1], z, sensor_variance)\n", - " positions.append(pos[0])\n", + "```python\n", + "pos = update(pos[0], pos[1], z, sensor_variance)\n", + "positions.append(pos[0])```\n", " \n", "For this I get this as the result:\n", "\n", @@ -1401,11 +1404,12 @@ "\n", "Before we continue I want to point out how few lines of code actually perform the filtering. Most of the code is either initialization, storing of data, simulating the dog movement, and printing results. The code that performs the filtering is the very succinct\n", "\n", - " for i in range(N):\n", - " pos = predict(pos=pos[0], variance=pos[1], \n", - " movement=vel, movement_variance=process_variance)\n", - " pos = update(mean=pos[0], variance=pos[1],\n", - " measurement=z, measurement_variance=sensor_variance)" + "```python\n", + "for i in range(N):\n", + " pos = predict(pos=pos[0], variance=pos[1], \n", + " movement=vel, movement_variance=process_variance)\n", + " pos = update(mean=pos[0], variance=pos[1],\n", + " measurement=z, measurement_variance=sensor_variance)```" ] }, { @@ -1611,41 +1615,46 @@ "source": [ "For many purposes the code above suffices. However, if you write enough of these filters the functions will become a bit annoying. For example, having to write\n", "\n", - " pos = predict(pos[0], pos[1], movement, movement_variance) \n", - " \n", + "```python\n", + "pos = predict(pos[0], pos[1], movement, movement_variance) ```\n", + "\n", "is a bit cumbersome and error prone. Let's investigate how we might implement this in a form that makes our lives easier.\n", "\n", "First, values for the movement error and the measurement errors are typically constant for a given problem, so we only want to specify them once. We can store them in instance variables in the class. Second, it is annoying to have to pass in the state (pos in the code snippet above) and then remember to assign the output of the function back to that state, so the state should also be an instance variable. Our first attempt might look like:\n", "\n", - " class KalmanFilter1D:\n", - " def __init__(self, initial_state, measurement_variance, movement_variance):\n", - " self.state = initial_state\n", - " self.measurement_variance = measurement_variance\n", - " self.movement_variance = movement_variance\n", + "```python\n", + "class KalmanFilter1D:\n", + " def __init__(self, initial_state, measurement_variance, movement_variance):\n", + " self.state = initial_state\n", + " self.measurement_variance = measurement_variance\n", + " self.movement_variance = movement_variance```\n", "\n", "That works, but I am going to use different naming. The Kalman filter literature uses math, not code, so it uses single letter variables, so I will start exposing you to it now. At first it seems impossibly terse, but as you become familiar with the nomenclature you'll see that the math formulas in the textbooks will have an exact one-to-one correspondence with the code. Unfortunately there is not a lot of meaning behind the naming unless you have training in control theory; you will have to memorize them. If you do not make this effort you will never be able to read the literature.\n", "\n", "So, we use `x` for the state (estimated value of the filter) and `P` for the variance of the state. `R` is the measurement error, and `Q` is the process (prediction) error. This gives us:\n", "\n", - " class KalmanFilter1D:\n", - " def __init__(self, x0, P, R, Q):\n", - " self.x = x0\n", - " self.P = P\n", - " self.R = R\n", - " self.Q = Q\n", + "```python\n", + "class KalmanFilter1D:\n", + " def __init__(self, x0, P, R, Q):\n", + " self.x = x0\n", + " self.P = P\n", + " self.R = R\n", + " self.Q = Q```\n", " \n", "Now we can implement the `update()` and `predict()` function. In the literature the measurement is usually named either `z` or `y`; I find `y` is too easy to confuse with the y axis of a plot, so I like `z`. I like to think I can hear a `z` in *measurement*, which helps me remember what `z` stands for. So for the update method we might write:\n", "\n", - " def update(z):\n", - " self.x = (self.P * z + self.x * self.R) / (self.P + self.R)\n", - " self.P = 1 / (1/self.P + 1/self.R)\n", + "```python\n", + "def update(z):\n", + " self.x = (self.P * z + self.x * self.R) / (self.P + self.R)\n", + " self.P = 1 / (1/self.P + 1/self.R)```\n", "\n", "Finally, the movement is usually called `u`, and so we will use that. So for the predict function we might write:\n", "\n", - " def predict(self, u):\n", - " self.x += u\n", - " self.P += self.Q\n", - " \n", + "```python\n", + "def predict(self, u):\n", + " self.x += u\n", + " self.P += self.Q```\n", + "\n", "That give us the following code. Production code would require significant comments and error checking. However, in the next chapter we will develop Kalman filter code that works for any dimension so this class will never be more than a stepping stone for us." ] }, @@ -1785,13 +1794,15 @@ "source": [ "Modify the values of `movement_variance` and `sensor_variance` and note the effect on the filter and on the variance. Which has a larger effect on the variance convergence?. For example, which results in a smaller variance:\n", "\n", + "```python\n", " movement_variance = 40\n", - " sensor_variance = 2\n", + " sensor_variance = 2```\n", " \n", "or:\n", "\n", + "```python\n", " movement_variance = 2\n", - " sensor_variance = 40" + " sensor_variance = 40```" ] }, { @@ -2467,9 +2478,11 @@ "\n", "Do you suppose that this filter works well or poorly with nonlinear systems?\n", "\n", - "Implement a Kalman filter that uses the following equation to generate the measurement value for i in range(100):\n", + "Implement a Kalman filter that uses the following equation to generate the measurement value\n", "\n", - " Z = math.sin(i/3.) * 2\n", + "```python\n", + "for i in range(100):\n", + " Z = math.sin(i/3.) * 2```\n", " \n", "Adjust the variance and initial positions to see the effect. What is, for example, the result of a very bad initial guess?" ] @@ -2745,7 +2758,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.3" + "version": "3.4.1" } }, "nbformat": 4, diff --git a/05-Multivariate-Gaussians.ipynb b/05-Multivariate-Gaussians.ipynb index f61379c..82f828d 100644 --- a/05-Multivariate-Gaussians.ipynb +++ b/05-Multivariate-Gaussians.ipynb @@ -303,7 +303,7 @@ "What might a **multivariate normal distribution** be? *Multivariate* means multiple variables. Our goal is to be able to represent a normal distribution across multiple dimensions. I don't necessarily mean spatial dimensions - it could be position, velocity, and acceleration. Consider a two dimensional case. Let's say we believe that $x = 2$ and $y = 17$. This might be the *x* and *y* coordinates for the position of our dog, it might be the position and velocity of our dog on the x-axis, or the temperature and wind speed at our weather station. It doesn't really matter. We can see that for $N$ dimensions, we need $N$ means, which we will arrange in a column matrix (vector) like so:\n", "\n", "$$\n", - "\\mu = \\begin{bmatrix}{\\mu}_1\\\\{\\mu}_2\\\\ \\vdots \\\\{\\mu}_n\\end{bmatrix}\n", + "\\mu = \\begin{bmatrix}\\mu_1\\\\\\mu_2\\\\ \\vdots \\\\\\mu_n\\end{bmatrix}\n", "$$\n", "\n", "Therefore for this example we would have\n", @@ -1427,7 +1427,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.3" + "version": "3.4.1" } }, "nbformat": 4, diff --git a/06-Multivariate-Kalman-Filters.ipynb b/06-Multivariate-Kalman-Filters.ipynb index 4134452..79612d0 100644 --- a/06-Multivariate-Kalman-Filters.ipynb +++ b/06-Multivariate-Kalman-Filters.ipynb @@ -452,9 +452,8 @@ "\n", "I have programmed the equations of the Kalman filter into the `KalmanFilter` class in FilterPy. You will import it with\n", "\n", - " from filterpy.kalman import KalmanFilter\n", - " \n", - "The class contains variables **blah**" + "```python\n", + "from filterpy.kalman import KalmanFilter```" ] }, { @@ -680,8 +679,9 @@ "\n", "and implemented it with\n", "\n", - " def predict(pos, variance, movement, movement_variance):\n", - " return (pos + movement, variance + movement_variance)\n", + "```python\n", + "def predict(pos, variance, movement, movement_variance):\n", + " return (pos + movement, variance + movement_variance)```\n", " \n", "If the current position is 5.4 meters, and the movement is 1.1 meters over the time step, then of course the new position is 5.4 + 1.1, or 6.5 meters.\n", "\n", @@ -693,12 +693,14 @@ "\n", "But we need to generalize this. The Kalman filter equations work with *any* linear system, not just Newtonian ones. Maybe the system you are filtering is the plumbing system in a chemical plant, and the amount of flow in a given pipe is determined by a linear combination of the settings of different valves. \n", "\n", - "$$ \\mathtt{pipe_1 flow} = 0.134 (\\mathtt{valve}_1) + 0.41(\\mathtt{valve}_2 - \\mathtt{valve}_3) + .34 \\\\\n", + "$$\\mathtt{pipe_1 flow} = 0.134 (\\mathtt{valve}_1) + 0.41(\\mathtt{valve}_2 - \\mathtt{valve}_3) + .34 \\\\\n", "\\mathtt{pipe_2 flow} = 0.21 (\\mathtt{valve}_2) - 0.6(\\mathtt{valve}_1 - \\mathtt{valve}_5) + 1.86$$\n", "\n", "Linear algebra has a powerful way to express systems of equations. Take this system\n", "\n", - "$$2x+3y=8\\\\3x-y=1$$\n", + "$$\\begin{cases}\n", + "2x+3y=8\\\\3x-y=1\n", + "\\end{cases}$$\n", "\n", "We can put this in matrix form by writing:\n", "\n", @@ -1081,8 +1083,9 @@ "\n", "We need to convert the temperature into a voltage so we can perform the subtraction. For the thermometer it might read:\n", "\n", - " CELSIUS_TO_VOLTS = 0.21475\n", - " residual = measurement - CELSIUS_TO_VOLTS * predicted_state\n", + "```python\n", + "CELSIUS_TO_VOLTS = 0.21475\n", + "residual = measurement - CELSIUS_TO_VOLTS * predicted_state```\n", " \n", "The Kalman filter generalizes this problem by having you supply a **measurement function** that converts a state into a measurement. \n", "\n", @@ -1325,27 +1328,31 @@ "\n", "The function `filter_dog()` implements the filter itself. Lets work through it line by line. The first line creates the simulation of the DogSensor, as we have seen in the previous chapter.\n", "\n", - " dog = DogSensor(velocity=1, noise=noise)\n", + "```python\n", + "dog = DogSensor(velocity=1, noise=noise)```\n", "\n", "The next line uses our helper function to create a Kalman filter.\n", "\n", - " dog_filter = dog_tracking_filter(R=R, Q=Q, cov=500.)\n", - " \n", + "```python\n", + "dog_filter = dog_tracking_filter(R=R, Q=Q, cov=500.)```\n", + "\n", "We will want to plot the filtered position, the measurements, and the covariance, so we will need to store them in lists. The next three lines initialize empty lists of length *count* in a Pythonic way.\n", "\n", - " pos = [None] * count\n", - " zs = [None] * count\n", - " cov = [None] * count\n", - " \n", + "```python\n", + "pos = [None] * count\n", + "zs = [None] * count\n", + "cov = [None] * count```\n", + "\n", "Finally we get to the filter. All we need to do is perform the update and predict steps of the Kalman filter for each measurement. The `KalmanFilter` class provides the two functions `update()` and `predict()` for this purpose. `update()` performs the measurement update step of the Kalman filter, and so it takes a variable containing the sensor measurement. \n", "\n", "Absent the bookkeeping work of storing the filter's data, the for loop reads:\n", "\n", - " for t in range(count):\n", - " z = dog.sense()\n", - " dog_filter.update (z)\n", - " dog_filter.predict()\n", - " \n", + "```python\n", + "for t in range(count):\n", + " z = dog.sense()\n", + " dog_filter.update (z)\n", + " dog_filter.predict()```\n", + "\n", "It really cannot get much simpler than that. As we tackle more complicated problems this code will remain largely the same; all of the work goes into setting up the `KalmanFilter` variables; executing the filter is trivial.\n", "\n", "Now let's look at the result. Here is some code that calls `filter_track()` and then plots the result. It is fairly uninteresting code, so I will not walk through it. The `DogSimulation` class from the previous chapter has been placed in `DogSimulation.py`. I have also implemented a few plot routines to visualize the data in `mkf_internal.py`. Both of these are in the *code* subdirectory if you wish to read them.\n", @@ -2660,16 +2667,19 @@ "\n", "First collect your measurements into an array or list. Maybe it is in a CSV file, for example.\n", "\n", - " zs = read_altitude_from_csv()\n", + "```python\n", + "zs = read_altitude_from_csv()```\n", "\n", "Or maybe you will generate it using a generator:\n", "\n", - " zs = [some_func(i) for i in range(1000)]\n", + "```python\n", + "zs = [some_func(i) for i in range(1000)]```\n", "\n", "Then call the `batch_filter()` method.\n", "\n", - " Xs, Ps, Xs_pred, Ps_pred = kfilter.batch_filter(zs)\n", - " \n", + "```python\n", + "Xs, Ps, Xs_pred, Ps_pred = kfilter.batch_filter(zs)```\n", + "\n", "The function takes the list/array of measurements, filters it, and returns a list of state estimates (Xs), covariance matrices (Ps), and the predictions for the same (Xs_pred, Ps_pred).\n", "\n", "Here is a complete example." @@ -2925,7 +2935,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.3" + "version": "3.4.1" } }, "nbformat": 4, diff --git a/07-Kalman-Filter-Math.ipynb b/07-Kalman-Filter-Math.ipynb index 4e89461..0b504de 100644 --- a/07-Kalman-Filter-Math.ipynb +++ b/07-Kalman-Filter-Math.ipynb @@ -350,28 +350,30 @@ "source": [ "This is still a little hard to compare to Bayes' equation because we are dealing with probability distributions rather than probabilities. So let's cast our minds back to the discrete Bayes chapter where we computed the probability that our dog was at any given position in the hallway. It looked like this:\n", "\n", - " def update(pos_belief, measure, p_hit, p_miss):\n", - " for i in range(len(hallway)):\n", - " if hallway[i] == measure:\n", - " pos_belief[i] *= p_hit\n", - " else:\n", - " pos_belief[i] *= p_miss\n", + "```python\n", + "def update(pos_belief, measure, p_hit, p_miss):\n", + " for i in range(len(hallway)):\n", + " if hallway[i] == measure:\n", + " pos_belief[i] *= p_hit\n", + " else:\n", + " pos_belief[i] *= p_miss\n", "\n", - " pos_belief /= sum(pos_belief)\n", + " pos_belief /= sum(pos_belief)```\n", "\n", "Let's rewrite this using our newly learned terminology.\n", "\n", - " def update(prior_probability, measure, prob_hit, prob_miss):\n", - " posterior_probability = np.zeros(len(prior_probability))\n", - " for i in range(len(hallway)):\n", - " if hallway[i] == measure:\n", - " posterior_probability[i] = prior_probability[i] * p_hit\n", - " else:\n", - " posterior_probability[i] = prior_probability[i] * p_miss\n", + "```python\n", + "def update(prior_probability, measure, prob_hit, prob_miss):\n", + " posterior_probability = np.zeros(len(prior_probability))\n", + " for i in range(len(hallway)):\n", + " if hallway[i] == measure:\n", + " posterior_probability[i] = prior_probability[i] * p_hit\n", + " else:\n", + " posterior_probability[i] = prior_probability[i] * p_miss\n", + "\n", + " return posterior_probability / sum(posterior_probability)```\n", "\n", - " return posterior_probability / sum(posterior_probability)\n", "\n", - " \n", "So what is this doing? It's multiplying the old belief that the dog is at position *i* (prior probability) with the probability that the measurement is correct for that position, and then dividing by the total probability for that new event.\n", "\n", "Now let's look at Bayes' equation again.\n", @@ -2910,7 +2912,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.3" + "version": "3.4.1" } }, "nbformat": 4, diff --git a/08-Designing-Kalman-Filters.ipynb b/08-Designing-Kalman-Filters.ipynb index 4b047b5..b0cc7a5 100644 --- a/08-Designing-Kalman-Filters.ipynb +++ b/08-Designing-Kalman-Filters.ipynb @@ -2792,7 +2792,8 @@ "source": [ "So to create a trajectory starting at (0, 15) with a velocity of $100 \\frac{m}{s}$ and an angle of $60^\\circ$ we would write:\n", "\n", - " traj = BallTrajectory2D(x0=0, y0=15, velocity=100, theta_deg=60)\n", + "```python\n", + "traj = BallTrajectory2D(x0=0, y0=15, velocity=100, theta_deg=60)```\n", " \n", "and then call `traj.position(t)` for each time step. Let's test this " ] @@ -3052,11 +3053,12 @@ "source": [ "We already performed this step when we tested the state transition function. Recall that we computed the initial velocity for $x$ and $y$ using trigonometry, and set the value of $\\mathbf{x}$ with:\n", "\n", - " omega = radians(omega)\n", - " vx = cos(omega) * v0\n", - " vy = sin(omega) * v0\n", + "```python\n", + "omega = radians(omega)\n", + "vx = cos(omega) * v0\n", + "vy = sin(omega) * v0\n", "\n", - " f1.x = np.array([[x, vx, y, vy]]).T\n", + "f1.x = np.array([[x, vx, y, vy]]).T```\n", " \n", " \n", "With all the steps done we are ready to implement our filter and test it. First, the implementation:" @@ -3606,7 +3608,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.3" + "version": "3.4.1" } }, "nbformat": 4, diff --git a/09-Nonlinear-Filtering.ipynb b/09-Nonlinear-Filtering.ipynb index e2c8b4b..0e97765 100644 --- a/09-Nonlinear-Filtering.ipynb +++ b/09-Nonlinear-Filtering.ipynb @@ -417,7 +417,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Unfortunately Gaussians are not closed under an arbitrary nonlinear function. Recall the equations of the Kalman filter - at each step of its evolution we do things like pass the covariances through our process function to get the new covariance at time $k$. Our process function was always linear, so the output was always another Gaussian. Let's look at that on a graph. I will take an arbitrary Gaussian and pass it through the function $f(x) = 2x + 1$ and plot the result. We know how to do this analytically, but lets do this with sampling. I will generate 500,000 points on the Gaussian curve, pass it through the function, and then plot the results. I will do it this way because the next example will be nonlinear, and we will have no way to compute this analytically." + "Unfortunately Gaussians are not closed under an arbitrary nonlinear function. Recall the equations of the Kalman filter - at each evolution (prediction) we pass the Gaussian representing the state through the process function to get the Gaussian at time $k$. Our process function was always linear, so the output was always another Gaussian. Let's look at that on a graph. I will take an arbitrary Gaussian and pass it through the function $f(x) = 2x + 1$ and plot the result. We know how to do this analytically, but lets do this with sampling. I will generate 500,000 points with a normal distribution, pass it through the function, and then plot the results. I do it this way because the next example will be nonlinear, and we will have no way to compute this analytically." ] }, { @@ -927,7 +927,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.3" + "version": "3.4.1" } }, "nbformat": 4, diff --git a/10-Unscented-Kalman-Filter.ipynb b/10-Unscented-Kalman-Filter.ipynb index 3cc4f04..bef1ee1 100644 --- a/10-Unscented-Kalman-Filter.ipynb +++ b/10-Unscented-Kalman-Filter.ipynb @@ -7,6 +7,13 @@ "[Table of Contents](http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/table_of_contents.ipynb)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The Unscented Kalman Filter" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -270,9 +277,9 @@ "source": [ "In the last chapter we discussed the difficulties that nonlinear systems pose. This nonlinearity can appear in two places. It can be in our measurements, such as a radar that is measuring the slant range to an object. Slant range requires you to take a square root to compute the x,y coordinates:\n", "\n", - "$$x=\\sqrt{slant^2 - altitude^2}$$\n", + "$$x=\\sqrt{\\text{slant}^2 - \\text{altitude}^2}$$\n", "\n", - "The nonlinearity can also occur in the process model - we may be tracking a ball traveling through the air, where the effects of gravity and air drag lead to highly nonlinear behavior. The standard Kalman filter performs poorly or not at all with these sorts of problems.\n", + "The nonlinearity can also occur in the process model - we may be tracking a ball traveling through the air, where the effects of gravity and air drag lead to nonlinear behavior. The standard Kalman filter performs poorly or not at all with these sorts of problems.\n", "\n", "In the last chapter I showed you a plot like this. I have altered the equation somewhat to emphasize the effects of nonlinearity." ] @@ -287,9 +294,9 @@ "outputs": [ { "data": { - "image/png": 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QDO053sjJiEyPTJBh7th/I+roRsQm7tZZeg8AEi7FIuFSLAJ9umJkn0fR1jOI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\n", 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RUctxSL0WKqsswbJNCxotqDt7BGDGXf/F/cNnsqAmAuDt7os59y9GeJdBeper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\nbYo9IiJnZveiWhRFxMbGQiaTYdeuXfbunoio3ebOnYu0tDT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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -319,23 +326,23 @@ "source": [ "I generated this by taking 500,000 samples from the input, passing it through the nonlinear transform, and building a histogram of the result. We call these points **sigma points**. From the output histogram we can compute a mean and standard deviation which would give us an updated, albeit approximated Gaussian.\n", "\n", - "It has perhaps occurred to you that this sampling process constitutes a solution to our problem. Suppose for every update we generated 500,000 points, passed them through the function, and then computed the mean and variance of the result. This is called a 'Monte Carlo' approach, and it used by some Kalman filter designs, such as the **Ensemble filter** and **particle filter**. Sampling requires no specialized knowledge, and does not require a closed form solution. No matter how nonlinear or poorly behaved the function is, as long as we sample with enough sigma points we will build an accurate output distribution.\n", + "It has perhaps occurred to you that this sampling process constitutes a solution to our problem. Suppose for every update we generated 500,000 points, passed them through the function, and then computed the mean and variance of the result. This is called a **Monte Carlo** approach, and it used by some Kalman filter designs, such as the **Ensemble filter** and **particle filter**. Sampling requires no specialized knowledge, and does not require a closed form solution. No matter how nonlinear or poorly behaved the function is, as long as we sample with enough sigma points we will build an accurate output distribution.\n", "\n", - "\"Enough points\" is the rub. The graph above was created with 500,000 sigma points, and the output is still not smooth. What's worse, this is only for 1 dimension. In general, the number of points required increases by the power of the number of dimensions. If you only needed 500 points for 1 dimension, you'd need 500 squared, or 250,000 points for two dimensions, 500 cubed, or 125,000,000 points for three dimensions, and so on. So while this approach does work, it is very computationally expensive. The Unscented Kalman filter uses a similar technique but reduces the amount of computation needed by a drastic amount by using a deterministic method of choosing the points." + "\"Enough points\" is the rub. The graph above was created with 500,000 sigma points, and the output is still not smooth. What's worse, this is only for 1 dimension. In general, the number of points required increases by the power of the number of dimensions. If you only needed 500 points for 1 dimension, you'd need 500 squared, or 250,000 points for two dimensions, 500 cubed, or 125,000,000 points for three dimensions, and so on. So while this approach does work, it is very computationally expensive. Ensemble filters an particle filters use very clever techniques to significantly reduce this dimensionality, but the computational burdens are still very large. The Unscented Kalman filter uses sigma points but drastically reduces the amount of computation by using a deterministic method to choose the points." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Sigma Points - Sampling from the Distribution" + "## Sigma Points - Sampling from a Distribution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Let's look at the problem in terms of a 2D covariance ellipse. I choose 2D merely because it is easy to plot; this will extend to any number of dimensions. Assuming some arbitrary nonlinear function, we will take random points from the first covariance ellipse, pass them through the nonlinear function, and plot their new position. Then we can compute the the mean and covariance of the transformed points, and use that as our estimate of the mean and probability distribution." + "Let's look at the problem in terms of a 2D covariance ellipse. I choose 2D merely because it is easy to plot; this extends to any number of dimensions. Assuming some arbitrary nonlinear function, we will take random points from the first covariance ellipse, pass them through the nonlinear function, and plot their new position. Then we can compute the the mean and covariance of the transformed points, and use that as our estimate of the mean and probability distribution." ] }, { @@ -350,7 +357,7 @@ "data": { "image/png": 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wWq243e7YfaJtyKJMJhMOhwO/38/48eMpLi5m3rx5TJ06VUpsblA4HGbTrk1k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\n7ASG6V2Wzt4G/p5HK+FPB8W6QmJrB0X9w2m4aOmgqCgKmqY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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -373,15 +380,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's look at that by running a bunch of points through a nonlinear function. We will write a function to pass randomly generated points with a Gaussian distribution through the system\n", - "\n", - "$$\\begin{aligned}x&=x+y\\\\\n", - "y &= 0.1x^2 + y^2\\end{aligned}$$ \n", - "\n", - "for the mean and covariance \n", + "To see that, let's write a function which passes 10,000 points randomly drawn from the Gaussian\n", "\n", "$$\\mu = \\begin{bmatrix}0\\\\0\\end{bmatrix}, \n", - "\\Sigma=\\begin{bmatrix}32&15\\\\15&40\\end{bmatrix}$$" + "\\Sigma=\\begin{bmatrix}32&15\\\\15&40\\end{bmatrix}$$\n", + "\n", + "through the nonlinear system:\n", + "\n", + "$$\\begin{cases}\\begin{aligned}x&=x+y\\\\\n", + "y &= 0.1x^2 + y^2\\end{aligned} \\end{cases}$$ " ] }, { @@ -396,14 +403,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Difference in mean x=-0.010, y=42.676\n" + "Difference in mean x=0.153, y=42.145\n" ] }, { "data": { - "image/png": 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Rb6WeQl3vTkXTG+Miz6euOpFNnRhyDJlMBtlsFj6fT2WhU0HUx6dfL1f9yWaz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yRPN3PaM+ceZ77HPOKakJCQMAt5toiJNbJ7ODzoQLe+vp5R6ICQ4tFst9shqsYY5vmZTKpJMSJF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\npqaioqIC+fn5wuuVlZV48cUXRSnDHg8fPkR9fT0yM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SL9mnPEf+yP5DoRA8Hg+SySQaGhpQVlaGyspKm7pJEiozz3XXPMfntUml0Ofz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\n3DZE86gxjXxmEUJf7iQsdm27S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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -433,7 +440,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This plot shows the strong nonlinearity that occurs with this function, and the large error that would result if we linearized the function at (0,0), which is what filters like the Extended Kalman filters do (we will be learning this in the next chapter)." + "This plot shows the strong nonlinearity that occurs with this function, and the large error that would result if we linearized in the way of the Extended Kalman filter (we will be learning this in the next chapter)." ] }, { @@ -442,7 +449,7 @@ "source": [ "## Choosing Sigma Points\n", "\n", - "I used 10,000 randomly selected sigma points to generate this solution. While the computed mean is quite accurate, computing 10,000 points for every update would cause our filter to be very slow. So, what would be fewest number of sampled points that we can use, and what kinds of constraints does this problem formulation put on the points? We will assume that we have no special knowledge about the nonlinear function as we want to find a generalized algorithm that works for any function.\n", + "I used 10,000 randomly generated sigma points to generate this solution. While the computed mean is quite accurate, computing 10,000 points for every update would cause our filter to be very slow. So, what would be fewest number of sampled points that we can use, and what kinds of constraints does this problem formulation put on the points? We will assume that we have no special knowledge about the nonlinear function as we want to find a generalized algorithm that works for any function.\n", "We can visualize this algorithm using the following diagram." ] }, @@ -458,7 +465,7 @@ "data": { "image/png": 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ZBsaD/Hj7iofaB+zz+fj000955513+OSTT0ilUqTTaRKJbMCy2WwYhkF5eTkv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\nbVte2hnuxEQwylR8CqdDwW4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oHpokFBqlo+8kj1Q8gXYftxvMBoqiUFJSQklJfvaVWpaJ\n33/puu0MXuD67QwuTDNFaWk77e2v09b2MqWlC3KtGNe3Y0SjI0Qig0QiI8Ri48Tj4ySTU3y7IpwN\ny98MbaXTEUhHSMagZ7KPnosnAdA0DVXNtkuYpolpmjdUXxVFQVXVXMCcaSm4lVgsllvVpWkahmHk\nVnV5PB4qKyupqamhqamJ5uZmmpubcyF2JtDW1NTgcDycKmwkniSRieM0iq/fXdy9cDRNpS7DbmJu\nKK5HeSHukqIo/PGzaxia+D3hcJArgTMsK3t0TvU63o07rSInk1MMDn5CT89bDAx8nLvkIlu5VbDb\nS8huZ9jCwoVv0NKyG5frxmqTrpdQWjr/O8+TTken2y0GGB8/xtDQ53i9J0kkfDMn/s6Xz2RuDMm3\nen9v9/cz77NlWbk2hpKSEioqKqirq6O1tZX58+fT2tp6U4W5tLT0vnyOxePxXF/v3YrEU8TTCQxD\nwo6ASDRDa6Wbco+0wIjiJ+FXzHkuh53/+pUNhKKHOX5ihN5wR1EOwN2r7+sfjsWGrrts4swNl01k\nN0MYaFoZ8+a9woIF+2ho2H7TWjTLMkkkJonFxqefxojFxolGRwiH+4lEhm4YvrOsNJrmQFGmV3WZ\n6dzg3Z3QNC3XpqDr+g2tCzO9uIlEgmQyic1my21RyP57ZP8uu+nCIh6PE4/H8Xq9dHZ2Aje2NgC5\nYTXTNCkpKckNn9XX1+cqwt9uw6ipqaGioiJXob7eBx98wE9+8hMaGxtZvnw5GzduZOXKlaxYsYJF\nixbd9jKPRDJNNJkAJYPdJpXfuS6eyIBpp8ThwuOUFhhR/KTnV4hpl/sn+A9vfcWpUyZNttU0uIpj\n6ON+9Pze+qpkE+gFTmMYl0inJwHlpssmSkrm0dr6AjU167HbS4nHs8E2Ehm4bkDOSyIxSTodRlXt\nqKqe2zf8/WvXVGy2mQCs5PYTK4oyvZ+4ClXVSacjRCLDQHaH8UyrxMyVx9u2beOnP/0pL7zwAtXV\nN15+Ypomk5OTTExM4PV6b3gaHBy8aX9uKBRCVdXcUBtkh+ISiQTp9O37lmde5vrBtmQySSqVwuVy\n5cJybW0tTU1NjI6O8sUXXxCPx3Mv73Zn+9ZjsRi1tbUsW7aMDRs2sGrVKpYvX86SJUuIpeDgqUv0\nRzpZvvD+XmssCs/oRIJYwMOTix5h3eKGfB9HiHsmA29C3IXD5/v563fPcvaswhL3Y1QYNfk+0j25\nX9seQqEQ7733HpFImGxV9W+Bk2Q3MKSYqbQqSnY4zWZzoSja9JBaKledBSW34uy7hte+vUPYNJPT\nF264MYxyHI4aXK7s/mCPpxWns3b6qS73+5n9wdezLAuf7wwXu/6W3t63yMQn0e0asVgs92+TTCZZ\nvnw5P/vZz9izZw8LFy6843/v69/O1NTUTUHZ6/UyPDycC8ter5fJyUmmpqZy68m+XSW+3UqzuzET\nihVFIRqNUl5RQWV9E41Lmli7YTEt7S00L2jOXQ0s5pbO3gjV9nnsWr2c5prSfB9HiHsm4VeIu/TO\n7zt467NuOi7YWV2+GZctP4Ni9+rW1dpv7NqV3fOb/bF/cnqwbOyGdoNIZJhwuI9AoI9AoB+IADMX\nT9xZa0GWct0NcgqmmcE0sy0DM7fHOZ11uN2NeDytuFyNNwXa+3nVsmVZXAh8hbPsEk16F1dO/IYz\nZ85gGAahULZC7nBkbyGrrq7mJz/5Cfv27WPDhg23bD+4H6LR6C3D8ujoKAMDA4yMjOTCcjAYJJlM\nYhhGLrj/IAo4nA5UVSURS+AuddM0v4mFKxcyf8l8Wha20NLegsvjun/vqJhVTNPi9KUQj9StZffG\nxRi6dEOKwifhV4i7ZFkW/+WDU3x0aJhr3S7WVDyJrhXe0vcbq7UA14DzgB/w8/+3d9/Bcd/nncff\n23eB3UUHFr2TKCwCSVEULYkSRYlWMS23yHKdc+I4E098k7kkl3/uzjdzuZIbx8nc3GRy59zNOa6K\nHVuWJdmSiyjJIiWxdxAEQPS6aNvr7/6gRKvRbAC3fV4zGGAkAPssZ7H7+X33+T5fkymI05kgHl++\nfIzxb1sHLoXTdPpqq44WLm0dMGE28+ZYMSd2eylOZ+XbVmcb3zwO+Z2B1mbzZGxzYTgZ4PjyS9x+\nu8G///xdlDjhueee4zvf+Q6//OUvsdlsBINB0uk0VqsVl8uF2Wxm3759PP7449x///23bDLD+4nF\nYjzyyCNXvLi5WSazCSN96eXBXeKmrrmOjg0dtHW3sWPPDrxlWiHMB8uBBBMTZu5s6eOezfnR6iWi\n8CtyAxLJFF978gAvHlxkYaqUjaV3Ys2xEWhTU5M8/fTTb/svLwJPcuWDJd65OntpXFgcw0hhs3lI\nJOwYhhsoAcrf/OwFPLhcNezd+wkqKlrePMwiNwwFzpD0DPLQveX8+Sd3Xg7i8Xic/fv38+STT/Kj\nH2cODaIAACAASURBVP2IWCx2uQXhrdFxsViMe+6554p9wrfCn/3Zn/Hyyy9f988FwjEC0RAOB9d9\n3LPNYeOP/8Mf09TZdN23K9lndDKCNV7Fru6NdDXd+sewyFpQ+BW5QSuhGP/tu69w8HCY8HwFG8ru\nwGyyZLqsa/be8HsY+EcutStYgSI8nlq83oa39c7WvKNv1uWqwW6/9Pd/rS0UuSSZTnLI/yt6N8X4\nk0/cxp29je/5HsMwOHbsGD/84Q/57ne/y+TkJCaT6Yp9wo899hjt7Zk9Qvlqnn9jkIOjh+hd78Ru\n07SHQnayP0Crt4sH+7qoKFF7i+QHhV+RmzC/HOavv/cbXj8cJbFUQ0/pNsym3AgLgUCAp5/+yeWN\nbpd6dcOAB7DhdrvZt28fbve19TSvxVHJ2WAmMsaUcYx773Lyn35/Nzbr777AGR0d5amnnuJb3/pW\nxvuEb9RPD5zn9fE32LrBc90rv5I/YvE0Z85HuL1hKx/c3pFzF68iV6LwK3KTpvwB/vv3XuWNI3FM\nwTq6SrbkxIvE9Wx4u57fmcmjkteCYRgcXXiZ5nXL/NHHetmzte2af3Zpael39gk7nU4sFkvW9AnD\npZaeZw72c2zmCFs36Lm9kM0txFnxF3FX52a2ra/LdDkiq0bhV2QVjEwv8bUnD3LoSAJHrIlOz6ac\nCHz5ulq72vyxGS4mXufeuxz81e/vvqEd72/1CX//+9/nxz/+8fv2Ccfj8Xf0CVdUVKzBvfndQpE4\nz75xjv7FU2zuys1JJrI6Bi6GKLc0sXtjL001ep2X/KHwK7JKLkws8DdPHuTw0RSeZBtt7p6cCMD5\nuFq72gzD4Pjib6hvX+RLH+1m7/brn+377t+XrX3Ci4EIPzusAy4KXSplcOxsgD7fFvbe3olTI84k\njyj8iqyi08Oz/N0P3+DosTTlxjpa3OszXZKskoXYLMPx17j3bgf/+Q/ux25bvc2NIyMjPPXUU3z7\n29/OeJ/w9EKQF46dZjY+zLrW4jW5Dcl+84txFucc7GjdzM4N793oKZLLrpZhs2sXhkiW622t5o/2\nbWHzRhN+znMxeI4rXD9KjimzV2FOlDI8GuOVk6Or+rubm5v5yle+wmuvvcbMzAz/8A//wKOPPorL\n5cJutxONRhkbG+Nv//Zv2bNnD+Xl5Xzuc5/jmWeeuXx08WqJJ1IkUkmsVq38F7KFpQTlrnLqKtX6\nIoVH4VfkOm1ZV8uXPtzHbZtNLJgGGAqeUQDOAyaTiabiTsbG4PlDgyRTV5qHfHNKS0t54oknePrp\np1laWuIHP/gBX/jCF6ioqMDpdBKLxVheXuZb3/oWn/rUpygrK2Pv3r1885vfxO/33/Ttx5Mpkukk\nNoXfgpVMGQRCacpcZdSWq/VFCo/Cr8gNuL2rni9/ZCt9m80ELENcCJxUAM4D5fYa0lEPw+MRjl+Y\nXvPbs9vtPPDAA3zjG99gbm6Ol156ib/4i7+gra0Nh8NBIpEgGo3y/PPP8+Uvf5m6ujr6+vr42te+\nxuDg4A3dZjxxKfxarXr6L1SLywm8jhJqyjw6zlgKknp+RW7CqeFZ/uePDnHseApHrJF13s3aSJbj\nJsMXWXae5CMPVvKnn7gzY3VcS59wVVUVjz/+OB/72MeuuU/45NAM+/uP4/SuUFOZe8d2y807OxjE\n52zlvg3dNFbr9V3yj3p+RdbQhtZq/vXHt7O1z0rcNca55SOkjbV5u1xujWpnPUt+CycH55ldDGWs\njvfrE37kkUfe0Sc8OjrK17/+9Xf0CT/77LO/s0/YMC596BqtMMXiaSIRE+WuMmor1O8rhUnhV+Qm\ndTVV8qefuINtfTbS7knOLh9WAM5hVrONCnsdMzPw8omRTJcD/LZP+Kc//elV+4SfeOKJq/YJGxh6\nh6JA+Zfilza6VXixWhQBpDCp7UFklYxML/H1H7zGkeNxEsuV9JRsw2q2ZbosuQEr8UXOR19h1912\n/vpLD2RtSLiRecJBo5j9/cdwl4eoKtchJ4XmZH+AVu967t/cRXWZRt1JftKcX5FbaHxuhb/74Wsc\nPRFladbLhtLtOCyuTJcl1+nSkccv0da9wp9/ZmvOHP16LX3CJWXl9Oy4kzsf2sLtH+hZs3nCkn0C\noSQXR1Nsq+/jgW1tWv2XvKXwK3KL+ZfD/I8fvc6hEwHGR5z0ltyB2+bNdFlynSbDF1lxnuTjD1Xz\nJx+9I9PlXLelpSWee+45vv3tb/OrX/0Km81GIBDAMAzMFgt2hw2LxcL23du5++G72bRjE3aHVoLz\n2YWRMB5THXev72Z9U2WmyxFZMwq/IhkQjib4+5+8watH/Vw4b2W9ZxtljqpMlyXXIZ6KcWjpBe65\ny8zf/PGDOT0SKh6Ps3//fr7//e/zzz/4IdFYlFQ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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -473,11 +480,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's consider the simplest possible case and see if it offers any insight. The simplest possible system is the *identity function*. In mathematical notation this is $f(x) = x$. It should be clear that if our algorithm does not work for the identity function then the filter will never converge. In other words, if the input is 1 (for a one dimensional system), the output must also be 1. If the output was different, such as 1.1, then when we fed 1.1 into the transform at the next time step, we'd get out yet another number, maybe 1.23. The filter would run away (diverge). \n", + "Let's consider the simplest possible case and see if it offers any insight. The simplest possible system is the **identity function**: $f(x) = x$. If our algorithm does not work for the identity function then the filter will never converge. In other words, if the input is 1 (for a one dimensional system), the output must also be 1. If the output was different, such as 1.1, then when we fed 1.1 into the transform at the next time step, we'd get out yet another number, maybe 1.23. The filter diverge. \n", "\n", "The fewest number of points that we can use is one per dimension. This is the number that the linear Kalman filter uses. The input to a Kalman filter for the distribution $\\mathcal{N}(\\mu,\\sigma^2)$ is $\\mu$ itself. So while this works for the linear case, it is not a good answer for the nonlinear case.\n", "\n", - "Perhaps we can use one point per dimension, but altered somehow. However, if we were to pass some value $\\mu+\\Delta$ into the identity function $f(x)=x$ it would not converge, so this is not a possible algorithm. We must conclude that a one point sample will not work.\n", + "Perhaps we can use one point per dimension, but altered somehow. However, if we were to pass some value $\\mu+\\Delta$ into the identity function $f(x)=x$ it would not converge, so this is not a possible algorithm. If we didn't alter $\\mu$ then this would the standard Kalman filter. We must conclude that a one point sample will not work.\n", "\n", "So, what is the next lowest number we can choose? Consider the fact that Gaussians are symmetric, and that we probably want to always have one of our sample points be the mean of the input for the identity function to work. Two points would require us to select the mean, and then one other point. That one other point would introduce an asymmetry in our input that we probably don't want. It would be very difficult to make this work for the identity function $f(x)=x$.\n", "\n", @@ -496,7 +503,7 @@ "data": { "image/png": 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jxsr4hSgsVgIAjA2bYcrgt9Fcbi5yMmroPJ07YFSvqUI7/UwKdqVtFDERETUG\nLNaJqEnZvHcFzlw6LrTHDYiCo3UrERNRY9LNZwBC7rrvYdv+NTiZkyZiIiJq6FisE1GTcSBzN5Iz\ntgntsKAX0NGji4iJqDEa0WMSPJzaAwC00OKHHYtw9cZlkVMRUUPFYp2ImoQLijOI2x0jtDt6BGFA\n4Kj77EH0aGQyA0wMn40Wza0BAGWqW/hu6wKUVdwSORkRNUQ6L9aXLVsGd3d3yOVy+Pv7IyUl5Z5j\nk5KSMGTIEDg6OsLU1BS+vr5YsWJFrXHJycnw8/ODXC6Hh4cHvvnmG13HJqJGRKvV4sKFCiQnlyM5\nuRyZp5VYvu0TVKkrAQAOLV3xr9AZkEp4voKeDDMTS0weHA1DmREAQHnjElbv/AK5uWXCvLxwoYI3\noBLRA+n0nSouLg4zZ87E3LlzkZGRgeDgYISFheHixYt1jk9NTYWvry82bNiAkydPYtq0aZg6dSrW\nrVsnjMnJyUF4eDhCQkKQkZGB6OhoTJ8+HRs38qYdIqqtpKQKcXHl6NHDEL16NUOfvjJ89P0iFN0s\nBADIjU0xeVA0mhnJRU5KjZ2LrQde6Peq0D6RcwgvR29Er17N0KtXM/ToYYi4uHKUlFSJmJKI9J1E\nq8OP9V26dEGnTp1qnPn28vLCyJEjMX/+/HodY8yYMVCr1fjll18AAHPmzMHmzZtx+vRpYcyUKVNw\n8uRJ7N+/X+grKioS/reFhcXj/lMahbS06pua/P39RU5C+qwxzROtVou4uHK88EIzABIAQO/Ry9Ah\nOOH2CAmmDXkXz7h1Fi1jQ9WY5snTtnnvCuxO3yK04/83B+ePdb3d0mLdunKMGdMMEolEnIA6xHlC\nD8I5UtuDalidnVlXqVRIT09HaGhojf7Q0NAaRfWDFBUVwcrKSminpqbWecy0tDSo1erHC01EjUpe\nngpvvmmMvwv1DsE77irUgRPJ42AqbSdSOmqqfF2ehzK3k9Du9+KXaOmQe7slwZw5xrh4USVKNiLS\nfzor1gsKCqBWq2FnZ1ej39bWFgqFol7H2LZtG3bv3o2pU++sU6tUKmsd087ODlVVVSgo4OOcieiO\n3FwtLl6sfllzcM9EjxHfCdtOp/XAH5uGISeH1wjT05V3QYIt3/4Hf12zBwAYGZcjYtICGJuUVG/P\nk3JeEtE96c3j+vbt24cXX3wRS5cufeyvRv7+ioWq8e9B9dEY5klxsQuAZjC1KED4xE8gk1V/+3b1\nYmvsjnuykzaQAAAgAElEQVQVgATFxUVISzshas6GrDHMk6etuNgFFbfssP37aIyKmgMj43JYWCsx\n8N+f4ddv34VWI2t085LzhB6Ec+QOT0/P+27X2Zl1a2tryGQyKJXKGv1KpRIODg733TclJQXh4eH4\n8MMP8fLLL9fYZm9vX+vMvFKphIGBAaytrXUTnogaBRubMri5lyNi0scwMau+BrCs1Bzx/3sLVZXG\ncHXVwMamTOSU1NTY2JTB1VWD64pWSFwzU+h3bXsUXSNWc14S0X3p7My6kZER/Pz8kJCQgBEjRgj9\niYmJGDXq3msZ79mzB4MGDcIHH3yA119/vdb2rl27YtOmTTX6EhMTERAQAJlMVucxedNCNd7EQfXR\nmOaJRqPBxDmLca38XHVbLUX8ijdRcsMWgBaffFKBLl1aQSJxEzNmg9SY5snTptVq8ckn1Tc+Zx8P\nwsGdoxE44CcAgF/fzXh+mDO6dOnbKOYl5wk9COdIbXffYFoXnS7dOGvWLKxcuRLff/89srKyMGPG\nDCgUCkRGRgIAoqOj0a9fP2F8UlISwsLCMG3aNLzwwgtQKBRQKBS4du2aMCYyMhKXL19GVFQUsrKy\nsHz5cqxatQpvvPGGLqMTUSOw5+h2XCvfI7T3bpqEK+c7wNVVg3XryhERYdgoVtyghkUikSAiwhDr\n1pXD1VWDAzueR86JO4VK7s1vcelajogJiUif6fSa9dGjR6OwsBDz5s1Dfn4+fHx8EB8fDxcXFwCA\nQqFAdna2MH7VqlUoLy/HwoULsXDhQqHfzc1NGOfm5ob4+HhERUUhJiYGTk5OWLp0KYYNG6bL6ETU\nwJ25eAyb9955qFp7114Y/mEfSCTlcHOTwNW1cSyNRw2TmZkBxoyRoWtXFXJztahUv4K9OXNxo/QK\nKtUqLN+2AG88/xnMTLj0MBHVpNN11sXEddZr41dNVB+NYZ4UFivx2bo3cLO8enWNVnaeeH3kRzA0\nMBI5WePRGOaJvlHeuIzP189GueoWAMDT2QevDP0/yGR6s/bDQ+M8oQfhHKntqa2zTkQkBlVlBZZv\nXSAU6mYmlpg06C0W6qT37Fo44d8DoiC5/VyAs5eOY3PKSnFDEZHeYbFORA2WVqvF2l1f4XJBLgBA\nJjXApIg5sGzeUtxgRPXUoXUAwru+ILSTM7bhQOZuERMRkb5hsU5EDdbvhzch/cxeoT2y1xS0dnxG\nxERED69/wEj4egQJ7bjdMbigOCNiIiLSJyzWiahBOpmThq37Vgvtbh0GoJvPABETET0aqUSKF0Nn\nwKGlKwCgSl2J5ds+RtHN6yInIyJ9wGKdiBoc5Y3LWLVjEbSovj++teMzGNFrssipiB5dMyM5Jg+K\nholxcwBA0c3rWL7tY1RWqURORkRiY7FORA3KrYpSfPfrR8IKGi2aW2Ni+BwYyAxFTkb0eGwsHTAh\nfDYkkuq35guKM/hpdywayaJtRPSIWKwTUYOh0ajxw44vcPWvKwAAQ5kRJg+OhrmppcjJiHTD29UX\nQ7uPF9oHsnZjz9Ht4gUiItGxWCeiBmNb6lpk5h4W2mP7vwYXWw8RExHpXq9OgxH4TG+hvWnP/3A6\n76iIiYhITCzWiahBOHx6D3albRDa/fyGw8+7h4iJiJ4MiUSCMX2moZWdJwBAo9VgxW+foaBIIXIy\nIhIDi3Ui0nsXr57H2l1fCe12rZ7FoOAXRUxE9GQZGhhh8qBomJu2AADcKi/Bd1vno0JVJnIyInra\nWKwTkV4rvvkXlm9dIKyKYWvpiH+HzYJUKhM5GdGTZdHcCpMi3oJMZgAAyC/Mw+qExdBoNSInI6Kn\nicU6EemtyioVlm9fgBulBQCAZkYmmDL4bWF5O6LGzt3BG2N6TxPax84fQHzqOhETEdHTxmKdiPSS\nVqtF3O4Y5OafBgBIJFK8NHAW7KycRU5G9HQFte+LXp2fE9oJh35G2qlkERMR0dPEYp2I9NLvhzfh\nYNYfQntIyEto7+4vYiIi8QwJeQnPtHpWaK/d9RUuKM6ImIiInhYW60Skd45nH8TWfauFdlC7vuh9\n15lFoqZGJpVhfNh/hG+WqtSV+G7rAtwoKRA5GRE9aSzWiUivXCnIxQ87FkGL6qc2eji2w+g+kZBI\nJCInIxKX3NgUUwe/A5NmZgCA4ls38N22+aioLBc5GRE9STov1pctWwZ3d3fI5XL4+/sjJSXlnmMr\nKiowfvx4+Pr6wsjICL179641JikpCVKptNbPmTP8+o+osSm5VYRvt94pPqzMbTExYg4MZIYiJyPS\nDzaWDpgY/qawGtKlq9n4MWEJV4ghasR0WqzHxcVh5syZmDt3LjIyMhAcHIywsDBcvHixzvFqtRpy\nuRzTp09HRETEfc+cZWZmQqFQCD9t2rTRZXQiElmVuhL/2/4JrhdfBQAYGzbD1MHvwMzEQuRkRPrF\ny8UHo3pNFdoZ5/Zjx4E4ERMR0ZOk02J90aJFmDBhAiZNmgRvb28sWbIEDg4OiImJqXO8iYkJYmJi\nMHnyZDg5OUGr1d7z2DY2NrC1tRV+pFJewUPUWGi1Wvy0Oxbnr2QCACSQ4N8DZ8HRupXIyYj0Uzef\nAejhGyG0dxyIQ/qZe3+TTUQNl84qXpVKhfT0dISGhtboDw0Nxf79+x/7+P7+/nB0dES/fv2QlJT0\n2McjIv2xK20j/sz8XWgP7jYOPq0DRUxEpP+G9ZgIb1dfob0m4Uvk5J8SMRERPQk6K9YLCgqgVqth\nZ2dXo9/W1hYKheKRj+vo6IjY2Fhs3LgRGzduhLe3N/r27Xvfa+GJqOFIP5OCrfvvrPwS+Exv9PUb\nJmIiooZBJpVhQths2LZwAlB9Kdm3W+ejoOjR33OJSP8YiB3gQby8vODl5SW0g4KCkJubi4ULFyIk\nJKTOfdLS0p5WvAaBfw+qDzHmybXiS9h54k6hbmfeCp6WQTh8+PBTz0L1w9cT/dPNfQjiS1aiouoW\nbpYVY3HcOwjrOB7GBnLRMnGe0INwjtzh6el53+06O7NubW0NmUwGpVJZo1+pVMLBwUFXvwYAEBgY\niLNnz+r0mET0dJWU38Afp36CRqsGAJjLW6JX25GQ3V7lgojqx0xuhd7PjIJUUv3fTnFZIZJP/QK1\nRi1yMiLSBZ2dWTcyMoKfnx8SEhIwYsQIoT8xMRGjRo3S1a8BAGRkZMDR0fGe2/39+ZRD4M6nVv49\n6H7EmCe3ykvxxU9vobzyFgDAVG6OGaPnwcZStx/sSXf4eqLv/GHvbIOVv30GAFAUXcC5vw5gbP/p\nT/UZBZwn9CCcI7UVFRXdd7tOL4OZNWsWxo0bh8DAQAQHByM2NhYKhQKRkZEAgOjoaBw6dAi7du0S\n9snMzIRKpUJBQQFKS0tx9OhRaLVadOrUCQCwePFiuLu7o127dlCpVFizZg22bNmCjRs36jI6ET0l\nVepKfL/9EyhvXAIAGMgMMWXQ2yzUiR7Ts14hKChSYNv+NQCAA1m7YW3pgAGBuj1hRkRPl06L9dGj\nR6OwsBDz5s1Dfn4+fHx8EB8fDxcXFwCAQqFAdnZ2jX0iIiJw4cIFAIBEIkHnzp0hkUigVld/fVdZ\nWYnZs2fj0qVLkMvl6NChA+Lj4zFw4EBdRieip0Cr1SLu9xicvXRc6PtX6Ay0dmwrYiqixqO//wgU\n/JUvrK60PfVHWFvYwc+7h8jJiOhR6fwG02nTpmHatGl1bluxYkWtvpycnPseb/bs2Zg9e7ZOshGR\nuBIO/YwDWbuF9qCuL+JZr7pvFCeihyeRSDCmzzRcL76KM7c/FK9JXALL5i3h4dRe5HRE9Cj4ZCEi\neir+PPk7tqeuFdpd2vVF/4CRIiYiapxkMgNMHDQHdlbOAAC1ugrfbp2P/MI8kZMR0aNgsU5ET9zJ\nnDSs//1roe3l0hFj+kQ+1RvfiJoSE+PmiHzuvzAzsQQAlFXcRMzm93GjpEDkZET0sFisE9ETlas4\ng//FfwqNVgMAcLJxx6SIt2AgMxQ5GVHj1tLCDpFD/gtjw2YAgL9KCxG75QPcKi8VORkRPQwW60T0\nxChvXMY3Wz5EZZUKAGBlbotpQ96F3NhE5GRETYOLrQcmRbwF6e3nF+QX5uG7rfOF/yaJSP+xWCei\nJ6Lo5nXEbH4fN8tLAFSvpf7K0P+DuWkLkZMRNS1tW3XCi/1fF9rnr2Tihx2LoOFDk4gaBBbrRKRz\nZRU3Ebv5A1wvvgoAMDIwxsvPzYVtCyeRkxE1TQFte2JIyHihffT8n/gleTm0Wq14oYioXlisE5FO\nVVZV4vttH+NyQS4AQCqRYkL4bLjZe4kbjKiJ6/PsEPTq/JzQTjn2GxIO/SJiIiKqDxbrRKQzGo0a\nqxO+ENZ3BoDn+76K9u58rDSR2CQSCYZ2H49nvboLfdtTf8S+4ztFTEVED8JinYh0QqPVYN2ur5Fx\ndr/QF9H1RQS17ytiKiK6m1QixYv9X4eXs4/Q99PuWKSdShYxFRHdD4t1InpsWq0WG5O/r/F00p6d\nBiGUDz0i0juGBoaYNCgarrZtAABaaLEm4UscO39A5GREVBcW60T02LanrsWeo9uFdpd2fTGsx0Q+\n9IhIT8mNTTBt6LtwaOkKoPqbsRW/LcTpvKMiJyOif2KxTkSPJTFtIxIO/Sy0O3t2wwt9X4FUwpcX\nIn1mKjfHK8Peg42FAwBAra7Cd1vnI/tKlsjJiOhufDcloke252g8tu77QWi3d/PHuAEzhQewEJF+\nszC1wqvD34dl85YAAFVVBWK3fIiLV8+LnIyI/sZinYgeyYHM3fgl6Vuh7ensgwkRs2EgMxQxFRE9\nLCtzW7w2/AOYyS0AAOWqW1i26T3kF14UORkRASzWiegRHDm7D2t3fSW0W9l7Ycrgt2FkYCxiKiJ6\nVLYtnPDKsPdhYtwcAHCzvATLNv0frt64InIyImKxTkQPJf1MClb99jm0Wg0AwMnaDdOGvItmRnKR\nkxHR43CyccO0oe/C2LAZAKDo5nUs3TAXyhuXRU5G1LTpvFhftmwZ3N3dIZfL4e/vj5SUlHuOraio\nwPjx4+Hr6wsjIyP07t27znHJycnw8/ODXC6Hh4cHvvnmG13HJqJ6SDuVjFU7FkFzu1C3a+GMV4a9\nB5NmzUVORkS60MreC1OfmwtDAyMAdxXs1y+JnIyo6dJpsR4XF4eZM2di7ty5yMjIQHBwMMLCwnDx\nYt3XvanVasjlckyfPh0RERF1LvOWk5OD8PBwhISEICMjA9HR0Zg+fTo2btyoy+hE9ACHTiVhdcKX\nwhl1eysXTB8xD2YmliInIyJd8nTugMgh/xUuayu+eQNLNsyF4jqvYScSg06L9UWLFmHChAmYNGkS\nvL29sWTJEjg4OCAmJqbO8SYmJoiJicHkyZPh5OQErVZba0xsbCycnZ3x5ZdfwtvbG5MnT8ZLL72E\nzz77TJfRieg+DmTuxpqddwp1h5aumD7iQ5ibslAnaow8nX0QOfRdGN2+JKbk1l9Y+stc5BfmiZyM\nqOnRWbGuUqmQnp6O0NDQGv2hoaHYv3//PfZ6sNTU1DqPmZaWBrVa/cjHJaL6+fPk71ibuBRaVH+Y\ndmzZCq8N/5Bn1IkauTZO7TFtyJ1r2EvKirB0w39xpeCCyMmImhYDXR2ooKAAarUadnZ2NfptbW2h\nUCge+bhKpbLWMe3s7FBVVYWCgoJa2wAgLS3tkX9fY8S/B9VHXfPkrPIIUs/deTJpC1M7hHiMwOnM\ns08zGukRvp40Pb3bjsHvmetQqVahtKwIX8RFI7TDi2hhWvv992+cJ/QgnCN3eHp63nc7V4Mhojqd\nyk+rUahbmdqjf/sX0czQRMRURPS02Zq7oF+7sTCUVd90WlF1Cwkn1qCghMs6Ej0NOjuzbm1tDZlM\nBqVSWaNfqVTCwcHhkY9rb29f68y8UqmEgYEBrK2t69zH39//kX9fY/L3p1b+Peh+/jlPtFotfjuw\nHgezdwhjnG1b49Vh78O0mZkoGUl8fD1p6vzxTLt2WLbpPZSrbqGiqgy7stZiyqBoeLv6CqM4T+hB\nOEdqKyoquu92nZ1ZNzIygp+fHxISEmr0JyYmIjg4+JGP27VrVyQmJtY6ZkBAAGQyPtKcSJc0Wg1+\nTvoWOw7ECX2t7L3w2rAPWKgTNXFu9l41PrSrKssRu+VDHDn76PelEdGD6fQymFmzZmHlypX4/vvv\nkZWVhRkzZkChUCAyMhIAEB0djX79+tXYJzMzExkZGSgoKEBpaSmOHj2KjIwMYXtkZCQuX76MqKgo\nZGVlYfny5Vi1ahXeeOMNXUYnavKq1JX4YccipBz7Tehr26ozXhv+AddRJyIAQCt7T8wYNR+WzVsC\nANSaKqyMX4h9x3eKnIyo8dLZZTAAMHr0aBQWFmLevHnIz8+Hj48P4uPj4eLiAgBQKBTIzs6usU9E\nRAQuXKi+s1wikaBz586QSCTCSi9ubm6Ij49HVFQUYmJi4OTkhKVLl2LYsGG6jE7UpFWqVfj2149w\nKu/OB2U/r+54MfR1GMgMRUxGRPrG3soFM0d9jGWb38PVG5ehhRZxu2NQWlYEK4l7nc9MIaJHJ9HW\ntbh5A3T39T4WFhYiJtEfvC6M6iMldQ92Z8ahoPTOI8V7+IZjeM/JkEp4DzpV4+sJ/VNpWTFit3yI\nPOWd1aGecQiEv3t/BAQEiJiM9BlfS2p7UA3Ld2KiJux68VXsPP5DjUI9rMvzGNFzCgt1Irqv5nJz\nvDb8A3i5dBT6svIPIuXMZlRWVYqYjKhx4bsxUROVk38an6+fjaKyAgCABBKM6jUVYUHP82tsIqqX\nZkZyvPzcf9GpzZ2FJHIKTuLrTe+itKxYxGREjQeLdaImKP1MCpZumIuSsuqv3qQSKf49cBa6+4aL\nnIyIGhpDA0OMD/sPuvkMFPqyr2Th87jZUFy/KGIyosaBxTpRE1K9hnocVv72GarU1V9TGxvI0b/9\nv+Dn3V3kdETUUEmlMozu/TL83O6s+FZYpMQXcXNw6kLGffYkogdhsU7URFRWqbB652L89uc6oc+2\nhRPCOk6AnYWriMmIqDGQSCRo7xSEXm1HwcjAGABQprqF2C0fIOXYjgfsTUT3wmKdqAkoufUXvtr4\nLtJOJwt9Xi4dMWv0JzCXW4mYjIgaG9eW3pgxagEsbq/FrtFq8NMfsdiY/D00GrXI6YgaHhbrRI3c\nxavn8Xncm8jJPyX0BXcIxbQh7/JhR0T0RLjYtsYbYxbCxdZD6EvK2Ipvf/0IN8tLRExG1PCwWCdq\npLRaLfafSMQXP72F68VXAVSv+DK0+wSM6TMNMplOn4lGRFSDRXMrvD7yI/h6BAl9mRfSsXDtLOQp\nz4mYjKhhYbFO1AipKiuwNnEp1v/+9Z0bSY3kmDw4Gn2eHcKlGYnoqTA2bIYJEW+iv/8Ioe96yTV8\n8fNb2Hd8JxrJcxmJniieWiNqZK79lY//bf8ElwtyhT6Hlq6YFDEHti2cxAtGRE2SVCLF4G7j0Mre\nE2sSlqBcdQtqdRXidscgJ/8URveOhJGhsdgxifQWz6wTNSLHzv+Jhev+U6NQD2jbC7PGfMpCnYhE\n1dEjCLNf+BxO1m5C38GsP7Ao7k1cvXFFvGBEeo7FOlEjUKWuxOa9K7F828coV90CAMhkBhjTZxr+\nFToDxobNRE5IRATYWDogaswn6NKur9B3pfACFq7/D46c3SdiMiL9xctgiBq4/MI8/LDzC1y+liP0\nWZnZYGLEHLjatRExGRFRbUYGxnix/3S0dmiLn5O+RZW6EhWqMqyIX4gTbQ9hZK8pkBubih2TSG+w\nWCdqoDRaDZIztmHrvtXCTaQA0K7Vsxg3MAqmzcxETEdEdH9dO/SHs21r/G/7pygsVgIADp1KwrnL\nJ/Gv0BnwdO4gckIi/cDLYIgaoBslBVi26T1s2vM/oVA3kBlieI9JmDpkLgt1ImoQXGw98ObYRQho\n20vou1FyDV9t+C82712JyqrKe+9M1ETovFhftmwZ3N3dIZfL4e/vj5SUlPuOP378OHr27AkTExM4\nOzvjww8/rLE9KSkJUqm01s+ZM2d0HZ2oQTh8eg8+/nEGzlw8JvQ52bhj9gufo1fnwZBK+BmciBoO\nubEpxg2YiQnhs2Fy+0SDFlrsTt+Mz9e/gcvXcsUNSCQynV4GExcXh5kzZyImJgYhISH4+uuvERYW\nhszMTLi4uNQaX1xcjP79+6NXr15IS0tDVlYWJkyYAFNTU8yaNavG2MzMTFhZ3XksurW1tS6jE+m9\n4ps3sCF5eY2bsCSQoK//cIQHPQ8DmaGI6YiIHk9nz25o7fAMfkxcglN5GQCqbz79LO4NDAwcg75+\nQ/k6R02STov1RYsWYcKECZg0aRIAYMmSJdixYwdiYmIwf/78WuN//PFHlJeXY9WqVTA2Nka7du1w\n6tQpLFq0qFaxbmNjg5YtW+oyLlGDoNFqsP94Arbu+wFlt1d6AQArc1uMC50JD6d2IqYjItIdi+ZW\nmDb0/7D3WDy27F2FSrUKanUVtqf+iMO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JoivfVBzCZ+e7XrrwfguUKxGi7ANVJ5fk+gRH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\nAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -518,15 +525,15 @@ "\n", "$$\\begin{aligned}\n", "\\mathbf{Constraints:}\\\\\n", - "\\sum_i{w_i^m}&=1 \\\\\n", + "1 &= \\sum_i{w_i^m} \\\\\n", "\\mu &= \\sum_i w_i^mf(\\mathcal{X}_i) \\\\\n", "\\Sigma &= \\sum_i w_i^c{(f(\\mathcal{X})_i-\\mu)(f(\\mathcal{X})_i-\\mu)^\\mathsf{T}}\n", "\\end{aligned}\n", "$$\n", "\n", - "If we look at this is should be clear that there is no one unique answer - the problem is unconstrained. For example, if you choose a smaller weight for the point at the mean for the input, you could compensate by choosing larger weights for the rest of the $\\mathcal{X}$, and vice versa. We can use different weights for the mean and variances, or the same weights. Indeed, these equations do not require that any of the points be the mean of the input at all, though it seems 'nice' to do so, so to speak.\n", + "These constraints do not limit us to a unique answer. For example, if you choose a smaller weight for the point at the mean for the input, you could compensate by choosing larger weights for the rest of the $\\mathcal{X}$, and vice versa. We can use different weights for the mean and variances, or the same weights. Indeed, these equations do not require that any of the points be the mean of the input at all, though it seems 'nice' to do so, so to speak.\n", "\n", - "But before we go on I want to make sure the idea is clear. We are choosing 3 points for each dimension in our covariances. That choice is *entirely deterministic*. Below are three different examples for the same covariance ellipse." + "But before we go on I want to make sure the idea is clear. We are choosing 3 points for each dimension in our covariances. That choice is *entirely deterministic*. Below are three different examples for the same covariance ellipse. The size of the sigma points is proportional to the weight given to each." ] }, { @@ -539,9 +546,9 @@ "outputs": [ { "data": { - "image/png": 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aGhr42te+utUrGAqF8Ps/fmVSOV6ttNtSagKnp0BDtdd095TmCypX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\nD0l8jPtbmd2vWv1UqlZ5V2ox8Z2njtDRDguxe9L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iizg3Z+HlqvkeRBeXkizHWhhNl9T5im0z6/Ktpum8MZ9gaQkGfSHZHCq2xW61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\nr7c7Ey57QYLfTbGrZ/vKsu2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A93g8nDlzhlOnTmEYhmzePETSpQKhARgd8HXtTvVyrUG9\nDv3u3us74vBKFfJMHOveFZP9ZL7gt9bOXvXiAH5QSqUSZ8+e5Wtf+xrz83MAHDs2zZNPPsmDDz6I\nz9ed2Zn9lCrlu3an+patpdte7jtyRN/hYhgGyWKeU+Pdu2IC7b5Tr4EzIGUP4mCU6zU0pc5A0CK3\nut2C6Z70Vx9CUnu1I9Vqheeee44vfOG3rwa+APPzc/zO73yBb3zjG1QqlQ628PBpahrZSpGBcHfu\nVN+yVfYgfUcclGy5dHWner+/O8tPdN2gWm/RaEipnTg4iXyO8GA74aKq3Zlw2U8mDH7Nu9ltLyws\nLPLnf/7fbvv6l7/8JRYWFg6wRYdfIp8l2K8zFvZ25U71LeVqg1oP1vyKw2s9l2F4CKaGu/fGu0q9\nSa1mYLdKqZ04GI1Wi1y1QGRQYXKor9PNOZRMF/xulT04enjpdr/ous7rr79+1/e9+uqrVy+/MDtN\n10kWcwwPwfTora+P7ga6blCpt2g2FOk74kAUqhV0tcbQoLWrd6rLpFEctHg+y8CAwfhgdydc9pPp\ngt9rdYvyC7FdhmHc8oa4G83OzmIYxgG06PBLFvN4/RojYVfXLtsCVOtN6nUDm8WKKtkrcQDWcxmG\nhuHocF/XbnSD68uFJPgV+6+laaSKOSJdnnDZb937RNmherNFs4XUXu1Qf//dO9N2b4vrVYZhkMhn\nGR6GY13+EKo3NRoNsFmk34j9V6nXqbbKRMJq1y/b1psazYaMOeJgbBTy9AV1RsNuAt7euIlzP5gu\n+G1pOroGFtnVvW2qqvLud7/rru977LHHZNc87VupHK4mw2EHQ/3dvdu2pelouvQbcTDWcxkiEZgc\nCnTlhTDXa2k6miYniYj9pxsGiUI74SJZ3zszVW+8+hBSTPVt7xlFUTh6dIqHHnrLbd9z3333MzU1\nJRs72NysMwzHRro/E77VdyT43Ru6rqNpGpqmSYnQDerNJvlakUhEYaoH+k6zpaFpYFW7O4gXh1+6\nWMDjbTE04CTc5+l0cw41U63DyAx890KhEP/yX/5Lnn76ab73ve+h6xoAqmrh8ccf58knn2RgYKDD\nrey8XKUM1jqRsJXRge7drLNFVkz2Rr1eZ3FxkXPnznH58iX8fj/vfOe7mJqaYmBgQCaNtDfrhMMG\nExE/Lkf318m2NJ1arYnSKmA3wOv1yhgk9pxhGKznMhyd7o2Ey34zX/Crg0UyvzumKApjY2N86lOf\n4kMf+hCJRByASCTCxMQRHA5Hh1t4OMRzGYZH2kc09cIZi1czv9J3dqxarfK3f/u3/P7v/z6Gce00\nlO985zucPHmKf/tv/y3j4+OmDoCbmka6nOeBqe5ftjUMg5WVFb77vb/jT7/8BqmFLE6LjSeeeIIH\nH3yQqakprFIHLPZIrlLG6mgQGbAxMiAXTd2NqXqeLN3uHYfDwfHjxzl+/Hinm3LoFKtV6nqFyKCF\nI12+WWeLrJrsjmEYXLx4kd/7vd+95eszM5f5sz/7M37xF38Rj8e8y5VbZ2KPhj343N07kTYMg0uX\nLvGpT32KF84ug/VhyAMtjT/7sz9FVS384i/+Iu9+97uxySkQYg+sZtOMTrQ3V5t5An2vTDWSydKt\nOAgrmSRjY+2lJ2sXH9F0vWZLQ9OlbnGnarUq3/jGN+74npdeepHl5eUDatHhU282SRazjI7AifFQ\np5uzKxsbG3z605/mhRdeAEUFLHBdbbeua/yn//SfOH/+vNR8i11LFQuo9hojQ7auvkX0IPXGyHyP\nJPMr9lu6VMSwVBkdtvZU3ZWmG9J3diGdzvDqq6/e9X3xePwAWnM4rWbThAd1Jod9BH3deyY2wLlz\n5/hf/+t/tf/jFsEvgGHo/M3f/A3VavXgGyh6hm4YxDIpJsbh5Hioq8/EPkim+inJ0q3YT1sPofEJ\nODk+0FMPoWZLQ5e+s+/MmgWs1OvkawVGRxVOHwl3ujm7ous6zz333LUP3Cb4hfZtmKursYNrnOg5\niXwWj7/JyKCjq29CPGimGsk0TUfX5agzsT82CjlcngajEQfjg731ENINY7PvSC3ZTgSDQR566KG7\nvm9oaOgAWnP4RNNJRkYNjo304XZ2dw2srutcuXLl2gcUFVDhlvMag0pFMr9iZ5qaRjyfYXwMzkyG\npdZ3G0wVBSqKQvt3w5zZFbF/WprGei7N+Dicnui9I6sURQHFvJnJ3XK73fz4j/+jO77n4YffypEj\nRw6oRYdHvlKmYZQZHbZwfKy7a32h3Vcikci1DxgGdxpz7Hb7/jdK9KT1bJr+kMbEkEfO9d0mUwW/\nqtoOfnUZwMUeW89l6OvXGIu4iXT5bW63oioKqgS/O6YoCvfddz8///P//JavT00d4+d+7udMd9KD\nYRispJOMj8PxsX7stu7fUKmqKj/xEz9x3Uc2g99bzIenpo4xPDx8UE0TPaTebJKu5HqiVKgTTHXU\nmXKvmtwAAB54SURBVMLmCpSM32IP1ZtNUqUc9z/QXnrqRYpCO/Pb6YZ0MbfbzQc/+EGOHz/Oq6++\nyoULF/D7/Tz++ONMTU0RiUR6bsXgblKlAhZHnZEhG0d75FhARVF46KGHOHbsGPPz8+3Mr2LQHoHe\n3IOefPJJAoFAR9oputtKJklkyODoSAC/p3uPBewUUwW/qtrOXknmV+ylWDZFOKIzOeynz+vsdHP2\nhWR+94bT6eTMmTOcOnUKXddRFAVVVU0X9AJous5qJs30CTg10VsbRI8cOcKXvvQlPvrRj7Jeuy7z\ne133+af/9OM89NBDpvx/L3anVKtRahSZHlY5NSE3qu6EqYJfi6qiqqBfd7uSELtRqtUo1Aq85ZTS\n0w8hi6XddzRN+s5eUFXV9CdnxPNZvP4moxFnz+1SVxSFt7/97XzrW9/id7/8Tf78v1+inK0B8Oij\nj/JjP/ZjnD59Gq+390qkxP6LpjcYHYPjY0GcdlOFcXvGVD8169YArssALnbPMAyWknEmJtoPoW7f\npX4nVouKxQJ6U/qO2L1qo0GikOb+++G+ni0VUnjwwQf5uO7DH0wQsgbxOl2EwwM4nS7J+Iod2Sjk\nUGxVxkesXX8FeCeZLvi1WttH0QixW/F8FpurzviIjRM9sEv9TqwWFdUiE0exN5ZTCUZHDabHAoQC\n7k43Z1/ZrVYGBoKM+sbxu3r7exX7q9lqsZpNceoM3H90sGduEO0EU/3krg7gUvYgdqnebLKeTzM5\nCQ8ei/RUveKtWC0qVgl+xR5IFvNoaoWJMaspdqlvrZpI3xG7tZzeIDyoMTnsZTjk63Rzulpvj9g3\nsFn///bu4zfy7Frs+LdyzlUsVmCRLGY22a3pkWZGetJ7C/0fAvQXCDBgwBstBAjaPC8FaG0bMGAY\nfga8k7zSysCD5DCa0GE6MVbO+Ze8qCK71dO5SVb4nQ9AkMNhDw7Yc3/3/M49997xBK5q2rRDEXPu\naaXIckonnwma4nxFh82KzQ6qLmNHfDhF0ziplVlbG7c7LMLRZm/jsMvYER+v0evSU9vkVqwc5pem\nHc7cM1nya8PtsmGx6iiSAIsPVGm3UCxdclnbwvYrvszjcuByjSveQnyoo0qJeEJjLeMjs2Cb3F5H\nxo74WJqu86xSZG0d9lbjeFyLu7/kppgq+QXwyoNIfISRqnJcK5Ffh8P8Ei6T7LT1uSfjRpVxIz5M\nvduhq7ZYzVm5nU++/Q8sCJ/bgVvGjvgIR9UyoYjCatrDemoxzsOeNvMlvxeTuDKadihiDj2rFEkk\nNdbSvoU7nulNXE47bqcVA03ahsR7UzSNp5Ui+TzcWk8s9MkoL5OCi/gYzV6X1rDB6qqFH2wuyykh\nV8R0ya/P7cDtlrdw8f6q7RZDo8Pqio07m8vTDufGed0OXDJ2xAc4qpaIxVVW017WFuQmt3f1vOAi\n40a8H03XeVopsrYG+2tx/B7ntENaGKZLfi/fwmUCF+9hqCgc1Uqsr8PBesKUB4v7ZBIXH6DaadNV\nWuRyVu5smKfd4YLbacftsqAZqpz4IN7L00qRYERhNeNmIx2ZdjgLxXzJ72QCH8gELt6Rbhg8Kp2T\nSmvks35yydC0Q5qKixdHGTviXQ2UEUfVApubcDufwGfCypXFYnle/ZWii3hH5XaTntoiv27lk62U\ntDtcMdMlvz63c7x0KxO4eEcntQoOT5/1VQefbJmv3eGCz+OUjTvinemGwXfFczJZnc2VAKsma3d4\nkfT9ivfRGw05qZXY3II7G0vS7nANTJf8elx2XE4Lqq6iG8a0wxEzrt7tUO/XyOctfLqdwmFf/HNJ\nX0cmcPE+jiol3L4B6zmnKXvkX+TzOMd7TWTsiLfQdJ1HxXOyOZ3tXIiVJXOuNF430yW/FouFgNeJ\n22PQGw6mHY6YYUNF4WmlwMYGHObjRAKeaYc0VQGvE68XujJuxFvUOm2aowYbG+OXRrNfwxrwOPHI\n2BHv4Khawhccks+5OFyXyyyuiymfSNGAh2AAWv3+tEMRM8qY9PkupzTyWR8bmei0Q5o6j8tByO/A\n4dToDYfTDkfMqKGi8KxaZHMDDvMJQn73tEOaumhwPOe0BzLniNertFt0lCb5vJVPt1PYTP7SeJ1M\n+ZuNBT34/dCRB5F4jZNaBbu7T37NwSdbqWmHMzOiQQ8BmcTFa4z7fM9IZzQ2cwHWU7JDHSDgdRHw\n2TCsirQ+iFfqj0Yc14psbo77fANe17RDWmimTH6jQQ+B4HgCN6TvV7yk0etS7Y37fO9upXA6zNvn\n+7JY0DtJfnvTDkXMoONqGadvwHrOYcpjzd5Eqr/idcZ9vmdkV3S2ckHTnih0k0yZ/HpcDkK+8fJt\nfyQ3vYnnhorCk/L5uM93I040aO4+35dJ5Ve8TrX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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -565,10 +572,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Van der Merwe's Scaled Sigma Point Algorithm\n", - "There are several published ways for selecting the sigma points for the Unscented Kalman filter, and you are free to invent your own. However, since 2005 or so research and industry have mostly settled on the version published by Rudolph Van der Merwe in his 2004 PhD dissertation [1] because it performs well with a variety of problems and it has a good tradeoff between performance and accuracy. It is a slight reformulation of the *Scaled Unscented Transform* published by Simon J. Julier [2].\n", + "## The Unscented Transform" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the moment assume an algorithm exists for selecting the sigma points and weights. How are the sigma points used to implement a filter?\n", "\n", - "Before we work through the derivation, let's look at an example. I will plot the sigma points on top of a covariance ellipse showing the first and second standard deviations, and size them based on the weights assigned to them." + "The **unscented transform** is the core of the algorithm yet it is remarkably simple. For each dimension in the state space we deterministically choose 3 sigma points and corresponding weights. We pass the sigma points through a (usually nonlinear) function yielding a transformed set of points.\n", + "\n", + "$$\\boldsymbol{\\mathcal{Y}} = f(\\boldsymbol{\\chi})$$\n", + "\n", + "We then compute a new mean and covariance of the transformed sigma points. The figure below depicts the operation of the unscented transform. The green ellipse on the right represents the computed mean and covariance to the transformed sigma points. " ] }, { @@ -577,125 +594,12 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "image/png": 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Ti/lDXakUV1bIJrAci3AYSJItxCRs7+612uiwZq2RSUecnMvJhBRxRZmUwSgc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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3.91\n" - ] - } - ], - "source": [ - "ukf_internal.plot_sigma_points()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the sigma points lie between the first and second deviation, and that the larger $\\alpha$ spreads the points out further. Furthermore, the larger $\\alpha$ weighs the mean (center point) higher than the smaller $\\alpha$, and weighs the rest of the sigma points less. This should fit our intuition - the further a point is from the mean the less we should weight it. We don't know *how* these weights and sigma points are selected yet, but the choices look reasonable.\n", - "\n", - "Van der Merwe's formulation uses 3 parameters to control how the sigma points are distributed and weighted: $\\alpha$, $\\beta$, and $\\kappa$. We will go into detail with these later. For now, $\\beta=2$ is a good choice for Gaussian problems, $\\kappa=3-N$ is a good choice for $\\kappa$, and $0 \\le \\alpha \\le 1$ is an appropriate choice for $\\alpha$, where a larger value spreads the sigma points further from the mean.\n", - "\n", - "Our first sigma point is always going to be the mean of our input. This is the sigma point displayed in the center of the ellipses in the diagram above. We will call this $\\boldsymbol{\\chi}_0$. So,\n", - "\n", - "$$ \\mathcal{X}_0 = \\mu$$\n", - "\n", - "For notational convenience we define $\\lambda = \\alpha^2(n+\\kappa)-n$. Then the remaining sigma points are computed as\n", - "\n", - "$$ \n", - "\\begin{aligned}\n", - "\\boldsymbol{\\chi}_i &= \\mu + (\\sqrt{(n+\\lambda)\\Sigma})_i\\,\\,\\,\\, &\\text{for}\\text{ i=1 .. n} \\\\\n", - "\\boldsymbol{\\chi}_i &= \\mu - (\\sqrt{(n+\\lambda)\\Sigma})_{i-n}\\,\\,\\,\\,\\, &\\text{for}\\text{ i=(n+1) .. 2n} \\\\\n", - "\\end{aligned}$$\n", - "\n", - "In other words, we scale the covariance matrix by a constant, take the square root of it, and then to ensure symmetry both add and subtract if from the mean. We will discuss how you take the square root of a matrix later.\n", - "\n", - "Van der Merwe's forumation uses one set of weights for the means, and another set for the covariance. The weights for the mean of $\\mathcal{X}_0$ is computed as\n", - "\n", - "$$W^m_0 = \\frac{\\lambda}{n+\\lambda}$$\n", - "\n", - "The weight for the covariance of $\\mathcal{X}_0$ is\n", - "\n", - "$$W^c_0 = \\frac{\\lambda}{n+\\lambda} + 1 -\\alpha^2 + \\beta$$\n", - "\n", - "The weights for the rest of the sigma points $\\boldsymbol{\\chi}_1 ... \\boldsymbol{\\chi}_{2n}$ are the same for the mean and covariance. They are\n", - "\n", - "$$W^m_i = W^c_i = \\frac{1}{2(n+\\lambda)}\\;\\;\\;i=1..2n$$\n", - "\n", - "It may not be obvious why this is 'correct', and indeed, it cannot be proven that this is ideal for all nonlinear problems. But you can see that we are choosing the sigma points proportional to the square root of the covariance matrix, and the square root of variance is standard deviation. So, the sigma points are spread roughly according to 1 standard deviation. However, there is an $n$ term in there - the more dimensions there are the more the points will be spread out and weighed less." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The update step converts the sigmas into measurement space via the `h(x)` function.\n", - "\n", - "\n", - "$$\\mathcal{Z} = h(\\mathcal{Y})$$\n", - "\n", - "The mean and covariance of those points is computed with the unscented transform. The residual and Kalman gain is then computed. The cross variance is computed as:\n", - "\n", - "$$\\mathbf{P}_{xz} =\\sum W(\\mathcal{X}-x)(\\mathcal{X_z}-\\mathbf{x}_z)^\\mathsf{T}$$\n", - "\n", - "Finally, we compute the new state estimate using the residual and Kalman gain:\n", - "\n", - "$$\\hat{\\mathbf{x}} = \\mathbf{x}^- + \\mathbf{Ky}$$\n", - "\n", - "and the new covariance is computed as:\n", - "\n", - "$$ \\mathbf{P} = \\mathbf{P}^- - \\mathbf{PKP}_z\\mathbf{K}^\\mathsf{T}$$\n", - "\n", - "This function can be implemented as follows, assuming it is a method of a class that stores the necessary matrices and data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The computation of the new mean and covariance is called the *unscented transform*. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The Unscented Transform" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The unscented transform is the core of the algorithm yet it is remarkably simple. For each dimension in the state space we deterministically choose 3 sigma points and corresponding weights using the algorithm above. We pass the sigma points through the nonlinear function yielding a transformed set of points\n", - "\n", - "$$\\boldsymbol{\\mathcal{Y}} = f(\\boldsymbol{\\chi})$$" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, "outputs": [ { "data": { "image/png": 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ZBsaD/Hj7iofaB+zz+fj000955513+OSTT0ilUqTTaRKJbMCy2WwYhkF5eTkv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\nbVte2hnuxEQwylR8CqdDwW4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oHpokFBqlo+8kj1Q8gXYftxvMBoqiUFJSQklJfvaVWpaJ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\nZIYzA1ESUQONRY3MrXbTWuOS44qFyKLLZVi5LBVCCHHV3EUWVrRWAXD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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -710,22 +614,24 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We then compute the new mean and covariance using these equations.\n", + "The mean and covariances are computed using these equations.\n", "\n", "$$\\begin{aligned}\n", - "\\mu &= \\sum_i w_i\\boldsymbol{\\mathcal{Y}}_i \\\\\n", - "\\Sigma &= \\sum_i w_i{(\\boldsymbol{\\mathcal{Y}}_i-\\mu)(\\boldsymbol{\\mathcal{Y}}_i-\\mu)^\\mathsf{T}}\n", + "\\mu &= \\sum_i w^m_i\\boldsymbol{\\mathcal{Y}}_i \\\\\n", + "\\Sigma &= \\sum_i w^c_i{(\\boldsymbol{\\mathcal{Y}}_i-\\mu)(\\boldsymbol{\\mathcal{Y}}_i-\\mu)^\\mathsf{T}}\n", "\\end{aligned}\n", "$$\n", "\n", - "These equations should be familar - they are the constraint equations we developed above. They perhaps don't look like much, but let's see an example of their power.\n", + "These equations should be familar - they are the constraint equations we developed above. \n", "\n", - "Earlier we wrote a function that found the mean of a distribution by passing 50,000 points through a nonlinear function. Let's now use 5 sigma points - we will pass them through the nonlinear function, and compute their mean with the unscented transform. This code is below. Under the comment `### create sigma points` I use code from FilterPy to generate the sigma points. It uses functionality which you will learn later in this chapter; pass by it for now. The key points are we generate 5 points deterministically, pass them through the nonlinear function, and then run the unscented transform on them to generate the estimated mean." + "### Unscented Transform Example\n", + "\n", + "Earlier we wrote a function that found the mean of a distribution by passing 50,000 points through a nonlinear function. Let's now pass 5 sigma points through the same function, and compute their mean with the unscented transform." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "collapsed": false, "scrolled": true @@ -735,14 +641,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Difference in mean x=-0.013, y=-0.691\n" + "Difference in mean x=-0.097, y=0.549\n" ] }, { "data": { - "image/png": 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s4+/Zswfnnnsu3G431qxZgwEDBlj2z5kzB7/73e+aZW6NhUII9xk1ahSef/55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ZuqRJbHOS+WKONu137NMQgSX50zx9TvM8xkzk0DgECajzBNtY3ZFnpOyDrMJU499VTzet26dTju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\nSgcHBwcHBwcHh+bHjOXRnArms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HliFircpEImFutyOIToRRzkFtL0mh7JMEkWul240VjUYRiUSQSCQs/TiRVuBQ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\nOHLp4OBwIIIyNxwOo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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -756,11 +662,13 @@ "def f_nonlinear_xy(x, y):\n", " return np.array([x + y, .1*x**2 + y*y])\n", "\n", + "#initial mean and covariance\n", "mean = (0, 0)\n", "p = np.array([[32., 15],\n", " [15., 40.]])\n", "\n", - "### create sigma points\n", + "### create sigma points - we will learn about this later\n", + "### in the chapter\n", "points = MerweScaledSigmaPoints(n=2, alpha=.1, beta=2., kappa=1.)\n", "Wm, Wc = points.weights()\n", "sigmas = points.sigma_points(mean, p)\n", @@ -770,13 +678,14 @@ "for i in range(5):\n", " sigmas_f[i] = f_nonlinear_xy(sigmas[i, 0], sigmas[i ,1])\n", "\n", - "### pass through unscented transform\n", - "ukf_mean, _ = unscented_transform(sigmas_f, Wm, Wc)\n", + "### use unscented transform to get new mean and covariance\n", + "ukf_mean, ukf_cov = unscented_transform(sigmas_f, Wm, Wc)\n", "\n", "#generate random points\n", + "np.random.seed(100)\n", "xs, ys = multivariate_normal(mean=mean, cov=p, size=5000).T\n", "\n", - "plot_monte_carlo_mean(xs, ys, f, ukf_mean, 'UKF Mean')\n", + "plot_monte_carlo_mean(xs, ys, f, ukf_mean, 'Unscented Mean')\n", "plt.subplot(121)\n", "plt.scatter(sigmas[:,0], sigmas[:,1], c='r', s=40);" ] @@ -785,18 +694,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "I find this result remarkable. Using only 5 points we were able to compute the mean with amazing accuracy. The error in x is only 0.054, and the error in y is 0.408. In contrast, a linearized approach (used by the predominant EKF filter) gave an error of over 43 in y. I told you to ignore the code to generate the sigma points, but if you look at it you'll see that it has no knowledge of the nonlinear function, only of the mean and covariance of our initial distribution. The same 5 sigma points would be generated if we had a completely different nonlinear function. \n", + "I find this result remarkable. Using only 5 points we were able to compute the mean with amazing accuracy. The error in x is only -0.097, and the error in y is 0.549. In contrast, a linearized approach (used by the predominant EKF filter) gave an error of over 43 in y. If you look at the code that generates the sigma points you'll see that it has no knowledge of the nonlinear function, only of the mean and covariance of our initial distribution. The same 5 sigma points would be generated if we had a completely different nonlinear function. \n", "\n", - "I will admit to choosing a nonlinear function that makes the performance of the unscented tranform striking compared to the EKF. But the physical world is filled with very nonlinear behavior, and the UKF takes it in stride. You will see in the next chapter how more traditional techniques struggle with strong nonlinearities. This graph is the foundation of why I advise you to use the UKF or similar modern technique whenever possible.\n", - "\n", - "> The UKF belongs to a family of filters called sigma point filters. By the end of this chapter you will be prepared to do a literature search and learn about the various sigma point filters that have been invented." + "I will admit to choosing a nonlinear function that makes the performance of the unscented tranform striking compared to the EKF. But the physical world is filled with very nonlinear behavior, and the UKF takes it in stride. I did not 'work' to find a function where the unscented transform happened to work well. You will see in the next chapter how more traditional techniques struggle with strong nonlinearities. This graph is the foundation of why I advise you to use the UKF or similar modern technique whenever possible." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## The Unscented Filter" + "## The Unscented Kalman Filter" ] }, { @@ -817,15 +724,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As with the linear Kalman filter, the UKF's predict step computes the mean and covariance of the system for the next epoch using the process model. However, the computation is quite different.\n", + "As with the linear Kalman filter, the UKF's predict step computes the mean and covariance of the system for the next evolution using the process model. However, the computation is quite different.\n", "\n", - "First, we generate the weights and sigma points as specified above.\n", + "First, we generate sigma points and their corresponding weights according to some function:\n", "\n", "$$\\begin{aligned}\n", - "\\mathcal{X} &= sigma\\_function(\\bf{\\mu}, \\bf{\\Sigma}) \\\\\n", - "W &= weight\\_function()\\end{aligned}$$\n", + "\\mathcal{X} &= \\mathtt{sigma\\_function}(\\bf{\\mu}, \\bf{\\Sigma}) \\\\\n", + "W^m, W^c &= \\mathtt{weight\\_function}(\\mathtt{n, parameters})\\end{aligned}$$\n", "\n", - "We pass each sigma point through the function f. This projects the sigma points forward in time according to our process model.\n", + "We pass each sigma point through a process function $f(\\mathcal{X})$ which you define. This projects the sigma points forward in time according to the process model.\n", "\n", "$$\\boldsymbol{\\mathcal{Y}} = f(\\boldsymbol{\\chi})$$" ] @@ -842,12 +749,15 @@ "\\end{aligned}\n", "$$\n", "\n", - "This computes the mean and covariance represented by the green ellipse above, and corresponds with the linear Kalman filter equations of\n", + "This table compares the Kalman filter with the Unscented Kalman Filter equations.\n", "\n", - "$$ \\begin{aligned}\n", - "\\mathbf{x}^- &= \\mathbf{Fx}\\\\\n", - "\\mathbf{P}^- &= \\mathbf{FPF}^\\mathsf{T}+\\mathbf{Q}\n", - "\\end{aligned}$$" + "$$\\begin{array}{l|l}\n", + "\\mathrm{Kalman} & \\mathrm{Unscented} \\\\\n", + "\\hline \n", + "\\mathbf{x}^- = \\mathbf{Fx} & \\mathbf{\\mu}^- = \\sum w^m\\boldsymbol{\\mathcal{Y}} \\\\\n", + "\\mathbf{P}^- = \\mathbf{FPF}^\\mathsf{T}+\\mathbf{Q} & \\mathbf{P}^- = \\mathbf{FPF}^\\mathsf{T}+\\mathbf{Q}\n", + "\\end{array}$$\n", + "\n" ] }, { @@ -861,7 +771,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we can perform the update step of the filter. Recall that Kalman filters perform the update state in measurement space. So, the first thing we do is convert the sigma points from the predict step into measurements using the $h(x)$ function that you define.\n", + "Now we can perform the update step of the filter. Recall that Kalman filters perform the update state in measurement space. So, the first thing we do is convert the sigma points from the predict step into measurements using a measurement function $h(x)$ that you define.\n", "\n", "$$\\boldsymbol{\\mathcal{Z}} = h(\\boldsymbol{\\mathcal{Y}})$$\n", "\n", @@ -875,28 +785,143 @@ "\n", "The $z$ subscript denotes that these are the mean and covariance for the measurements.\n", "\n", - "All that is left is to compute the residual and Kalman gain. The residual is trivial to compute:\n", + "All that is left is to compute the residual and Kalman gain. The residual of the measurement $\\mathbf{z}$ is trivial to compute:\n", "\n", - "$$\\mathbf{y} = \\boldsymbol{\\mathcal{Z}} - \\boldsymbol{\\mu}_z$$\n", + "$$\\mathbf{y} = \\mathbf{z} - \\boldsymbol{\\mu}_z$$\n", "\n", - "\n", - "The Kalman gain is not much worse. We have to compute the cross variance of the state and the measurements, which we state without proof to be: \n", + "To compute the Kalman gain we first compute the [cross covariance](https://en.wikipedia.org/wiki/Cross-covariance) of the state and the measurements, which is defined as: \n", "\n", "$$\\mathbf{P}_{xz} =\\sum w^c(\\boldsymbol{\\chi}-\\mu)(\\boldsymbol{\\mathcal{Z}}-\\mathbf{\\mu}_z)^\\mathsf{T}$$\n", "\n", "And then the Kalman gain is defined as\n", "\n", - "$$K = \\mathbf{P}_{xz} \\mathbf{P}_z^{-1}$$\n", + "$$\\mathbf{K} = \\mathbf{P}_{xz} \\mathbf{P}_z^{-1}$$\n", + "\n", + "If you think of the inverse as a *kind of* matrix division, you can see that the Kalman gain is a simple ratio which computes:\n", + "\n", + "$$\\mathbf{K} \\approx \\frac{\\mathbf{P}_{xz}}{\\mathbf{P}_z^{-1}} \n", + "\\approx \\frac{\\text{belief in state}}{\\text{belief in measurement}}$$\n", "\n", "Finally, we compute the new state estimate using the residual and Kalman gain:\n", "\n", - "$$\\hat{\\mathbf{x}} = \\mathbf{x}^- + \\mathbf{Ky}$$\n", + "$$\\mathbf{x} = \\mathbf{x}^- + \\mathbf{Ky}$$\n", "\n", "and the new covariance is computed as:\n", "\n", - "$$ \\hat{\\mathbf{P}} = \\mathbf{\\Sigma}^- - \\mathbf{KP_z}\\mathbf{K}^\\mathsf{T}$$\n", + "$$ \\mathbf{P} = \\mathbf{\\Sigma}^- - \\mathbf{KP_z}\\mathbf{K}^\\mathsf{T}$$\n", "\n", - "This step contains a few equations you have to take on faith, but you should be able to see how they relate to the linear Kalman filter equations. We convert the mean and covariance into measurement space, add the measurement error into the measurement covariance, compute the residual and kalman gain, compute the new state estimate as the old estimate plus the residual times the Kalman gain, and then convert both back into state space. The linear algebra is slightly different from the linear Kalman filter, but the algorithm is the same." + "This step contains a few equations you have to take on faith, but you should be able to see how they relate to the linear Kalman filter equations. We convert the mean and covariance into measurement space, add the measurement error into the measurement covariance, compute the residual and kalman gain, compute the new state estimate as the old estimate plus the residual times the Kalman gain, and adjust the covariance for the information provided by the measurement. The linear algebra is slightly different from the linear Kalman filter, but the algorithm is the same Bayesian algorithm we have been implementing throughout the book. \n", + "\n", + "This table comparing the Kalman filter equations with the UKF equations should help clarify the relationship between the filters." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\\begin{array}{l|l}\n", + "\\textrm{Kalman Filter} & \\textrm{Unscented Kalman Filter} \\\\\n", + "\\hline \n", + "\\mathbf{x}^- = \\mathbf{Fx} & \\mathbf{\\mu}^- = \\sum w^m\\boldsymbol{\\mathcal{Y}} \\\\\n", + "\\mathbf{P}^- = \\mathbf{FPF}^\\mathsf{T}+\\mathbf{Q} & \\mathbf{P}^- = \\mathbf{FPF}^\\mathsf{T}+\\mathbf{Q} \\\\\n", + "\\hline \n", + "\\mathbf{y} = \\boldsymbol{\\mathbf{z}} - \\mathbf{Hx} &\n", + "\\mathbf{y} = \\mathbf{z} - \\sum w^m h(\\boldsymbol{\\mathcal{Y}})\\\\\n", + "\\mathbf{S} = \\mathbf{HP^-H}^\\mathsf{T} + \\mathbf{R} & \n", + "\\mathbf{P}_z = \\sum w^c{(\\boldsymbol{\\mathcal{Z}}-\\mu^-)(\\boldsymbol{\\mathcal{Z}}-\\mu^-)^\\mathsf{T}} + \\mathbf{R} \\\\ \n", + "\\mathbf{K} = \\mathbf{P^-H}^\\mathsf{T} \\mathbf{S}^{-1} &\n", + "\\mathbf{K} = \\left[\\sum w^c(\\boldsymbol{\\chi}-\\mu)(\\boldsymbol{\\mathcal{Z}}-\\mathbf{\\mu}_z)^\\mathsf{T}\\right] \\mathbf{P}_z^{-1}\\\\\n", + "\\mathbf{x} = \\mathbf{x}^- + \\mathbf{Ky} & \\mathbf{x} = \\mathbf{x}^- + \\mathbf{Ky}\\\\\n", + "\\mathbf{P} = (\\mathbf{I}-\\mathbf{KH})\\mathbf{P}^- & \\mathbf{P} = \\mathbf{\\Sigma}^- - \\mathbf{KP_z}\\mathbf{K}^\\mathsf{T}\n", + "\\end{array}$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Van der Merwe's Scaled Sigma Point Algorithm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are several published ways for selecting the sigma points for the Unscented Kalman filter, and you are free to invent your own. However, since 2005 or so research and industry have mostly settled on the version published by Rudolph Van der Merwe in his 2004 PhD dissertation [1] because it performs well with a variety of problems and it has a good tradeoff between performance and accuracy. It is a slight reformulation of the *Scaled Unscented Transform* published by Simon J. Julier [2].\n", + "\n", + "Van der Merwe's formulation uses 3 parameters to control how the sigma points are distributed and weighted: $\\alpha$, $\\beta$, and $\\kappa$. Before we work through the equations, let's look at an example. I will plot the sigma points on top of a covariance ellipse showing the first and second standard deviations, and size them based on the weights assigned to them. I do this by adjusting $\\alpha$." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Ti/lDXakUV1bIJrAci3AYSJItxCRs7+612uiwZq2RSUecnMvJhBRxRZmUwSgc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\nWS2WOdcsHvf5i7NCkuwpSMb140qHNO6J2zcYeWzt27TMFq1imQutkgR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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.91\n" + ] + } + ], + "source": [ + "ukf_internal.plot_sigma_points()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "We can see that the sigma points lie between the first and second deviation, and that the larger $\\alpha$ spreads the points out. Furthermore, the larger $\\alpha$ weighs the mean (center point) higher than the smaller $\\alpha$, and weighs the rest of the sigma points less. This should fit our intuition - the further a point is from the mean the less we should weight it. We don't know *how* these weights and sigma points are selected yet, but the choices look reasonable.\n", + "\n", + "Van der Merwe's formulation uses 3 parameters to control how the sigma points are distributed and weighted: $\\alpha$, $\\beta$, and $\\kappa$. We will go into the details later. For now, $\\beta=2$ is a good choice for Gaussian problems, $\\kappa=3-N$ is a good choice for $\\kappa$, and $0 \\le \\alpha \\le 1$ is an appropriate choice for $\\alpha$, where a larger value for $\\alpha$ spreads the sigma points further from the mean.\n", + "\n", + "### Sigma Point Computation\n", + "\n", + "Our first sigma point is always going to be the mean of our input. This is the sigma point displayed in the center of the ellipses in the diagram above. We will call this $\\boldsymbol{\\chi}_0$. So,\n", + "\n", + "$$ \\mathcal{X}_0 = \\mu$$\n", + "\n", + "For notational convenience we define $\\lambda = \\alpha^2(n+\\kappa)-n$. Then the remaining sigma points are computed as\n", + "\n", + "$$ \n", + "\\boldsymbol{\\chi}_i = \\begin{cases}\n", + "\\mu + (\\sqrt{(n+\\lambda)\\Sigma})& \\text{for i=1 .. n} \\\\\n", + "\\mu - (\\sqrt{(n+\\lambda)\\Sigma})_{i-n} &\\text{for i=(n+1) .. 2n}\\end{cases}\n", + "$$\n", + "\n", + "In other words, we scale the covariance matrix by a constant, take the square root of it, and then to ensure symmetry both add and subtract if from the mean. We will discuss how you take the square root of a matrix later.\n", + "\n", + "### Weight Computation\n", + "\n", + "This forumation uses one set of weights for the means, and another set for the covariance. The weights for the mean of $\\mathcal{X}_0$ is computed as\n", + "\n", + "$$W^m_0 = \\frac{\\lambda}{n+\\lambda}$$\n", + "\n", + "The weight for the covariance of $\\mathcal{X}_0$ is\n", + "\n", + "$$W^c_0 = \\frac{\\lambda}{n+\\lambda} + 1 -\\alpha^2 + \\beta$$\n", + "\n", + "The weights for the rest of the sigma points $\\boldsymbol{\\chi}_1 ... \\boldsymbol{\\chi}_{2n}$ are the same for the mean and covariance. They are\n", + "\n", + "$$W^m_i = W^c_i = \\frac{1}{2(n+\\lambda)}\\;\\;\\;i=1..2n$$\n", + "\n", + "It may not be obvious why this is 'correct', and indeed, it cannot be proven that this is ideal for all nonlinear problems. But you can see that we are choosing the sigma points proportional to the square root of the covariance matrix, and the square root of variance is standard deviation. So, the sigma points are spread roughly according to 1 standard deviation. However, there is an $n$ term in there - the more dimensions there are the more the points will be spread out and weighed less." ] }, { @@ -961,7 +986,7 @@ "data": { "image/png": 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AcAs9/Z0q/TCYl9WfU//g1FvdeXh4Kjk6U4mWjCvBPDxBloh4BfgGubBjYHYR1AEAwJxx\nGA6drzyhP55+VfWtldPWRofHKztxhTIT8pURv0x+Pv4u6hKYGwR1AADgcoZh6EL1Sb1+4hU1t9dO\nWhPoH6LshOXKSlyhrMTlCguOdG2TwBwjqAMAAJcxDEMXa07pjROvqLGtesI5Tw8vpcbmKDtxhbKT\nViguKkUeJo8prgQsfAR1AAAw67r62nS69F19UHpEts7GCed8vHx1/fLbdPOqv1BwAM9PAT5CUAcA\nALNieGRQ56pO6IOSI6pouCBDE5+x6O3lo+vzt+pzq+9UcMCSOeoScF8EdQAA4BSGYaizr1W1LWUq\nrj2rc5XHNTI2fFWdj5evNiy9VbcW/JVCAsPmoFNgfiCoAwCAT2VkbFgNtirVWstU01Km2pYy9V7u\nmrTWJJMyE/K1JudGLU9bL192bAGuiaAOAACu6fJwv6wdDWpur1NzR50abJVqbKuR3TE27ThLeILW\n5tyk1Vmb2bUF+IQI6gAAYNzI6LBau5vU0lGv5vY6tbTXqaWjXl397TMa7+vjr+ToTCXHZCk/bZ3i\no1J5KijwKRHUAQBYZByGQ119bWrtalZrV9PH/ts040D+EfOSWCXHZCklJlspMVmyhCfIw8NzljoH\nFheCOgAAi0BjW7WOXXxTVU2X1NbdojH76Cca7+nhpejweMVGJCkmIlGxkUlKtmQq0D9kljoGQFAH\nAGCBGh4Z1Nnyozp28bDqbBUzGuNh8lBEqEWW8HjFRiYpJuLKy7wkRp6exAbAlfg/DgCABebycL9e\nP/5/dbLkLQ2PDE5aE+QfKnNYrMxhcYoOi5P5w1dEiFlent4u7hjAZAjqAAAsII1t1fqX//6h2nus\nE457enppRfpGrc/9nBLMaQrwC5qjDgHMFEEdAIAF4mTxn/Sbt17SqH1k/Jg5LE4bl27RupybWE8O\nzDMEdQAA5hm7fUyt3c1qbq9VU3udmttr1dxeq+7+jvEaX28//fXntmtV5nVsjwjMUwR1AADcnGEY\namitUlHFMZXUF8ra2SC7feoHDVnCE/St2/9B0WFxLuwSgLMR1AEAcEOGYajOVqGiimMqqjymzt7W\na47x8vTWmuwb9ZebvylfH38XdAlgNhHUAQBwE3aHXXXWchVVHte5imPTPnwoLDhKsZFJiotMVmxk\nsmIjkxS1JFaePGwIWDAI6gAAzBGH4VBTW60qGs+rouGiKpsvTbmdor9PgJalrdPy9A1Ki8tVgC+7\ntgALHUEdAAAXcRgOWTvqVdF4URWNF1TZeEmXh/unrA/wC1Z+6lqtyNiozIR89jcHFhmCOgAAs8Rh\nONTcXqvKxkuqbLqkqqZLGhjqm3bMkqAI5SStuhLO45fxNFBgEeP/fgAAnMThsKuxrUaVTRdV2XhJ\nVc3FGhwemHZMcMASZcYvU0bCMmXEL1NkqIXtFAFIIqgDAPCpjYwNq85aoermYlU1FavGWjblGvOP\nBPmHKi0uVxnxy5SZsEzRYfEEcwCTIqgDADBDo2OjKqsvUlVzsaqai9Vgq5LdMfV+5tKVO+YZ8UuV\nFpen9LilsoQTzAHMjFODektLi/7hH/5Bb7zxhvr6+pSamqoXX3xRmzdvHq/ZvXu3Dhw4oK6uLq1b\nt0779+9Xbm6uM9sAAMCpOnvb9P6F3+v4pT+qf7Bn2trQoAilx+VdecUvlXlJLMEcwKfitKDe3d2t\nTZs2afPmzXr99dcVFRWl6upqmc3m8Zp9+/bpmWee0cGDB5WZmaknnnhCt956q8rKyhQUxDZTAAD3\n4TAcKqs/p6Pn39DFmtMyDMekddHh8UqLzVFqbK7S4nIVHmwmmANwCqcF9R/+8IeKi4vTz3/+8/Fj\nSUlJ4382DEPPPvusdu7cqTvvvFOSdPDgQZnNZh06dEjbtm1zVisAAHxqXX3t+qDkLZ0o/pM6emxX\nnQ8LitSKjI1Ki8tVSkyOggNC56BLAIuB04L6a6+9pq1bt+pLX/qS3n77bcXGxupv//ZvtX37dklS\nTU2NbDabtmzZMj7Gz89Pmzdv1rFjxwjqAIA5Y3eMqbGzQqdee12l9UWT3j3PSliu65dvVV7KGp7+\nCcAlnBbUq6ur9cILL+ihhx7SI488osLCQt1///2SpO3bt8tqtUqSoqOjJ4wzm81qbm6e8rqnT592\nVotYoJgjmAnmCf63MfuorD21auysUF1HiYbHrt6txcfTT6nmZcqyFCg0IEIjXVJhV+EcdAt3wXsJ\nppORkeHU6zktqDscDq1du1ZPPfWUJGn58uWqqKjQ/v37x++qT4W1fAAAVxgc6VdjV6UaO8vV0l2j\nMcfopHWW0GRlRK9QQngWTwMFMGecFtRjY2Ov2r0lOztb9fX1kiSLxSJJstlsio+PH6+x2Wzj5yZT\nUFDgrBaxwHx0V4M5gukwT3B5uF+F5e/rVMnbqmkplSFj0roAnxBtXrFV6/JuVkRI9KQ1WLx4L8FM\n9PRMvyvUJ+W0oL5p0yaVlpZOOFZeXq7k5GRJUkpKiiwWiw4fPqzVq1dLkoaGhnT06FE9/fTTzmoD\nAADZHXaV1Rfpg5IjOl91UmP2ye+cm8PitDRljbxHghUVkqC1a9a6uFMAmJrTgvqDDz6ojRs3as+e\nPbr77rtVWFio5557Tnv37pV0ZXnLjh07tGfPHmVnZysjI0NPPvmkgoODdc899zirDQDAItV3uUfN\n7bUqrS/UqZJ31Hu566oak8lDqbE5Wpa6RktT1sgcFieJdccA3JPTgnpBQYFee+01PfLII/rHf/xH\nJSUl6cknn9S3v/3t8ZqHH35Yg4OD2r59u7q6urR+/XodPnxYgYGBzmoDALDAORx2tXW3qKm9Vk1t\nNVde7bXqGeicckx8VKrW5tyk1VnXKzhgiQu7BYBPz6lPJr3tttt02223TVuza9cu7dq1y5lfFgCw\nwI2MDqu49owKK95Xce0ZDY8OXXNMcMASrcm+QWtzblJsZPLsNwkATubUoA4AgLOMjo2opO6sCsvf\n14WaUxq5Rjj39vRRTGSS4qOStSx1nbKTVrLfOYB5jaAOAHArXX1tevP0qzpV+raGR67e21ySQgLC\nFBeVcuUVmaz4qBRFLYmRB8EcwAJCUAcAuIXO3ja9efrfdeLSH2V3jF113hwWp1UZ12ll5ibFRCTO\nQYcA4FoEdQDAnGrrbtFbZ17TieI/XRXQo0JjtDLzOq3M2KTYyCQekAdgUSGoAwBcpu9yt+ptlapv\nrVKDrVL1rZXqHbh6G8WUmGx9ft2XlJ24gnAOYNEiqAMAnG50bEStXc2ydjbI2tmglo46Ndiq1NXf\nPu241JgcbV3/18pMyCegA1j0COoAgE/NMAy1dNSrsa1a1s5GWTsbZOtoUHuvTYbhmNE1fLz9lBqT\nrc+tvpOADgAfQ1AHAHwil4f6VdZwTiW1Z1VSVzjtg4b+N29PH8WZU5RoTldidLoSzOmKDotltxYA\nmARBHQBwTW3dLSqqPK6L1R+o1lp+zbvlJpkUHmqWJTzhw1e84qNSZQlPkKcn//QAwEzwbgkAmFRL\nR4POVR7TucrjamqvnbIuwDdIaXG5iolIkiU8XpaIBJnD4uTj5eu6ZgFgASKoAwAkSQ6HXY1tNbpQ\nfVJFlcdl62yctM4kkxItGcpJWqmcpFVKik5n6QoAzAKCOgAsUoZhyNbVqPKG8ypvuKDKxou6PNw/\naa2Xp7eyk1ZqRfoG5SavVpB/iIu7BYDFh6AOAItIV1+byurPXwnnjecn3cP8I95ePspNXq0V6RuV\nl1IgPx9/F3YKACCoA8ACNjo2qurmYpXUXdmhpaWjftr6kIAwZSbkKz9tnXKSV8nX289FnQIA/jeC\nOgAsMG3dLSqpK1RJ3VlVNFzQyNjwlLX+voHKiF+mzIRlyojPlyU8nn3MAcBNENQBYJ5zGA7VWct1\nvuqkLlR/oNaupilrvTy9lRaXq6yE5cpMyFd8VAofBAUAN0VQB4B5aHRsVOUN53S+6qQu1pxS3+Xu\nKWujlsR+uEPLSqXHL2U5CwDMEwR1AJgnHA67Khov6kzZuzpXdUKDwwOT1vl4+SojYZlyklYpJ2ml\nopbEuLhTAIAzENQBwI0ZhqFaa5nOlL2nwor3p7xzHuwfqrzUNcpPXafMxHweNgQACwBBHQDciN0+\npsa2GlU3l6i6uVjVLaVThvPwELNWpG9Ufto6JVsyWWsOAAsMQR0A5tDg8GXVWss+DOYlqrOWT7tL\nS3DAEq3KvE6rMq9XsiWTHVoAYAEjqAOAi3X2tupC9Qe6UHVSlc3Fcjjs09YH+AUrP22dVmder4z4\npdw5B4BFgqAOALNsdGxEtdZylTec18XqD9TUXjttfXhwlFJjc5Uam6PU2BxZIhLkYfJwTbMAALdB\nUAcAJxsZG1ZtS5kqGy+psumiaq3lGrOPTlkfF5mstLhcpcbmKiUmW2HBkS7sFgDgrgjqAOAEdodd\nl2pO6/jFN1XaUCS7fWzKWk9PL2XG52tZ6lotTV2jJUERLuwUADBfENQB4DPo6LXp+MU/6kTxH9U7\n0DVlXdSSWKXH5Sk7aYVyklbJz8ffhV0CAOYjgjoAfEJj9lFdrD6lY5feVFldkQwZV9VEh8crPW6p\n0uPylB6Xp9Cg8DnoFAAwnxHUAWCGmtpqdbL4TzpV9o4GBnuvOh8csETrcz+n9Xm38DRQAMBnRlAH\ngGlcHurX6bJ3dbL4T2porbrqvEkmZSet1MalW7Q0pUCenrytAgCcY9b+Rdm7d68effRRbd++Xc89\n99z48d27d+vAgQPq6urSunXrtH//fuXm5s5WGwDwiTkcdpU1nNfJ4j/pfNXJSXdsCQuK1Nrcm7Uh\n7xaFh5jnoEsAwEI3K0H9xIkTOnDggPLz8yc8NW/fvn165plndPDgQWVmZuqJJ57QrbfeqrKyMgUF\nBc1GKwAwY7bORp0sOaJTJUfUM9B51XkvT2/lp63X+tzPKTNhGQ8eAgDMKqcH9Z6eHn3lK1/Ryy+/\nrN27d48fNwxDzz77rHbu3Kk777xTknTw4EGZzWYdOnRI27Ztc3YrAHBNvQPdOld1XKdK3lattWzS\nmkRzutbl3qzVWZsV4MdNBQCAazg9qG/btk133XWXbrjhBhnG/+yEUFNTI5vNpi1btowf8/Pz0+bN\nm3Xs2DGCOgCX6ext0/mqEzpXeVzVzSWT7toS7B+qguwbtDbnZsVFJbu+SQDAoufUoH7gwAFVV1fr\n0KFDkjRh2YvVapUkRUdHTxhjNpvV3Nw85TVPnz7tzBaxADFHMBNvvXdY9R2lqusoVUf/5O85HiYP\nxYdnKt28XLFLUuXh4amWuna11LW7uFvMFd5PcC3MEUwnIyPDqddzWlAvKyvTo48+qqNHj8rT88q6\nTcMwJtxVn8rHAz0AfFJj9lF1X27V4OiAhkYHNDR6WUMj//PngeEe9Q5dveZcurJrizkkQUmROUqO\nzJOfd4CLuwcAYHJOC+rHjx9Xe3u78vLyxo/Z7Xa99957eumll3Tx4kVJks1mU3x8/HiNzWaTxWKZ\n8roFBQXOahELzEd3NZgji0//YK+qm0tU3VysquYSNbRWyeGwz3i8h4enMhPytTxtvfLT1ik4YMks\ndov5gPcTXAtzBDPR09Pj1Os5LajfeeedWrt27fjfDcPQN77xDWVmZuqRRx5RRkaGLBaLDh8+rNWr\nV0uShoaGdPToUT399NPOagPAAjM0MqjWria1dNSrpqVEVc0lsnU2fuLreHl6KydppZanb9DSlDV8\nKBQA4PacFtRDQ0MVGho64VhAQIDCwsLG90nfsWOH9uzZo+zsbGVkZOjJJ59UcHCw7rnnHme1AWCe\n6hnoVL2tUm3dzWrtalJrV7Nau5vVO9A1o/HRYfGKCI1WsH+oggJCFRwQqiD/ULU0tsrPO0A3brpV\nvt5+s/xdAADgPLP6CD2TyTRh/fnDDz+swcFBbd++XV1dXVq/fr0OHz6swMDA2WwDgBuzdTbq8Kl/\n05myd+UwHDMa4+nhpYToNKXF5ig1NlepMdkK9A+ZtPb0wJVfVxPSAQDzzawG9SNHjlx1bNeuXdq1\na9dsflkA80Bze60On/o3FZa/P+n2iB/x9PBS5BKLzEtilRidrtTYXCVFZ8jH29eF3QIA4HqzGtQB\n4OMGhy/c//tVAAAbcElEQVSrrL5Ip8ve0fmqk1edT7ZkKT4qReawOJnDYhW1JFbhIWZ58gRQAMAi\nRFAHMGsMw1BrV5Mu1Z7WpZozqmounnR3ltykVfqzdXcrJSZ7DroEAMA9EdQBOF11c4nOlh/VpdrT\n6uixTVmXn7ZOW9bcpcTodBd2BwDA/EBQB+A01c2lev3EIZU3nJ+yJj4qVbnJq7Uq8zrFRia5sDsA\nAOYXgjqAz6zeVqn/Pn5IJXVnrzrn4+2n7MTlyk0uUF7yaoUGhc9BhwAAzD8EdQCfWp21Qn/44De6\nWHNqwnEPk4cKsm/QmuwblRqbK28v7znqEACA+YugDuATq24u0e8/+I1K6wonHDfJpNVZm/X5dV+S\nOSx2jroDAGBhIKgDmJHhkUFVNZforTP/n8obL0w4Z5JJyzM2aOu6LysmImGOOgQAYGEhqAO4isNh\nl7WzUXXWctXZylVrrVBLR72M//XkUJPJQ6syr9OWNXcR0AEAcDKCOrCIGYahvss96uprVUdvq5ra\naq6E89ZKDY8MTjnOw+ShNdk36tY1fyVzWJwLOwYAYPEgqAMLnGEY6uprU621XO09VnX1tqmjr1Wd\nva3q6mvT6NjIjK5jMnkoJjxBGQnLdMOK2xUZapnlzgEAWNwI6sACM2YfVVNbjapbSlXTUqqaljL1\n9Hd84uuEBIYp2ZKpJEuWki0ZSjSny9fHfxY6BgAAkyGoA/OcYRhqbKvRhaqTqmi8oHpbpUbtM7tL\nLkn+PgEKDzErPMSsqCWxSrJkKNmSqSVBkTKZTLPYOQAAmA5BHZiHHA67qltKdb7yhM5XnVBnX9u0\n9b7efkq2ZCkmMkkRH4by8OAohYVEKcA3yEVdAwCAT4KgDswTdoddFQ0XVFT5vs5XfaD+wZ4payNC\nopUSk62UmCylxuYoJiJRHh6eLuwWAAB8VgR1wI3Z7WMqb7ygoopjOl91QgNDfZPW+fsEKC9ljZam\nrlFaXK5CA8Nd3CkAAHA2gjrgZkbGhlXZeElFlcd0oerklOE8JCBMy9LWKT9tnTLil8rL09vFnQIA\ngNlEUAfmmN1hV0Nrlcrrz6m84byqW0o1Zh+dtDY0MFwrMjZqRfpGpcRmy8Pk4eJuAQCAqxDUARcz\nDEOtXU0qrS9SecN5VTZe1ODI5SnrQ4MitCJ9g1ZmbFJyTBbhHACARYKgDriAYRhqbq9VUeUxFVUc\nl62rcdr66LB45SSt1IqMTUqOySScAwCwCBHUgVliGIYaWqtUVHlc5yqOqa2nZcra0KAIZSXkK/PD\n15KgCBd2CgAA3BFBHXAywzB0vuqE3jjxipo76iat8fHyVXbSCmUmLFdWQr7MYXE8XAgAAExAUAec\nxDAMXaw5pTdOvKLGtuqrzvv6+GtpyhqtSN+onKSV8vH2nYMuAQDAfEFQBz4lh8MuW1ez6m0Vamit\nVGXjpavuoPt4+2lF+gYtT9+g7MQV8vbymaNuAQDAfENQB2aod6BblU0XVW+r/DCcV2l4dGjSWm8v\nH21efptuXnWnggNCXdwpAABYCAjqwBQMw1BTe40u1ZzWxZrTqrdWyJAx7RgvT29dt+zzuqXgLxUS\nGOaiTgEAwEJEUAc+ZmRsWBUNF3Sx5rQu1ZxSd3/HtPXBAUuUGJ2uxOgMJUWnK9mSpQC/IBd1CwAA\nFjKCOhY9u8Ou0rpCnSp9RxeqT2p0bGTSOpPJQykxWUqNzVVSdLoSo9O1JCiS3VoAAMCsIKhjUfpo\nWcsHJW/rTNm76rvcPWmdv2+gcpJWaWlKgXKSVynQL9jFnQIAgMXKaUF97969evXVV1VeXi5fX1+t\nX79ee/fuVV5e3oS63bt368CBA+rq6tK6deu0f/9+5ebmOqsNYEoOw6GmtlqV1J3V2bL3ptzj3Lwk\nVktT1ygvZY1SY7Ll6cnPswAAwPWclkDeeecdffe739WaNWvkcDj0+OOP65ZbblFxcbHCwq58qG7f\nvn165plndPDgQWVmZuqJJ57QrbfeqrKyMgUFsa4Xztc70K3S+kKV1hWprL5IfYM9k9aFBISpIPsG\nrcm+UXFRya5tEgAAYBJOC+q///3vJ/z9l7/8pUJDQ3Xs2DF94QtfkGEYevbZZ7Vz507deeedkqSD\nBw/KbDbr0KFD2rZtm7NawSLVd7lH1s4GWTsbZOtsUFVziZraaqas9/by0fK0DVqTc6MyE/Ll6eHp\nwm4BAACmN2u/0+/t7ZXD4Ri/m15TUyObzaYtW7aM1/j5+Wnz5s06duwYQR0zZhiG2rqbVWk7p/b+\nJr1f86qsnQ0aGOq75thA/xBlJyxXTvIq5aetl5+Pvws6BgAA+ORmLag/8MADWrlypTZs2CBJslqt\nkqTo6OgJdWazWc3NzVNe5/Tp07PVIuYJh8Outr5GtfY1qq23UW19jRoeG5zRWJPJQ+bgeMUuSVVs\nWJrCAy1XdmkZkC6evzTLncOd8F6CmWCe4FqYI5hORkaGU683K0H9oYce0rFjx3T06NEZbV3H9naY\nTM/ldlW2nlNV63kNjQ5cs97Lw1uhAZEK9Y9UaECkwgLMig5JlLeXrwu6BQAAcC6nB/UHH3xQv/nN\nb3TkyBElJyePH7dYLJIkm82m+Pj48eM2m2383GQKCgqc3SLc2NDIoAor3teJS39UTUvplHUBfsEK\n84+WOThe61ZuliU8QUuCI+Rh8nBht5gPPrr7xXsJpsM8wbUwRzATPT2Tb1rxaTk1qD/wwAP67W9/\nqyNHjigzM3PCuZSUFFksFh0+fFirV6+WJA0NDeno0aN6+umnndkG5pnRsVHVtJTodOk7OlvxvkZG\nh66qCQkMU15ygZJjspQak62osFidPXNWkpSbvMrVLQMAAMw6pwX17du361e/+pVee+01hYaGjq9J\nDw4OVmBgoEwmk3bs2KE9e/YoOztbGRkZevLJJxUcHKx77rnHWW1gHjAMQ7auRpXWFam0vkiVjRc1\nMjZ8VZ2Hh6eWpqzRhrxblJ20kl1ZAADAouK0oP7iiy/KZDLpc5/73ITju3fv1uOPPy5JevjhhzU4\nOKjt27erq6tL69ev1+HDhxUYGOisNuCGHIZDrV1NqmkuVXVzicoazqm7v2PK+ujweG3Iu0Vrsm9U\ncMASF3YKAADgPpwW1B0Ox4zqdu3apV27djnry8INDY8Mqs5WoZqWUtU0l6rGWqbB4ek/DBoZalF2\n0kqtyb5RyZZMPmAMAAAWPZ6Njs/M4bCrvrVKJbVnVVJXqHpbhRzG9D+4+fsEKDMhX1mJK5SVuFxR\nS2Jc1C0AAMD8QFDHp9Iz0KnSukKV1BWqtP6cLl/jYUNB/qFKiclSSky20uJylRidwZpzAACAaRDU\nMWO2riadKnlbl2pOqam9dso6k0yKiUxSSkz2eDiPDLWwnAUAAOATIKhjWgNDfTpb9p4+KH1bddby\nKetCAsOUk7RKOUkrlZW4XIF+wS7sEgAAYOEhqOMqhmGopO6sjl98UxdrTsvuGLuqxtPDS6mxOcpJ\nWqmcpFWKjUzijjkAAIATEdQxzuGwq6jyuA6f+jc1T7K0xdPDS3kpBVqTfYOyElfIz8ff9U0CAAAs\nEgR1aMw+qlMlb+uPp19VW0/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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1035,9 +1060,10 @@ "source": [ "You need to tell the filter how to compute the sigma points and weights. We gave the Van der Merwe's scaled unscented transform version above, but there are many different choices. FilterPy uses a `SigmaPoints` class which must implement two methods:\n", "\n", - " def sigma_points(self, x, P)\n", - " def weights(self)\n", - " \n", + "```python\n", + "def sigma_points(self, x, P)\n", + "def weights(self)```\n", + "\n", "FilterPy provides the class `MerweScaledSigmaPoints`, which implements the sigma point algorithm in this chapter." ] }, @@ -1047,11 +1073,12 @@ "source": [ "Other than these two functions, everything else is nearly the same. When you create the UKF you will pass in the two functions and sigma point class object like so:\n", "\n", - " from filterpy.kalman import MerweScaledSigmaPoints\n", - " from filterpy.kalman import UnscentedKalmanFilter as UKF\n", - " \n", - " points = MerweScaledSigmaPoints(n=4, alpha=.1, beta=2., kappa=-1)\n", - " ukf = UKF(dim_x=4, dim_z=2, fx=f_cv, hx=h_cv, dt=dt, points=points)" + "```python\n", + "from filterpy.kalman import MerweScaledSigmaPoints\n", + "from filterpy.kalman import UnscentedKalmanFilter as UKF\n", + "\n", + "points = MerweScaledSigmaPoints(n=4, alpha=.1, beta=2., kappa=-1)\n", + "ukf = UKF(dim_x=4, dim_z=2, fx=f_cv, hx=h_cv, dt=dt, points=points)```" ] }, { @@ -1080,7 +1107,7 @@ "data": { "image/png": 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zs1OrV6/Wvn37lJOTM11tAMBfbHBoQMVVhTpW8o4uXrowbk1cZLJW53xWK7Nv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\nRjkcIzpeeliHTvxaHT0t15z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V7tdMn25ef+01M3rrckkNGkiffirVqiX99JM0fLhUo4Y0ZIgJuA0bSsOGmSkPkuTrK11/\nvQnNf/5z8X0mQH4IvwAAlAPvvWfCpiTdfbc0apT01VfSffcVfM2TT+aOykrSjBnSuHEmAJ/Xpo37\nNQ89lPuc86ZPz/26USPp55/NKPSQIVLFiiaUS2bKw3kVKkiVK7sfA0oC0x4AACjjdu+W1qyRoqPN\n9z4+0qBBeac+XKxdu9yvjx6VDh+Wbrml8NecN2+eOR4SYgLt669LBw4U7T0AJYWRXwAAyrh335Wy\ns8282/POz7k9dEiqXz//6ypWLPqzLr5m0SIzWjx7ttS5s1SlivTmm9KXXxb93kBJYOQXAIAS5nK5\ntG9fuvbtS5fL5bqqe2VlmSa1//kfadMm91+tW0sLFhTuPiEhJiQvW1a0569eLXXsKI0caaZIhIWZ\nkejLrebg52cCO1DSCL8AAJQgl8ul2Nh0derkq06dfBUbe3UB+LvvpORk01AWHp77q2VLMz83Jqbw\n93r2WTNl4fXXpZ07pV9+MY1tl9KihVlfePFiadcuM284Li535LkgTZqY8w4flo4dK3yNwNUi/AIA\n4EkLFkgvvih9/bV08GCeFHjgQKaio/2VlORUUpJT0dH+OnCgkAvmFvC4qCipWrW8r91/v1lSbOnS\nvK/lNzL7+OPSW29J77xjlju7/XZp69ZLP3/ECLNpxYABUocO0v790vjxee9/8fcvvGDmBV9zjdn5\nDSgpDsvK/99mKSkpOV8Hn2/TBAAAl3funNnWbMMGkz4PHDDpdO5c7dtnRn2Tksz4U506LsXHZ6px\nY38vFw2UD5fLsIz8AgDgaUlJZr2vAwfMLg5nzkgDB0qSGjb0VUxMuurUcalOHZdiYtLVsKGvlwsG\n7IORXwAArlZqqrRypZSQYLq4QkOlXr3MArfPPis984zZ6eG/XC5XzlSHhg195XQyFgV4yuUyLEud\nAQBQVJZlllNYtkw6ccKs/3XzzdLzz+duqXb4sDR5sukAq17d7XKn06ngYH9VqmQGhgGUHMIvAACF\n8ccfpnPsP/8x3VutW5stzC4KtpJM19fs2dKrr0pBQfnebsgQs8Xv1KnFXDcAN4RfAADyk5kp/fij\nWY8rLU2qWVPq2VPq3//yi9jWqmXCr0/+f80eOmQy8b59UkaGWfMWQMlgzi8AAOft2SMtWWLSqY+P\ndMMNUteuUmCgRx8zdaqZFly9unTkiFmjF4BnMOcXAICCpKZKK1ZI69ZJLpdJpH36uDWnedqhQ1Kl\nSmbkt2lTs0Mao79AyWHkFwBgHy5XbqPayZMmhXbvLrVvL1WoUCIlTJ0qPfWUGWBu1cr0zq1dy+gv\n4CmM/AIA7O3oUSk2Vtq2zczVvf56aejQ/LdEK2ZpaWY3swv/Pm7Z0gRhACWDkV8AQPmSkeHeqBYS\nYhrVrrvu8o1qJeiTT8zIb3i4tysByhdGfgEA5d9vv5nR3UOHJF9f06j29NMeb1QDUPYRfgEAZc/p\n06ZRbf16M483LEy6806pQQNvVwaglCP8AgBKP5dL+uUXaflyKSUlt1Ft6tQSa1QDUD4QfgEApdOR\nI2Yqw/btZg/gNm3MkghVq3q7MgBlGOEXAFA6ZGRIa9ZI//63lJ5uGtVuvVV65JFS1agGoGwj/AIA\nvGf3brPO1+HDZpeHzp2lv/xFCgjwdmUAyinCLwCg5Jw+Lf3wg/Tzz2Ye7zXXSHffLdWv7+3KANgE\n4ReAZ9x8sxQRIb3xhrcrQWnickkbN5pGtVOnpMqVaVQD4FWEXwCF88cfJrB8/72UmGiajlq1kiZO\nlHr0MHMyPT0v8/33pdGjzWghyo6kJNOotmOHaVT705+k4cNpVANQKhB+ARTOffeZ3bIWLJCaNjWd\n+KtWScePe7syeFt6umlUW73afF2njtSrl/ToozSqASh1CL8ALu/kSRNsli0zP7KWpIYNpXbtCr7m\nH/+Q5swxo3+BgVK3btLrr0v16pnXV66UoqLMPSdNkrZsMfu8zp9vRgpXrpSGDDHnOp3m92nTpOef\nL6Y3iUKzLGnXLjO6m5hoGtVuvJFGNQBlAuEXwOVVqmR+ffWVCTn+/pe/JjNTmjFDuvZaM2XimWek\n/v3NaPGFJk+WZs40o4VjxkgPPyxt3Wqe8/rr5vU9e8y5FSt6/r2hcE6dcm9Ua9ZMuvfe3H/MAEAZ\nQfgFcHk+Pmb+7bBhuSOzN94oPfCA1KFD/tdER+d+3aSJ9Le/mZHdw4fdA9OMGWZUWDKjujfdlHtO\nlSrmx+YhIcX1zlAQl0vasMEE3vONalFRZgthGtUAlGGEXwCFc++9Uu/eZgOCH3+UFi+WZs+WXnrJ\nTFuwLPfzN2yQpk+XNm0y84LPv75/v3v4bd069+u6dc3vR48yougNiYlmKsPOnWaqSWSkNGKEFBzs\n7coAwGMIvwAKz9/frOzQo4c0ZYoZCZ42TXr6affzzpwxO3P16mXm/oaEmKkPXbqYXbwu5Oub+/X5\n5iiXq1jfBv4rPd3M5V692vy51K1r/swGDqRRDUC5RfgFcOWuu07KzjarQFxo+3YpOVn661+lxo3N\nsS1bin5/Pz9zf3iGZZlR3dhYsxyZn5+ZZjJxYuHmcQNAOUD4BXB5yclmfu9jj5mNLCpXltavN41q\nt9xivpdypzY0amTC1BtvSCNHStu2mZHiomrSxATrZcukNm1Mw1tgoMfeli2kpOQ2qlmW1Ly5dP/9\nuVNMAMBmCL8ALq9yZemGG8zSZbt3mx+X168vPfKI9Nxz5pwLN7moVUtauNCs1PDWW9L110v/7/9J\nt9/uft/8frR+4bHOnaXHHzerRCQns9RZYWRn5zaqnT5tmgajoqS77spdMg4AbMxhWRd3qRgpKSk5\nXwfT7AAApdfhw2Yqw65dZiWGyEgTeKtU8XZluIRPPjGbJIaHe7sSoHy5XIZl5BcAypq0NNOktmaN\nWU/5fKPaoEE0qgHAZRB+AaC0syyzU15srFkGzt+fRjUAuEKEXwAojU6elJYvlzZuNOG3RQupXz+z\nEx4A4IoRfgGgNMjONito/PCDlJpqNpa45RbpnntoVAMADyL8AvCslBQzSrl2rRQWVvB5c+dKK1ZI\nX35ZcrWVNocOmakMu3ebRrW2baUnnqBRDQCKEeEXgGe9+qrZAe5SwVeShg83WyOvWye1b18ytXlb\nWprZHnrtWtOoVq+eaVSLjvZ2ZQBgG4RfAJ6TkSG984708ceXPi8rSwoIMBtnvPWW9P77JVJeibMs\ns9tdbKzZ3tnf32zxPGmS2V0NAFDiCL8APGfZMuncObPG7HkrV5rvv/tOmjpV2rTJTHW44w6pb1/p\nvvuk994zP/YvD06cMI1qv/xivr/2Wumhh6Tatb1bFwBAEuEXgCfFxZkNFvJba3biRGn2bKlpU6lS\nJXOsfXvpzBnT6NWxY8nW6inZ2WbqxooVplGtalUz7ePee2lUA4BSiPALwHN27ZIaNcr/tWnTTCi8\nULVqZuvknTvLVvg9eFBaskTas8eMWLdrJ40aZd4LAKBUI/wC8JzTpwv+8X67dvkfr1LFrBBRmp07\n596o1qCBaVR77DFvVwYAKCLCLwDPCQ42ATg/FSvmf/zUKTNVoDSxLGnbNmnpUtOoFhBgGtUmT6ZR\nDQDKOMIvAM9p2tSMjhbWiRMmLDdrVnw1FaWWZctMQ54kXXed1L+/FBLi3boAAB5F+AXgOV26mKXL\nLCv/preLJSRIQUFmc4eSlp1tnr9ihWm6q1bNzEm+7z4a1QCgHCP8AvCcHj3MFIHly92b2woKwl9/\nLd1/v+RTQv8pOnDANKr9/rtpVGvfXnryydzVJwAA5R7hF4Dn+PmZndtiYnLD7803m1HWi507J332\nmfTNN8VXz7lzZvm1tWvNxhoNGki33nr53ecAAOUW4ReAZ02YILVoYZYBu1TIfOcd6cYbpQ4dPPds\ny5K2bjWNaseOmVHorl2l556TfH099xwAQJnlsCzLyu+FlAuWHgoODi6xggCgSI4fN41qmzeb78PD\npZ49pVq1vFsXcBmffCK1amX+JwvAcy6XYRn5BVC2ZGWZRrWVK6WzZ3Mb1R54oHBNdgAAWyP8Aij9\n9u83jWr79plGtQ4dpDFjCl47GIAbp9Opzz77TPfee2+B50ybNk2ff/65fv31V48//9ixYwoJCdHK\nlSvVtWtXj98fKArCL4DS5+xZadUqKT7ejPQ2bGga1UJDvV0ZUObt3btXYWFhWr9+vSIjI3OOT5gw\nQWPGjMn5fvDgwUpOTtY3xdmUCngB4ReA91mWtGWLaVRLTpYCA6Vu3WhUA4rRxS0/FStWVEV+mgIb\nYCV3AN6RnCwtWmQC7pQpJvwOHCi99JI51qULwRcopMWLF6tLly6qXr26atSoodtuu03bt2/P99yw\n/67C0r59ezmdTkVFRUky0x4iIiJyvv7ggw/03Xffyel0yul0Ki4uTnv37pXT6dSGDRvc7ul0OvXF\nF1/kfL9u3Tq1bdtWgYGBioyM1E8//ZSnjq1bt6p3796qUqWKateurQEDBujIkSMe+TyAS2HkF0DJ\nyMqSfvrJTGc4e1aqXt00qvXrR6MacJXOnj2rp556Sq1bt9a5c+c0Y8YM3Xnnndq2bZt8LtpEJiEh\nQR06dNCSJUt0/fXXy8/PL8/9JkyYoO3bt+vEiRP68MMPJUnVqlXToUOHLltLamqqevfure7du+vD\nDz/UwYMH3aZTSFJiYqK6du2qYcOG6bXXXlNmZqYmT56su+66Sz/++KMc/DcBxYjwC6D47NtnGtX2\n7zeNah070qgGFIOLG9kWLFig4OBgJSQkqHPnzm6v1axZU5JUo0YNhYSE5Hu/ihUrKiAgQH5+fgWe\nU5CPP/5YmZmZiomJUVBQkMLDw/Xcc8/p0UcfzTnn73//u9q0aaOXX34559jChQtVo0YNrV+/Xu3b\nty/SM4GiIPwC8JwzZ8zI7k8/mZHexo2lXr2kJk28XRlQrv3222+aMmWKEhIS9Mcff8jlcsnlcmn/\n/v15wm9x27Ztm66//noFBQXlHOvUqZPbOT///LPi4uJUuXJlt+MOh0N79uwh/KJYEX4BXDnLkn79\n1WwykZwsBQXRqAZ4QZ8+fdSoUSPNnz9f9evXV4UKFRQeHq6MjIyruu/F0w+cTtMqdGGzXGZmZp7r\nCtg/y+31Pn36aNasWXleK+pIM1BUhF8ARZOcbFZl+PVXM1e3VSvTqPbfH6UCKFnJycnasWOH5s2b\np27dukmSNmzYoKysrHzPPz/HNzs7+5L39fPzy3OPWv/dOfHw4cNq27atJOmXX35xOyc8PFwLFy7U\n2bNnc0Z/4+Pj3c6JjIzUJ598okaNGuWZkwwUN1Z7AHBpmZnS6tW5qzB88IHUsqX04ovm10MPEXwB\nL6pWrZpq1qyp+fPna/fu3Vq1apUef/zxAkNlSEiIAgMDtXjxYh05csRtK9gLhYaGasuWLdq5c6eO\nHTumrKwsBQYGqlOnTnrllVe0detWrV27Vk8//bTbdQMGDJCPj4+GDBmirVu3aunSpXrppZfcznni\niSeUkpKiBx98UAkJCdqzZ4+WLVumESNGKDU11TMfDFAAwi+AvPbuld5+24TdF1+UTp+Wxo0zX48b\nJ0VEsEIDUEo4nU4tWrRImzdvVkREhEaPHq0XX3xR/v7++Z7v4+OjuXPn6t1331X9+vV1zz33SDJT\nHC6c5jBs2DBdd911ateunWrXrq21a9dKMs10klkq7c9//nOeYFuxYkV9++232rVrlyIjI/WXv/xF\nM2fOdLt33bp1tWbNGjmdTt12221q1aqVRo0apYCAgALrBjzFYRUwMefCfwkGBweXWEEAvODMGWnl\nSikhQcrOzm1Ua9zY25UB5dYnn5hZQ+Hh3q4EKF8ul2GZaAPYkWVJmzebRrXjx83SYzffbDabYP4d\nAKAc4285wC7++MM0qv3nP2bKQkSENHiwVKOGtysDAKDEEH6B8iozU4qPN+vunjtnmtJ69pT692e+\nLgDAtgi/QHny++9mR7WDB830hU6dpKeeMuvvAgAAwi9QpqWm5jaquVxmJ7U77pAaNfJ2ZQAAlEqE\nX6AssSxp0ybTqHbyZG6j2vPP06gGAEAh8LclUNodPWoa1bZuNXN1r79eGjJEql7d25UBAFDmEH6B\n0iYzU1qTSPxxAAAN7klEQVS7VoqLk9LSpFq1TKPagAE0qgEAcJUIv0Bp8NtvplHt0CHJ11e64Qbp\n6aelwEBvVwYAQLlC+AW8ITVVWrFCWrfO7KgWFibdeafUsKG3KwMAoFwj/AIlweUyjWrLl+c2qnXv\nLk2dKlWo4O3qAACwDcIvUFyOHpViY6Vt28xc3TZtpMcek6pV83ZlAADYFuEX8JSMDNOo9u9/m0a1\nkBDTqPbwwzSqAQBQShB+gauxe7cZ3T3fqNa5M41qAACUYoRfoChOn85tVHO5pGuukfr2lRo08HZl\nAACgEAi/wKW4XNIvv5hGtZQUqVIl06g2bRqNagAAlEGEX+BiR46YqQzbt0tOp2lUGzZMqlrV25UB\nAICrRPgFMjKkNWtMo1p6umlUu/VW6ZFHaFQDAKCcIfzCfiwrt1Ht8GHJz880qv3lL1JAgLerAwAA\nxYjwC3s4dUr64Qfp55/NPN6mTaW775bq1/d2ZQAAoAQRflE+uVzSxo3SsmUm+FauLEVFmS2EaVQD\nAMC2CL8oP5KSzFSGHTtMo9qf/iSNGEGjGgAAyEH4RdmVnm4a1VavNl/XqSP16iU9+iiNagAAIF+E\nX5QdliXt2mVGdxMTTaPajTfSqAYAAAqN8IvSLSUlt1HNsqRmzaR775Xq1fN2ZQAAoAwi/KJ0cblM\n0P3hB9OoVqWKaVTr25dGNQAAcNUIv/C+xERpyRJp504TcCMjpccfl4KDvV0ZAAAoZwi/KHnp6aZJ\nbfVqs7ta3bqmUW3QIBrVAABAsSL8ovhZlhnVjY01y5H5+Uk33SRNnCj5+3u7OgAAYCOEXxSPlBRp\n+XJpwwYTfps3l+6/34zyAgAAeAnhF56RnZ3bqHb6tGlUu+UWs4Ww0+nt6gAAACQRfnE1Dh82jWq7\ndplGtbZtpZEjTfAFAAAohQi/KLy0tNxGtcxMs9Zur17S4ME0qgEAgDKB8IuCWZa0Y4dpVDt61DSn\n3XSTNGkSjWoAAKBMIvzC3cmTplFt40YTflu0kPr1k+rU8XZlAAAAV43wa3fZ2dL69aZRLTXVbCxx\nyy3SPffQqAYAAModwq8dHTpkGtV27zaNau3aSU88UayNaikpZhB57VopLKzYHgMAAHBJhF87SEuT\n4uJM8szMlOrXN41qQ4aUWAmvvir16EHwBQAA3kX4LY8sS9q+3YzuHj0qBQRIXbtKkyeb3dWu0JEj\n0pw5UmioVLmylJhoZk2MGSP5+hZ8XUaG9M470scfX/GjAQAAPILwW16cOGEa1X75xYTfa6+V+veX\natf2yO3375f69JH++U+pZcvc4wsXmuPffSf5FPC/pmXLpHPnpKgoj5QCAABwxQi/ZVV2trRunbRi\nhWlUq1rVNKrde2+xNKqNGCE9+KB78JWkQYOkt9+WZs+Wnnkm/2vj4qTISJYCBgAA3kf4LUsOHjRT\nGfbsMQG3fXtp1CgzB6EYJSaaxw4bJn3xhcnX582fb+byxsQUHH537ZIaNSrWEgEAAAqF8FuanTuX\n26iWlWUa1W69VXrssRItY98+83vdulLPnu6Pb9pUGjo095z8nD7tsdkXAAAAV4XwW5pYlrR1q7R0\nqWlUCww0jWrPPntVjWpXq2FD87uvr5lhcbGpU6XGjQu+PjjYBGAAAABvI/x62/HjpiNs0yYTfsPD\nPdqo5gn165sR36VLzZLAF1u+XIqOLvj6pk3N4DUAAIC3EX5LWlaWe6NatWpm0uz995fqHdXefVe6\n4w5TZrNmucfff9+spPb00wVf26WL9NZbJtvT9AYAALyJ8FsSDhwwHWO//252VGvfXnrySalSJW9X\nVmgNG5odkN94Q6pXz2wGl5hoAu2SJeZtFaRHDxOQly83XwMAAHgL4bc4nDsnrVol/fijGelt0MA0\nqpXx7c1q1ZJeeKHo1/n5ScOHmxUhCL8AAMCbCL+eYFnSf/5jJsUeO5bbqPbcc5fe+sxGJkyQWrQw\nq7SV8X8DAACAMozwe6WSk02j2ubN5vuWLaVHHjHDo8gjOFhKSvJ2FQAAwO4Iv4WVlSUlJJhGtTNn\npOrVzRII/frRxQUAAFBGEH4vZf9+0821d6/p6OrQQRozpkw1qgEAACAX4fdCZ8+aRrX4eDPS27Ch\naVQLDfV2ZQAAAPAAe4dfy5K2bDGNasnJplGtWzca1QAAAMop+4Xf5GQTdn/91XzfqpX06KM0qgEA\nANhAmQ+/LpdLBw5kSpIaNvSV8+Jd0rKyzDSGlSvNtIaaNc1isw8+SKMaAKDYrV8vLVokDRwoRUR4\nuxoAZTr8ulwuxcamKzraX5IUE5OuXr385Ty/o9revZKPj9SxozRunFSxoncLBgDYTrt2UuvW0ocf\nSh98QAgGvM1hWZaV3wspKSk5XwcHB5dYQUWxb1+6OnXyVVKSGe2tXt2lBe9mqO7GZVJkpNmHFwCA\nUiIzU/r+ezM206yZ9MADUni4t6sCypfLZdgyPfJ7McuSTqRUkG+nPubAMe/WAwDAhSzLzLhLTZWu\nuUZq2tTbFQH2U6ZHfi+e9tC+/UidPr1DK1as8HJlAADksizpq6+kuDipb1/p5pu9XRFQfl0uwzrz\nHCkBgwcPltPplNPplK+vrxo0aKBBgwYpMTGxSPdxOp3q1ctf8fGZio/PVMOGFeSgiQ3wOMuy1LVr\nV/Xt29ft+NmzZ9WiRQuNHDnSS5UBpd/GjdL48VLVqtJrrxF8AW/zSvh1OBzq2bOnkpKStG/fPsXE\nxGjFihUaOHBgke/ldDrVuLG/Gjf2l8PhUAED2YWWlZV1VdcD5ZHD4dDChQu1YsUKxcTE5Bx/5pln\nZFmWZs+e7cXqgNKtdWtCL1CaeCX8WpYlf39/hYSEqF69eurZs6ceeOABxcfHSzLTGR577DGFhYUp\nKChIzZs316uvvuoWbLOzs/X000+revXqql69usaNG6fs7Gy35yxevFhdunRR9erVVaNGDd12223a\nvn17zut79+6V0+nUv/71L0VFRSkoKEjz588vmQ8BKGNCQ0M1a9YsjRs3Tvv379fy5cs1b948vf/+\n+woMDPR2eUCpVaGCtysAcCGvhF9JbkF2z549Wrx4sdq3by/JhN8GDRro008/1fbt2/XSSy/pr3/9\nq9uI0+zZs/Xuu+9q/vz5io+PV3Z2tj7++GO3aQ9nz57VU089pXXr1mnVqlUKDg7WnXfeqczMTLda\nJk2apFGjRmnbtm266667ivmdA2XXiBEj1KlTJz3yyCMaMmSIxo8fr86dO3u7LAAACs0rDW+DBw/W\nRx99pICAAGVnZystLU29e/fWwoULVb169XyvmThxon7++WctXbpUklSvXj2NHj1akyZNkmTC9LXX\nXqv69evrhx9+yPceZ86cUXBwsOLi4tS5c2ft3btXYWFhmj17tsaNG+fR9wiUV+f/f9OsWTNt2bJF\nvmwFDgAoRUplw5skdevWTZs2bVJCQoJGjx6tVatW6ciRIzmvz5s3T+3atVNISIgqV66s119/XQcO\nHJBk3lRSUpJuuOGGnPMdDoc6duzoNqL822+/acCAAWratKmCg4NVp04duVwu7d+/362Wdu3aFfO7\nBcqP9957T0FBQTp48KD27Nn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2WXwWvaeHN9ITs6FW5kIRk8blUkREDmZzuH/zzTfx7//+7wgPD8f8+fMxb968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\nGo0G+fn5Ldbc//rrr9BoNNi+fTsyMjIQFBSEnj1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dgl+urn5fZV/Pljxx3z/1PrD3XgX7duLpMfNYsOYdSsqKKCot4IsVb/DEyDm3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\nPadmzZrJZrNp0qRJLivs4sWLKigokJ+fn9Nxf39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KUVyRi6LOFoHyPCiri3Qa2C0e39wKHi6+4lP/zt9ujp6wt3HUy7+1IAi4lX8F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\nCwQKAAAAAElFTkSuQmCC\n", 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oqCjNONHR0bCysoJcLmc4ICLqQ3FFPg6deQ/FFXlax50krlgx81kE+05/oP8+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\nIQQAwMvLCwYGBlCpVNL2oqIinD17Fn5+fm03ASI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Ef39/4uPjOXbsmFObgoICEhISCAwMJDAwkNGjR1NYeOl6zyIiIjeagpITfLRq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\nRVpaGmJjY6W4oKAgaVx3j4mIqFcTRET01PnLX/4iZDKZuHTpklZ7RES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29OkyxKjn/60RfSZjY20LwKWSbLILM0x6PSFEy2OjdgCNdb2m/6uvvuLw4cMkJycDKLMj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\n69SiC9Oyt3NkUI/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ntjP/txvfq6SkJIYPHw4YvjGIjY0lNjaWGTNm8NlnnzFnzhzKy8uZ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b7ij4NwHVTWUa7RrRbYh7ukb59PRjCmU+h4oLp9V+iSb+qP/8mUtga21nxN5M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lBtVE31JVFW98uU+z+pn2QZfiK91jdNpY/OiuFzEuYzI6vl7d7fz4GBNMWTmo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\nPpLEMZw6BvOuryH3sHSiEp+E9nnL4IlP9HPPiEJ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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1784,26 +1811,18 @@ "source": [ "We can compute the bearing between a sensor and the target as follows:\n", "\n", - " def bearing(sensor, target):\n", - " return math.atan2(target[1] - sensor[1], target[0] - sensor[0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ + "```python\n", + "def bearing(sensor, target):\n", + " return math.atan2(target[1] - sensor[1], target[0] - sensor[0])```\n", + "\n", "So our filter will need to receive a vector of 2 measurements during each update, one for each sensor. We can implement that as:\n", "\n", - " def measurement(A_pos, B_pos, pos):\n", - " angle_a = bearing(A_pos, pos)\n", - " angle_b = bearing(B_pos, pos)\n", - " return [angle_a, angle_b]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ + "```python\n", + "def measurement(A_pos, B_pos, pos):\n", + " angle_a = bearing(A_pos, pos)\n", + " angle_b = bearing(B_pos, pos)\n", + " return [angle_a, angle_b]```\n", + "\n", "The design of the measurement function and state transition function can remain the same as nothing has changed that would affect them. " ] }, @@ -1819,7 +1838,7 @@ "data": { "image/png": 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HBi2Xej8pB4iPruR/PDyURdlgi9KHO0VEBisFdBGRm5BhGOw9Chu2wqYdUH0R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\nqm4EJ8tmUHzuTto6Qj1ec/Qw1/SVtOg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X7xcuXIjVaiU2Npbbb78dAMMw8PDwwGazXXAfmZmZzJs3j/nz59O/f3/nfsLCwli9ejUD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\n1klsbKzExsZKeXm5DA0N6bbV3d0tJSUlYrFYJD4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InZaZfY7v1s0vtc/EgTMYdNG0hPJKzzDoe2F++bdrYMt+SP4RZlyjYF4feLib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\ntm3btDFCCFhaWuKbb77B6NGjtdsvXbp00ydHRERERGSq7O3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8q/nBRO+ONa+Ln55h+UCi3cc3lEBmIlJWqDgXkTIhM/sChmG5DKIJEwGVClaA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\npBFszomIiIiINILNORERERGRRrA5JyIiIiLSCDb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HU5NBTU2MXbscLc5du9bF5ZenSKUcXHut/f/Z3LkxwuEUOTkNnHGGRZ8+XTCM\nY85biIhIJ3VcYdvpdNKrV68Wr1uWxdKlS5kzZw5XXXUVAKtXr6ZXr148/fTTTJ48uXVHKyJyEg6v\n8Q6Hwyxdatd4G4aFaUJlpclPf2rXaM+aFeeJJ9xMn55g/vxDG+gsXOhr1sP7iy8aAejaFfr1C+B2\nu9vm4kRE5LR0XNMxW7dupbCwkD59+lBdXc22bdsA2LZtG3v27OGyyy7LHOvz+fjWt77F22+/nZ0R\ni4i0gtzcXEaO7Mro0V0ZOtRDXh4MHJhi9eomqqsTPPGEm6lTE0ec8T68h/dvfuNh716DL75w8Oc/\nR/jTnxrYvj2EaZptcFUiInK6cViW1XLF0GFeeuklwuEwFRUV7NmzhwULFrB582Y2btzI5s2bufDC\nC9m5cydFRUWZc2688Ub+9re/8dJLL2VeC4VCmccff/xxFi5FROTkdenShS++6E0gAE1NFqbp4PPP\n7WANh3p4799vUFGRoqYmTk3Nof7eK1Z4mD8/Rt++aaJRB0VFB9i7d29bXpKIiJyk/v37Zx4Hg8ET\nOveYZSRjxozJPB48eDCjRo2ivLyc1atXM2LEiK89z+FoORskInK6a2xsxONpJJWCnj270tBwJmed\nlWb16iZ27zZwu61MD+9/+qc4M2YcaiVYW+tj3Lgk06cHePrpMH/8o4uLLsojJycPgO7d9zSbeBAR\nkY7vhFv/BQIBKisr+eSTT7jyyisB2LNnT7OZ7T179pCfn/+1H2P48OHfYKjydTZs2ADovrY23dfs\naK/3tagoxAcfWLhcFr/8ZZj9+w3efdf5tceHww7+7/+1y1Nqa33k5ZksXZrPGWecSU6ORWlp67YS\nbK/39XSn+5oduq/ZofuaPSczUXLCS+hjsRgffvghvXv3pry8nPz8fF555ZVm77/55puMHj36Gw9K\nROR0EwzarQRHjAgyfLhFSUmaK69M8NhjkUwrwZqaGG+84eSRRyLceaefiy5KU1vrI5GASZOS3HBD\nDuPG5bJ5s5MPP0zy1lsNrFvXoNluEZEO7Jgz27NmzeKKK66guLiYvXv3Mn/+fKLRKBMmTABg5syZ\nPPDAA1RUVNC/f38WLFhAly5duOaaa7I+eBGRthAMBjlYRRcKhfjNb8Ikk/bzM89M8+GHzr/vWpkG\nYPz4JEuWeDPlJnfd5WP27DiLF3sZOzbF5Zcn6d07RDTqYMgQlzbPERHpQI4Ztj/77DOqq6vZt28f\nPXv2ZNQW1I7BAAAgAElEQVSoUaxbt47i4mIA7rzzTqLRKNOmTePLL79k5MiRvPLKK+Tk5GR98CIi\nbS0YDHLeefbjUCiEwwHl5UmGDDG56y4fNTUxduxo/kvEsWNTLF7sZdIkO4TX1Xn42c8idO1qsn59\nikAghNsNAwZo10oRkfbumGG7rq7umB9k3rx5zJs3r1UGJCLSXgWDQUaNsh83NDTw1FMRDMOiqsrB\neeeluf12u2vJqFEpgGaz3bfcEsj07547N8aQIUneey+FYTSQk2Nx1ll+PB5Pm1yXiIh8c9r2TEQk\nC7p27crw4V2pqsqhTx+L/v1TPPlkEzfcEKex8VDgPtzB/t0LF/rYu9fJ+PG5XHVVLlu2OHn33Sjv\nvhvik08a1MNbRKQdOeFuJCIicvxcLhelpUFKSyEejxMMxnE4LNxuePTRSIv+3QetXetqNus9bVqM\nCy5IE4tZfPFFIw6Hg7POsk6436uIiJxaCtsiIqeI1+tl5EgvYAdvvz/GmjVhGhsdbN9uZPp3L1oU\nZfFib+a80tI0/fpZXHONvRbm4OY5CxfGKC0NEY87OOOMLjQ2NrbJdYmIyNdT2BYRaQNer5eBA+1A\nHYvF6NYtzpo1YeJx2LvXwdSpCWpr7ZntefNi3HhjTovNc6ZNC7B6dROpFBhGPtCbd95pYOBAu4xF\nRETansK2iEgb8/l8DB9ul5CEw2E2bkxTXGyyZk2KAwccmZB9JC+95ObFF13cc0+cO+6wF2AuXx6h\ntDSEaTqorDTIzc09JdchIiItaYGkiMhpJDc3lxEjgowcGWTwYIP8fJPBg1MsWxZtsXlOTU2M1as9\nXHRRmjvusLeNr683mD49wL//u4crr8zl5ZfhnXdCvP9+iEQi0daXJyLS6WhmW0TkNJWbm8vZZ9uP\n+/cP8fzzYQBM02L2bIvFi73s33/kOZPiYov6eoNbbgnw0EMRysrSrF8fw+WKkZdn0bdvDi6XvgWI\niGSb/qcVEWkHgsEgo0fbj0OhEC5XigcesLj55gBvvOFkyZIos2bZZSQ1NTG2bLFDeF6eicMB69e7\nqa21S1UeeSRCONwEwJlnQkFBFwxDv+gUEckGhW0RkXYmGAwyciSkUin69g0TicCXX0J1tV0mUlBg\nsmKFh/x8k/vvj7F2rYu6Ok+m9vvWW+2FlYGAyc6dDnbsCONyWQwc6NDCShGRVqawLSLSTrlcLqqq\n7HC8efNmqqsLSCbB7bb4t3+LkEg4ePnlI/83v3u3gd/vYMYMu8/3woURolELj6eBLl0sysoMunTp\ncsquRUSko9LvDUVEOoBwOIzDsYXRo7syZIgXpxN69jQZPTrFwIFpampimQWWDz0UZccOO2jX1xvk\n5Jh4vQ6qq3MYPz6XDz908dFHJuvWhdi0KUQkEmnryxMRabc0sy0i0sH4fD5GjLDrs8vKmvjwwxTp\nNDz7bIodOwweecTL3LmxzPG3355g1ix/psxkxgw/8+dHaWpyUFJiEoslsawQ6bSDykonOTk5bXJd\nIiLtkcK2iEgHlpOTw/Dh9uN4PI7HE2fp0igHDsDDD0e5/XY/Ho/V4rxEwkFtrY/q6gR+v0UyCT6f\nxQcfpEinG7AsOOccN36//xRfkYhI+6KwLSLSSXi9XkaMsHetDIfDbN6cYs2aMA6HxbJlUWbMsIPz\ngw9Gue++Q9vFH5zxXrzYy9SpCVas8DB2bIqGhiS9e4fo00cLK0VEvo7CtohIJ5Sbm8vw4WCaJjt3\nNhIIpHj66SYSCZg3z0dTk0FNTYzevU22bDF49FF7i/gVKzxMmpRkyRIvdXUeHnkkQkODidcbAqCi\nwu6WIiIiNoVtEZFOzDAMysrscByPx3n//RgPPxzFsmDHDoOHH/Zyxx3xzPFjx6ZYssTbrI3gjTfG\nOfvsNC6XxeefQ36+Xd9dVaUyExERhW0REQHsMpPzzrPLR5qamnC70zz0UBSv12L58gj33OPj/vtj\n1NV5Mufk5Zn07Wty6612C8FFi6JEo7Bpk5NEIoXbHcLlgooKJ7m5uW1yXSIibUmt/0REpIWcnBzO\nP78rF1zQlaFDA5x9dpp/+zd72/ef/SySaSP4T/8UZ+5cu5NJfb3BnDl+unaFFSt8TJ7s54svHDQ1\nGbz7rsnmzSG2bWvENM22vjwRkVNGM9siInJUbrebfv2C9Otnz3ibpr2wMhRy8NprLb+NvPyym0QC\nbr89zoEDTiZNsktJli+PUFSUZt++RkzTQX6+QXFxQFvFi0iHprAtIiLHLScnhxEj7McNDQ2ccUaC\n885LM22aXUZysNxk/PgkDgfN+ndPnx6gujrBqFEp0mmLVMpk9247eJ99totAINBWlyUikjUK2yIi\n8o107dqV4cOhqipBYWGYdBoCAYsHHojxxhtH/vZimvC3vxnU1tqb7sydG2P3bgcNDSl69AgRjzsY\nMgS1EhSRDkNhW0RETorH42HECHvRpGmanHFGI6WlafbscbBkSZRZs+wyklmz4uzcaW+Wc3C2e+FC\ne+Oc227z88//HGX/foNIJE3fviG++AKqqnx4vd6v/dwiIqc7hW0REWk1hmFQXBykuNhuJfjBBzGe\nfz5MY6ODmTP9jB2banFOOg2TJiWZPNneBn7u3BgeTxKv18Gf/xzHMGJ4PDB4sB+Px9PifBGR09kJ\nrUpZtGgRhmEwffr0zGsNDQ3cfPPNFBcXEwgEqKioYOnSpa0+UBERaV+8Xi/nnhtk9OiuXHSRk3/7\ntwg/+EGc5csPdTOpqYnhcJDp3V1fb/Doox62b3dx1VW5jB+fyzvvuNm82cmbb8Z4990Q0Wi0rS9N\nROS4HffM9rp161i5ciVVVVU4HI7M6zNnzuT111/nl7/8JeXl5bz++uvcdNNN9OjRg+uuuy4rgxYR\nkfYlEAgwerT9eMiQCAUFYZxO2LvXwY4dzed9xo5NUVNzaGFlba1dauL3W3zrWylisSSBQAKXC846\nK4Db7T7VlyMictyOa2Y7FApx3XXXsWrVKrp169bsvfXr13P99ddz8cUXU1JSwo9+9CNGjhzJn/70\np6wMWERE2jc7eHfl3HMD9O8P1dVxHnvs0Gz3qFEtS028Xou+fU1uuCGH8eNz+egjF59/Du++G+Wt\ntxr4859DJBKJNrgaEZGjO66wPXnyZK6++mouvvhiLMtq9t7YsWP5zW9+w6effgrA22+/zXvvvceY\nMWNaf7QiItJhuFwuKiq6cN55Qb7zHTf/8R9hnn8+TG6uydy5sWalJuefn262ec7MmX4SCYMrr8zl\n//v/ctm1y2D9+hjvvNPAli0hUqmWgV1EpC0cs4xk5cqVbN26laeffhqgWQkJwOLFi7n++uspKSnB\n5bI/3M9+9jO+853vfO3H3LBhw8mMWb6G7mt26L5mh+5rdrT3++rxQHGxj169inj+eTswR6Pwn//Z\nslTk5Zfd1NcbdO9usmOHkzlzPHzve0n+z/9JsX9/BJfLomvXPTQ0NJz0uNr7fT1d6b5mh+5r6+vf\nv/83PveoYfujjz5i7ty5vPnmmzidTgAsy2o2uz1r1izeeecdfvvb31JaWsrrr7/OHXfcQWlpKZdf\nfvk3HpiIiHROsVgM+ASPB3w+H15vMVdemWD48DS33NJ88xyA8eOTrFjhYdq0BOk0XHPNoa4mRUW9\nKSw8E8MAv/8zIpFIW12WiHRSDuurdSGHefLJJ7nxxhszQRsgnU7jcDhwOp3s27ePbt268etf/5px\n48ZljrnpppvYvn07/+///b/Ma6FQKPM4GAy29nV0agd/gh0+fHgbj6Rj0X3NDt3X7Ojo99U07d0m\nt293YJoOAgGTTz5xMXOmn+rqQ7XadXWezMLK/HyTG26I8+1vpzAMyMsziccd5Oe7KCvzHdc28R39\nvrYV3dfs0H3NnpPJsUed2b7qqqs4//zzM88ty2LixIkMGDCAu+66K1NS8tX/sAzDaFHbLSIi8k0Z\nhkFhYZDCQkilUmzaFKVfvxTPPx8mGoUDBwzeeqvlt7Rzzklz/fU55OWZ3HdfnJkz7Q12Hn00QkFB\nGp8PKivV0UREsueoYTsYDLZI74FAgG7dujFo0CAA/vf//t/U1NSQm5tLSUkJr7/+Ok899RQPPvhg\n9kYtIiKdlsvloqqqC2AH723bmsjLS+PxWJSWmpmt4Gtro9x7r71b5bhxSWbOPNROcNq0ADfcEOdb\n30rR0BAlNzfCwIEe/H5/m12XiHRMJ7yDpMPhaLZI8le/+hVz5szhuuuu44svvqCsrIwFCxYwbdq0\nVh2oiIjIV7lcLvr3tyeFKitjbNwY57nnUtjfpiwOHPj68DxsmMmNN9qz3g8/HCWRSGCaSbxeqKry\na7ZbRFrFCYftV199tdnznj178sQTT7TagERERL4Jn8/Huefas9rRaJSPP06wbFmUGTP8vPGGk6VL\no5kykoULo8yb5yWRsLeKv/12P1OnJlixwsPYsSm++CJGr14RnM4SHI6/teVliUg7d8JhW0RE5HTn\n9/upqvJTUZGgpKQJ07RwOi2efTZMY6ODt95yceCAwfjxSZYs8TJunN3RZNIk+3ldnYelS6N06wY5\nOX14++0GSksd9O6dc1wLK0VEDlLYFhGRDsvj8TBypAew67u3bGnC77e44ooEw4alefvtQ98Gx45N\nsWSJN1PXPXOmn8cfb2LCBLuV4M9+FqGwsJF02sE557hV3y0ix0VhW0REOgWXy8WgQXZ9dzKZxOuN\nUFaW5sILU8yd6+P++2PU1XmanfPqq+5M+L7llgDV1Qnq6jwsWxalT58QHo+TQYN8mU3dRES+Sv87\niIhIp+N2uzn7bDt4Dx4co7AwgtNpsXx5hOnT7Y1zli6Nct993mbnhcMO6usNZszwZ4L34483UVoK\nXbu6KSnxqMxERJpR2BYRkU7N5/MxYoS9sHLw4BgFBWEsCwzDYto0BwsX2uG5pibG/ff7MueFww4S\nCdi61cnkyfbr//qvEfr2hWDQRVGRgreIKGyLiIhk+Hw+Ro/2YZomn36awOsN8+yzKbxe+PRTA4/H\n3pnyYPAePz5Jba0vU2ryk58EuOuuKFVVCXbsSJKbazF4sNoIinRmCtsiIiJfYRgGJSU+9u79gJwc\ngzPOOIvc3BTPPhvG4YC9ex14PJCb23K35AEDTKZODXDllUkuvjjFgQNRiooiuN1elZmIdEL6ihcR\nETkK0zQpLc1h0KAg55/vxe+3KCszWbXK7mwyd26M/HyT/HyT2too8+b5mDIlwZNPepk4MYf333ex\nebPBZ5/F+dOfGtmwIUQikWjryxKRU0Qz2yIiIsfJ6/Vy7rn2osl4PE63bnGSSXjwwQgbNzopKkoz\nenSaxYsPlZbU1vqorrbDdZ8+Jn36pFi/PorDEcPjcTB0qE9lJiIdmGa2RUREvgGv18uIEV057zwv\nffpY/OM/JikosLj00uQRjw+HHTz6qIemJgfbt7sYPz6XceNyWLMmwaZNDaRSqVN8BSJyKmhmW0RE\n5CR4vV5Gjz7UIrC8PMZjj0W4+Wa7hWBNTQynE+bN8zFhQoJQyOCee/yZme8ZM+zNcz79NEpREQQC\naiEo0pEobIuIiLQin8/HuHEp+vdvoqHBIhKB6dMDeDwwalSKUMjR4pxXX3VTV+dh3rwoO3ZYXHJJ\nExUVToqKfArdIu2cwraIiEgrc7lcDB7cBbB3q3zqqQiJBIRC0NhosGRJlFmz7O3eD+/fHY87ePJJ\nL08+6WXRoihFRY0EAg4Mw8E553jxeDxf+zlF5PSksC0iIpJFbreb4cPtBZCpVIrNm2OEQikef7wJ\ngDvv9LN/v8FNN8Wb9eyeM8fPE0808aMf5ZCXZ7JsWZRgMAZAly4OBgzwa5t4kXZAX6UiIiKniD3j\nnYtpmuzYEefLL5MsXBhj2rTAEXt2/+EPbhIJmDIlwYQJOQDMmxelTx+T/fsjdO8OAwYEFLpFTmP6\n6hQRETnFDMOgvNxPebmfqqoU/fpFSaXSXHhhimnT7IWVDz8c5f77vYwfn8y0Euze3SQed2SCd01N\njL/+Ncrll6vEROR0pbAtIiLShlwuF1VV9rfjqqoU/ftH+eILkw0bDO65J86GDc7MsV/dHv5gD++c\nnBi5uTGSSQd+v8WQIdoiXuR0obAtIiJymnC5XAwZ4vr7rpUxvvwySVlZitGjU0yffuRSE6/XYt8+\ng2uvPdRqsL4+SlFRnK5dXWojKNLGFLZFREROM3aZSYDycnu7+E8/TfDyyxHicbNZqUlNTYz+/VP8\n+Me5mdnuFSs8zJ5t8eMf291OHn88ytixXtV1i7QRfeWJiIicxgzDoKTER0mJ/fzss5OUlYU5cMDB\nH/7gIi/P2ez4sWNTzJlzaNOcyZP91NVFKCtz4XA4KC52a6Zb5BRS2BYREWlH3G4355zjJpVK0aNH\nAqfT5JFHItx6qz3bPWpUirq65oslP/8cqqvt1x5/PEpVlYHD4cCyFL5Fsk1hW0REpB06fGFl//4J\nSkrCpFKQSlnMnRtj4UJ7o5xFi6Lce6+v2Ux3dXWCAQPS7Nnj4OKLE5SVOSkp0W6VItmgsC0iItLO\neTweRoywZ65N06SsLM6QIU289JKLjz82OHCgeYhOp+3dKles8LFihd27u6KiibIytxZUirSyE/pq\nWrRoEYZhMH369Gavb9myhe9973t069aNnJwczj33XDZv3tyqAxUREZFjMwyD0lI/F12Uw9SpBlOm\nOPjFL2Lk55vk55vMmhXH4SDTQjCRsIN3dXUOo0Z5+I//iLJpU5j3329i+/YYpmm29SWJtGvHHbbX\nrVvHypUrqaqqwuFwZF7ftm0bF1xwAX379uXVV19l48aNLFy4kNzc3KwMWERERI7NDt1eSkp8XH65\nj7VrE/zqV1GeeMLN4RPXh/furq83uPNOL+vXO7j8cj+jRnn43e+ipFKptrsQkXbuuMpIQqEQ1113\nHatWreK+++5r9t7cuXMZM2YMDz74YOa1srKy1hyjiIiInATDMCgr81FSYvLSS0ksy+TCCyNH3CZ+\n7NgUNTWHuplMmeLnV7+K0Levk1DIIi/PoKhIpSYix+u4vlImT57M1VdfzcUXX4xlHfqiNE2T3/3u\ndwwcOJAxY8bQq1cvzj//fJ599tmsDVhERES+mYOz3WVlAa64wsfLL0e59tokjz8ezZSZXHJJssV5\nv/udi2XLHFx+uY81a0z+679UXiJyvI4ZtleuXMnWrVtZsGABQLMSkr179xIOh3nggQcYM2YM//mf\n/0l1dTXXXnstv//977M3ahERETkpdjeTHM49twv/8A9+1q5NUFcXwTAsFi2KNqvxfvFFF+Gwg/p6\ng9paH7//vcGuXUlM02T79hjvvx9h504FcJEjcViHT1V/xUcffcRFF13Em2++yYABAwD4X//rfzFk\nyBCWL1/O3/72N4qKirjmmmv45S9/mTnv2muv5csvv2wWuEOhUObxxx9/nI1rERERkZNgGAaW1YO9\ne4OEw07WrnXx4osupk1LMG+ej/37DfLzTaqrE/zgB3vYvbsLP/lJHmDvZtm3b4yCgu0K3dLh9O/f\nP/M4GAye0LlHrdleu3Yt+/bto7KyMvNaOp3mjTfe4Oc//znhcBiXy8WgQYOanVdRUcEzzzxzQgMR\nERGRtmWH5L2ceeY+evQ4g8JCPz/8YZTPP++Cx+MjP9/MhGrDgJ/8JC9T211b66O62qC6+gzSaXse\nz+PZr+Atnd5Rw/ZVV13F+eefn3luWRYTJ05kwIAB3HXXXXg8Hs4777wWbf62bNly1EWSw4cPP7lR\nSzMbNmwAdF9bm+5rdui+Zofua3bovtpM02Tt2gQNDebfF0jmsWtXTovj/H6L+voeTJ7sB2DVqjO5\n7DJvi8WUuq/ZofuaPYdXaJyoo4btYDDYYqo8EAjQrVu3zGz2nXfeyfe//30uuugiLrnkEl599VWe\neeYZXnjhhW88KBERETl9HOxmcrjiYje/+EWMG2+0X6+piTF0qEl1dSAz2z1xopc//SnOl1/aM92D\nBnlwubSfnnQuJ/wv3uFwNFsk+d3vfpfHH3+cBx54gBkzZjBgwACeeuopxo4d26oDFRERkdOHYRiZ\n/t0HZ7xNs/kMdmlpmnXrTG691Z7pXr48yne/a5FK9QDsGXO1EJSO7oTD9quvvtritQkTJjBhwoRW\nGZCIiIi0D1+d8TZNk1Wr4kyc6AVg8eIYP/xhTmame/p0P3l5TfzoRyUArFoVP2KZiUhHot/liIiI\nSKswDIPLLvOybp3dqzsUahmif/97d7Myk3XrkpSWek/pOEVOJf0oKSIiIq3m4MY5paVeBg3ysnz5\noZ7dP/tZlBdf1DyfdC76Fy8iIiJZ4XK5uPJKGDAgBsDAgR5ycpJMnGjP9a1aFae4uOWstmma7Nx5\nePcTbQ8v7ZfCtoiIiGSNvVPlobhx2WVO1qzZCcD555e0CNGmafLyy4e6nDz8cJTi4jAjRvhxu92n\nbuAirUQ/JoqIiMgpYxgGLtc+XK59R5y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p06fp+PHjlJ2d7a+YqmYwGCg6OprOnz9PlZWV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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2275,14 +2294,7 @@ } ], "source": [ - "d = 100\n", - "xs, ys = [], []\n", - "\n", - "for i in range (3000):\n", - " a = math.radians(30) + randn() * math.radians(1) \n", - " xs.append(d*math.cos(a))\n", - " ys.append(d*math.sin(a)) \n", - "plt.scatter(xs, ys);" + "ukf_internal.plot_scatter_of_bearing_error()" ] }, { @@ -2327,7 +2339,7 @@ "data": { "image/png": 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Zl8gDAHw3SyAQCIRzgQEDBiguLk7PPvus2rZtq1WrVmncuHHKyMjQ559/LkkqKSkJvn7n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\nU6dq9uzZysvLU//+/TV8+HD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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2335,21 +2347,7 @@ } ], "source": [ - "pos = np.array([5., 5.])\n", - "for i in range(5):\n", - " pos += (0.5, 1.) \n", - " actual_angle = math.atan2(pos[1], pos[0])\n", - " d = math.sqrt(pos[0]**2 + pos[1]**2)\n", - "\n", - " xs, ys = [], []\n", - " for i in range (100):\n", - " a = actual_angle + randn() * math.radians(1)\n", - " xs.append(d*math.cos(a))\n", - " ys.append(d*math.sin(a))\n", - " plt.scatter(xs, ys)\n", - "\n", - "plt.axis('equal')\n", - "plt.plot([5.5, pos[0]], [6, pos[1]], c='g', linestyle='--');" + "ukf_internal.plot_scatter_moving_target()" ] }, { @@ -2378,9 +2376,9 @@ "outputs": [ { "data": { - "image/png": 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fn34a1pIlLi1d6tRTTwU0aBALhgCof6oNytnZ2dqzZ88J26+77jr993//d1yKAgDUXy6X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\ngC+//FKRSEStWrWqsj0tLU3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Rj3/+00VJicmWq4finT073qVLDVVpD/HLL79k4MCBHDp0iCZNmtC9e3dyc3NJ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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2388,65 +2386,7 @@ } ], "source": [ - "from math import sin, cos, atan2, pi\n", - "\n", - "def isct(pa, pb, alpha, beta):\n", - " \"\"\" Returns the (x, y) intersections of points pa and pb\n", - " given the bearing ba for point pa and bearing bb for\n", - " point pb.\n", - " \"\"\"\n", - " \n", - " B = [pb[0] - pa[0], pb[1] - pa[1]]\n", - " AB = math.sqrt((pa[0] - pb[0])**2 + (pa[1] - pb[1])**2) \n", - " ab = atan2(B[1], B[0])\n", - " a = alpha - ab \n", - " b = pi - beta - ab\n", - " p = pi - b - a\n", - " \n", - " AP = (sin(b) / sin(p)) * AB \n", - " x = cos(alpha) * AP + pa[0]\n", - " y = sin(alpha) * AP + pa[1]\n", - " return x, y\n", - "\n", - "\n", - "def plot_iscts(pos, N=4):\n", - " for i in range(N):\n", - " pos += (0.5, 1.) \n", - " actual_angle_a = math.atan2(pos[1] - sa[1], pos[0] - sa[0])\n", - " actual_angle_b = math.atan2(pos[1] - sb[1], pos[0] - sb[0])\n", - "\n", - " da = math.sqrt((sa[0] - pos[0])**2 + (sa[1] - pos[1])**2)\n", - " db = math.sqrt((sb[0] - pos[0])**2 + (sb[1] - pos[1])**2)\n", - "\n", - " xs, ys, xs_a, xs_b, ys_a, ys_b = [], [], [], [], [], []\n", - "\n", - " for i in range (300):\n", - " a_a = actual_angle_a + randn() * math.radians(1)\n", - " a_b = actual_angle_b + randn() * math.radians(1)\n", - "\n", - " x,y = isct(sa, sb, a_a, a_b)\n", - " xs.append(x)\n", - " ys.append(y)\n", - "\n", - " xs_a.append(da*math.cos(a_a) + sa[0])\n", - " ys_a.append(da*math.sin(a_a) + sa[1])\n", - "\n", - " xs_b.append(db*math.cos(a_b) + sb[0])\n", - " ys_b.append(db*math.sin(a_b) + sb[1])\n", - "\n", - " plt.scatter(xs, ys, c='r', marker='.')\n", - " plt.scatter(xs_a, ys_a)\n", - " plt.scatter(xs_b, ys_b)\n", - " plt.axis('equal')\n", - " plt.show()\n", - "\n", - "pos = np.array([4., 4.])\n", - "sa = [0., 2.]\n", - "sb = [8., 2.]\n", - "\n", - "plt.scatter(*sa, s=100)\n", - "plt.scatter(*sb, s=100)\n", - "plot_iscts(pos, N=4)" + "ukf_internal.plot_iscts_two_sensors()" ] }, { @@ -2455,7 +2395,7 @@ "source": [ "I placed the sensors nearly orthogonal to the target's initial position so we get these lovely 'x' shape intersections. I then computed the (x, y) coordinate corresponding to the two noisy bearing measurements and plotted them with red dots to show the distribution of the noisy measurements in x and y. We can see how the errors in x and y change as the target moves by the shape the scattered red dots make - as the target gets further away from the sensors, but nearer the y coordinate of sensor B the shape becomes strongly elliptical.\n", "\n", - "Next I will alter the starting positions and rerun the simulation. Here the shape of the errors in x and y changes radically as the target position changes relative to the two sensors. " + "Next I will alter the sensor positions and rerun the simulation. Here the shape of the errors in x and y changes radically as the target position changes relative to the two sensors. " ] }, { @@ -2468,9 +2408,9 @@ "outputs": [ { "data": { - "image/png": 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KKc5v5J4xQw7LkpmVpf2XXaavIpE4fxoAOD7t2rWr2/b5fMf1mpjCdVpamiZNmqRbbrml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\n", 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2478,13 +2418,7 @@ } ], "source": [ - "sa = [3, 4]\n", - "sb = [3, 9]\n", - "pos= np.array([4., 4.])\n", - "\n", - "plt.scatter(*sa, s=100)\n", - "plt.scatter(*sb, s=100)\n", - "plot_iscts(pos, N=5)" + "ukf_internal.plot_iscts_two_sensors_changed_sensors()" ] }, { @@ -2506,12 +2440,12 @@ "\n", "$$ \n", "\\begin{aligned}\n", - "weights &= \n", + "\\text{weights} &= \n", "\\begin{bmatrix}\n", "w_1&w_2& \\dots & w_{2n+1}\n", "\\end{bmatrix} \n", "\\\\\n", - "sigmas &= \n", + "\\text{sigmas} &= \n", "\\begin{bmatrix}\n", "\\mathcal{X}_{0,0} & \\mathcal{X}_{0,1} & \\mathcal{X}_{0,2} \\\\\n", "\\mathcal{X}_{1,0} & \\mathcal{X}_{1,1} & \\mathcal{X}_{1,2} \\\\\n", @@ -2521,7 +2455,7 @@ "\\end{aligned}\n", "$$\n", "\n", - "In other words, each column contains the $2n+1$ sigma points for one dimension in our problem. The $0th$ sigma point is always the mean, so first row of sigma's contains the mean of each of our dimensions. The second through nth row contains the $\\mu+\\sqrt{(n+\\lambda)\\Sigma}$ terms, and the $n+1$ to $2n$ rows contains the $\\mu-\\sqrt{(n+\\lambda)\\Sigma}$ terms. the choice to store the sigmas in row-column vs column row format is somewhat arbitrary; my choice makes the rest of the code a bit easier to code as I can refer to the ith sigma point for all dimensions as `sigmas[i]`." + "In other words, each column contains the $2n+1$ sigma points for one dimension in our problem. The $0^{th}$ sigma point is always the mean, so first row of the sigmas is the mean of each of our state variables. The second through nth row contains the $\\mu+\\sqrt{(n+\\lambda)\\Sigma}$ terms, and the $n+1$ to $2n$ rows contains the $\\mu-\\sqrt{(n+\\lambda)\\Sigma}$ terms. The choice to store the sigmas in row-column vs column row format is somewhat arbitrary; my choice makes the rest of the code a bit easier to code as I can refer to the ith sigma point for all dimensions as `sigmas[i]`." ] }, { @@ -2549,11 +2483,12 @@ " \n", "Our code will look something like this.\n", "\n", - " lambda_ = alpha**2 * (n +kappa) - n\n", - " Wc = np.full(2*n + 1, 1. / (2*(n+lambda_))\n", - " Wm = np.full(2*n + 1, 1. / (2*(n+lambda_))\n", - " Wc[0] = lambda_ / (n + lambda_) + (1. - alpha**2 + beta)\n", - " Wm[0] = lambda_ / (n + lambda_)" + "```python\n", + "lambda_ = alpha**2 * (n +kappa) - n\n", + "Wc = np.full(2*n + 1, 1. / (2*(n+lambda_))\n", + "Wm = np.full(2*n + 1, 1. / (2*(n+lambda_))\n", + "Wc[0] = lambda_ / (n + lambda_) + (1. - alpha**2 + beta)\n", + "Wm[0] = lambda_ / (n + lambda_)```" ] }, { @@ -2567,19 +2502,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "###### The equations for the sigma points are:\n", + "The equations for the sigma points are:\n", "\n", "$$\n", - "\\begin{aligned}\n", - "\\mathcal{X}_0 &= \\mu \\\\\n", - "\\mathcal{X}_i &= \\mu + \\bigg[\\sqrt{(n+\\lambda)\\Sigma} \\bigg]_i\\,\\,\\,\\, &\\text{for}\\text{ i=1 .. n} \\\\\n", - "\\mathcal{X}_i &= \\mu - \\bigg[\\sqrt{(n+\\lambda)\\Sigma}\\bigg]_{i-n}\\,\\,\\,\\,\\, &\\text{for}\\text{ i=(n+1) .. 2n}\n", - "\\end{aligned}\n", + "\\begin{cases}\n", + "\\mathcal{X}_0 = \\mu \\\\\n", + "\\mathcal{X}_i = \\mu + \\left[\\sqrt{(n+\\lambda)\\Sigma} \\right]_i, & \\texttt{for i=1..n} \\\\\n", + "\\mathcal{X}_i = \\mu - \\left[\\sqrt{(n+\\lambda)\\Sigma}\\right]_{i-n} & \\texttt{for i=(n+1)..2n}\n", + "\\end{cases}\n", "$$\n", "\n", - "The Python for this is not much more difficult once we wrap our heads around the $[\\sqrt{(n+\\kappa)\\Sigma}]_i$ term.\n", + "The Python for this is not difficult once we understand the $\\left[\\sqrt{(n+\\lambda)\\Sigma} \\right]_i$ term.\n", "\n", - "The term $[\\sqrt{(n+\\kappa)\\Sigma}]_i$ has to be a matrix because $\\Sigma$ is a matrix. The subscript $i$ is choosing the column vector of the matrix. What is the square root of a matrix? There is no unique definition. A typical definition is that the square root of a matrix $\\Sigma$ is the matrix $S$ that, when multiplied by itself, yields $\\Sigma$.\n", + "The term $[\\sqrt{(n+\\kappa)\\Sigma}]_i$ is a matrix because $\\Sigma$ is a matrix. The subscript $i$ is choosing the column vector of the matrix. What is the square root of a matrix? There is no unique definition. One definition is that the square root of a matrix $\\Sigma$ is the matrix $S$ that, when multiplied by itself, yields $\\Sigma$.\n", "\n", "$$\n", "\\begin{aligned}\n", @@ -2595,16 +2530,17 @@ "\\Sigma = SS^\\mathsf{T} \\\\\n", "$$\n", "\n", - "This method is frequently chosen in computational linear algebra because this expression is easy to compute using something called the *Cholesky decomposition* [3]. \n", + "This method is frequently chosen in computational linear algebra because this expression is easy to compute using something called the **Cholesky decomposition** [3]. \n", "SciPy provides this with the `scipy.linalg.cholesky()` method. If your language of choice is Fortran, C, or C++, libraries such as LAPACK provide this routine. Matlab provides `chol()`.\n", "\n", - " sigmas = np.zeros((2*n+1, n))\n", - " U = scipylinalg.cholesky((n+kappa)*P)\n", + "```python\n", + "sigmas = np.zeros((2*n+1, n))\n", + "U = scipy.linalg.cholesky((n+kappa)*P)\n", "\n", - " sigmas[0] = X\n", - " for k in range (n):\n", - " sigmas[k+1] = X + U[k]\n", - " sigmas[n+k+1] = X - U[k]" + "sigmas[0] = X\n", + "for k in range (n):\n", + " sigmas[k+1] = X + U[k]\n", + " sigmas[n+k+1] = X - U[k]```" ] }, { @@ -2621,9 +2557,10 @@ "\n", "We implement the sum of the means with\n", "\n", - " X = np.dot (Wm, sigmas)\n", + "```python\n", + "x = np.dot(Wm, sigmas)```\n", "\n", - "If you are not a heavy user of NumPy this may look foreign to you. NumPy is not just a library that make linear algebra possible; under the hood it is written in C to achieve much faster speeds than Python can reach. A typical speedup is 100x. To get that speedup we must avoid using for loops, and instead use NumPy's built in functions to perform calculations. So, instead of writing a for loop to compute the sum, we call the built in `numpy.dot(x,y)` method. If passed a 1D array and a 2D array it will compute the sum of inner products:" + "If you are not a heavy user of NumPy this may look foreign to you. NumPy is not just a library that make linear algebra possible; under the hood it is written in C to achieve much faster speeds than Python can reach. A typical speedup is 20x to 100x. To get that speedup we must avoid using for loops, and instead use NumPy's built in functions to perform calculations. So, instead of writing a for loop to compute the sum, we call the built in `numpy.dot(x,y)` method. If passed a 1D array and a 2D array it will compute the sum of inner products:" ] }, { @@ -2658,14 +2595,21 @@ "source": [ "All that is left is to compute $\\mathbf{P} = \\sum_i w_i{(\\mathcal{X}_i-\\mu)(\\mathcal{X}_i-\\mu)^\\mathsf{T}} + \\mathbf{Q}$\n", "\n", - " kmax, n = sigmas.shape\n", - " P = zeros((n, n))\n", - " for k in range(kmax):\n", - " y = sigmas[k] - x\n", - " P += Wc[k] * np.outer(y, y) \n", - " P += Q\n", + "```python\n", + "kmax, n = sigmas.shape\n", + "P = zeros((n, n))\n", + "for k in range(kmax):\n", + " y = sigmas[k] - x\n", + " P += Wc[k] * np.outer(y, y) \n", + "P += Q\n", + " ```\n", "\n", - "This introduces another feature of NumPy. The state variable $X$ is one dimensional, as is $sigmas[k]$, so the difference $sigmas[k]-X$ is also one dimensional. NumPy will not compute the transpose of a 1-D array; it considers the transpose of `[1,2,3]` to be `[1,2,3]`. So we call the function `np.outer(y,y)` which computes the value of $\\mathbf{yy}^\\mathsf{T}$ for a 1D array $\\mathbf{y}$." + "This introduces another feature of NumPy. The state variable `x` is one dimensional, as is `sigmas[k]`, so the difference `sigmas[k]-X` is also one dimensional. NumPy will not compute the transpose of a 1-D array; it considers the transpose of `[1,2,3]` to be `[1,2,3]`. So we call the function `np.outer(y,y)` which computes the value of $\\mathbf{yy}^\\mathsf{T}$ for a 1D array $\\mathbf{y}$. An alternative implementation could be:\n", + "\n", + "```python\n", + "y = (sigmas[k] - x).reshape(kmax, 1) # convert into 2D array\n", + "P += Wc[K] * np.dot(y, y.T)\n", + "```" ] }, { @@ -2683,22 +2627,31 @@ "\n", "$$\\boldsymbol{\\mathcal{Y}} = f(\\boldsymbol{\\chi})$$\n", "\n", - "Then we compute the predicted mean and covariance using the unscented transform. In the code below you can see that I am assuming that this is a method in a class that stores the various matrices and vectors needed by the filter.\n", + "Then we compute the predicted mean and covariance using the unscented transform. In the code below you can see that I am assuming that this is a method in a class that stores the various matrices and vectors needed by the filter." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def predict(self, sigma_points_fn):\n", + " \"\"\" Performs the predict step of the UKF. On return, self.xp and\n", + " self.Pp contain the predicted state (xp) and covariance (Pp). 'p'\n", + " stands for prediction.\n", + " \"\"\"\n", "\n", - " def predict(self):\n", - " \"\"\" Performs the predict step of the UKF. On return, self.xp and\n", - " self.Pp contain the predicted state (xp) and covariance (Pp). 'p'\n", - " stands for prediction.\n", - " \"\"\"\n", + " # calculate sigma points for given mean and covariance\n", + " sigmas = sigma_points_fn(self.x, self.P)\n", "\n", - " # calculate sigma points for given mean and covariance\n", - " sigmas = sigma_points(self.x, self.P, self.kappa)\n", + " for i in range(self._num_sigmas):\n", + " self.sigmas_f[i] = self.fx(sigmas[i], self._dt)\n", "\n", - " for i in range(self._num_sigmas):\n", - " self.sigmas_f[i] = self.fx(sigmas[i], self._dt)\n", - "\n", - " self.xp, self.Pp = unscented_transform(\n", - " self.sigmas_f, self.W, self.W, self.Q)" + " self.xp, self.Pp = unscented_transform(\n", + " self.sigmas_f, self.W, self.W, self.Q)" ] }, { @@ -2726,7 +2679,7 @@ "\n", "\n", "$$K = \\mathbf{P}_{xz} \\mathbf{P}_z^{-1}\\\\\n", - "\\hat{\\mathbf{x}} = \\mathbf{x}^- + \\mathbf{Ky}$$\n", + "{\\mathbf{x}} = \\mathbf{x}^- + \\mathbf{Ky}$$\n", "\n", "and the new covariance is computed as:\n", "\n", @@ -2737,7 +2690,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 40, "metadata": { "collapsed": false, "scrolled": false @@ -2745,15 +2698,6 @@ "outputs": [], "source": [ "def update(self, z):\n", - " \"\"\" Update the UKF with the given measurements. On return,\n", - " self.x and self.P contain the new mean and covariance of the filter.\n", - "\n", - " **Parameters**\n", - "\n", - " z : numpy.array of shape (dim_z)\n", - " measurement vector\n", - " \"\"\"\n", - "\n", " # rename for readability\n", " sigmas_f = self.sigmas_f\n", " sigmas_h = self.sigmas_h\n", @@ -2766,7 +2710,7 @@ " zp, Pz = unscented_transform(sigmas_h, self.W, self.W, self.R)\n", "\n", " # compute cross variance of the state and the measurements\n", - " Pxz = zeros((self._dim_x, self._dim_z))\n", + " Pxz = np.zeros((self._dim_x, self._dim_z))\n", " for i in range(self._num_sigmas):\n", " Pxz += self.W[i] * np.outer(sigmas_f[i] - self.xp,\n", " sigmas_h[i] - zp)\n", @@ -2777,6 +2721,17 @@ " self.P = self.Pp - dot3(K, Pz, K.T)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### FilterPy's Implementation\n", + "\n", + "FilterPy has generalized the code somewhat. You can specify different sigma point algorithms, specify how to compute the residual of the state variables (you can not subtract angles because they are modular), provide a matrix square root function, and more. See the help for details.\n", + "\n", + "https://filterpy.readthedocs.org/en/latest/#filterpy-kalman" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -2788,20 +2743,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The Kalman filter is designed as a recursive algorithm - as new measurements come in we immediately create a new estimate. But it is very common to have a set of data that have been already collected which we want to filter. Kalman filters can always be run in a *batch* mode, where all of the measurements are filtered at once. We have implemented this in `UnscentedKalmanFilter.batch_filter()`. Internally, all the function does is loop over the measurements and collect the resulting state and covariance estimates in arrays. It may seem a bit trivial, but you will almost always want to do this if you can for several reasons. First, it will execute a bit quicker than if you implement the loop yourself. Second, the logic in your code will be a bit cleaner, and you will have a reduced chance of bugs. Third, and most importantly, it you batch process your data you can then use an extremely powerful technique to generate far smoother results than you have seen so far. We will present that technique in the next section; here I will show you how to use `UnscentedKalmanFilter.batch_filter()`.\n", + "The Kalman filter is designed as a recursive algorithm - as new measurements come in we immediately create a new estimate. But it is very common to have a set of data that have been already collected which we want to filter. Kalman filters can always be run in a *batch* mode, where all of the measurements are filtered at once. We have implemented this in FilterPy's `UnscentedKalmanFilter.batch_filter()`. Internally, all the function does is loop over the measurements and collect the resulting state and covariance estimates in arrays. It may seem a bit trivial, but you will almost always want to do this if you can for several reasons. First, it will execute a bit quicker than if you implement the loop yourself. Second, the logic in your code will be a bit cleaner, and you will have a reduced chance of bugs. Third, and most importantly, it you batch process your data you can then use an extremely powerful technique to generate far smoother results than you have seen so far. We will present that technique in the next section; here I will show you how to use `UnscentedKalmanFilter.batch_filter()`.\n", "\n", - "All you have to do is collect your measurements into an array or list. Maybe it is in a CSV file, for example.\n", + "Collect your measurements into an array or list. Maybe it is in a CSV file, for example.\n", "\n", - " zs = read_altitude_from_csv()\n", + "```python\n", + "zs = read_altitude_from_csv()```\n", "\n", "Or maybe you will generate it using a generator:\n", "\n", - " zs = [some_func(i) for i in range(1000)]\n", - " \n", + "```python\n", + "zs = [some_func(i) for i in range(1000)]```\n", + "\n", "Now we call the `batch_filter()` method.\n", "\n", - " Xs, Ps = ukf.batch_filter(zs)\n", - " \n", + "```python\n", + "Xs, Ps = ukf.batch_filter(zs)```\n", + "\n", "The function takes the list/array of measurements, filters it, and returns an array of state estimates (Xs) and covariance matrices (Ps) for the entire data set. \n", "\n", "Here is a complete example drawing from the radar tracking problem above." @@ -2809,7 +2767,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 41, "metadata": { "collapsed": false, "scrolled": false @@ -2819,7 +2777,7 @@ "data": { "image/png": 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2WXwWvaeHN9ITs6FW5kIRk8blUkREDmZzuH/zzTfx7//+7wgPD8f8+fMxb968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\nGo0G+fn5Ldbc//rrr9BoNNi+fTsyMjIQFBSEnj1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dgl+urn5fZV/Pljxx3z/1PrD3XgX7duLpMfNYsOYdSsqKKCot4IsVb/DEyDm3\nrKJkSirU5UTHHVKWOwSaRjJUHYO6TCAxPVZpnV+85Uu8XJvR1L1FjY+9NyqSy1cHFltZ2nBfz4dr\nfEwLc0tG9pzCjxs+BmDH8XX0CRmBi1PtJMw1lVeYzeLNXyoT6IHuc2pYt0kMDBtf63NotPUPo2tw\nhDLIeOnWrwlo0hZ7G8dajeNGZRWl5BZkKYn99cn93//nFeUolbhutOP4Wvy9gxjSdSLBvqF15vs5\nNSuJrUdWceD0VqV0r4WZJb1bjdF7QQGhf3Uy+b+Tmr7xDx8+fPed6rnswnQ2n15McZluBlUVKroF\nDMXNPIAjR47c5dE3M/Q5DWnah8MXdV25NuxfinmJE43s3Az6nMZUXFZA5HWJvwoVfVqOoSzbwqDn\n2gFvRnaYwaH4SBIzzwJQVJLPliMr9fo8VuY2+Lm3JcxvIDFnYvV67Kq61XkcEDyZzdGLKa0ooqy8\nhC9XzsXe2gkXe29cHbxwsffCxcELOyvjJhP34lL2BYrLigBwsHYmNSGLtMRsgz2fsT9f27j1IeHy\neXKLMymvKOOr5W8xosM0rC2r34WxtLyYNUd/Vpbbenfn/Nk4IK7G8Wq11rg6+JBZcJkKdTm/rPuc\nXi1H3bSfsc/rjRKunGF/7HpKK6619jrbutG71Whczbw5dvSYUeLydwrllOUhissLyC/K4dsVH9C7\n1Zjb7q+v81quLiMp8xz5JdkUleXr/pXmUVSWX+kcVVd8ylm+Xv02rvbetG/Wm2YurYxyEaDVaknL\nS+T0pf0kZ1fuhmtv7Uz/4PtpbO9Z596vpq5ly5Z6P2adTP69vHRlHdPS0mjatKmyPi0tTdnm5eWF\nWq0mMzOzUut/amoq4eHhtRuwCUnPS2Lr6aWUqXWtu2Yqc/q0Houva92dYjvIpyvxV06TWXAZjVbN\nvgtrGdp+ap1pAdGn4rJCNkX/Rm6xbgIvFSrd78dNfxMy3Ym9tRP9giaQnHWeA3EbKSzNveP+Zipz\nbCztsLa0w8bCDmtL2+t+ttNts7BV9rG2sKuzs+q6OngzpP0jbI7+jaKrF8aFpXkUluaRlHVO2c/W\n0gEXBy9c7b1wcfDGxcELeyunOvl+TLhyVvm5uWtwnYxRn6wsrOkXdD/rT35HubqMgtIcdsasZECb\nB6rdlfFE0k4lgXOwdqZNE/3dPVGpVHT2609k1K8AxKafpI1PNxrb126FtaoqqyjhYNxfxGWcqrQ+\n2LsrnXw6rL1FAAAgAElEQVQjjN4l09rClu6Bw9l25ncA4jKi8HVrQzMXw3ThU2sqOJd6hKjkPZSU\nF9XoWDaW9thZOer+WTthZ+VIQWkOceknla6SmYUpbD+7jEZ27rRv2htft+Ba6aKr0WpIzDxL9KX9\nZBbcXBTBy9mPPq3GYmtlmCptQv/qZPLv7++Pl5cXkZGRdO6sm92zpKSE3bt38/HHuluknTt3xtLS\nksjISCZPngxAcnIyZ8+epWfP21ezCAur/kBJUxcVd4gt+xcrXTWsrWx5YuQ/adWsfbWO9/fVfW2c\n0yZ+Hny05GU0GjUZ+cmUWGXQp8Nwgz9vbSoozuOL5f8mp+hqBRxUTB32slFKpIURxrDysRw6s53s\n/AwcbJ2xt3XEwdbp2s82TlhZ2phUQlmV92ynjp1YtnUBMcmnbln5qLi8gEvZF7iUfW2sjL2NI009\nWtDMPUD3v0cAbs5eRj03ao2a5Uf/pywP7jVG76UW/1abnwVV4d6kEQvXvgdASk4caWXnuK/XvXfV\nSclMImbvtbuhkwY+aYCuU2EkF5xRKjLFZh/hyb5vAHXrvMYkneS3yK/JvjqzOOjG7Dw0+DlldvG6\nIIww8rVpyuy/RxI3MSR8VKVJ7Wp6XtXqCg6c2crGA0vJKci8475mZuY42zXG2dGVRg6uNLJ3pZGj\nK40c3HC2d6GRoytOdi5YWtz6wik7P4MtR1axL2qT8t2dU5TBrpiVnEs/wKAu4wlr3RdzA8xUXlpe\nwoHTW9h2dA2ZeZW7U6tQ0a5FFwZ0Hou/dxAqlapOvV/rk9zcOzfCVYfRkv/CwkLOn9fdNtJoNCQk\nJHD8+HFcXV1p1qwZL7zwAu+++y5BQUG0bNmSd955B0dHRx588EEAnJ2dmTZtGq+++ioeHh64uLjw\n0ksv0aFDBwYOHGisl1VtRSUFHL+wD0sLK1ydPHF19sDJrrHekocDp7ewePOXSguCo60zT46ZSzOP\nmveHrQ1N3P0YFDaOvw4uA2DNnp9p16JLrQ0oM7TC4jy+WPGG0q9YhYrerUYbtTaytaUNvUOGGu35\njcXVyZMnx7yBWl1BWnYySelxJGfEkZQWS/KV+EoDG/9WWJLPucQTnEs8oayztbKjiUcLmrm3oKlH\nAM08WuDZuGmtXRDEXjpN4dWSh872Lvh5148BzFUREtCNIV0nKp8Xmw4vp7ln4D0NeNZqtazc+Z3y\nmdmyaXuDDSAd1ethzlw8ihYtpxOOEpN0qtqNMvpWVlHK2j2/sv34n5XWhwX1rVSSuS4Z33c65xJP\nkF+UQ15hNit2fseUwc/X+LgarYaj53axfv9ipQLb3xo7uBHaug+NHd10Sb6DG84OLjjaOteo2lFj\nR3cm9HuCwV0msO3Yanad3Kh8BqXnXOa3TZ+z4cBSBoWNp2tw/9teRNyLvMIcdp1cx66TGykqya+0\nzcLckq7BEUSEjr7l5J/CNBgt+T906BD9+/cHdLc+586dy9y5c3n00Uf5/vvvefXVVykuLmbmzJlk\nZ2fTvXt3IiMjsbe/dlvp008/xcLCgkmTJlFcXMzAgQP59ddfTaolEnT1kb9a9SaJaZX70FmaW9HY\nyV13MeDkgauzJy5OHsqynY1jlV7rliMrK5W+c3X25Okx83Bv5K3312JIg7vcz/Hz+0jLTqa0vITf\nty5gxqh/mdzv+0aFJfl8sXKuUmJSpTKjV+B9+Lu3M25gDZy5uQU+bn74uPnRDd1nlUajJiMnhaT0\nWJIz4khMjyU5PY6Ssptv+ReXFXEhOYoL15VNbd28A9NHzqmV2YSvn9grJKB7najgVZuGdXuApLRY\nTl+djffXyM/wuIdSp9Hxh5WZp1UqM8aFTzPYZ42Pmx9dgyM4cGYrAGt2/8RLD3xokOe6F0npsfzy\n16ekZiUp6+xsHJnU/yk6VWEmXWOxt3FkUv8nWbj2fUBXvKBTy17VLpGs1Wo5FXeAdfsWkZKZWGmb\no60zg7tOpGe7IXpJvG/Hyb4xo3s/ysDO49h+fC07j69VxvNk5aWzdOt8Nh5YyoDOY+nZbjBWltb3\n/Bxp2ZfYdnQ1B89su+mup52NI31ChtInZARO9o308pqE8ai0Wq3W2EEY2vW3TJydjTNp1Z3sOrmB\nZdsW3PPjrK1scXX6+4Lg6kWBs+7CwMXJEytLa9bs/omtR6/NSNnEzY+nxszVS4UVY9zii7t8hs+W\n/VOpLjB16Et0bm26YzyKSnSVZf4us6lCxUODn8OsUDeoVG6f6pch3rMarYbM3DTd3YH0OJLTY0lK\nj6Xwhhazv4W26sPUoS8Z9KJVo9XwxnfTyCvUDe59ZtzbBm1Jrqu3+4tKCvh4ySylldajkQ8vP/DR\nXWeQrlCX894vz5GRq5v1ulf7oUzq/6RBY83Oz+Cdn2YqXTseHTYLTZ7uIrG2z6tao2bz4eVsOLAU\njUatrG/jG8rkQc+YTO32nzZ8wpGrc9g4O7gyZ8pn2Fk7VPn9qtVqOZd4grX7frupcc7O2oEBnccS\n3nFErVzM36i4tJBdJ9az7diamz5rHG2diQgdTe+QYXedr0er1RJ3+Qxbj64iKu6Q8t36N1cnTyJC\nR9GtzYC7vs66+jlg6gyRw9bJPv8NSUFxHuv2/qYs+3q2RK1Vk5WbTlHpnSd9KS0r5vKVi7edlMjG\nyq5Si2Rgk7Y8cd8/7/rFV5e18Ammd8gwdp3UzYj5x46FtG7e8ba16OuyotICvlw5t1Li/+CgZ+ga\nHCHVEkyImcoM90beuDfyplPLXoDuCzU7/wrJGbEkpceRkBqjtCIfjdlFc88A+ofevgpJTSWkxiiJ\nv72NIwFN2hjsueoyOxsHpo+czX+XvkZZRSnpOZf5JfIzpo+cfcc7ITuOr1MSf1tre4Z3n2zwWBs7\nuhPecYRSXevPvb8wtM3jtT5APj37Mr9GfsbF1GuD3K0srBkb/jg92w02qTut4/s9QUzSSfKLc8kt\nyGTVzh94cNCzVXps3OUzrN37KxcuRVdab2VpQ0Sn+4gIHW3ULk+21vYM7jqRvh1Hsicqkq1HVpFX\npPubzy/OZc2en9l8eAV9O91H3w4jKo15AN1dzFNxB9lyZFWl3/Xfmnu2ZEDnMXQI6G7wSdpE7ZPk\n38jW7v1VSfJdnT15bsJ/sLSwAnRX9ll56WTmpZGZq/tfWc5Lv2Xf4+tdn/iHBHRn6tCXlGObsvt6\nPcypuAPkFGRSWJzHip3f8ciQF40d1j0pLi3kq5VvkpR+rdTlAwNnVpqmXpgulUqFi5M7Lk7uSj/x\npVu/Zs+pjQCs3v0zTdz8DTaPwPVdftoHdKuzFZZqg4+bH5MHPsNPGz8BICruIJEHlzG026Rb7p9X\nmMNfB39Xlod1ewBHu9q5YzwobDz7ojZRVFpAZm4aMalHCfbRz6R+d6PVatl9aiOrd/2ozAgNugm7\nHh78gsl1EwVwsHViYsQ/+H69rgvV/tNb6Hj1Av12ktLjWLfvN2UA9t8szC3pEzKMgWHja+39UBXW\nVrb0Dx1N75Ch7I/ewpbDK5RB2UWlBWzYv5itR1cRHjKcfp1GYW1pw4EzW9l+dI1ygXu9tv5hDOg8\nlgCfNiZ1oSfujST/RpSYdoF9UZuU5XHh0yol57bW9jRx96eJu/9Nj9VqtRQU51W6GMjKTSMz/9r/\nanUFoLtlPbHfE/Xm6t3GypZJ/Z9SZnM8fHYHYa370sYv1MiRVU1mbhrf/vmuMrgX4IEBT9OjrekN\nVBdVN77vNC5fuUh8ylm0Wg0/bviYWZM/xtVJv2UdtVotx69L/jvW01l970Xn1n1ISr+gdIHcsH8J\nzTwCbtkHfN2+35SGE8/GTekTMqzW4rSzcWBw1wms2vUjACeTdhHgYfhKOrkFWSza/AVnro6PAF2V\nmuHdHmBA2DiTvnjs2LInnVr24tj5PQAs2fIlQ9s+hpVF5S4saVnJrNu/iOPn91Zab2ZmTo82Axnc\ndSKNHevu/DJWFtaEdxhOz3aDOHRmO5sOL1e6u5WWFbPp8HJ2HF+LpYXVTd2EzM0t6BLUj/6ho/Fy\nqdqYGGHaJPk3Eo1Wwx/bv1X617XxDaWdf9VbeFQqFY52zjjaOePrdfMEEBqthrzCbNSaCr0nF3VB\nW/8wOrfqo/TnXLp1PnOm/O+u/RuN7VziCX7Y8HGlCgqT+j9Fz3aDjRiVqA0W5pY8PuJVPlr8MnmF\n2RSW5LNw7fu8OPH9ag3Ou53kjDiy8tIBXde/ulSG0Zju6/UIyelxxCSfQouWnzf+l1mTP6nUop2U\nHsv+6M3K8ri+0wxSQvFO+oQMZ8fxdWTnZ1BaUUT0pX305M6t1TVx7Pwelm79utJnkpdLMx4e8qLJ\nVIO7mwn9ZhCTfIrC4jxyCjI5cnELPQJHALrZ7jfuX8rBs9srzbKrQkXnoHCGdXvApO56WJhb0qPd\nILq26c/RmN1EHlpGWlYyoKvcdP1dHVtre/qEDKNPh+EmM45D6EfDKv9Qhxw6s13pZ2duZsG4vvqt\nJGGmMqORg2u9TPz/Nq7vdGXq9uz8DNbu/dXIEd2eVqtlx/G1zF/1pvIla25uwUODnqNX+yFGjk7U\nFmd7Fx4f/hrmZrqE8lJGPEu2fIU+6y6cuLBf+bmdfxejT7xUV5ibmTN12CylPHBxWREL175HaZlu\nAi+tVsvyHQuVBpm2/mEE+3aq9TgtLawY0eNBZfnM5QPkFmbp9TnKykuJijvE9+s+5If1HymfSSpU\nRHQaxSuTP6k3iT+Ao50z90f8Q1k+n3aMuPRTLNv2De/8NJMDZ7ZWSvw7BHRn9pTPeGTIiyaV+F/P\n3MycLkF9mTPlfzw+/NVKPQhcHN0Z33c6bz2+kJE9p0ji3wBJy78RFJcWsua60psRoaPxkHq598zR\nzplxfafxy1+fArDrxHo6t+6Dv3fdmq24vKKM37d+rZTxA13ZtmkjZuPv3dqIkQljaOETxIR+T7B0\n63wADp/bQTPPACI6jdLL8a/v738vde0bAkc7Z6aNeI1Pl82hQl1OSmYiizZ/waPDZnHs/B7iLp8B\ndA0yY/s8ZrQ4w4L6su3oai5duUiFppwN+5fwwICna3TMzLw0ouOPcDr+MOeTo5SqQn9r7OjOlMHP\n0bJp3ZhfQN86tezF0cDdyt/H7vOrb9onyLcTI3s8RHPPwNoOz2DMVGZ0bNmTDoE9dL/3ilKCfDuZ\ndFcuUXOS/BvBhgNLyS/WlW5ydnBlSJcJRo7IdIW17suRszs5naCbIGfR5i94dfL/GbTe8r3ILchi\n4br3SUiNUdb5erVi+ojZODtIa0tD1bPdYN2Yn2jdmJ/Vu36kiZt/jctxpmQmkZatu8VvZWFtlJbr\nuq65ZyCT+j/Jb5s+B3TdXrxdm7Pvuu4+fTuOMGqDjJnKjFG9pzJ/1ZsA7I/eTESnUXi6NK3yMdTq\nCuJSznL64mGi449UqtV/o67BEYzvO92kK8FVxcR+/+BCctRNfd5b+AQzsucUApu0NVJkhqdSqerM\nxHHC+CT5r2UpmYnsPL5WWR7T+1Gs63g/9bpMpVJxf/8neffX5ygrLyEtK5lNh/5geA/Dl+a7m/iU\nc3y37n2l5CLovmQn9X+qXlRdEtWnUqmY0G8GlzMTSEiNQaPV8MOGj3jlgU9wcar+rNUnLlwbrNjG\nr7NexxLUJ93aDCAh7QK7T24AYP3+xco2B1tnhnS931ihKYKad8TL2Y/U3ItotBr+3PsL00fOueNj\n8otyOZNwVDdBWcIxZRKoW/F2bU4bv850COyBn1fDmP3Zyb4R9/d/ih/Xf4QWLU09WjCyxxSCfTtJ\nZRvRoEjyX4u0Wi3Lt3+rTBcf2KQtoa16Gzkq0+fi5MF9PaewfMdCADYdXk7Hlj3xcfM1Wkz7o7ew\ndNt8peKSmcqMMX0eo2/HkfIlIwCwtLBk2ojX+Gjxy+QX5VBYnMd3697n+YnvYmVRvaT9ROy1/v4d\nArvrK9R6aVz441zOuEhcyplK60f2nFInWsBVKhWd/Qaw7sR3AJyMPUDc5TO08AlW9tFqtSRnxBEd\nf5joi0dITD1/0yRNf7M0t6Jls/a09etMG//O9Xo82J10atmTKx2nU6EpZ2i/0fJ5LBokSf5r0fEL\ne4lJPgXoksEJ/Z6QDx496RMyjCMxu7iYcg61poLFW77kxYnv1Xp5U7W6glW7f2THdXd37GwceWzY\nLIPVdBemq5GDK48Pf4XPV7yBRqMmKT2W37d+zUODnrvnz4YrualcyogHdIPJ2/jJLJt3YmFuyWMj\nXlGqLwE0dW9B9zb9jRzZNa4O3vi5teXiFd1EU6t3/8RTY+YSk3SCqPjDnL54pNKdxRs1dnCjjX8Y\nbf0606pZiNwJuqqxve7CR75/RUMlyX8tKS0vYdXOH5TlPh2G4+PmZ7yA6hkzM3MmD3iGDxe/iFpd\nQUJqDN+v/4iITqNo4RNcKx/yhcV5/LD+I+UCD8DH1Zfp983BzdnL4M8vTFNAk7aMD5/Gsu3fAHDw\nzDaaewYS3mHEPR3n+oG+Qc07Ymttp9c46yNnexemjZjNwj/fRa1R88CAp+vcfCidfPuRlKVr1IhP\nOcvsBVPQaNS33FelMsPfuzVt/cJo698Zb1dfSXCFEDeR5L+WbDq0XJl1z8HWmWHdHzByRPWPt2sz\nBneZyIar/XdPxu7nZOx+vF2b06v9ULoE9TNYQnQp4yIL175HZl6asq5DYA+mDHpOxnSIu+odMozE\n9FgOnN4CwIqd3+Pj5ndPAxBlYq/q8fduzbzHF2JmZlYnK6A42jSmd8hQ5W7ijYm/nY0jbXxDaevf\nmSDfTkr5YyGEuB1J/mtBRk4KW46uVJbv6/UwdtYORoyo/hoUNo5LGfGcvK7vc0pmIn9s/4Y1e34m\nrHUferUfSjOPAL0957Hze/kt8rNKk6cM7z6ZwV0nYqaSqTTE3alUKu6P+AcpVxJITL+ARqPmh3Uf\nMmvyJ1WaVTQ7/4pSUcpMZXZPEwYK6kx1sNsZ0vV+jp/fq9T793Hzo61fZ9r6h+Hn1arO3a0QQtRt\nkvzXghU7v1MGfvp6tqRbHepTWt9YmFsyfeRsEtMusDfqLw6f3akk5WXlJeyN2sTeqE34erakV/uh\nhLbqXe1+sBqthg37F/PXwWXKOmsrWx4Z8iLtW3TVy+sRDYelhRXTRr7GR4tnUVCcS35xLt+t+4Dn\nJ/znrtWhrr/Ybdm0Pfa2ToYOV9QiB1snZj3wMUnpsTRx91MmKhNCiOqQ5N/AouMPEx1/GNDNnjih\n3wxpDa4FzT0Dae4ZyOjej3Lo7A72nNpISmaisj0h7TwJaedZuet7ugZH0Kv9ELxcmlX5+MWlRfwS\n+SlRcQeVde7O3ky/7594u1b9OEJcr7GjO48Nf4UvV7yBRqshMe08v29bwIMDn7lj322Z2Kv+c3Zw\nkblBhBB6Icm/AZVXlCnlJwG6tx2Ir1dLI0bU8Nha2xPeYTh9QoYRd/kMe079xbELe5Q7McWlhew4\nvpYdx9cS2LQdvdsPJSSgGxbmt+8GkJ59mW/XvktaVrKyLsi3E48OfRk7G+nOJWqmZdN2jA1/XPns\nOHB6C809A+kTMuyW++cX5RB7dWZaFSpCArrVWqxCCCFMjyT/BrTt6Gqu5KYCuiR0ZM8pRo6o4VKp\nVAQ0aUNAkzaMK57GgdNb2HPqL+X3A3AhOYoLyVE42jrTve1AerYbjKtz5VrYZxKO8eOGjykuLVTW\nDeg8hvt6Piz9boXehHcYQWLaBQ6d3Q7A8h0L8XH1JaBJm5v2PRV3EO3VuUP8fYJwsm9cm6EKIYQw\nMZL8G0h2fgaRh/5Qlkf0eBBHO2cjRiT+5mDrxIDOY4kIHU1M4kl2n9pIVNxBZfK1/OJcNh1ezubD\nKwj27USvkKG08evM9mNrWLPnFyXRsjS34oGBM+kS1NeYL0fUQyqVikkDniIlM5HkjDg0GjXfr/+Q\nVyZ/QiMH10r7HpcuP0IIIe6BJP8GsmrXj8pAUx83P3q1H2rkiMSNzFRmBPl2JMi3IzkFmeyL3sze\nqEhyCzIB0KLldMJRTiccxdbavlJrfyMHV6aPnENzz0BjhS/qOSsLa6aPnM1HS2ZRWJxHflEO36/7\nkGfHv6NUpykqKSAm6aTymA4BkvwLIYS4Mxl5agAxSac4dn6Psjyh3xN1sn60uKaRgyvDuk1i3mPf\nMH3kHIJ8O1Xafn3i38I7mFkPfCKJvzA4FycPHhv2ilIk4GLqOZbv+FbZHhV/SKn73twjEBcnqQIj\nhBDizqTlX8/U6opKX86dW4ff00Q9wrjMzcwJCehGSEA3ruSmsvdUJPtOb6awOA+AXu2GML7f9DsO\nCBZCn1o1a8/o3o+yctf3AOyNiqSZRwC92g+RKj9CCCHumST/erbr5AalpKS1pQ1jej9q3IBEtbk5\nezGq9yMM6z6Zc4nHsbayoWXT9sYOSzRA/TrdR2L6BY6c2wnAH9u/xc3Zi7MJx5V9JPkXQghRFZL8\n61FeYQ7r9y9Wlod0vV/qMtcDlhaWtGshM6YK41GpVEweMJPUzEQuXbmIWlPB16vfRq3Rlaz1cfXF\no7GPkaMUQghhCqTPvx79ufcXSsqKAPBo5EO/TvcZOSIhRH1hZWnN9JFzsLNxBFASf4CQwO7GCksI\nIYSJkeRfT+JTznHg9BZleXy/J6RfuBBCr1ydPXl06MuobpglvKN0+RFCCFFFkvzrgUaj5o/t3yjL\nIQHdCL6hWowQQuhDkG9HRvV6WFn2aNwEb1dfI0YkhBDClEiffz3Yf3oLSemxgG7ip7F9HjdyREKI\n+qx/6Bg0Wi0xSScY0eMhVCqVsUMSQghhIiT5r6HCknz+3POLsjwgbCyuzp5GjEgIUd+pVCoGhY1j\nUNg4Y4cihBDCxEi3nxpav28xhSX5gG5CnoHyZSyEEEIIIeooSf5r4FJGPLtPbVSWx/Z5HCsLayNG\nJIQQQgghxO1J8l9NWq2WZdu/QavVABDUvCMhAd2MHJUQQgghhBC3J8l/NR0+t5O4y2cAMDezYHy/\nJ2TQnRBCCCGEqNMk+a+GkrJiVu/+UVnu12kkno2bGC8gIYQQQgghqkCS/2rYc2ojeYXZADjZN2ZI\n10lGjkgIIYQQQoi7k+S/Gi6mxig/D+4yERsrWyNGI4QQQgghRNVI8l8N6dmXlJ+bewYaMRIhhBBC\nCCGqrsrJf/fu3Zk/fz5ZWVmGjKfO02jUZOSkKMsejX2MGI0QQgghhBBVV+Xkv6ysjJkzZ+Lj48PY\nsWNZsWIF5eXlhoyN/Px8XnjhBfz8/LCzs6NXr14cPnxY2Z6Wlsajjz5KkyZNsLe3Z9iwYVy4cMGg\nMWXnX6FCrXvdjrbO2Fk7GPT5hBBCCCGE0JcqJ/9Hjx4lOjqal156iWPHjjFhwgS8vLx48skn2bt3\nr0GCmz59Ops2beLnn38mKiqKwYMHM3DgQC5fvoxWq2XMmDHExsayevVqjh07hq+vLwMHDqSoqMgg\n8QCkXdflx0Mq/AghhBBCCBNyT33+g4ODeffdd4mPj2f79u2MHz+e33//nd69exMYGMi8efP01vJe\nXFzMihUreP/99wkPD6dFixbMnTuXwMBA5s+fz/nz5zlw4ABfffUVYWFhtGrVivnz51NcXMzixYv1\nEsOtpEvyL4QQQgghTFS1BvyqVCrCw8P55ptviI+PZ+LEicTFxfHWW2/RqlUrevfuzcqVK2sUWEVF\nBWq1Gmtr60rrbWxs2L17N2VlZQCVtqtUKqysrNizZ0+NnvtOJPkXQgghhBCmSqXVarX3+iCtVsu2\nbdv49ddfWb58Ofn5+XTo0IGpU6diaWnJwoULOXHiBK+99hrvvfdetYPr1asX5ubmLFmyBE9PTxYv\nXsyjjz5Ky5YtOXXqFIGBgYSFhfHtt99ib2/P//3f/zFnzhyGDBnChg0blOPk5uYqP58/f77a8QBE\nRv1Kau5FACKC76eZS6saHU8IIYQQQohbadmypfKzs7OzXo55Ty3/p06d4rXXXqN58+YMHDiQDRs2\n8MQTT3DixAmOHTvGCy+8wMyZMzl27BgzZszgm2++qVFwv/zyC2ZmZjRt2hQbGxu++OILJk+ejEql\nwsLCghUrVhAbG4urqyv29vbs2LGDYcOGYWZmuAqmecWZys/Otq4Gex4hhBBCCCH0zaKqO3bo0IFT\np05hY2PDqFGjmDp1KkOGDLltot23b98aJ/8tWrRg+/btFBcXk5eXh6enJ5MmTSIgIACA0NBQjh07\nRn5+PmVlZbi6utKtWze6du1622OGhYVVO57SsmJ+3pMPgJmZOX17DsDcvMqnsN75u/JSTc6puDU5\nt4Yh59Uw5LwahpxXw5DzahhyXg3j+t4r+lLlzNXBwYEFCxZw//33V+m2w+jRo4mLi6tRcH+ztbXF\n1taW7OxsIiMj+eijjyptd3R0BHRdeo4cOcJ//vMfvTzvjdJzLis/uzl7NejEXwghhBBCmJ4qZ6+L\nFi3C3d0dOzu7W24vKiriypUrNG/eHAA7Ozv8/PxqFFxkZCRqtZqgoCAuXLjAK6+8QnBwMI899hgA\ny5Ytw83NDV9fX06dOsXzzz/P2LFjGThwYI2e93bSs68l/zLYVwghhBBCmJoqd4739/dn1apVt92+\nZs0a/P399RLU33Jzc3n22WcJDg5m6tSphIeH89dff2Fubg5AamoqU6dOJTg4mOeff56pU6fWWplP\nT5nZVwghhBBCmBi99VupqKjQ16EUEydOZOLEibfd/uyzz/Lss8/q/Xlv5/rk372RtPwLIYQQQgjT\nopeyODk5OWzcuBEPDw99HK7OSsuRln8hhBBCCGG67pj8v/nmm5iZmSndbKZMmYKZmdlN/1xcXFi0\naBGTJ0+ulaCNQavVkiF9/oUQQgghhAm7Y7efLl268PTTTwPw1VdfMWjQoEqTDYBuVl17e3u6dOnC\nuHHjDBepkeUWZlFaXgKArbU9Drb6mWhBCCGEEEKI2nLH5H/48OEMHz4cgIKCAp588km6d+9eK4HV\nNSZHNX4AACAASURBVNf39/do3ASVSmXEaIQQQgghhLh3VR7w++OPPxowjLovrVKlH+nyI4QQQggh\nTM9tk/+dO3cC0KdPH1QqlbJ8N+Hh4fqJrI6p1PLfSAb7CiGEEEII03Pb5L9fv36oVCqKi4uxsrKi\nX79+dz2YSqVCrVbrM746Qyb4EkIIIYQQpu62yf/WrVsBsLS0rLTcUN3Y518IIYQQQghTc8eW/zst\nNyTlFWVk5aUDoEKFeyNvI0ckhBBCCCHEvavyJF8FBQUkJibedntiYiKFhYV6CaquychJQYsWABcn\nDywtrIwckRBCCCGEEPeuysn/Sy+9xOjRo2+7fcyYMcyaNUsvQdU10uVHCCGEEELUB1VO/jdt2sSY\nMWNuu33s2LFERkbqJai6pnLyL5V+hBBCCCGEaapy8p+SkkKTJrdv9fb09OTSpUu33W7K0nOk0o8Q\nQgghhDB9VU7+3dzciI6Ovu32M2fO0KhRI70EVdfIBF9CCCGEEKI+qHLyP2LECL755hsOHTp007aD\nBw+yYMEChg8frtfg6gKtVit9/oUQQgghRL1w21KfN5o3bx7r16+nZ8+eDBs2jHbt2gFw6tQpNmzY\ngJeXF2+//bbBAjWWguJcikt1VYysLW1wtncxckRCCCGEEEJUT5WTf29vbw4dOsTs2bNZuXIla9eu\nBcDJyYmHH36Y9957Dy8vL4MFaizXt/q7N/ZBpVIZMRohhBBCCCGqr8rJP4CXlxc//vgj33//PRkZ\nGQC4u7tjZlbl3kMmJy372mBfz0bS5UcIIYQQQpiue0r+/6ZSqZSEv763hEt/fyGEEEIIUV/cU5P9\n+fPnmThxIk5OTnh6euLp6YmzszOTJk3iwoULhorRqKTMpxBCCCGEqC+q3PIfHR1Nr169KC4uZtSo\nUQQFBQFw9uxZVq1aRWRkJLt376Zt27YGC9YYpOVfCCGEEELUF1VO/mfPno2trS2HDx8mMDCw0rbY\n2Fh69+7N7Nmz+fPPP/UepLGo1RVcyU1Vlj0aeRsxGiGEEEIIIWqmyt1+du3axcyZM29K/AECAgKY\nOXMmO3fu1GtwxpaZl4ZGowbA2cEVaytbI0ckhBBCCCFE9VU5+a+oqMDW9vbJr62tLRUVFXoJqq6o\nNLNvIx8jRiKEEEIIIUTNVTn579y5M99++y3Z2dk3bcvOzubbb78lLCxMr8EZW3q2DPYVQgghhBD1\nR5X7/L/11lsMHDiQ1q1bM3XqVFq3bg3oBvz+/PPP5OTksGDBAoMFagwy2FcIIYQQQtQnVU7++/bt\nS2RkJC+//DKffPJJpW2hoaH8/vvv9O3bV+8BGpMk/0IIIYQQoj65p0m+IiIiOHr0KCkpKSQkJADg\n6+uLt3f9rIJzffLvKcm/EEIIIYQwcdWa4dfb27veJvx/KyotIL84FwALc0saO7oZOSIhhBBCCCFq\n5rbJf3XLdoaHh1c7mLrk+sG+7v/f3p0HR1Wlbxx/ugPZIISwJJCEARICBFChEpBFwo5YMCwKArIo\nDDAzKsroqKD+2GR1K7QEHJcZwyZBJy44zpAgEEBkhl02BYY4gJGwJYFgCJCc3x9ISxMSAnToTt/v\npypV3bdP933z1il5cj33dNXastt93FgNAAAAcOuKDf8dO3a84Q+z2WwqKCi4lXo8Buv9AQAA4G2K\nDf+rVq26nXV4HNb7AwAAwNu49Mq/N8nkyj8AAAC8TKm/5OtK+/fv19dff63s7GxX1+MxWPYDAAAA\nb3ND4X/x4sWqU6eOGjVqpISEBG3dulWSdPz4ccXExCgpKalMirzdCgsLdDz7J8fz0JBwN1YDAAAA\nuEapw//f//53DRs2TE2aNNGrr74qY4zjtZo1ayo2NlYLFy4skyJvt6wzJ3Sx4IIkKSggWIF+ld1c\nEQAAAHDrSh3+p0+fri5dumjFihUaPnx4kdfvvvtu7dixw6XFuQvr/QEAAOCNSh3+9+7dq/vvv7/Y\n10NDQ3Xs2DGXFHXZmTNnNG7cONWrV0+BgYFq166dNm/e7Hj99OnTevTRR1WnTh0FBgaqcePGmjNn\nzi2fl/X+AAAA8Eal/obfSpUqKTc3t9jXDx48qBo1XPstuKNGjdKuXbu0YMECRUZGauHCheratav2\n7Nmj8PBwjRs3TmlpaVq0aJHq16+vtLQ0jR49WjVq1NDQoUNv+ryEfwAAAHijUl/579y5sz744APl\n5+cXeS0jI0Pvvvuu7r33XpcVlpeXp+TkZM2aNUsJCQmKiorSpEmT1KBBA82fP1+StGnTJg0fPlwd\nOnTQb37zGw0bNkytW7fWf/7zn1s6t3P452ZfAAAAeIdSh/9p06YpIyND8fHxmjdvniTpyy+/1HPP\nPadmzZrJZrNp0qRJLivs4sWLKigokJ+fn9Nxf39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KUVyRi6LOFoHyPCiri3Qa2C0e39wKHi6+4lP/zt9ujp6wt3HUy7+1IAi4lX8F\nh5P/jVt3TbsMAKE+YxEfvRxhfhE6nc/UvweGKiYHBsLkoGd/3vNb5JZmAABeWPoKRgdE9ev9hvoi\naGlrxhs7fiHeNM2fvAILp/QvcTG2usYa5JXeSQRySzN0mvbRwdYZLtaeUDj4YXbMAni4+ppsRd2L\nGWfxecc4hE5zJ/4EC6esNNiT2Na2FpxLP4pjF/ejoqbUIOe4m72NEyaNisWU8Di493PA7r0Ulefg\nvS9/L/Yhljt5YcNjf4S9jX6/p5TVxXj3XxvFv0GFszdeevwtvXUHGyo3Bc2tTTh56Tscu/B1t+lm\nXRwUCPQIg2/HU3RfRbBBp0JtaW3ChYwfkZh2WOxi1pVEIoWXUxBC3SdgadwKoz8UGE50/XtVqVW4\nXZSOy5lJuJKVhOq7uqh1kkplCPYajebWRpRU5vc4SUFvZFIzuDt7w9PVT/Pj5g8vV384O8gH9bs/\np+QWjqR8hStZ57pt81OEIH7icowNntxnTEPle2CoYXJgIEwOumtrb8XGbaugUmv6Wf7x55/2u7qo\nIb8IktKOYteR/wOgeULzylPb4Gjnovfz6IOmINBt5JTc0iQEJRmoqNXtxtXN0QPB3uEI8R6NIK/R\ncHP0QGpqKoCh8QVbVJ6Lv3/3R5TXlIjrRvtHYs2Cl/TaPaGhqRanrxzAycvfd+uLay6zQERIDCwt\nrCEIKqjUagiCGmq1GmpB1fFb81NVVQlBUMPe3h4qQQWhc5taJe4jqNVQCSq4O3tj0qhYhAdEG/TG\nLLMwDR/s2yw+0fd3D8Uvl/9BbzO/NDTX4S//+h3KOgZR21k74qUVb8HN0UMvxweG3k1BY0s9Tlz4\nFscvfdNr3Q8JJFA4e8PXXZMo+ClC4KMIGtC/iyAIyCvNRGLaYaTeOt3juV3s5ZgSHofJo+cg62YO\ngKFzXYeK+/l77fy3u5KVhCtZ5+5rpjhXR3d4ufrD09UfXm7+8HT1g9zJ06QGyBdX5OFIyl6k3jzV\nraXD3dkHcdGPIDrsoR6/E4fa98BQweTAQJgcdHe76Aa2fPn/AAAKJy/891Mf9PsYhvwiUKtVeHv3\nf6Koo8DR5NFz8ET8er2fp7/UahVKKvPF7kG5pRkoLs/VqbnY2tIW/u6h8PcIhZ97KPzdQ+Fg69xt\nv6H2BdvYXI9PDv4ZN3Iviuvkjp54bvHL9z2wrVNlbRmOX/wGidcOi+NQOtlY2WPmuIWYEbFQ5yft\npnptL2f5SkYDAAAgAElEQVQm4uPv3xYrzI72j9RLFeW29jZ88PVmZBWmAdDMqLR++R8Q6DlywDF3\nZarX9V4ammpx9MJ+nL78PVp0qAjddS7/zmk8vd0C79nFrLG5Hik3TyLx2mGxaFtXMqkZxgZPQkx4\nPML8IsSns0P1upo6fVzXksp8XMnUJAp399t3sHGGp5sfPF01CYCXqz88XH1NYqpXXVXUluJY6n4k\npR3p1v3V2c4Ns6OWISY8Xutvn3+vhsHkwECYHHR37MJ+fH36nwA0gxKfnPvrfh/D0F8EN/Mu46/7\nNgHQPMX77ao/G3zAZk+aWhpx4uI3uFVwFfllWb0OVuvKTGYOH3mQViIgd/Ictv029T0OoVCZjaOp\nX+PCrdPdEi9nezlmRy7FlPC4fv/P1pSv7ekrB/Dl8Q/F5YEOFhYEATsTtiDlxklx3dMLf4sJodMG\nHOvdTPm66mIgxb+kEik8XHzh6x4Cv46EwcstAGYyc2QWpiEx7TAuZyT2OL7I3dkHMWPiMHHkLNjb\ndJ9rfqhfV1Ol7+taWatETslN2Ns4wtPVXywkOBzUNlTj5KVvcfrKgW4TZthaO2DW+EXwkQdBpW7H\nrYxbUAtq+Pv7iXU21F3qbfRYh0Otglrcrlk3b9LjLLTZhb7vYdlBkXrVtfiZMesb9CXMLwKjA6KQ\nnpMKAQK+Pv0J1j3y2qAOumtqacBf927qcUaHTp0FgfzdQ+HnoUkEvNz8Taqp2NCkUhkWT1sNH0UQ\nPk/Yitb2FrS2NePjH97G3ImPYeGUn95zHIIgCMgouIYjqXu1WiE6ebsFYE7UI5gQOm1Y9r+eMW4B\nahsqcej8nSrKjrYuWHyfVZQPnNujlRgsnrbGIInBcGBuZgF/jxHw7zI7S2t7CwqVOcgvyxSLhJVU\nFnSrJ6AW1CiqyEVRRS7OdUzDLJXKYGflgNrG7jNdmZtZYELoNMSExyPIaxQHEQ8DLg5yuDjIjR2G\nQTjYOmHxtNWIi35U07Xz4rfiFNsNTbX4PnFXt/ecudVtVb9MHTMXAJMDQxl+//ckvckt7lL8zLN/\n05UNpqXT1+JG7kWoBXVHpctUhAcOzlO05tYmbNv/erfEwMnOtSMRGAF/91D4KoKH3Tzg92tC6DS4\nO3vjo+/+KA4YTkj+EgXK21gz/8UexyGo1SpczkrC0ZR9PSZhI3zGYk70oxjpN37Y30gtnLIKNQ1V\nSEo7AgA4nPIVHO6jivL568dx8Ny/xOWY8HjERT2i11iHOwszSwR6hiHQ887Dk5a2ZhQqs5FXmom8\nskzkl2ahrKpQ7A7WSa1WdUsMfBRBiAmPR3TYTFhb2g7KZyDSF2tLW8yd+BPMmrAYSWlHcSx1n05F\nOe9H51hIMgwmB9Sj6voKsYy8hZklPF1NN0P3dPVFzJi5+PHqQQDA12c+wUj/CQYvXtTa1oLt37yh\nNYvI0ulPITrsIZMdGG0qvNwC8JufvoMdB98VWwDSc1Lx592/1RqH0NregvPpx3HswtdaA5oBTd/u\niJApiIt6FH7uIYP+GYxFIpFgxez/QF1jNdKyNV0f+ltFOaPgGnYf+au4HOYXgcdjnx/2idVgsDS3\nQpDXKAR5jRLXNbc2oUB5W9O60NHC0Dn428rCBtFhMxEzJh6+imBjhU2kNxZmlpgZsRDTxsxF6q3T\nuJKVhPb2NkhlZqitqYVUIoXcTXGnvoZMBmnHlLsy8XdnzQ0zcUrerjU4vN0Cjf0xhzUmB9Sjrje8\nfu4hJl0lFAAWTvkpUm6eREtrE0orC3D2WgJmjFtgsPO1tbfio+/eRGbHIE4AWP7Qc/1+evsgs7Wy\nxwtL/hvfJe7CkY5xCMoazXSaj89+AZW1Spy69F23CtDmMgtMHj0bsZFLIXfyNEboRieTyvD0gt/i\n/b2vIqfkJgRoxg7YWTtihO/YPt9bWlWIf3z3J/HJm6erH55ZuHFYdsMyFVYW1gjxDkeId7i4rqml\nAVV1Srg5eg64HgaRKZLJzDBpVCwmjYoV13GMzNBgmpOjk9HllHTtUqTfWUsMwd7GCfHRy8XlA0l7\n0NTSdyXh+9WuasPH37+Nm3mXxXVLp69lYnAfpFIZlkxbjbULfiNWEm1pa8bOQ1vwfeLnWomBjaUd\n5k16HJuf2Y7HZ7/wwCYGnSzMLfH8kv+Cu7MPAEClasffv/sjCpXZvb6nrrEGf9v/Ohpb6gFoZkx5\nfskr7MJiBNaWtvByC2BiQEQmh8kB9Uh7MLLpjjfoataExXC21wz4qm+q0ZoVR19UqnZ8cuDPSMtJ\nEdc9HLMKc6KW6f1cD5LIEdPx4uNv9Vj519lejkdnPovXnvkID8es6nHGlgeVrbUD/mPZq3C01XRj\na25txLavX++xhkZrews++u5NcZyHuZkFfr7kv4btIEkiIro/TA6oG5WqHfmlWeLyUEkOLMwssWjq\nk+LyiYvfoLK2TG/HV6tV2JnwHq5kJYnr5k58DPMmPa63czzIvOWacQidg8m9XP2xet4GvPrUNsya\nsBiWBqxEO5S5OCjwH8tehbWFZsB7bWMVtu17DfVdCsGpBTU+T9gqdheUQIKn5v/nAzVWg4iIdMPk\ngLopLM8R59t2cVD0WITLVEWFzYCfQnPD065qw7dnP9PLcdWCGruOvI8Lt06L62ZHLsXDMav0cnzS\nsLWyx/NL/htvvbALv3tiCyaOnMW+8DrwcgvAc4t/L06NW1ZdhA+/eUMs2vX92c9xMeNHcf9lM5/G\nuODJRomViIhMG5MD6mYo1DfojVQixSMznxaXU2+eQm7JwCZUFgQBXxz7G85fPy6umzFuIZZOX8vZ\nXQzE2tKG17afQn3GYM28FyGB5rrlltzCP3/4X5y5clCri92McQsxa/xiY4VJREQmrt/JQU1NDRIS\nEvD555+jpKTk3m+gISenS32DrvN3DxXB3uEYFzxFXP769Ce430LggiBg76l/4Oy1BHFdTHg8ls96\njjevZHLGh07FT2b9TFxOz0nFF8f/Ji6HB0Tj0Yee5d8uERH1ql/Jwf/8z//Ay8sL8+fPx5o1a5Ce\nng4AUCqVsLa2xrZt23Q+1qlTp7BkyRL4+PhAKpVix44d3fbZvHkzvL29YWNjg9jYWPF8nVpaWrB+\n/XrI5XLY2dlh6dKlKCws1NqnqqoKq1evhpOTE5ycnLBmzRqtMtPU3VAcjHy3JdPWiNV2s4rScSXr\nXL+PIQgCvvlxB05e+k5cN3HkLKyY/QKkEja6kWmaEbEQcyc+1m29tzwQaxf8p8lPS0xERMal8x3O\n3/72N7zyyit44okn8K9//UvrSaxcLseyZcvw73//W+cTNzQ0YNy4cXjvvfdgbW3d7UnWW2+9hXff\nfRfvv/8+kpOToVAoEB8fj/r6enGfDRs2YO/evdizZw9Onz6N2tpaLFq0CGr1ndL1q1atwqVLl3Do\n0CEcPHgQFy5cwOrVq3WO80FT11gjFpsyk5nDWz40C40onL206hx8c2YH2lVt/TrGgaQ9OJr6tbg8\nIXQaVsWvF5MOIlP1cMwqTBk9R1x2tHPF80v+m4O6iYjonnQe6bd161b85Cc/wfbt21FeXt5t+/jx\n47FlyxadT7xgwQIsWKC5eVu7dq3WNkEQsGXLFrz88st45JFHAAA7duyAQqHArl278POf/xw1NTX4\n+OOP8cknn2DOHM3/BHfu3Al/f38cOXIEc+fOxfXr13Ho0CH8+OOPmDxZM/juww8/xIwZM3Dr1i2M\nGDE0n4obUtdWAx9FkDjAcSiaP3kFzl8/jqaWBihrinHmykHMmqBbX+uE81/i4Pl/ictjgyZhzbwX\n+dSVhgSJRIIVc34BexsnlFUVYtHUJ+Fk52rssIiIaAjQueXg9u3biIuL63W7s7MzKisr9RJUdnY2\nSktLMXfuXHGdlZUVZs6cibNnzwIAUlNT0dbWprWPj48PRo0ahcTERABAYmIi7OzsEBMTI+4zdepU\n2NraivuQtq6DdwOH2GDku9la2WtNM3rw/BdobK7v4x0axy7sx3eJn4vLo/0jsXbBbzlrDg0pMqkM\ni6etxrOL/h/cXXyMHQ4REQ0ROt/tODk5oays9znj09PT4empn4qlnQOd3d21CyIpFAoUFRWJ+8hk\nMri6aj8Nc3d3F99fUlICuVy7wI9EIoFCoehzMHVnee8H0ZVbdz67qtFMb9fCWNfUVu0OOysn1DdX\no7G5Dp9++3+IDozvdf8bxSk4f/uguOzhGIDxnvG4fOlyr+8xpgf5b9XQeG0Ng9fVMHhdDYPX1TB4\nXfUrNDRUr8fTueVg0aJF2L59OyoqKrptu3LlCj766CMsXbpUr8H15F6zbNzvrDSkmcu/vK5IXJbb\nexsxGv2QSc0Q5X+n7/WN4mTUNfXcwpVRelErMVA4+CJ21ONDumsVERERUX/o3HLwhz/8AYcPH8bY\nsWPx8MMPAwA+/vhjfPjhh/j666/h4+ODV155RS9BeXh4AABKS0vh43OnOby0tFTc5uHhAZVKhYqK\nCq3Wg9LSUjz00EPiPkqlUuvYgiCgrKxMPE5PoqOj9fI5hpqi8hy0n9UUP3O0dcHMqbMHPOVh59MB\nY17TKCEKebVpyC6+AbWgRnbtJTwzY6PWPsk3TiDpxx/E5QCPMPzikc2wMtEBnKZwXYcrXlvD4HU1\nDF5Xw+B1NQxeV8PQ9yycOrcceHp6Ijk5GYsWLcJXX2kK6uzatQsHDx7Ek08+iaSkJLi5ueklqMDA\nQHh4eCAh4c7c8s3NzThz5gymTp0KAIiKioK5ubnWPgUFBbhx44a4T0xMDOrr67XGFyQmJqKhoUHc\nh+7ILtaewnS4zIUukUiwbMadwmiXMs/idtF1cflixo/4LGErBGhanXwUQXhh2SsmmxgQERERGUq/\nRlgqFAps374dH374IZRKJdRqNeRyOWSy/s/g0tDQgIyMDACAWq1Gbm4uLl26BFdXV/j6+mLDhg14\n8803MXLkSISGhuKNN96Avb09Vq1aBQBwdHTEs88+i40bN0KhUMDFxQUvvfQSIiIixIHTo0aNwvz5\n8/H8889j+/btEAQBzz//PBYvXqz3/lnDQU6XwcgBniONGIn+BXqGIXLEdFy4dQYAsO/0P/HS42/h\n6u3z2HHwXQiCZvpbL1d/rFu2GTaWdsYMl4iIiMgo7mv6lc5BvQORnJyM2bNni8fbtGkTNm3ahLVr\n1+Ljjz/Gxo0b0dTUhHXr1qGqqgpTpkxBQkICbG1txWNs2bIFZmZmWLFiBZqamhAXF4fPPvtM64n3\nrl27sH79esybNw8AsHTpUrz//vsDin24Gg7Fz/qyeOpqXM5KgkrVjtySW/jyxHYkph2GWq0CALg7\n+2Ddo6/B1trByJESERERGUevycFrr712X91KXn31VZ32mzVrllaxsp50Jgy9sbCwwNatW7F169Ze\n93FycsLOnTt1iulB1thSj9LKAgCAVCqDryLYyBHpn6ujO2aNXyQWNjtz5YC4Te7oiV8++jrsbZyM\nFR4RERGR0fWZHNwPXZMDMi25JRnia2+3AFiYWxoxGsOJn/gTJKUdRUNznbjOxUGBXy5/HY52LkaM\njIiIiMj4eh2QrFartX7y8vIwduxYrF69GsnJyaiurkZ1dTXOnz+P1atXY9y4ccjPzx/M2EmPcrQG\nIw/t4md9sbG0w4IpPxWXnexcsf7RP8DZXt7Hu4iIiIgeDDqPOVi3bh3CwsKwY8cOrfXR0dHYsWMH\nHnvsMaxbtw5ff/213oMkw9MejDx8kwMAmD52PuqbalFeU4KFU1bC1dH93m8iIiIiegDonBwcP34c\nb731Vq/bY2Nj8bvf/U4vQdHgUgtq5HZNDobhYOSupFIZFk5ZaewwiIiIiEyOznUOLC0tcfbs2V63\nnz17FlZWVnoJigaXsroYjS31AABbawe4OfZeII6IiIiIhi+dk4Mnn3wSn3/+OX75y1/ixo0baG9v\nR3t7O65fv45169Zh165deOKJJwwZKxlITvEN8fVwKn5GRERERP2jc7eiP/3pTygvL8cHH3yADz74\nQLyBFARNVdmVK1f22e2ITFdO8Z0uRYHDeDAyEREREfVN5+TA0tISO3fuxG9+8xv88MMPyM3NBQD4\n+/tj4cKFiIiIMFiQZFhaxc+G+WBkIiIiIupdvyskR0REMBEYRlpam1BUkQcAkEACP/dQI0dERERE\nRMai85gDGp7yyjIhCJpK1Z6ufrCysDZyRERERERkLDq3HEilUkgkEnGMQVed6yUSCVQqlV4DJMPK\n7lr8zHN4T2FKRERERH3TOTl49dVXu61TqVTIzc3Fvn37EBYWhsWLF+s1ODI8reJnHiONGAkRERER\nGZvOycHmzZt73VZcXIwpU6ZgxAg+eR5KBEFALlsOiIiIiKiDXsYceHp64oUXXsAf/vAHfRyOBkll\nbRnqmmoAANYWNlA4exs5IiIiIiIyJr0NSLa1tcXt27f1dTgaBF27FPl5hEIq4fh0IiIiogeZXu4G\nr169iq1bt7Jb0RBToMwSX/u789+OiIiI6EGn85iDwMDAHmcrqq6uRk1NDWxtbbFv3z69B2gMnTMv\nDXf5ZXdaenzkgUaMhIiIiIhMgc7JwUMPPdRtnUQigbOzM0JCQvDTn/4ULi4ueg3OWIrKc+A9zG+W\nBUFAgTJbXPZVBBsxGiIiIiIyBTonB5988okBwzAtKTdPDfvkoKpOicbmOgCAtaUtXBwURo6IiIiI\niIxN5zEHzzzzDM6dO9fr9vPnz+OZZ57RS1DGduHWGag7qgYPV9pdioIeiG5URERERNQ3nZODTz75\nBFlZWb1uv3379rBpXaiqUyK76IaxwzCoAuWd5MBXEWTESIiIiIjIVOht7srKykpYWlrq63BGl3rz\nlLFDMKiCLi0H3nImB0RERER0jzEHJ0+exMmTJ8UZivbu3YvMzMxu+1VWVmLPnj2IiIgwTJRGcDHz\nLJY/9BxkMp2HZQwpbDkgIiIiorv1eed7/PhxvP766+Ly3r17sXfv3h73DQ8Px9atW/UbnRE1NNXi\nZv5ljA6IMnYoelfbUIWahkoAgIWZJRROXkaOiIiIiIhMQZ/din73u9+hrKwMZWVlAIBt27aJy50/\nSqUSDQ0NuHr1KiZNmjQoQQ+WlGHatahrq4GXPABSqcyI0RARERGRqeiz5cDa2hrW1tYANAOOFQoF\nbGxsBiUwU3A16xxa21pgYT58xlIA2uMNfOWsb0BEREREGjoPSA4ICHhgEgOFszcAoKWtGdeyk40c\njf7lK1kZmYiIiIi667XlIDY2FhKJBAkJCTAzMxOXeyMIAiQSCY4dO2aQQAdT1IgZOHBuDwDgwq3T\niBwx3cgR6VfXbkU+rIxMRERERB16bTkQBEGcpahzWa1W9/pz9/5DWVTYDPF1Wk4qGpvrjRiNfjU2\n16OiphQAIJOawdPV18gREREREZGp6LXl4MSJE30uD2cKZ2/4KoKRX5YFlaodlzMTETMm3thh6UWB\nMlt87enqBzOZuRGjISIiIiJTovOYg1OnTkGpVPa6XalU4tSp4TO7T1TYTPF16q3TRoxEv7S7FLG+\nARERERHdoXNyMGvWLBw+fLjX7UePHkVsbKxegjIFkSOmQwLNGIuM/KtiXYChrutMRT6sjExERERE\nXeicHNxLa2trnwOWhxonO1cE+4QDAAQIuHDrjJEj0g9WRiYiIiKi3vRZ56CmpgY1NTXiQOPy8nLk\n5eV126+yshK7d++Gt7e3YaI0kuiwmcgsuAYAuHDzNGInLDFyRAPT0taM0qpCAIBEIoWXW4BxAyIi\nIiIik9Jny8GWLVsQEBCAwEDNXPgbNmxAQEBAt5/IyEgcOnQIv/jFLwYl6MESERIDmVSTP+WWZkBZ\nXWzkiAamqDwHgqAGACicvWBpbmXkiIiIiIjIlPTZchAfHw9bW1sAwMaNG7Fy5UpMmDBBax+JRAJb\nW1tMnDgRUVFRhovUCGyt7DHKf4JYCC315inMn7zCyFHdP1ZGJiIiIqK+9JkcTJ06FVOnTgUA1NfX\nY/ny5Rg7duygBGYqosJmdkkOTmPepMeH7NgKrcrIClZGJiIiIiJtOg9I3rx5s1ESg7q6OrE7k42N\nDaZNm4aUlBRxe2lpKdauXQtvb2/Y2tpiwYIFyMzM1DrGrFmzIJVKtX5WrVql0/nHBE2ERUf3m9Kq\nAhSWZ9/jHaZLaxpTthwQERER0V16bTnYsWPHfT0hX7NmzYACuttzzz2Ha9eu4dNPP4WPjw927tyJ\nuLg4pKenw9PTE8uWLYOZmRn2798PBwcHvPvuu+J2GxsbAJquT8888wzefPNN8bjW1tY6nd/S3Apj\ngyYh9aamhkPqzdNDcgrQdlUbisvvDCZnywERERER3a3X5ODpp5++rwPqMzloamrC3r17sXfvXsyc\nqSlKtmnTJnz77bfYtm0bVq9ejXPnzuHy5ctiq8a2bdvg4eGB3bt349lnnxWPZW1tDYVCcV9xRIfN\nFJODCzdPY/G01ZBK9DYL7KAorsiHSt0OAHB1cIeNpZ2RIyIiIiIiU9NrcnD79u3eNg2a9vZ2qFQq\nWFpaaq23srLCmTNnsGKFZnBw1+0SiQQWFhY4c+aMVnKwZ88e7NmzB+7u7liwYAE2bdoEOzvdbpDD\n/CJgY2WPxuY6VNWXI7voOoK9w/XwCQcPKyMTERER0b1IhM4iBiZq2rRpkMlk4o397t27sXbtWoSG\nhuLq1asICQlBdHQ0PvroI9ja2uIvf/kLXn75ZcybNw8HDhwAAHz00UcICAiAl5cXrl27hpdffhmh\noaE4dOiQeJ6amhrxdUZGRrc4kjJ/wK3SCwCAER5RmBK8wMCfXL/OZR3EzRLNWI3xfrMwzne6kSMi\nIiIiooEKDQ0VXzs6Og74eCbfN2bnzp2QSqXw8fGBlZUV3n//faxcuRISiQRmZmbYu3cvsrKy4Orq\nCltbW5w8eRILFiyAVHrno/3sZz9DfHw8wsPDsWLFCnzxxRc4fPgwLl68qHMcgfI7LQW55elQq1V6\n/ZyGVtlQIr52tfMwYiREREREZKr6nMr0biUlJfjHP/6B1NRU1NbWQq1Wi9sEQYBEIsGxY8f0GmBQ\nUBBOnDiBpqYm1NbWwt3dHStWrEBwsGa2ncjISFy8eBF1dXVobW2Fq6srJk+ejEmTJvV6zMjISMhk\nMmRmZnar2wAA0dHR3daphUicy/kB1fUVaGlvgq1chvDA7vuZIrVahT3n/ldcjp06Hw62ToNy7s6Z\npXq6pnT/eF0Nh9fWMHhdDYPX1TB4XQ2D19UwuvZ+0QedWw6uXbuG8PBwvPHGG8jKysKxY8egVCpx\n8+ZNnDhxAvn5+TBkDyVra2u4u7ujqqoKCQkJWLp0qdZ2e3t7uLq6IiMjA6mpqd22d3X16lWoVCp4\nenrqfH6pRIrIETPE5dRbp/v/IYykrLoIre0tAAAHW+dBSwyIiIiIaGjROTl4+eWXYWVlhfT0dBw9\nehQAsGXLFhQWFuLzzz9HdXU13nnnHb0HmJCQgAMHDiA7OxuHDx9GbGwsRo0aJc6m9OWXX+L48eO4\nffs29u/fj/j4eDzyyCOIi4sDoBlY/frrryM1NRU5OTn44Ycf8NOf/hSRkZGYNm1av2KJCpspvr6S\ndQ6tbS36+6AGxMrIRERERKQLnZODM2fO4Pnnn0dgYKBY/6CzpWDlypV4/PHH8Zvf/EbvAdbU1GD9\n+vUYNWoUnnrqKcycOROHDh2CTCYDoOnq9NRTT2HUqFH49a9/jaeeegq7d+8W329hYYFjx45h3rx5\nGDlyJH79619j/vz5OHLkSL/rOPjIA6Fw9gYAtLY1i5WTTV0BKyMTERERkQ50HnPQ2toKb2/NjXFn\nAbHq6mpx+/jx4/Hpp5/qOTzgsccew2OPPdbr9vXr12P9+vW9bvfx8cGJEyf0EotEIkFU2EwcSNIk\nH6k3TyFyhOnP+pNfxsrIRERERHRvOrcc+Pn5IS9PU2HXxsYGHh4eOHv2rLg9LS1N57oBQ1lUl3EH\n6TkX0Nhcb8Ro7k0QBK2WA1/WOCAiIiKiXuicHMyePRv79u0Tl5988kls3boVzz77LJ5++mn89a9/\n7XMQ8HChcPaCnyIEAKBSt+NyZqKRI+pbZW0ZmloaAAA2lnZwtpcbOSIiIiIiMlU6dyvauHEjZs+e\njebmZlhZWeH1119HVVUVvvzyS5iZmWHNmjUGGZBsiqLCZiKvLBOApmtRzJh4I0fUu7srI/d3nAUR\nERERPTh0Tg78/f3h7+8vLltZWeGjjz7CRx99ZJDATFnkiOn4+vQ/IUBARsE11NRXwtHOxdhh9Uh7\nvAG7FBERERFR70y+QrIpcrRzQYjPGACAAAEXMs4YOaLeFZRlia853oCIiIiI+sLk4D51rXmQetN0\nC6IVKLPF1z4KzlRERERERL1jcnCfxofEQCbV9MrKK81AWVWRkSPqrqahErWNVQAAC3MryJ10rwhN\nRERERA8eJgf3ycbKDqMCIsXlC7dMr/Wga2VkH7dASCX85yYiIiKi3vFucQCi7+pa1Fkx2lSwMjIR\nERER9QeTgwEYEzgRFuZWAIDSqgIUlmff4x2Di5WRiYiIiKg/mBwMgIW5JcYFTRaXU2+eMmI03bEy\nMhERERH1B5ODAYoKmyG+Tr15GmpBbcRo7mhorkNlbRkAQCYzg4eLr5EjIiIiIiJTx+RggEb6jYet\nlT0AoLq+AtlF140ckUZhlylMvVz9IZPpXO+OiIiIiB5QTA4GSCYzw/jQaeJyionUPGBlZCIiIiLq\nLyYHetC1a9GljB+hUrUbMRqNrpWRfTjegIiIiIh0wORAD4K8RsHJzhWApq//jbxLRo5IuzKyLysj\nExEREZEOmBzogVQi7TYw2ZhaWptQVlUIAJBIpPBy9TdqPEREREQ0NDA50JPIEXcKol25fQ6tbS1G\ni6WwPBcCNAXZPFx8YGFuabRYiIiIiGjoYHKgJz7yQLg7+wAAWtuacS072WixFCjvjDfwlrMyMhER\nEVwwCq8AAB4BSURBVBHphsmBnkgkEq2uRSlGLIjWdaYiX1ZGJiIiIiIdMTnQo6iwO12LrudcQGNz\nvVHi6FoZmTMVEREREZGumBzokdzJE37uoQAAlbodlzITBz2GtvY2FFfkics+7FZERERERDpicqBn\n2rMWDX7XopLKPKjVKgCAm6MHrC1tBz0GIiIiIhqamBzoWWTodEggAQBkFlxDTX3loJ6flZGJiIiI\n6H4xOdAzRzsXhPqMAQAIEHDh1plBPT8rIxMRERHR/WJyYACRXQYmD3bXIlZGJiIiIqL7xeTAAMaH\nxEAmNQMA5JVlorSjWrGhqdUqFJbfSQ44GJmIiIiI+oPJgQHYWNlhdECkuHz68veDct7SqiK0tbcC\nABztXGFv4zQo5yUiIiKi4YHJgYHMGLdQfJ2UdhQNzXUGP2fXyshsNSAiIiKi/mJyYCBhfhHwcgsA\nALS2t+Ds1QSDn5OVkYmIiIhoIJgcGIhEIkHshCXi8qnL36Nd1WbQc7IyMhERERENBJMDA4ocMQMO\nNs4AgJqGSoNOayoIAgpZ44CIiIiIBoDJgQGZm5ljZsSdsQfHLuyHIAgGOVdFbSmaWhsBALZW9nC2\ndzPIeYiIiIho+GJyYGDTxs2HhZklAKCoPAe38q8Y5Dx3V0aWSCQGOQ8RERERDV9MDgzM1soek0bP\nFpePX9hvkPOwMjIRERERDRSTg0EQO2EJJNA8yU/PvYDiiny9n4OVkYmIiIhooEw6Oairq8OGDRsQ\nEBAAGxsbTJs2DSkpKeL20tJSrF27Ft7e3rC1tcWCBQuQmZmpdYyWlhasX78ecrkcdnZ2WLp0KQoL\nB6dicSe5kyfGBk8Sl09c/EavxxcEQbvlgDUOiIiIiOg+mHRy8Nxzz+Hw4cP49NNPce3aNcydOxdx\ncXEoKiqCIAhYtmwZsrKysH//fly8eBH+/v6Ii4tDY2OjeIwNGzZg79692LNnD06fPo3a2losWrQI\narV6UD9L7ISl4uvkGydQ21Ctt2PXNlShrqkGAGBpbgU3J0+9HZuIiIiIHhwmmxw0NTVh7969+NOf\n/oSZM2ciKCgImzZtQkhICLZt24aMjAycO3cOH3zwAaKjozFixAhs27YNTU1N2L17NwCgpqYGH3/8\nMd555x3MmTMHEyZMwM6dO3HlyhUcOXJkUD9PkNco+LmHAgDaVW04c+WA3o6d36XVwFseCKnEZP9Z\niYiIiMiEmexdZHt7O1QqFSwtLbXWW1lZ4cyZM2htbQUAre0SiQQWFhb48ccfAQCpqaloa2vD3Llz\nxX18fHwwatQonD17dhA+xR0SiQSzI++0Hvz/9u48KIoz/QP4t2dgYEYRkVPuQyIEEiWgETSARI1G\no1JqFOMRz93VEF0tjbiu1xqVWHGNpcRN9ig2rtFokc2x7oKJyLHqbxU8UBINAVcwQgQRRbmceX9/\noL2OnMLgDPD9VE3VdM/b3W8/PiXzTPfbb0bOP1F7v8Yg+y58ZPIzjjcgIiIiorYy2eLAysoKoaGh\n2LRpE3766SdotVrs3bsXJ0+eRHFxMfz8/ODu7o7Vq1ejvLwctbW1iI+Px7Vr13D9+nUAQHFxMZRK\nJWxtbfX27ejoiJKSkqd+TgP6hcLGyh4AcLfqNk59d8wg+712g5OfEREREVH7mRm7A8355JNPMHfu\nXLi6ukKpVCI4OBgxMTHIysqCmZkZkpKSMG/ePNja2kKpVGLkyJEYM2ZMu4/76KBnQ/OxHYDTd+pv\nafrnic+gqu7T7jkJfiz6Tn5/+0ZVh/a/rUyxT10B49pxGNuOwbh2DMa1YzCuHYNxNSxfX1+D7s9k\nrxwAgLe3N44dO4a7d++iqKgIJ0+eRG1tLXx86m+deeGFF3DmzBlUVFSguLgYhw8fRmlpKby96389\nd3JyglarRVlZmd5+i4uL4eTk9NTPBwD6OQbBXFl/K9TtqjJcK89rYYvmVdfdw92a2wAAhaSEtZoz\nIxMRERFR25j0lYOH1Go11Go1ysvLkZKSgm3btul9bmVlBQD44YcfkJWVhXfffRcAEBwcDHNzc6Sk\npCAmJgYAUFRUhO+//x5hYWFNHi8kJKSDzqTez3WXcfTBZGhX71zExFExbd7Xpavn5Peu9l4YPPjF\ndvfPkB7+OtDRMe1uGNeOw9h2DMa1YzCuHYNx7RiMa8eoqKgw6P5MujhISUmBVquFn58f8vLysGLF\nCvj7+2POnDkAgIMHD8LOzg4eHh7IycnBkiVLEB0djREjRgAArK2tMW/ePKxcuRIODg7o06cPli1b\nhgEDBshtjCF8wDgcO/MVdEKHvKILKPz5xzYPJC7kzMhEREREZCAmfVtRRUUFYmNj4e/vj9mzZyM8\nPBzJyclQKpUA6m8Pmj17Nvz9/bFkyRLMnj1bfozpQzt27EB0dDSmTp2KYcOGoVevXvjqq6/afZ9/\ne/TpZY+BvkPl5dTstk+KxpmRiYiIiMhQTPrKwZQpUzBlypQmP4+NjUVsbGyz+1CpVNi5cyd27txp\n6O61S9QLE5B9OQMAkP1DJl4bOhM2Vk8+XoAzIxMRERGRoZj0lYOuzN2xH3xcAgAAOp0W6ef+8cT7\nqK6two1b9Y9tVUgK9LXzMGgfiYiIiKh7YXFgRMODxsvvj+cko7q26om2v3ajAAICAODYxxUqM4sW\ntiAiIiIiahqLAyMK9B4E+97OAICq2ns4efGbJ9q+iDMjExEREZEBsTgwIoWkQGTQa/LysbNfQavT\ntnr7op85MzIRERERGQ6LAyN70T8KGsv6eRpu3v4Z53/8v1ZvW/jIlQM+xpSIiIiI2ovFgZGpzC0w\n7LnR8nLqg8nRWlJ3vxbFNwvlZRc7PqmIiIiIiNqHxYEJCB/wKpTK+qfKXim+hPyfvm9xm+tlV6F7\ncAuSvXVfqC00HdpHIiIiIur6WByYgF49bBDSP0JeTs3+e4vbcGZkIiIiIjI0FgcmYvgjA5PP//h/\n8vwFTXl0ZmRXPqmIiIiIiAyAxYGJcLbzhJ/7QACAgEDa2a+bbc+ZkYmIiIjI0FgcmJDhL0yQ35/M\n/Rb3qisbbafVafFT6X/lZT7GlIiIiIgMgcWBCfFzH4i+tu4AgNq6avz7Qkqj7UpuFqFOWwsA6N3T\nFlYa66fWRyIiIiLqulgcmBBJkjA86H9XD9LPfo372roG7Yr05jfgeAMiIiIiMgwWByYmuH84emls\nAAAVd28i+3JmgzaPzozsxluKiIiIiMhAWByYGHMzc7w04FV5OTX7Cwgh9NpwZmQiIiIi6ggsDkzQ\nsOdegbmZCgBwrfQKfijKkT/TCR2uPfoYU145ICIiIiIDYXFggnqoe+FF/yh5+Wj2F/L7sooSVNfe\nk9v17mn71PtHRERERF0TiwMTFRk0HhIkAEDulSwU3ywEoD8zspu9NyRJMkr/iIiIiKjrYXFgohxs\nnBHoPUheTs3+EgBnRiYiIiKijsPiwIQ9Oinaqe+P4c69W5wZmYiIiIg6DIsDE+bj/CzcHfoBAO5r\n65Bx/p96Vw7ceOWAiIiIiAyIxYEJkyRJ7+pB6pkvUVlVAQCwUKlha+1orK4RERERURfE4sDEDfQN\ng42VPQCgprZKXu9q7w2FxH8+IiIiIjIcfrs0cUqFEhEDxzZYz5mRiYiIiMjQWBx0AqEBI2GhUuut\n48zIRERERGRoLA46AbVFD4QFjNRbx5mRiYiIiMjQWBx0EhEDx8ljDCxVGjj2cTVyj4iIiIioq2Fx\n0En06eWAaS8vhqdTf0yN+hWUCqWxu0REREREXYyZsTtArTck4GUMCXjZ2N0gIiIioi6KVw6IiIiI\niAgAiwMiIiIiInqAxQEREREREQFgcUBERERERA+wOCAiIiIiIgAsDoiIiIiI6AEWB0REREREBIDF\nARERERERPWDyxcGdO3ewdOlSeHp6QqPRYOjQoTh9+rT8+e3bt7Fo0SK4ublBo9HAz88PO3bs0NtH\nZGQkFAqF3mv69OlP+1SIiIiIiEyayc+QPH/+fFy4cAF//etf4erqik8++QQjRoxAbm4unJ2dsXTp\nUqSlpWHv3r3w8vJCWloaFixYADs7O8yYMQMAIEkS5s6di82bN8v7VavVxjolIiIiIiKTZNJXDqqq\nqpCUlIStW7ciPDwc3t7eWLduHfr164cPP/wQAHDq1CnMmjULERERcHd3x8yZMzFkyBD85z//0duX\nWq2Gg4OD/LKysjLGKRERERERmSyTLg7u378PrVYLCwsLvfWWlpb497//DQAYM2YMvvzySxQVFQEA\njh8/jrNnz2L06NF62+zfvx/29vYIDAzEihUrUFlZ+XROgoiIiIiokzDp24qsrKwQGhqKTZs2ITAw\nEI6Ojvj0009x8uRJ+Pr6AgDi4+Mxa9YsuLu7w8ys/nR27dqFV199Vd7P9OnT4enpCWdnZ1y4cAFx\ncXE4f/48kpOTjXJeRERERESmSBJCCGN3ojn5+fmYO3cu0tPToVQqERwcDF9fX2RnZ+PixYtYvnw5\nvvrqK/z+97+Hh4cH0tLSsGrVKhw6dAivvPJKo/s8ffo0Bg8ejKysLAQFBQEAKioqnuZpEREREREZ\nlLW1dbv3YfLFwUNVVVW4ffs2HB0dMXXqVNy7dw8HDhyAlZUV/v73v+O1116T2y5YsABXrlzBkSNH\nGt2XTqeDhYUF9u3bhylTpgBgcUBEREREnZshigOTHnPwKLVaDUdHR5SXlyMlJQUTJkyATqcDACgU\n+qehUCjQXM2Tk5MDrVaLvn37dmifiYiIiIg6E5O/cpCSkgKtVgs/Pz/k5eVhxYoV0Gg0yMjIgFKp\nxKhRo3D9+nXs2rUL7u7uSEtLw6JFi7Bt2zYsXrwY+fn52Lt3L8aOHQtbW1vk5uZi+fLl6NGjB06d\nOgVJkox9ikREREREJsHki4ODBw8iLi4ORUVF6NOnDyZPnox3331XfhTpjRs3EBcXh+TkZJSVlcHT\n0xPz58/HsmXLAABFRUWYMWMGLly4gMrKSri5uWHcuHFYt24devfubcxTIyIiIiIyKSZfHBARERER\n0dPRacYcdLSEhAR4eXlBrVYjJCQEmZmZxu5Sp7F+/XooFAq9l7Ozc4M2Li4u0Gg0GD58OHJzc43U\nW9OVnp6O8ePHw9XVFQqFAomJiQ3atBTHmpoaxMbGwt7eHj179sSECRNw7dq1p3UKJqmluL755psN\n8jcsLEyvDePa0JYtWzBo0CBYW1vDwcEB48ePx8WLFxu0Y84+mdbElTnbNrt378aAAQNgbW0Na2tr\nhIWF4fDhw3ptmK9PrqW4Ml8NY8uWLVAoFIiNjdVb3xE5y+IAwIEDB7B06VKsWbMGZ8+eRVhYGMaM\nGYPCwkJjd63T8PPzQ3FxsfzKycmRP4uPj8f27duxa9cunDp1Cg4ODhg5ciQnonvM3bt38fzzz+OD\nDz6AWq1uMB6mNXFcunQpkpKSsH//fmRkZOD27dsYN26cPHi/O2oprpIkYeTIkXr5+/gXBsa1obS0\nNLz11ls4ceIEjh49CjMzM4wYMQLl5eVyG+bsk2tNXJmzbePm5ob33nsPZ86cQVZWFqKiojBx4kSc\nO3cOAPO1rVqKK/O1/U6ePImPP/4Yzz//vN7fsA7LWUFi8ODBYuHChXrrfH19RVxcnJF61LmsW7dO\nBAYGNvqZTqcTTk5OYvPmzfK6qqoqYWVlJf7whz88rS52Oj179hSJiYnycmvieOvWLaFSqcS+ffvk\nNoWFhUKhUIjk5OSn13kT9nhchRBi9uzZYty4cU1uw7i2TmVlpVAqleLrr78WQjBnDeXxuArBnDWk\nPn36iI8++oj5amAP4yoE87W9bt26JXx8fMSxY8dEZGSkiI2NFUJ07P+x3f7KQW1tLbKzszFq1Ci9\n9aNGjcLx48eN1KvOJz8/Hy4uLvD29kZMTAwKCgoAAAUFBSgpKdGLr6WlJcLDwxnfJ9CaOGZlZaGu\nrk6vjaurK/z9/RnrZkiShMzMTDg6OqJ///5YuHAhbty4IX/OuLbO7du3odPpYGNjA4A5ayiPxxVg\nzhqCVqvF/v37UV1djfDwcOargTweV4D52l4LFy7ElClTEBERofeY/o7MWbMOOI9OpbS0FFqtFo6O\njnrrHRwcUFxcbKRedS5DhgxBYmIi/Pz8UFJSgk2bNiEsLAwXL16UY9hYfH/66SdjdLdTak0ci4uL\noVQqYWtrq9fG0dERJSUlT6ejndDo0aMxadIkeHl5oaCgAGvWrEFUVBSysrKgUqkY11ZasmQJgoKC\nEBoaCoA5ayiPxxVgzrZHTk4OQkNDUVNTA7Vajc8++wz9+/eXvygxX9umqbgCzNf2+Pjjj5Gfn499\n+/YBgN4tRR35f2y3Lw6o/UaPHi2/DwwMRGhoKLy8vJCYmIgXX3yxye04x4RhMI7tM3XqVPl9QEAA\ngoOD4eHhgX/84x+Ijo42Ys86j2XLluH48ePIzMxsVT4yZ1unqbgyZ9vOz88P58+fR0VFBQ4ePIhp\n06YhNTW12W2Yry1rKq4hISHM1za6dOkSfvOb3yAzMxNKpRIAIIRodpLfh9qbs93+tiI7OzsolcoG\nFVRJSQlnUG4jjUaDgIAA5OXlyTFsLL5OTk7G6F6n9DBWzcXRyckJWq0WZWVlem2Ki4sZ6yfQt29f\nuLq6Ii8vDwDj2pJf//rXOHDgAI4ePQpPT095PXO2fZqKa2OYs61nbm4Ob29vBAUFYfPmzRgyZAh2\n797dqr9VjGvTmoprY5ivrXPixAmUlpYiICAA5ubmMDc3R3p6OhISEqBSqWBnZwegY3K22xcHKpUK\nwcHBSElJ0Vt/5MiRBo/aotaprq7Gd999h759+8LLywtOTk568a2urkZmZibj+wRaE8fg4GCYm5vr\ntSkqKsL333/PWD+BGzdu4Nq1a/KXBca1aUuWLJG/wD7zzDN6nzFn2665uDaGOdt2Wq0WOp2O+Wpg\nD+PaGOZr60RHR+PChQs4d+4czp07h7NnzyIkJAQxMTE4e/YsfH19Oy5nO2JkdWdz4MABoVKpxB//\n+EeRm5sr3n77bWFlZSWuXr1q7K51CsuXLxdpaWkiPz9fnDx5UowdO1ZYW1vL8YuPjxfW1tYiKSlJ\n5OTkiKlTpwoXFxdRWVlp5J6blsrKSnHmzBlx5swZodFoxMaNG8WZM2eeKI6/+tWvhKurq/jmm29E\ndna2iIyMFEFBQUKn0xnrtIyuubhWVlaK5cuXixMnToiCggKRmpoqhgwZItzc3BjXFixatEj06tVL\nHD16VFy/fl1+PRo35uyTaymuzNm2e+edd0RGRoYoKCgQ58+fF6tWrRIKhUKkpKQIIZivbdVcXJmv\nhhURESHeeustebmjcpbFwQMJCQnC09NTWFhYiJCQEJGRkWHsLnUa06ZNE87OzkKlUgkXFxcxefJk\n8d133+m1Wb9+vejbt6+wtLQUkZGR4uLFi0bqrelKTU0VkiQJSZKEQqGQ38+ZM0du01Ica2pqRGxs\nrLC1tRUajUaMHz9eFBUVPe1TMSnNxbWqqkq88sorwsHBQahUKuHh4SHmzJnTIGaMa0OPx/Pha8OG\nDXrtmLNPpqW4Mmfb7s033xQeHh7CwsJCODg4iJEjR8qFwUPM1yfXXFyZr4b16KNMH+qInJWEaMXI\nBiIiIiIi6vK6/ZgDIiIiIiKqx+KAiIiIiIgAsDggIiIiIqIHWBwQEREREREAFgdERERERPQAiwMi\nIiIiIgLA4oCIiIiIiB5gcUBE1E1ERkZi+PDhRu3D+++/Dx8fH+h0OqP1YdCgQXjnnXeMdnwiIlPG\n4oCIqIs5fvw4NmzYgIqKCr31kiRBkiQj9Qq4c+cOtmzZgpUrV0KhMN6fn9WrV2P37t0oKSkxWh+I\niEwViwMioi6mqeLgyJEjSElJMVKvgD//+c+orq7GrFmzjNYHAJg4cSJ69eqF3bt3G7UfRESmiMUB\nEVEXJYTQWzYzM4OZmZmRelNfHIwdOxZqtdpofQDqr6BMnjwZiYmJDWJERNTdsTggIupC1q9fj5Ur\nVwIAvLy8oFAooFAokJaW1mDMwZUrV6BQKBAfH4+EhAR4e3ujR48eGDFiBK5evQqdToff/e53cHV1\nhUajwYQJE1BWVtbgmCkpKYiIiICVlRWsrKwwZswYnDt3Tq9NQUEBcnJyMHLkyAbbf/vttwgPD0ef\nPn3Qo0cP9OvXD7GxsXptampqsGHDBvj6+sLS0hKurq5YtmwZqqqqGuxv//79GDJkCHr27AkbGxu8\n9NJL+PLLL/XajBgxAoWFhcjKymp9cImIugHj/YREREQGN2nSJPzwww/49NNPsWPHDtjZ2QEA/P39\nmxxzsH//ftTU1ODtt9/GzZs38d5772HKlCmIjIxERkYG4uLikJeXh507d2LZsmVITEyUt923bx9m\nzpyJUaNGYevWraiursZHH32El156CadOnUL//v0B1N/qBAAhISF6x87NzcXYsWMxYMAAbNiwARqN\nBnl5eXq3PwkhEB0djfT0dCxcuBDPPvsscnNzkZCQgIsXLyI5OVluu2nTJqxduxahoaFYv3491Go1\nTp8+jZSUFIwfP15uFxwcLPfr8T4REXVrgoiIupRt27YJSZLEf//7X731ERERYvjw4fJyQUGBkCRJ\n2Nvbi4qKCnn96tWrhSRJ4rnnnhP379+X10+fPl2oVCpRXV0thBCisrJS2NjYiHnz5ukdp7y8XDg4\nOIjp06fL69asWSMkSdI7jhBC7NixQ0iSJMrKypo8n7/97W9CoVCI9PT0BuslSRIpKSlCCCHy8vKE\nQqEQEydOFDqdrtkYCSGEhYWF+MUvftFiOyKi7oS3FRERdXOTJk1Cr1695OXBgwcDAGbMmAGlUqm3\nvq6uDoWFhQDqBzjfunULMTExKC0tlV/379/HsGHDkJqaKm9bVlYGhUKhdxwA6N27NwDg888/b/Lx\npp999hmeeeYZPPvss3rHCQ8PhyRJOHbsmLwPIQR++9vftuqpTDY2NigtLW1FhIiIug/eVkRE1M25\nu7vrLVtbWwMA3NzcGl1fXl4OALh8+TIANDqOAIBeYQE0HCANAFOnTsWf/vQnLFiwAKtWrUJUVBQm\nTpyI119/Xd7+8uXLuHTpEuzt7RtsL0kSfv75ZwDAjz/+CAAICAho5mz/R6fTGfXRrkREpojFARFR\nN/f4l/iW1j/8kv/wl/7ExES4uLg0eww7OzsIIVBRUSEXGQBgaWmJtLQ0pKen4/Dhw0hOTsYbb7yB\n7du3IyMjA5aWltDpdAgICMAHH3zQ6L6dnZ1bPMfG3Lp1Sx6TQURE9VgcEBF1MU/r13AfHx8A9V/8\no6Kimm3r7+8PoP6pRQMHDtT7TJIkREREICIiAvHx8dizZw8WLVqEzz//HDExMfDx8UF2dnaLx+jX\nrx8A4MKFC/KA46Zcu3YNdXV1cr+IiKgexxwQEXUxPXr0AADcvHmzQ48zevRo9O7dG5s3b0ZdXV2D\nzx+9n3/o0KEAgFOnTum1aayPQUFBAOp/2QeAadOmoaSkBB9++GGDtjU1NaisrAQAREdHQ6FQYOPG\njU2OX3jo4SNMw8LCmm1HRNTd8MoBEVEXM2jQIABAXFwcYmJioFKp8PLLLwNo/L7/trKyssKePXvw\nxhtvICgoCDExMXBwcMDVq1fxr3/9C4GBgfjLX/4CoH5cw8CBA3HkyBEsWLBA3sfGjRuRlpaGsWPH\nwsPDA+Xl5dizZw969uyJcePGAagfGH3o0CEsXrwYaWlpGDp0KIQQuHTpEg4ePIhDhw4hPDwc3t7e\nWLt2LdavX49hw4YhOjoaGo0G2dnZUKvV2LVrl3zcI0eOwM3NjY8xJSJ6DIsDIqIuJjg4GFu2bEFC\nQgLmzp0LIQSOHj3a5DwHjWmq3ePrX3/9dTg7O2Pz5s14//33UV1dDRcXFwwdOhS//OUv9drOnTsX\nq1atwr1796DRaAAAEydORGFhIRITE3Hjxg3Y2toiLCwMa9eulQdES5KEpKQk7NixA4mJifjiiy+g\nVqvh4+ODxYsX47nnnpOPsXbtWnh5eWHnzp1Yt24dLC0tERgYKE8MB9SPlTh06BDmz5/fqlgQEXUn\nkjDkz0hERERNqKyshLe3NzZu3NigcHiakpKSMHPmTOTn58PR0dFo/SAiMkUsDoiI6KnZvn07EhIS\ncPnyZSgUxhn2NnjwYERFRWHr1q1GOT4RkSljcUBERERERAD4tCIiIiIiInqAxQEREREREQFgcUBE\nRERERA+wOCAiIiIiIgAsDoiIiIiI6AEWB0REREREBIDFARERERERPcDigIiIiIiIAAD/D1N5ChQN\nCwQKAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2887,7 +2845,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 42, "metadata": { "collapsed": false, "scrolled": false @@ -2901,7 +2859,7 @@ " [ -5.26709207, 0.01521311, 195.7945557 ]])" ] }, - "execution_count": 41, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, @@ -2909,7 +2867,7 @@ "data": { "image/png": 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nOjVh84dDn8gcWKWOxe27Gbw4fxtHL0YD0K2lB0sm98G7Xs1KzVHVVNffjXZt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\nAyn48UAKgPrZKoYGemJoDy8M7uEJB1terr45yr9aiT0nMrA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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2947,7 +2905,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 43, "metadata": { "collapsed": false, "scrolled": false @@ -2964,7 +2922,7 @@ "data": { "image/png": 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55ZVXcDqdtccLCgr41a9+xa9+9atrFv+beQ2Hw1F7195qtZKXl8fYsWNrz/H0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\nUV1drbc1EBEZMjb3REQGpKPmPiMjA+np6XpaFfD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8PAN5fvj795QZqrzmr/2Uk5eV6N7HLYCXRn3K7mMSHy+G7WF3fu2Tg2DOK0rF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\n9oX+7r9kBhA1U3drNsPCwjSOu3Xrhl27dsHW1vbJ4lqyBF5eXli3bh1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PmAZba3vIBDnkcs2bfnUiPLQf5A2W/j6v7R1tuJp/HuezT+PStZ/7HJHLwswS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\ndYWDgwOysrIwZ84cnD17Frt379bJdRERERER6SOJ0PNp7S5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Lf2ho6EPbent7A6h5a1GP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\nQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3014,8 +2972,6 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "** author's note: this entire section needs a lot of work. Ignore for now.**\n", - "\n", "I have found the literature on choosing values for $\\alpha$, $\\beta$, and $\\kappa$ to be rather lacking. Van der Merwe's dissertation contains the most information, but it is not exhaustive. So let's explore what they do. \n", "\n", "Van der Merwe suggests using $\\beta=2$ for Gaussian problems, and $\\kappa=3-n$. So let's start there and vary $\\alpha$. I will let $n=1$ minimize the size of the arrays we need to look at and to avoid having to compute the square root of matrices" @@ -3023,7 +2979,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 44, "metadata": { "collapsed": false, "scrolled": false @@ -3070,7 +3026,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 45, "metadata": { "collapsed": false, "scrolled": true @@ -3114,7 +3070,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 46, "metadata": { "collapsed": false }, @@ -3144,7 +3100,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 47, "metadata": { "collapsed": false }, @@ -3167,7 +3123,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 48, "metadata": { "collapsed": false }, @@ -3190,7 +3146,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 49, "metadata": { "collapsed": false }, @@ -3233,7 +3189,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 50, "metadata": { "collapsed": false }, @@ -3242,7 +3198,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3326,7 +3282,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 51, "metadata": { "collapsed": true }, @@ -3361,7 +3317,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 52, "metadata": { "collapsed": true }, @@ -3425,7 +3381,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 53, "metadata": { "collapsed": true }, @@ -3447,7 +3403,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 54, "metadata": { "collapsed": true }, @@ -3484,7 +3440,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 55, "metadata": { "collapsed": true }, @@ -3518,7 +3474,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 56, "metadata": { "collapsed": true }, @@ -3556,19 +3512,20 @@ "\n", "With that done we are now ready to implement the UKF. You've seen this sort of code many times, so I will not describe it in detail. There is one new thing here that is important to discuss. When we construct the sigma points and filter we have to provide it the functions that we have written to compute the residuals and means.\n", "\n", - " points = MerweScaledSigmaPoints(n=3, alpha=.00001, beta=2, kappa=0, \n", - " subtract=residual_x)\n", + "```python\n", + "points = MerweScaledSigmaPoints(n=3, alpha=.00001, beta=2, kappa=0, \n", + " subtract=residual_x)\n", "\n", - " ukf = UKF(dim_x=3, dim_z=2, fx=fx, hx=Hx, dt=dt, points=points,\n", - " x_mean_fn=state_mean, z_mean_fn=z_mean,\n", - " residual_x=residual_x, residual_z=residual_h)\n", + "ukf = UKF(dim_x=3, dim_z=2, fx=fx, hx=Hx, dt=dt, points=points,\n", + " x_mean_fn=state_mean, z_mean_fn=z_mean,\n", + " residual_x=residual_x, residual_z=residual_h)```\n", "\n", "The rest of the code runs the simulation and plots the results, and shouldn't need too much comment by now. I create a variable `landmarks` that contains the coordinates of the landmarks. I update the simulated robot position 10 times a second, but run the EKF only once. This is for two reasons. First, we are not using Runge Kutta to integrate the differental equations of motion, so a narrow time step allows our simulation to be more accurate. Second, it is fairly normal in embedded systems to have limited processing speed. This forces you to run your Kalman filter only as frequently as absolutely needed." ] }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 57, "metadata": { "collapsed": false }, @@ -3631,7 +3588,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 58, "metadata": { "collapsed": false, "scrolled": true @@ -3639,9 +3596,9 @@ "outputs": [ { "data": { - "image/png": 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jf35xM68+uRiAcbNnccUvLsRoNDBmHMTHw+zZvUfD7YbK1lo2767lzILcAfs2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\nAEAEGKQBAACACDBIAwAAABH4D9krXfCOrCGAAAAAAElFTkSuQmCC\n", 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vJvDfAKFQmFMK0iLa+3w+fvXz23jjmSUAjJ09i6t/fgl6vY7RYyE+HmbP7jkb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\nmc6mmJvPY8TKlL0yRdtF0yL4QUgYhued4pgvBMSNBHVVMeJRk1hcwTI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Lh6GUyiHZnM/k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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3681,7 +3638,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 59, "metadata": { "collapsed": true }, @@ -3716,7 +3673,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 60, "metadata": { "collapsed": false, "scrolled": true @@ -3724,9 +3681,9 @@ "outputs": [ { "data": { - "image/png": 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WffTo0cMwm83GmjVrjJ9++smoUqWK4eDgYHh4eBi9e/e+7TaGYRjHjh0zevToYXh7exu2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\n", 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px47IxWRnZuPoXQMqv43hP5q4cx1UaN8BKyszVQM9eKyaHyVd/2/Yn4eHB4MG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\nV199Vaxbt05IkiTKy8tbjZk5c6ZISkoShw8fFnl5eSIxMVH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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3756,16 +3713,16 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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RhPqef65l6NChzJkzBzj/ieCXX33L5yu/JiX+JKnRiaRGJ4JmIyWhXajqfxtT\nRvShb9fgi56zuLyaLYmHSLUkUG7yQJfvwe1RYYT7tzzn6+l771ok1+/yyPVrO7l25/1ydcXFNJtM\nz5kzhy+++IJ169bh7OxMTs753bYcHR2xt7ensrKShQsXcuedd+Lj40Nqaip/+9vf8Pb2brDEQwgh\nLkV+SRXZZXmklqSis1E4V+JNdlEZo3t1qN+COzo6mvfff5+VK1dSUVEBgE6n49Zbb+WBBx5gwoQJ\n2NvbA7A/9hybj9lzKjmWDhEKYENooIGfT2YRm+rHoC4BrSqXB2Bn1HHPyK4ExToTnexFSWU5/u5u\nDdY7tziGrZ5pI7oQeMqZ2BRPSipNdAvxatUW5QCdQzwpKA2lpq6WU0lx1NSq2NtrqDBXUVhWVZ9M\n/1JERAQvLvgbPUZM5bsD2zm6ZxsZx09QmplMadLP7Er6mUlbPmDe3D/x8MMP4+fn16B/SYWJqppq\n9IY6sC3gRFYl2v0Kd4/scklzF0KIG02zyfSSJUtQFKX+4ZcLXnzxRV544QW0Wi2xsbEsX76ckpIS\nfH19GTVqFN9++239/8SEEOJS2dvqMNoYcXbS0iXSlrMpBRxKK6Wkoow1q8+ydf3X7Nu3r779wIED\nefDBB7n77rtxd29c5m1QlwAqqmuojaklgXhy8lV8PHW4u2nIKM0mOjGHEb1CWx2fRqMwtHsQg7sG\nkltUgZer/SWvI9ZoNAzpFsSgLgGUV9Xg7GC8pP7DegRTZ1XRxmo5k3GW8hoTUR5adNrm120P7xFE\nUnZ3KjU13PHoKNatKMHPeJh93++nOD+XhQsX8vLLL3P77bfzxz/+kdGjR6MoCjYaDRpFQaOFqA5G\nziRXE5N9BsdDBh4a2x29/pKeZxdCiBtGsz/9Lnxc2hSj0dioFrUQQlyuMB9XvB3dSUpLwmxWCfWB\njds2su773Zgrzm+F7eTkxPTp03nsscfo1KlTs+MpisLo3qHUWVV08TrOpJ2hrNyEi5MNOZll5BRX\ntilOjUbB16Plh/aaH0NzyYn0BSN7heDmaOTQaWeyikvwdHQkwLP5u8RuTnb0iwwgv6KIM0lnGTTS\nnajISUSOHEXSoRyKY3/m8N5trF69mtWrV9OtWzeeffZZbr39Dhz19lRXq6iqSmSokWMxxcRnZbAn\nxqXFTWOEEOJGJbcShBDXHDtbPT06eBOf6sA3767g7N79mKtMABhdfRl3+908/9QT9OvS+rvJWq2G\nCf074OlWjGfCAAAgAElEQVRsi/1xI4kFqSTm5+OgN1JTa2l5gGtUj3AfuoZ6kZ5Xiq+bA8ZWbJc+\npFsQabmllCSWUKcUAXZ4exnIDvFjwqipfPX5xyxbtpQlS5YQExPDgw8+SGDg3xky4S5sQgMoKavD\nzUVHVAcDsXEpHI53ISrIo8VEXgghbkSSTAshrjmZmZms+uhNPvvsMyy1NQCE945i9H23sGFvKHZd\nbdhzKotAHw983Ft/Z1hRFPp29MfPw4n9sS6k55ajotIroukHpq8HWq3mouukm2Kj1TBpYATFFVUc\nTD9OyrlqQgNtUbS1lFRWYXB0ZcGCBTz77LN8+eWXvPXWW5w+fZqv/vtPDHb2dB07jKmPjMfJwRZv\nbwvJRWnsjXHj7pFdUBSpVS2EuLlIMi2EuGYUFRXxxhtv8N5772Eynb8T3bHPEHwH90PnFMGWQzZk\nnYNdu6tI9knA0c6Wh8Z1x3CJ63X9PBz5/fDOmMwWai11OLVxmcX1zN3ZjvH9IqiuqeFEdgy1lipU\nVcViraXWcn6Jn8FgYNasWcyYMYMffviBf7y2iEMHD3Dsu43EbdvJ8GljsXqOxNEzn4SMPFKy/Qnz\naz6pL68yY2/UywYxQogbhlTeF0JcNQnnColNzqWgpPk1yZWVlSxatIiwsDDefPNNTCYTd955J7Gx\nsbzzwVL69RiDs7uJWX+oIywM5j9hpEPXYs7kpnEoPrNNsSmKgq1Rd1Mm0hd0DPJgyqBO9Pbvjlrp\njmLVY9QZsf/VUhGNRsOUKVM4eGA/z7/1CT7hHamuqGbTp+vZ+s/nyDiwlbPZCRw9k9XkueqsKqt3\nxfHh+sN8ufUkOYUVV3t6Qgjxm5A700KIq+Lw6Uw2/3yacnMVLkYnOgV4M7p3aIPkVVVVvvrqK555\n5hkyM88nxWPGjOG1116jX79+AHSss1JSYaLmbA0nT6fQsZMtWq2WiGAjcWfO8XOCD30ifFtdVk40\n1CXECzcHWw7EeZBZUEHPDt64NrMZy0N334bWK5Dl36wh6+B2TPkJHFq9kSMbdpA9+V76d/gHIUGN\nl80UVZg5lVXKz1nReBS4UVBayV3DuxDg1Xg3MSGEuJ5IMi2EuCpyispJKzpHcU0RiqqQWeLPufxS\nRvUOo2uoFydPnmTu3Lns3r0bgD59+vD6668zZsyYBuNotRpuH9qRGouVuuQ6cE2nvNIWZycbHBzN\npBdnczwxh6HdgtpjmjcEXw9H7hjWGVVVW1zzHO7vRpiXF8PG9MPpns5889807Eu/J/H4Gbat+Yxu\nW1bz9FN/Zt68eQ223S2pNFNhrsTFSYOiK+VEdhyG/TY8NK5HqzaqEUKIa5Us8xBCXBUOtnoMOiPe\nHjb06WFHtU02+1N/5qutB5n2wCx69erF7t278fDw4OOPP+bw4cONEukLDHob7hzWib6hEQQ7RHDy\nlJn0TDPeHnoKq4rIzC/7jWd3Y2rNw4OKovC77kF0cA8hM7uWnr8L47F//Zn7X52Hd3gUFeVlvPji\ni4SEhPDqq69SVnb+a6PVaFAUDRqNQscOtqArJy4nmc1Hk672tIQQ4qqSZFoIcVVEBrjj5+hNbn4d\nGgW6Rtlhyj3JP595iG++/AyAuXPnkpCQwB/+8Ac0muZ/HNkaddw3uitjenSit29PSvLtOZ1owqpa\nMdVcv6Xtrkehvq70DAsgwDEQDx8zAD0Hd2Two4/wyHPvMmTIUEpKSliwYAGhoaF89tlnaK012NoY\nqTZZURSFjuG2ZFdkEXcum8TMwnaekRBCtJ0s8xBCXBUBXs5E+ntzrtSL06fTiN2wgWNbDgHg6BvM\n9McXMPueKbi6XkJJNxst4/qFE+bnxr5YV3KKylGU87WWxW9rdO9Q0nNL2J9SRGqGiZAAIw52Gjzc\nI/nsj+s4lxDNwoUL2bt3Lx988AFffvklQyfdh7ZTMLW1Kga9hkB/HYn5KeyKdiXUx/WSd5EUQohr\ngSTTQoirZkSPEDZsWMuqj97AXFGBjV5H2O9uw7XTUPINZjYdOYuLg5EAr0vb7CPc341wfzeqqmuo\nsdTh4tj0A3Pi6rA16BjXrwPl1dUcyzyJVmtGr9dQY6mhylTLqFGjGDlyJNu2beOpp57i5MmT/LBi\nCQZ7B0qnjmH8/aMI8NGTk1dKUm4Op1Ly6C6/FAkhrkOSTAshroqqqiqe+8t8lv/3vwA4BYThN/BB\n4lP84AAUl1ZTnB+Ps72RmRN6YmOjveRz2NnqsbvSgYtWC/d3Z2yfCCzWOuKyz1BursTXx4Cz/fkH\nChVFYcyYMXz88cccPHiQjz5ZSmz0z+z6Yh3HftjGqHvHU2IYiD4ok2NnfenWwbvZdduqqlJSbsLJ\n3iB3sYUQ1wxJpoUQV1x0dDT33nsvp0+fRq/XM2POM7h27k9SaSI+IRb0OhvGjjXyc0wpibnZHI73\nZHBXqcZxPeod6YdeZ4PbSUfyykoI9PDA29WhQRtFURg0aBDTZz3C/FcWs3bFvyk+l876D75Fa/yJ\n0gkjCXL0JTUnpMmdHGstdXy94xQZBcX4u7twS/9wPFzsf4spCiFEsySZFkJcMaqq8v777/P0009j\nNpvp2LEjX331Fd26dWfd3njURIW8/LP4+BnRaGwICzFy9mwaR8940ifS75J3MhTXhq6hXkQFupNZ\nUIa/hxO6Jj5lcLI3MmXyZDT+PuzfvofEnZuoKUknet13xP20i4JTj/POa89hNDbeSCezoIyz2dmc\nyI7Bp8ib0koT94/u3mxNbCGE+C3I52RCiCuiqqqKBx54gLlz52I2m5k9ezbHjh2jR48eaDQKtw2J\nYkB4OIOjIqmoNpOaYcLFUYvOUEN2aSFJWcXtPQVxGXQ2WkJ8XJtMpC/oFeFDoIsfnYZG8cq3f8N7\nyJ/wCQugtqqE//zrVcLDw/nggw8wm80N+uUWV1JaXY6nuw1mbQExWWf5/mACVqt6NaclhBAtkmRa\nCNEqJnMtldU1F30vJSWFIUOGsGLFCuzt7fn666/5z3/+g53d/1Y0a7UabhsSxahunejj34vSAnuO\nRFdSWWWlutZERRNjixtLsLcLHXw8cNZ5cC6rlgHjevD0J88zZNYMvANDyczMZM6cOURERPDhhx9S\nU3Ph++L8WmqdTkPnCFuKa3OJy8zgYFxG+01GCCGQZR5CiFY4l1fKur3xVJpNONna0TPCh/4d/bHR\nati6dSt33303RUVFhIeHs27dOrp06XLRcbRaDWP7diDc343tx51Izy+gzFSBh4M77vJx/U1jWI9g\nUvOK+TnrOP0HWFEUDd2H96Jnn3H4mEr5+rP3iY2N5bHHHmPRokU8//zz9Bs+Eb2NnjKzFRsbDZGh\nRs4mpnAswYPeET4YDbr2npYQ4iYlybQQokUnEnOIzU4iuzwLW52RtKIg4tPyyYndxYK/PY3VamXi\nxIl8+eWXuLi4tDheqK8rM71dSMkupqC0Cg8XOzr4uf0GMxHXgkAvZ3qG+ZJbns/ZlAy6dbTF0U5D\nfqmJLv1GED1vNt9++y0vvfQScXFxzJ49m8CgYHqNvRNdR28A3Fx0GOwqSS3M4tBpb4b3DGnfSQkh\nblqyzEMI0SKDTosChAYaiAhXSC07w7/++RzP/eXPWK1WnnvuOTZs2NCqRPoCjUahg78bAzoHSCJ9\nExrWI5hIryAs1faknqvBaFAwW2qoNNWg0WiYNm0aJ0+eZOXKlXTs2JFz6Wms/+RtNi16g33r91Jn\nqSMs0EB6aQYnErMxyy6YQoh2Ism0EKJFQV4ueNh5kJNfg4NR5cz6VcRu24Si0XDn7L9w96y5LW4H\nLsQvOdoZmNA/nG4+HcnP03IqwYxeq8fW8L8PTLVaLffccw+xsbF88cUX+AWGUFlUyJp/LufN6S+y\n/tMjGAxmssoKiE3Ja/Z8VqvKnug0lv8UzdEzWVd7ekKIm4gs8xBCtCgiwI1wH09Sco0snvcvMuOT\nMNgZiZg4E01kABsPn8HWoCPcX+4wi9YLD3BjfL8ItMe0JBeew8XojKdz49rRWq2W+++/n8jew3jx\nrX+yZ8MXFGTmUbB2KWmHvOg0agqdfPzpE+XX5LnO5ZeyMyaRswUpJOWEUFRWzbh+Ha7m9IQQNwlJ\npoUQLdJqNXQJcOS5Oe+RlZKEo7sLs994nLWbA/H0qiY6+xT6AzruHdUNbzeHlgcU4v/0DPchwMOR\ng6c9cLY3MLBTQJNtu4R4M2r8XaQr/mRHx1B48keKsvLY98UnJGzfQs3LL/GHGQ+i1TYuz3cut4z8\nykKq6kqJzYsBFHzcHOjewfsqzk4IcTOQz2WFEC3Kzc1l1n1TyUpJwMXLF//xc/h2oz/JybBxgy1J\naWZic+LZfCRJ6v6KS+bhYs/kQZH8rnswOl3TdartbPWE+7kxuJcP987rQ+iUl5n27EM4uLuRn5XO\now/PpHv37qxatQqr1dqgb5W5FrOlhiB/PaHBOs4UJLA7Oq3Jco9CCNFakkwLIZqVkZHBsGHDiI2N\npVOnTry5ZAUTh/en/5AqgkOszJkDd9xqpMxSxNmcLE4m5bZ3yOIG1r2DN36OvmTnWujSVcOAiUN4\n5L0FDLlnBh7evsTFxXH33XfTo0cPvv322/qkWm+jRavRUmdV8fXSo7c1kZSfyRFZPy2EuEySTAsh\nmpSZmcnw4cNJSEigR48e7Nq1i4cmD6V/eBhBDuE4e1RRXGbBRqshNMBASnEaR89koapyd1pcHaG+\nroT7euJk40ZoxPldEj08DHj37slz737DkiVLCAgIIDY2lrvuuotevXqxdu1a7G11GGz0VJvOf2+G\nBhnILM/kZFIu1aba9pySEOI6J8m0EOKi8vPzGTNmDMnJyfTt25cdO3bg6emJQW/D1KEdGRgZzqT+\nnYg/U8eZ5GqcnbRU11WSV15KeZV8dC6uniFdAwl1CyIr20JtrYrRoEXRWKi2WrjvgRkkJiby/vvv\n4+/vz8mTJ7njjjuYcdctZJ+KoaLifAk9JwcbbO1rSS/K5XhiTjvPSAhxPZNkWgjRSElJCePGjSM+\nPp5u3bqxefNmXF1d69/X/19CPaFPZwYE9kZT5cXRE2bqLIAKphq50yeunhBfVzoGeONp58vZFBMA\nBr2CqdZMhakGg8HAn/70JxITE/n3v/+Nr68vcbExfP3uS2x88x1i9kajqiqBfgYyy7I4lZonn6YI\nIdpMkmkhRAMVFRXccsstnDhxgoiICLZs2YKbW+OSd1qthmE9gpk+vifje/RiWNhABgUNoHuoP54u\njcubCXEljekTRkevUCrL9aScM3HhuVfrL5Jio9HI448/TlJSEu+88w7Obh6UZGaw9PkPePex19my\nKo5aKsgqKuFcXmmL56y11JFXXEldnbXFtkKIm4eUxhPiJpSWU8K5vDIc7PREBrhhZ9QDUFtby513\n3snBgwcJDg5m27ZteHs3XzrM08We24d2pK7OSrW5Fgc7w28xBXGTc3W0ZVy/DlSazcTlncFUW4GT\n0QFv18alGW1tbXnyySfpNGA8r7y9iGM/reFcfCrn4t8n7WAw5on3MaRzCEHeTe/gWVldw5dbY8gv\nKyfM14PbB0dha9RdzSkKIa4TkkwLcZPJyCvl650xnCvJxmhjwM/Zk35RAQzo7M+cOXPYvHkznp6e\nbN26lcDAwFaPq9VqJJEWv6lOwZ5oNF1xPmpLWbWJzsGe6GyaLq3XLcKfcbdNp8Qpiqwjxyg6vYXc\n5DS+X7yI01t/YMl7bzFmzBgURWnUNymrmKT8LOLzE8ivCMZiqeP+Md3RaBq3FULcXJpd5rFo0SL6\n9euHs7MzXl5e3HrrrZw6dapRuxdffBF/f3/s7OwYOXIkcXFxVy1gIcTlKSyrJq+8iOzKc+Rbktmf\ndpQNR09w7yPz+e9//4vRaGT9+vWEh4e3d6hCtCgq0J2ZE3ry2K39uWVARLNtfd0d8XNzoXtXd/74\n0ihCprzKxNlT0dvZkRR/knHjxjFs2DC2b9/eaA11Rn4pxdUlBPjqyKpK53RmJofjM6/m1IQQ14lm\nk+ldu3bx+OOPc+DAAbZv346NjQ1jxoyhuLi4vs0bb7zBP//5TxYvXsyRI0fw8vJi7NixVFRUXPXg\nhRCXzs6ow2hjQKdT6NbRjk6RWvbt+YbVS/+Noii8v+RjBg4c2N5hCtFqdrZ6XByMrWrbOdgTP0cf\nMnJq6N7TyOj7JnDHK88z/I7pOLu4snfvXkaPHs2IESPYtWtXfT9LnYrVWoeDnYaIMAOJhckcPp1B\nlZTVE+Km12wyvWnTJqZPn07nzp3p2rUry5cvJz8/n/379wOgqirvvPMOf/vb35g6dSpdunRh2bJl\nlJeXs2LFit9kAkKISxPq44q/qzuKxY78wloqcrM5tOJrAPreehdVzuEUlVW3c5RCXB29wn0IdPWm\nqtKGXn3Ol8lz97Cny5hbWPn9bl599VVcXV3ZvXs3I0aMYNSoUezZswedjQZF0VJnBXcXHbYOtaQV\nZXMiMbudZySEaG+XVM2jrKwMq9VaXyIrJSWF3Nxcxo0bV9/GaDQybNiw+oRbCHFtsbHR0L9zAKGu\nIZyOK+KzBUuw1NTiGDqUzhMGEJ15hjV7TssdN3FDMhp09OjgQ4CzH2kZ5zd9sTVqMFvMWDV6nnvu\nOVJSUnjppZdwdnZmx44dDBs2jOefmElhagrmmvOVPAJ99WSV5RCbki9l9YS4ySnqJfwUmDZtGklJ\nSRw9ehRFUdi/fz9Dhw4lPT2dgICA+nazZs0iKyuLTZs21R8rLf1f2aGzZ89eofCFEG1htVr58dg5\nlrz1d/KSE9C5BFMb+gx+ARYc3cvoGeZCv6BQhnfxae9QhbjiqkwWvjuSRkzBGfz8alA0UJDtxO9C\nOjGqu299u/LyclauXMmKFSuorKwEwDM8lFH3D8enQwBxiSqBxg7c0iOEEC/H9pqOEOIqi4j43/MY\nzs7Ojd5v9Z3pP//5z+zfv5/Vq1df9EnnX2tNGyFE+9BoNJzd9x15yQno7ez53SO34hdgYdq0AkYN\nM5NnyiMpt5js4qr2DlWIK87OaEPPEHeCHAI5lw1lFSoaFLS/qszh6OjI7NmzWb9+Pfc9MB2dwUh+\nYgpfv7SUdW+thLJMCquLSMuvbNV5y6pqiE4pIj1fnikS4kbSqtJ48+fPZ9WqVezYsYOQkJD64z4+\n5+9a5ebmNrgznZubW//exfTt27eN4V7/jh49Ctzc16Ct5Npdnl9evw0bNrDiy+VotVr++Nf/R5WL\nA6VV+Xh7+6PXa9DqTFSXWDHp3Onbt0s7R35tkO+/trsWr12fPir2e+I5nOhJQkECEUEd+N2AXvTt\n5H/R9iNGjKDLqHtZtvwtUg/uIe1kEmknk/Dp2JnwGc/Q8/5bsGmmLJ+qqiz57igJRWV413oSFhFA\nz/DWffJzLV6/64lcv7aTa3feL1dXXEyLd6affPJJvv76a7Zv305kZGSD90JDQ/Hx8eGnn36qP2Yy\nmdi7dy+DBw9uY8hCiKspOzubWbNmAefLXz4/9yH6hXRmQBd/jkZXkZldg6+njqKqQrIL5Q6auDEp\nisJtQ6IY1a0Tg4MH0Nk/iMiAxjt9XqDRaIgIDWDw7fcye/GLjLx3PIrWQE58HG//dSYTJk7m+PHj\nTfbPLCgnv7yE5OJUTmTHsPFIApn5ZVdjakKI31izyfScOXNYunQpX375Jc7OzuTk5JCTk1O/dkxR\nFObNm8cbb7zB2rVriY2NZcaMGTg6OnLffff9JhMQQrSe1Wpl+vTpFBQUMHbsWJ566ik8Xey5Z2QX\nRnTqSlevHhTk2nL4RBVajZY6qxWrVR6uEjcmrVbDuH4dePz2/syc0BMXR9tm2wd7u+Bm68qRk0bS\nLHegdvkH9uGj0NgY2LZlE71792bq1KlER0c36ptbXEGJqRwfTxs8PKwkF6SyKzr1Ks1MCPFbajaZ\nXrJkCRUVFYwePRo/P7/619tvv13f5tlnn2X+/PnMmTOHfv36kZuby08//YS9vf1VD14IcWlWrlzJ\nli1b8PDwYNmyZWg0538EODkYuWtEF+4d0YNRHfsyNGQQAwL7MqhLoOzwJm54RoOuVc/5RAS44W7v\nhp9/HX/8o0pYlCNz37iD3y98jcl3TcdoNLJu3Tp69uzJ73//e2JiYur71tWpqKoVrY1CSKCB4toC\nErPzSc4quppTE0L8BppdM221Wls1yMKFC1m4cOEVCUgIcXUkJCSwePFiAD799FN8fX0btYkK8iAq\nyIPa2jpq66zYGXW/dZhCXLNcHW0J8nTlTL4LeQXVdO2qx8Feg2q0YeJ9c/jve6/zxhtvsGTJEtas\nWcOaNWu46667WLhwITY6NxRFg2oFrVbBz9uGjOJsYpL9CfNrenmJEOLad0l1poUQ1yeLxcIrr7yC\nxWLhscceY8qUKc221+m0kkgLcRE9w33wd/IlK7eG4cPB1qiljhpKq004Orvxr3/9i+TkZObOnYvB\nYOCbb76hW7duPP/0nyjPza2vU+3rpaewuoDErCKqqmvaeVZCiMshybQQN4G3336b+Ph4fH19efPN\nN9s7HCGuWx0D3Qlw9aDWrKek9PzGRga9BlOtiYr/S4r9/Px47733SEpKYs6cOeh0OjZuWMenL8xh\n+3+Xk5eeg0GvwcFBJaesgNPpBe05JSHEZZJkWogbSGpOCWt2n+aHAwnEJuditaokJCTUL8P6+9//\njoODQztHKcT1y8ZGS/cwb4JdAklON6OqKjZasKpWaix1Ddr6+/uzePFiEhMTmT37UTQaLenHjvPm\njBdZ8Y9P0VuKya8sIKmV66bTckrYeiyZM5J8C3FNaVWdaSHEta+s0sTaPXGcyj2Loip4O3kRctqT\nj16Zi9lsZtKkSQwcOLC9wxTiute/kz+xqflkJ+WSnmnCUgcaRYOmiYcYAwMD+c9/PiRswGQ++u/r\npB45wLEthzi29TBBvfviP92RqUM7odM1XafaXGth9e44kgrTCXL1Z6rSmchA96s1RSHEJZA700Lc\nIKpMtZSaKiiqzsfRs5TE4jiWfrmYI4cO4Obuwfz589s7RCFuCHqdDSN7htDJK5LcHC1mkwZXO0e8\nXZuvYtW1UySj7n+UGf9cQP+JQwCF9GNHWDTvHu578CGSk5Ob7JteUEVuRRHnSjOIzjrFDwcTqKiS\ntdZCXAskmRbiBuFgq8fWxhZQ8PbQ0aWDhp9/+BaAkXc9THpJXfMDCCFaLSrIgykDO9EnoDs9/boR\n7u/W7A6IAAGeTrjaOhOT6EyB/UPQ5WVsgwagqvDt1yuIiorikUceITU1tVHf4gozpdVlhAbpMdib\nSC3K5GDcuas0OyHEpZBkWogbhIOdgQ6+7njYeZGeVcO2L36kuqwCJ79QCPPmUGIuZ7NlxzUhrpTu\nHbx5aGwvZo7ry8SBES22D/V1xc3OhYBAK489phLWyZPH33qAO59fxMgJt2G1Wvn444+JiIjg0Ucf\nJT09vb6vpU6lTq1DZ6MhNMhAVnkmMSl5VJtqr+YUhRCtIMm0EDeQId0CCXUL5GxsFntWb0NRFBw7\n30dQoA3JZWkcTMglu7C8vcMU4obh7eZAoJdzqzZ9cbI34O/ujIPOiYIiC127gq2tBp2LM9PnvcTp\n06d54IEHsFqtfPTRR4SHh/OnP/2J3NzcBuM42Gmxs7eSXZpP/Dl5GFGI9ibJtBA3EF93R/pFBJG0\ncSvWOiv2IUPILA7iuzV6cvIgrSyL3dFp7R2mEDetjkEeeDt4kZN/vk61va0Wk6WKknIT4eHhLF++\nnFOnTnHvvfdisVhYsmQJU6dOZc3yDzGXlVFbe75OtZeHjvyqQhIyZAdFIdqbJNNC3GA0ZWkkxx5D\nb2vHyBljCA6pY84cGD7UTHldCQlZeaTnlrR3mELclDoHe+Lj5EF5OZhrrNjYKKCpw1RbS03t+eca\nOnbsyIoVK4iNjWXatGlYLBZ2/bSBFQvns33pasoKS/F001FqLiYtt5iKKnOrzq2qaqt3NhZCtJ4k\n00LcQFRV5fnnnwPgtrtnEuXXC2eParJza9BqwM1VJacsn7NyN0uIdmFvqyfC3wNPe2/SM89X49Ao\nClbVSp1VbdC2c+fOfP3116xYsYJBQ4dTZ6kldutu/nHvc/zw4TforBUUVBaTktPyL8eZ+WV8tOEY\n7645THyaLA0R4kqSOtNC3EB++OEHDhw4gKenJ//556vsjM3GTm8kKTOV/BIVFXA1WKi1yN0pIdrL\ngE7+nErP4WhGLr5eFqyqikbR0NSq6/DwcBa+/CrfHTjN+q/+TWZMDLu/3YbN+t3/n707j4+yvPf/\n/7rve/bJTCb7ThJCQtj3XVFErLiBRyvqcWv7PdZTt9Z6Wq31q7+WarWttj1q22OtoufY2n6PBTcU\nkCIii+z7lhAChOx7ZjLbfd+/P6LRlC0JCUnw8/QxD8Lc2zWfDPGda677uhh+wRzGZyQxanDKaa+5\ntaiCbcf20xhswTQMvG4b6Ynenn9xQnwFSc+0EOcJwzB45JG2Xukf/ehHxMX5uPbCYdxw4VguzBtP\npiOPJEsG2XEZZCXL/0SF6Csp8TGMy8sgNy6XzTtbsSpO4mKcuJy2Ux7jc9koKBjF5Fvu4P7fP4Ir\nfQzRcIQdK9/j9vmz+MEPfkB1dfVJjzUMk5LyeqoD1WiOAHsri/l4x5GT7iuE6DrpmRbiPPHmm2+y\nY8cOsrKyuOuuu9qfH5GbTOGgRJKsLQRCOtOnjGFwWlwftlQIMWtsDnVNraiKiqZo5KTGnnZ/TVNJ\n9jk5Uuxhc42dQNp3SB9WSvPBxTQf2cMvfvELXnjhBe69914efPBBEhK+WB0xGI4SCAcxFZ1RQ2P4\ndFs1B4/XUFbTRIb0Tgtx1iRMC3EeME2Tp59+GoCHHnoIh8PRYbumqQxKigGQIC1EP2C3Wbj+omGk\n74rBarUwsSDtjMekJ3qYMtYLMZW8/7aVu+/OZtOOb+GqtVHyydusXLGMn//85zz33HPcd999fP/7\n3xU6dFoAACAASURBVCc+Pp5IVEc3dDQVLBaF5EQLxxsr2FFcJWFaiB4gwzyEOA98/PHHbNy4kYSE\nBO64446+bo4QohNsVgsXj8tlxsgs7LYz921lJnpJcPmoq2+boxrAblNJGJTDc3/8b9avX8/ll19O\nS0sLTzzxBDk5Ofzf//t/qa+r48u3NqYl26hpraGkvB5dl/snhDhbEqaFOA/88pe/BODuu+/G5XL1\ncWuEEL0hN81HijeOaNjCxMlt0+g5HCqhSJBGf4gpU6awdOlS1q5dy5w5c2hubuanP/0pY0cPZ9Wb\nrxFoCQDgdqkolhBVTU0cl0WchDhrEqaFGGDKqpvYf7SWqvoWAPbt28fbb7+Nw+Hg7rvv7uPWCSF6\ni6apDE6PI8GZQHVN2zLiDptCMBqmpTXcvt+0adNYtmwZa9asYfbs2TQ2NrLif19h2ZNP8v4r7xD0\ntxLvs1AbqONQeefmnG8JhFi9vZRPdh4hKr3ZQnQgY6aFGEC2HiznvU8P0BxqwWN3k5+RxNsvt/VK\n33777SQnJ/dxC4UQvSk/I4GUfUnsq6kkO9NEUcHEwDDNE/adMWMGK1asYPXq1fzbPQ9wYOdmlr/y\nNmv+90MmXz2blJGXcbSqsVPXXbn1MGv278OmWQiEIsyZmNfTL02IAUvCtBADyP6jNRyoLiKkNBKt\ng/1HHPz59f9BURQeeOCBvm6eEKKX5WXEkZWQSHFdDJU1YUwDlM/+O5WZM2fy+LMv86e/v8KBVW9x\nZHcRH73+NjbXSvzzb+eqSU/g8516NpFQOMqh8nqKa0owFQOn1cHgtDjyMuJ74yUKMeDIMA8hBhDD\nMDExycu2M2msm52fLEaPRhg2YQZRm8zSIcT5TlEUJg/LICduECVHQzQ269gtNlz20/eNJXhd5A0f\nx/wf3cu3f/Vd7Al5hAN+3n39BfKG5PH000/j9/tPemxFfQt1/ibsTpOsDI1DdaVs2n+8N16eEAOS\nhGkhBpDMpFjinXFUVEcwomH2r/4EgNQpk3ln/b5Of2QrhBi4Rg1OpjAjnVRnJjV1URLc8eSeYcrL\nZJ+bGJub9Z8aLF8/jFD2f+CbdhfelELqamv54Q9/yODBg/nVr35FIBDocGwgGCEUieCwK6Sl2GgI\n1VNSVUt9c2tvvkwhBgwJ00IMIGPyUsj0pdLQYPLx4o9pbQ5gj88lf+Igdlfs5511BwgEw2c+kRBi\nwFIUhX+5cBjjsguYOmgig1OSSEuIOe0xqfExeB0x5OTq3H03DM5TuOXfh3PrY/8fv3zhFSZPnkxV\nVRUPPvgggwcP5te//jWtrW1hORLV0U0dTVWwWTTifRqVzVXsO1JzLl6uEP2ehGkhBpDYGAdj8lLJ\n9ebw4esfAhDyXcaydx3sLm5mf2Up6/eW9XErhRC9zeWwcsulo/j2lZP510tHoSinHjMNkORzEefy\nokc0gkGdkSPBZlOI6BFGTZzB+vXreffdd5k4cSKVlZV873vfIy8vj9/+9rcEQ8EO50qIs1DX2siR\nSvkkTAiQMC3EgDNrbA6BwwcIN9cRk5hAzrgx3HOPwtWX2ylrKmd3SRXhSLSvmymE6GU2q4WEWNcZ\ngzS0TauXnRKLz+6jpiHKRReB1aoQ1sP4gxEUReGKK67g008/5a233mLcuHGUl5dz//33c/Ul09ix\n6n2Cwbbp+HxejZZQE+V1zUSieqfaGgxF+HRvGVsPlmOeZOYRIQYyCdNCDDCapvLxu68DMPbSa4hP\nbSUQ1PHEWLA7IpQ31nG4QnqMhBAd5abFkeCOp66+7Zdtu1UhrEcIfBaSoW0IydVXX83mzZtZvHgx\nY8aMoaqyguWv/443H/0Za5d8hGoaOFxQF2jgWHVTp669cuthFq/fxpJ1u/hUPj0T5xkJ00IMMB98\n8AG7du4gJSWFG6//FheMymLbziAHSloJR0x0MyqLKgghTpCXHk+iK46mZpNwxEBV23q0T9ZPrCgK\n8+bNY8uWLby06L9Jzswl0NDA/z77Oj+98VGObVpPXUsdFXUtZ7xuOBLl4LEa9tccZHfVPj7acZiq\n+pPPHCLEQCRhWogB5uc//zkADzzwAN+4chKXjR7D+PRxWIOpJNrTSHLHkeyTJcWFEB25HFby0hNJ\ncCVyvDKMrpsoioqmnnqYiKqq3HjDDdz/xCIm33IrKTnptNTVs/qVv7Ho0fv5n9cWEYlETnk8wLHq\nJmr8jTidBnHxBqX1bcM9hDhfSJgWYgD55JNP+Oijj4iNjeWuu+7C5bBx5bQCvvG1CXx92lTmT5rC\nglmjSPS5+7qpQoh+aHx+KoN8GRyviNDYEsWmWXHarac9xm7ViPM6caVPZtj1j0Du/8EVn0pLXS3P\nP/UoQ4cO5eWXXyYaPfm9Gv7WCK2RIA6HQlaajSp/FQeO1RGNyido4vxwxjC9evVqrrnmGjIzM1FV\nlUWLFnXYfscdd6CqaofH9OnTe63BQnxVmabJI488AsB9992H1+tt35aW4GHmmGwum5RHVvKpVzIT\nQny15aTFUZCWQlpMJiVHwiS44shK8p72GE1Tifc4sCpOdB2In8S0b/2YoVf9K8npgygpKeGb3/wm\nhYWFLFq06IRQHYxEiRpRrFYVt0vDaotQ2VRHSUVDL75SIc6dM4Zpv9/P6NGj+c1vfoPT6TzhrmFF\nUZgzZw4VFRXtj/fee6/XGizEV9Xy5cv56KOPiIuL4/vf/35fN0cIMUBdNa2A0Rn5jEgpJNWbyNBB\niWc8JtHnYkhWDOMm6EycCHOvsDBy1ji++4uX+eOfXiY/P5/i4mLuuOMOhg8fzqJFXwz/iEQNDPOL\nMdrx8RbqWxs5Vi03SovzwxnD9Ny5c1m4cCHXXXcdqnri7qZpYrPZSE5Obn/4fL5eaawQX1WmafKj\nH/0IgIceeojYWOl9FkJ0j9tpY8Gs4fzrRZP41txxxDhtZzwmNT4Gj91LQ5POpEltHWmaCrpp8i/X\nL2DPnj0sWrSIvLw8Dh48yB133EFeXh7PPvsskVArqqJi6G23Ovo8FpqCzRyvae5Su3ceqmTllhIq\nO3HToxDn0lmPmVYUhTVr1pCSksLQoUO58847qa6u7om2CSE+8+abb7J582bS0tK45557+ro5QogB\nzut2MHJwCj6Ps1P7D0qKJd7lpcVvkJvbFootFoWoESUYjmKxWLjtttvYt28fr7zyCsOGDePo0aM8\n8MADXH3JZNYv+QtNdW3T6HljNPyRZqrqA4TDnZsT/0hlA2+t28viTzfz7vqDRCKdm99aiHNBMbsw\ne7rH4+H555/ntttua3/ujTfewO12k5ubS0lJCT/+8Y/RdZ3Nmzdjs33x225j4xcf5xw8eLCHmi/E\n+aWqoZWtJXWoqkJKrIPhWT4MPcqNN97I0aNH+eEPf8j111/f180UQnzFGIbJe1vK2HBsHxmZfjwx\ncOCwQYqWy7zxBWQkuP5pf4M1a9bw2muvsW3bNgBUi8aImWMYP3cqFS1xZNnzmT8pl5ROzD60dl8V\nHxUdoDpYQ3bMIC4Yks2EIQm98lqF+Gf5+fntX5/sk2HL2V5gwYIF7V+PGDGCCRMmkJ2dzbvvvsu1\n1157tqcX4itD1w1W7qzgYP1hoqZOgiOOQ5VJlG1dxtGjR8nNzWX+/Pl93UwhxFfQ57/geyo81De1\nhenPnWwBRlVVmTlzJjNnzuTDjzfwwh9f4sierexcuYWdK7eQXJBHaPJc6odldCpMVzS00hRpIjsD\nyssrKK6MZ2xuHJomk5KJvnfWYfqfpaWlkZmZSVFR0Sn3mThxYk9fdsDYtGkT8NWuQXed77WrbQzg\nOxhAjZYyMi+Go2V+Dta1sPi/XwXgD3/4A1OnTu32+c/3+vU2qV/3Se3OTn+pX1p2EzURBzuqw6Sn\nu6hq8JPlzWTCuLFknGZGkMEFI4gkD+OTXR9Qt+MTNr6/gaoDxVQdeI6DH73Dgw/czze+8Y1T3gti\nmiarD0VwBw4zZmQKuunH7vUQnzGE/Mz4M7a7v9RvIJLatfny6IqT6fFf6aqrqykrKyMtLa2nTy3E\nec1us2BRNRRFJSnOyviRbrYs/QvhUCujJ1/EpKkX9nUThRBfYWkJHnJT43FrPo5XRQiFTexW+xlv\nYPS67Tgsdo7VJlPtuhVz1FN4R1yDLSaBsqOH+d73vkdGRgbf+c532L59+wnHt4aihPUomtrWQ56Q\nYKEmUMeh43Vdan9DcytLNxxk2cYiQp0cqy1EZ3Rqarxt27axbds2DMOgtLSUbdu2cfToUfx+Pw8+\n+CDr16/n8OHDrFq1imuuuYaUlBQZ4iFEF8U4bWQkeImxeKmpi3B4VzFF6zahahZGXjmf9zcW0YVb\nHIQQokepqsKEoWnkxuVQeiSKVXGS5InF67af9jiLphLrdjCswM43vqkzuCCG+5/8Gtc9vpDvPvYs\ns2fPxu/387vf/Y6xY8cyceJEfv/737f3BkaiOoaho2pt54uPtdDQ2kh5bddm9Vi7+ygrduziwx37\nWL3jSLdqIMTJnDFMb9y4kfHjxzN+/HiCwSCPPfYY48eP57HHHkPTNHbt2sW8efMYOnQod9xxB8OG\nDWPdunW43bICmxBdVZidSIY3neJDfv72y9cAGDT5MsIxOnuOHWPHoco+bqEQ4qtsaFYiU4cPYnji\ncArih1A4KOGE9SdOJsHrwm1z0ezXGTkSLJqCgcHoKRexYsUKdu/ezT333IPP52Pz5s38+7//O2lp\nadx+++2s+2QNJvB5X0KMSyOoB6hp9BPu5KwekajOgWO1HK4/wsHaIjYfPCZT7Ikec8Yx0xdffDGG\nceolP99///0ebZAQX2XjhqSy+3A1f3vp91QdqQBHKoeDV+DaadCUeoyCtHTG5KX2dTOFEF9Rqqpw\nybgckmKdaKrKyNzkTh2XlhBDrN1DU0szF10EwaCCYepE9bZ8MXz4cP7zP/+Tp59+mr///e+89NJL\nrFy5kldffZVXX32VhJQMkkYNZ0jSBSQPSsXlhOaQn/LaZrJTz7y2RXVTiFp/AIdLxxOjcayhnN2l\n1aTEx5zx2H8WCkdRVQWrRevyseL81OM3IAohus9i0ciw+ynZ8B4oCsOuvJGsAitz5pis3dzIsZoG\nWgIhYlyn/1hVCCF6i9WiMb4gvUvHZCZ5iXV6qWo8CoCmKeiGTiTasbPO6XRy8803c/PNN1NcXMzL\nL7/Myy+/zPHjZdRWlrFvxXIyCwaROW4crnFeKuqGdypM+0NRgpEQTodKSoKF/QdqOVRWzyXjcrv0\nOg4crWXphgMYpsl1M0cwKEUW0BK9cAOiEKL7otEoDz14H4auM23OtaTm5WNxthKOmDhsCq2RIA3+\nUF83UwghuiQtwYPP6SHY2ra8uKKAiYlumKe8FyQvL4+FCxdSWlrKtx/5DVkTJmJ3OTh24Ajr31jC\nHx++k7tuv4E//OEPVFRUnPb64YhBRI9gtap4PRYiZoCqpmbqm1u79Dq2FVewvXw/Oyv2snJrCYYh\n97EICdNC9Cs//elP2bJlC4MGDeLZX/6CqyeOIychlc3bg0TCVlw2J3Exjr5uphBCdIlFU8lIjCXW\nEUtNXQTdMNFUFYumnHHMtcViYezk6UxecAsP/+XnjL72ThKGjEFVNbZtWsddd91Feno6F154Ic8+\n+yyHDx8+4RwRXSdq6FjUtpWbY70W6gONHK1q6vRraAmEOFxRT12gjsZwA8WV5ew7UtPVUgBQ1xTg\no+2H2VNS1a3jRf8iwzyE6Cc+/vhjFi5ciKIovPLKK0wZNZj87FQ+3hnHkcpcdMNg2ogs3GeYhkoI\nIfqjgsx4NpckUV5dTIzbgkWx4rRZO3Ws22GlodbBm4t1dhyZALFjybrKzwhnC1rzJpYvX86aNWtY\ns2YNDzzwAOPHj+faa6/lyiuvxDRNorqJburYtbbg7vNqNNY2cqy6idF5KZ1qQ3VjgKbWFjwxCokJ\nVipqqygqq2V4TlKXa7F0QxGbSw6RGOPD47KTJcNFBjQJ00L0Aw0NDdxyyy0YhsFDDz3ErFmzAIj3\nupg3oxDTNDEMU1b7EkIMWIWDEkn3JnGoroRj5SGcNh9ed+c6B2LdDrIz7EyY2EJqCui6SmyChVn5\nV3DPtY/T1NTE0qVLefPNN3n33XfZsmULW7Zs4dFHHyUhIYH8EeOIGVLIkKmZgJ0Yt0pleYC6Lgzz\naA6ECUZDOOwqiXEWjh6r52h1E6ZpdmpGk8/VNgYorWqguL6YxlACq7bHccuc0V06h+hfJEwL0cdM\n0+Suu+7iyJEjTJo0iZ/85Ccn7KMoCpomP2iFEAOXw25lXEE6lc1D2F21l2FJiQzJSOjUsV6XDYfF\nQSDYRF5e2zR5x2t1whEd0zTxer0sWLCABQsWEAwGWbFiBUuWLGHp0qWUlZVRu3oFrF7Bh6+oZA/P\nJX/CcMKuXOoyR3a6/cFwlKihY7UrOB0aqqZT19JMdUOA5LjOTwdcUtFAjb+OhDiNxpZaSqtrqKr3\nd2tmEdE/SJgW4hzRdeOkPcsvvvgib7zxBm63m9dffx2rtXMfewohxEBzwchBHK1sxGG1kRgTS+Gg\nzoXpZJ8bjz2GSn85owoBFMrr2m5g1HUTi+WLzgaHw8FVV13FVVddhWmavPHGG/z9vQ9Zu2Edx4v3\ncnhXMYd3FQOw9k//xVt/uIDZsy9h1qxZTJgw4ZQ/g0ORKLoRRfssOcV6NBqDTRyrbupSmG5obiUQ\naSU2TsNqgRp/HUXH6yRMD2ASpoXoZbpusGLzIfaUVuOwWRiencSMUYOwaCobNmzg3nvvBeB3v/sd\nQ4YM6ePWCiFE71FVha9fPIKq+myS49zYrJ2LIWkJHrz2GIrqv5hKT1XBMA10w8ByivkUFEVhyJAh\n/MsNyQyaeTWVrXvQGkt585W94N9HU0UlH364gg8/XAFATEwM06ZNY9q0aUydOpUpU6YQHx8PQDiq\nEzV1bJ9dyuvVaKxu4nhtM+NJ63QN/MEIYT1MrE3B5rBQeayRsprmTh8v+h8J00L0suLjdazdd4id\nFbuwaFYO1QziUHk90wviuO666wiHw9xzzz3ceuutfd1UIYTodTarRmZy1264i/M6iXW7MQ0LwZCO\nw66hKgqGaRDVDc40877NomLVrOw9YKeuchxNvnGkjPAz2pfM9VOD7N2+kVWrVrF//36WL1/O8uXL\n248dOnQoU6dOxZc+hCpdIdXlABy4nCqVkSBN/mCXXks4qhM1olitKm6XSlGohcq6li6PvRb9h4Rp\nIXpZRZ2fukA9KSkaSfEaB0qKaCxq4KmHnqOsrIzp06fzq1/9qq+bKYQQ/VqKz43X7qG+yU9akoZh\nmqiKiqae+cZst92Kw6qTN8Tkpuvh+edh1mUKccQwe/Yc7v/3bwFw/Phx1q1bx/r161m3bh2bN29m\n//797N+/v/1ciqqSkp1K6uBMDGcaiS0GXxuXQUJC54ashCI6uqFj0RTsNhXNqtPQ2kJNY4AkX+eH\ni4j+Q8K0EL3Mabdg1ayEdBOf18K4ES5++d0Xqdm1ibiEJP72t79hs8l0d0IIcTq56T4SiuKprWsk\nJcGGoSvYrBZs1jOH6RiHBafFQSjctkjMyJEKVotKNBylNRRp3y89PZ3rrruO6667DoBwOMz27dtZ\nv3497y5bycZNm6ivLKOi5DgVJccB2PHOEn7z6HdITU2lsLCw/TFs2DAKCwvJzMxE/VLgj0R1dDOK\n5bN7aDxuleZQMxV1LRKmBygJ00L0sswkL3FOH0er2lbLWvf3f1Cz6xNUTeP6ux8lqrn6uolCCNHv\nFWQmkORO4FDZIZr9OjbNjttu7RBUT8Vm1YhxqKhYCIUNLrpIo+QoRINRQmH91MfZbEyaNIlJkyYx\nc+7XefUfG6iLHMCr1PH6H4/idRwiUtVA7fFSKioqqKioYNWqVR3O4XK5yM3NJScnh5ycHKqDVo6F\nW/BZEknPScRu0wgGQzQHZHXbgUrCtBC9LDU+hqzEeA7VxfGnX3/C3rf+HwDesTezo9LDp3uPMyjF\n18etFEKI/s3jsjM4LZ5DdUls3VVJiju5Sz25Xrcdp8VBa2sUh11D0xSCRpRw9NRh+sucdgtW1cLu\nfVZqK3KpUXJRE8czZsxwXnpsKnprA/v27TvhUVlZye7du9m9e3eH8619re1PVdNwemL5ID2ToXnZ\npKamkpqaSkpKCvHx8fh8Pnw+H3Fxce1fOxyyEm5/ImFaiF6mKAoXjclm/fq1HFj6GmASN3IeP3hy\nOp9uredYTRORiI7VqvV1U4UQol+bNTaX0qp6WsItpMemMDw7sdPHJsS6iLHF0OSvJc5nxTRNNEVr\nH25xJm6HDbvFxpAhJjde1zbu+oJZChlOnbBukPdZz/Pll1/e4biGhgYOHz7c/li+ZjM79+8i1FJN\nQ1U94UAAf0MdBxrqOLBnR6faYrfbcbvduN1uVq5cKTNB9TEJ00KcA6HGSv7y20fRIxFyJk8m/9I5\n2GwqFqtJU2uABn9QxsoJIcQZJPpczB6bh8/lIiXOzZCM+E4fm5XkxeeMpbypiuwMiOqgKmqnw7TX\nbcducRCOtE3PN3IkWCwQ1aMETzNUxOfzMXbsWMaOHQtA7sR9LNmynuRMP1s3WQkEWvF5mhgbn82E\nXG/7cJGKigoaGhraH/X19e1/hkIhQqEQdXV1WCwS5fqafAeE6GUlJSVceumlNNbXMXrSDOZ88z84\n3HSYnfsjhMPgsNhxO+QGRCGE6IxxBWmMK+j8vM6fG5Qci88Zy/4aA+OzxV5sqgV7Jz8VdNmtuOw2\nDF1F100uukhhf7GCbuhEIp0bKgJgt1qoqrCyYYvJnl0ANrLzvORPGs78+RPPeLxpmgSDQfx+P36/\nn4yMjE5fW/QOCdNC9KKysjJmz55NWVkZF154Ie+++x4bD1bx6f54alrqSU+yMyQjHpdDVj0UQoje\n5HHbSY3z4KqIoaEpSjhs4NUsOG2di0KapuJxWrGoNoIhA7dLQ9NAj3Z+3DWAy2ElN8tK7nCTzHQw\nDIWYeJ2c3M6dQ1EUnE4nTqeTxMTOD3MRvUfCtBC9pLq6mksvvZSSkhImTpzIO++8g8cTwyXjYxg9\nOJXjtU2gKIzITurrpgohxFdCXloc20oTKK86hr/VYFC8k1hP52/m83kcuG0umv1+3C4NwwRFUdHU\nzi+2EuOyYdXstIQM8vIAFI7XGkR0HV030Do57ET0H/IdE6IXlJeXM2vWLPbt28fIkSN5//338Xq9\n7dsTfS5G56UyenCK/OAUQohzZNTgZAbFpdPUqEHUTrLHR1q8p9PHpyd48Nq8NLW09SLrUdAUC1ZL\n528g9zhtOCw2QmGDIUMgLw80VSFqRInoxplPIPod6ZkWopsamlux2yw47R2HaBw9epTZs2dz8OBB\nhg8fzvLlyzu9MpYQQoje4/M4mTo8i8ZgM7ppMCw7EbULvcoZSV58Ti8HGj4L04aJ1WrBYet8mPa6\n7DisNsLNZvtzqga6qROO6Dg6OexE9B/yHROiiwzD4K21B9h7pBpVUchN9TF7/GASYl0UFxcze/Zs\nSktLGTt2LMuWLSMpSYZxCCFEfzF1eCYOm4ZumIzJS+3SsWlxMfhcHsJVGqGwQSRiYnVYsHchAMe6\n7bisDoIhE8MwURQwTVBRujRcRPQfEqaF6KKisnq2Fh9he8UuFMWkpC6N6oYAY9IUFlw3n+PHjzNl\nyhSWLl1KXFxcXzdXCCHEl6iqwviC9G4da7VqDErx4iuPo6auidagiSPOjs/d+XHXDruVxFgXTi2G\n5hYdr0dD1000xYJN1hsYkGSwphBdVFbTRLW/jqx0C1PHxxDUKnl35d+57NJLOH78ODNnzmT58uUS\npIUQ4jw0NDOBdG8qRYdDWHCQEOPB67Z36Rwp8R48Dg9NzTq6aWKaClZL18Zei/5DeqaF6CKbRUNT\nFXQTrFaF7ct2suOtVzF0nQtmXcb77y7G6XT2dTOFEEL0guE5SRQUpVLVUkOMw8nQrEQUpWvDM9IT\nYoi1e6luqSIpYsGiap2e71r0PxKmheii9EQP8a449tUd4/DHK9n29yUADLnwIq6/6zEsFlmARQgh\nzleqqnLJuBwAbFaNcfldG3cNkJ0SS5wrlqLjBv5WHbtmx+OS/3cMVBKmheii7BQf8Q4Xy59/g/qi\njYBCwtiv4x09hSp/E4cqGhiaJbN3CCHE+Sojycutl43u9vE+j5OMBC/7qmI5UNxAgiOFBK+rB1so\nziUZMy1EFx05UsrvHr+L+qKNaDYbSVP/Dz/69WwmT7LgD7fQ0BLs6yYKIYTo5yYXppMbn004Aime\nRAoHyWqGA5X0TAvRBStWrODGG2+ktraW1IwsLr/zQbaURyirCNHcHMXtsGKRqY2EEEKcQWF2EoWZ\nqdgsFlJ9sWSn+Pq6SaKbJEwL0Qm6rvPkk0/y2GOPYRgGc+fOZdGrr7FqVwWDjhzlaFU5DhTSvNK7\nIIQQonMWzBpBTUOAhFhXlxaPEf3LGYd5rF69mmuuuYbMzExUVWXRokUn7PP444+TkZGBy+Vi1qxZ\n7Nmzp1caK0RfOHbsGLNnz+bRRx/FMAweeeQR3n77bZISE7j+ouFcN2Mc88ZP4/LRk/n6RSNwO+Um\nEiGEEGemKApJcW4J0gPcGXum/X4/o0eP5vbbb+e22247YfqXp556imeeeYZFixZRUFDAT37yE+bM\nmcP+/fuJiYnptYaLnrF3b5DS0lNvz86GYcM6Pxn9QBUKR7FZtRPe34sXL+Zb3/oWdXV1pKSk8Oqr\nr3LZZZe1b1cUhdF5KYzOSznXTRZCCCFEP3DGMD137lzmzp0LwB133NFhm2ma/PrXv+bhhx/m2muv\nBWDRokUkJyfz+uuvc+edd/Z8i0WPKi2FuXNPHZaXLg0ybNg5bNA5FgxFeHfDQUrK67BaLIzMSWbm\nmGwC/hb+4z/+gxdffBGAK664gpdffpnk5OQ+brEQQggh+pOzGjNdUlJCZWVlh546h8PBzJkzaC+5\nsAAAG79JREFUWbt2rYRp0e+t3X2M9QcPcrCmCE3VKGvMZdkH7/M/LzxBWVkZNpuNp59+mvvuu6/L\nk/ILIYQQ4vx3VmG6oqICgJSUjh9xJycnc/z48VMet2nTprO57Hmhv9SguTkbOHXPdHNzM5s27Tp3\nDeqEnqzdh+uPsK18F9mDwoQDAV56+mXq9u8AYMSIETz66KPk5eWxefPmHrtmX+sv772BSurXfVK7\nsyP1OztSv+77qtcuPz//tNt7bTYP6cUTA0FUN9ANnUMbtrPhf/9BoMmParFwwRU38ZMHv43bae/r\nJgohhBCiHzurMJ2a2raEZmVlJZmZme3PV1ZWtm87mYkTJ57NZQe0z3+76y81qKk5/QIjHo+n37S1\nN2r3wcZidv75NWpKS9qeiMkn/6rryRw6g+RBBQzLTuqxa/W1/vbeG2ikft0ntTs7Ur+zI/XrPqld\nm8bGxtNuP6sVEHNzc0lNTWXZsmXtzwWDQdasWcP06dPP5tRC9Krq6mq+/e1v8+jdN1FTWoItxsPI\nq74BBd/Hm5hKq95CbWOgr5sphBBCiH6uU1PjHTx4EADDMCgtLWXbtm0kJCSQlZXFd7/7XZ544gkK\nCwvJz89n4cKFeDwebr755l5vvBBd1dzczDPPPMMvf/lLWlpasFgsXHzVjSRNnYo7IYrtUxg12sRr\nWLFatL5urhBCCCH6uTOG6Y0bN3LJJZcAbeOgH3vsMR577DHuuOMO/vSnP/GDH/yA1tZW7r77burr\n65k6dSrLli3D7Xb3euPF2cvObpv+7nTbzwehUIjf//73LFy4kJqaGqBturtf/epXxMSn8ddVu9lT\nWYwrtgZ/k53CjBTy0uP7uNVCCCGE6O/OGKYvvvhiDMM47T6fB2wx8Awb5jhv5pFuCYTQDROPy4aq\nto1gam1t5eWXX+app57iyJEjAMyYMYMnn3ySCy+8sP3YGy8ZyUfbPQxP8WOzqEwflUWiz9Unr0MI\nIYQQA0evzeYhxLlimibvf1rEjkMVRA2DVJ+HyfkJvPvmn/n1r39NZWUlACNHjuTJJ5/kyiuvPGG2\nmfRELzdeMpKo3vaLowzxEEIIIURnSJgWA96n+8r4ZF8xe6r20NpQx7ENWyha9zHBQAsAEyZM4OGH\nH2b+/Plo2qlDsqIoEqKFEEII0SUSpsWAt/dwNevWLeXollUUbd6JaZgAjBg7iWef/hmXXnqpzHsu\nhBBCiF4hYVoMWBUVFfzP//wPv3j2P6ksKwVAs2g4MsYz8eppXHXhDVw4c7IEaSGEEEL0GgnTYkAJ\nBoO8/vrrvPbaayxbtqz95liXL47kUdMxfBdz5LiX3Uebca2O8M0rIjjs1j5utRBCCCHOVxKmRb8Q\njeocOFZHOKozOC0Or/uLZbxbWlr44IMPeOmll1i9ejV+vx8Aq9XK1VdfzazL51NjT2Fv7V4Kh9h5\n7bUIE6dqXJBjJ0aWAxdCCCFEL5IwLfpcbWOANz/eS0l1FRE9THJMAhNzYinetYHFixezYsUKQqFQ\n+/6TJ0/mtttuY8GCBSQmJmKaJks+2Y+OQfGhUnwJOgUJ+YzIScZmlRsKhRBCCNF7JEyLPrdsUzGb\nS/dztHwnO/6xi9bju6krPYxptt1IqCgK06ZNY8KECVx88cVcd911HY5XFIWrpxWQFOti75E0Zg7W\nyc+MZ9a43L54OUIIIYT4CpEwLfqMYRj846M1/OE3/8WWDctprqpq36ZZLEyeNpNv3HojV199Namp\nqWzatOmU59I0lRmjBjFj1KBz0XQhhBBCCEDC9IBiGJ/31DJgZ6gIhUKsXLmSJUuW8NZbb1FeXt6+\nTbW6MDwjiRsygoIRM/jZvZdI77IQQggh+jUJ0/1YJKoTjuiEozqGYWKYYJptYVpVQFXbFhmxWbQ+\nHxvsbw1T0xgg1m3H53F22NbQ0MB7773HkiVLWLp0Kc3Nze3bsrKyyB01DTUrhRHTBvPO23amXBAg\nLzYVn9txrl+GEEIIIUSXSJjuZ0zTJBiO0hqKEgqbRCIQiYBhAKaCorQFahMTTTOxWKLYbFFsVnDa\nrTjtlnPaa22aJut2H2Xt7qM0tvqJsbuYOjyTwfEab731FosXL2bVqlVEo9H2Y0aPHs38+fOZP38+\nY8eOZXtRBW9v2EdRzSF88a2kObMpSMtgdF7KOXsdQgghhBDdIWG6HwmFo/iDEVqDJq2toKBitWi4\n7RoWTT1hf103CEd1Wv06fgyCzghBRxSXw4rDdm6+tbsPV7Nsy352Ve5h9+ZjqA17+M3+IsoPH2jf\nR1VVLr74YubNm8e8efPIze04dGNsfhpWi8a2ogQuGBIhK9nLhaOy0U7ymoUQQggh+hMJ0/2EvzVM\nS2uUlhZQTBW33YrVcvqhG5qm4tRUnHYrUd0g0BomGDQIu8O4nToeV+/OsRyNRvnr4nf537f+TNme\nLTRW1bZvszucXDH3cubPn8+VV15JQkLCac81IjeZEbnJvdpeIYQQQoieJmG6H2hsCeJvNQgEwGmz\nEQpHqWpoIRiO4rBZiHU7zrj4iEVT8bodhCM6Lc1hdF3HMIJ43fYeHfYRCARYvnw5ixcv5u2336a2\n9osAjcWDK2MEGYVT+OmDt7Pg0nE9dl0hhBBCiP5IwnQfa/KHaAkYtAYUahr97DlymMrGBgIRP1Ez\nik214bS6SPX5yE9PZEh6wmmHP9isGppqpzkQwjQNFCXcYTXBf6brBhV1LWiqQkp8zEmDd3V1Ne+8\n8w5Llixh2bJltLa2tm9LSc8isXA4+VOHsnnfEKZfGCHbU0Belox3FkIIIcT5T8J0HwoEI/hbdZqb\nTXaVlLPv+HEONxUT0BtxuxQsFoVI2CTQaFJUH0txVQaDjqRx4chcEmPdpzyvpql4XQ6aAkE0Tcei\nRXA5rCfsV1bdxNJPizhWW4+mauQkx3PVtHziPE6Ki4tZsmQJixcv5pNPPsEwjPbjJk2axPz585k3\nbx5Wbwp/X7ObPVUHSayJkObOoCAtndGDJUwLIYQQ4vwnYbqP6LpBIBihpcVka3EZO48d4nDzAbIy\nNAoTHKjqFz3EhmFS1xDgSPkeqssqaQwEmFqYQ2HWqccYq6qC22HD7w+haRFs1o43MYbCUd76ZD+f\nHtmFX6+jogKSzCB/e6WUkp3r2LVrV/u+VquVOXPmMG/ePK655hoyMjI6XOvaC0aStT+eWUN10hM9\nXDQ6u8+n6hNCCCGEOBckTPcRfzCCPwAHjtWy7/hRDrfsZ/hQOy7niUM4VFUhMd5CvE/jSFkD2yo3\nE9GjuB02spJ8p7yG1aJh060EAhHs1jCxMV/M21xUVkdJVRml+9cTKtvPxhU7iPjr27d7vV6uuOIK\n5s+fz+WXX05sbOwpr1OQlUBB1ulvMBRCCCGEOB9JmO4DUd0gGNZpaolwsKya4oYD5OfZThqkv0xV\nFXKybNisEQ5W7cW9y8k1U4cT47RjmuZJxzs7bRYa/VGCYQNXVKc14Of999/nxVdeZ/Wq5YRbA+37\nWl2xDBo2jQfvuZ1v3vwv2Gy2Hn/tQgghhBDnEwnTfSAcNWhthdLKRo43H8fliRLr7fw0dumpVppb\nAhysPcj/+zhKXIyLcFQnMdbN+CEZxH5p5UBFUWioq+avbyzhHx++w+qPVhEOh9u3u5NSSBoyjsP1\nYxgyLYHJOWOZe/kkCdJCCCGEEJ0gYboPRHSDSAQq6pqobq1iUE7Xvw15OTY2bDnG7oPNWB0RvDEW\nfHYfxWW1XDW1kKaaMt57bzHvv7+EzZs3tB+nKAoXXHAB11wzj6A3j9JwIw2RWoyNMHNiKpNzExmU\ncuohHUIIIYQQ4gsSps+xqG4QjUIorFPfEqBVb8HzpbHMnWWxtM32UdpSRJLNQkFGMns37+STt4p4\n5qHdVJeXtu/rcDiYccGlXHn1Fdxw/Xwy0tMAqK738+76A5TV1RM7AmYMSeDKKfnndDlyIYQQQoiB\nTML0OWaYJrqu0BQI0RJtxuVUOszc0RWh1gjNR3cS2FLKjhcO09rU0r7N4/Vxxdx5zJ07j4svvgw0\nK5o1SrzviynykuLc3H75WGoaAu1/F0IIIYQQnSdh+hwzTDAM0FQVTFBPf8/hCQJNfv6xZCfVB7ex\nZ/1u9C+Nf9ZcCYy6cBS+QRP5+mU3c8PFX6xAGAxH0fW2afa+TFEUCdFCCCGEEN0kYfocM00T0wSb\nRUNVNHT9zMcUFcGQIW1fL3n+b2z6YF37NmdKMmpmGlrcJJrqxlHrUglHI7RGOp5YUcA0oGOUFkII\nIYQQZ0PCdB+xWFQ0RTuhp/hkiovbwnRREUQ948BTT+HUsWSPH0bQbnKssYycdA/7NkS49RYbO3ab\nGKbEZiGEEEKI3iZh+hxTUFAUE01RUFX1tD3TRUVtQXrZsra/5+XBrfeMITl/DF/7GkQiBpu2t1JR\n30RllUlGVpjDZQZxjkSSY2M6nMswTFQVVLm5UAghhBCix3RxxO6JHn/8cVRV7fBIT0/vibadlxSl\nbZy01WLBaXWgR1UikZP3Ig8ZAl/7Glx2Wdufnw/1yMtr+9NqVRlWYCfRmYI1nITXo0GrjyEJg5lY\nkNXhXIb5WZju5s2OQgghhBDiRD3SM11YWMiqVava/65pWk+c9rykqQqaZgIm6fGxxNbGUd/YRHLi\nqb8Vn4fnz30eqgF8XgsjCuxUH4unIHkQ+RmJjM/PICWuY890VDewO9uuL4QQQgghekaPhGlN00hO\nTu6JU533LJqKxQIoJhkJXhKOJVFRW3faMP3l8HwyLqdGdnoMc0bnMybvxE8FDMPEMA1sVgWrZWD9\norN3b5DSz6bMbm7OBqCmJti+PTsbhg3r+jzdQgghhBA9oUfC9KFDh8jIyMButzNlyhSeeOIJcnNz\ne+LU5yWrpmK1QlqClxR3MoePFxMIGLhc3Rt10xo0idMcuBwnXwI8FIlitYLVctajes650lKYO/fz\nsHxiaF66NMiwYee2TUIIIYQQnzvrdDV16lQWLVrEBx98wIsvvkhFRQXTp0+nrq6uJ9p3XrJZVJxO\nMEyDvPQE0mIyKTka7tTMHv/MNE2aW3Q8tlhSfDEn3R6KRHE6wWGT+02FEEIIIXqSYpo9O4daIBAg\nNzeXhx56iO9973vtzzc2NrZ/ffDgwZ685IDkD0ZpajEJhVQ2Hapif+MB7N4G0ro4WqbFD+VlLkbH\nj+KSkZknbA9GohhE8HogxmE9yRn6t5KSbG64IemU2//612pyc0tPuV0IIYQQ4mzk5+e3fx0bG3vC\n9h7/3N/lcjFixAiKiop6+tTnFYdNw+Ew0TSTUYMSGOTKobHBTmNz185TUa2QaE8mK+HEXmndMAhH\ndRwOE4d1YI2VFkIIIYQYCHr8c/9gMMjevXu55JJLTrnPxIkTe/qyA8amTZsAmDJ5Eq2hCI0tEZqb\nIWNQLqv3JrCvfgdZsVZivWcOv2XlERJjHUzOmMT86SM63FxoGCZNgSDuGBOv24LbefLx1P3dl282\nPBmPx/OVfj91xefvPalX90j9uk9qd3akfmdH6td9Urs2Xx5dcTJn3TP94IMPsnr1akpKStiwYQPX\nX389ra2t3H777Wd76vOe027F7dRwuSAzycew9CzyfSMoKjYpPRZG108+Asc0TY4dj1BZoVIYP5wp\nhYM6BOmobtDcGsThNHE7tQEbpIUQQggh+ruz7pkuKyvjpptuoqamhqSkJKZNm8b69evJyso688EC\nj8uOaYZQFJ0xeal4XDbcJW5KGovZWlNDQrxGrEfFblfRdZNAq0FVTRSLHsuIxAKmD8tjULKv/Xyh\nSJRAKIzbDW6nisclQVoIIYQQorecdZj+85//3BPt+Erzuu2oahhNi1JgSSDe4yKp1Et5QwM1rZVU\nNTUT0oNoigWHxUmWI5EMXwozhueQluAFIBLVaQ1FMBUDrxfcTgsx50GPdHZ22/R3AM3NbQPKPR5P\nh+1CCCGEEH1F5krrJ2KcNqyais0aweFwEu/NoaElxPHaDFpaQwTDEWwWC26HjfRELznJcZhAIBQh\nEtUxMXA6welQcDus2M+TafCGDXO0zyO9adMuQMZuCSGEEKL/OD8S13nCbrNgt1lw2CI47FFiYuwk\nxycRiYBhgGmCqiiYJjQGgmgaWK3gjgGbVcFpt+CwWVAUWTJcCCGEEOJckDDdDzntVpx2K5GoTjii\nE9GNz5YENzEME0UBVQFVVbBZNGxWbcAtEy6EEEIIcT6QMN2PWS0dQ7JpmpgmKArS+yyEEEII0Q9I\nmB5AFEVBMrQQQgghRP/R4ysgCiGEEEII8VUhYVoIIYQQQohukjAthBBCCCFEN0mYFkIIIYQQopsk\nTAshhBBCCNFNEqaFEEIIIYToJgnTQgghhBBCdJOEaSGEEEIIIbpJwrQQQgghhBDdJGFaCCGEEEKI\nbpIwLYQQQgghRDdJmBZCCCGEEKKbJEwLIYQQQgjRTRKmhRBCCCGE6CYJ00IIIYQQQnSThGkhhBBC\nCCG6ScK0EEIIIYQQ3SRhWgghhBBCiG6SMC2EEEIIIUQ3SZgWQgghhBCimyRMCyGEEEII0U0SpoUQ\nQgghhOgmCdNCCCGEEEJ0k4RpIYQQQgghuqnHwvQLL7xAbm4uTqeTiRMnsmbNmp46tRBCCCGEEP1S\nj4TpN954g+9+97v8+Mc/Ztu2bUyfPp25c+dy9OjRnji9EEIIIYQQ/VKPhOlnnnmGb3zjG3zrW99i\n6NCh/Pa3vyUtLY3f/e53PXF6IYQQQggh+qWzDtPhcJgtW7Zw2WWXdXj+sssuY+3atWd7eiGEEEII\nIfqtsw7TNTU16LpOSkpKh+eTk5OpqKg429MLIYQQQgjRb1n64qKNjY19cdl+IT8/H/hq16C7pHZn\nR+p3dqR+3Se1OztSv7Mj9es+qV3nnHXPdGJiIpqmUVlZ2eH5yspK0tLSzvb0QgghhBBC9FtnHaZt\nNhsTJkxg2bJlHZ5fvnw506dPP9vTCyGEEEII0W/1yDCPBx54gFtvvZXJkyczffp0fv/731NRUcFd\nd93Vvk9sbGxPXEoIIYQQQoh+o0fC9A033EBtbS0LFy6kvLycUaNG8d5775GVldUTpxdCCCGEEKJf\nUkzTNPu6EUIIIYQQQgxEPbacuDgzWXK9c1avXs0111xDZmYmqqqyaNGiE/Z5/PHHycjIwOVyMWvW\nLPbs2dMHLe1/nnzySSZNmkRsbCzJyclcc8017N69+4T9pH4n9/zzzzNmzBhiY2OJjY1l+vTpvPfe\nex32kdp1zpNPPomqqtx7770dnpf6ndzjjz+OqqodHunp6SfsI7U7tfLycm6//XaSk5NxOp2MGDGC\n1atXd9hHanhyOTk5J7z/VFXlqquuAsA0TandaUiYPkdkyfXO8/v9jB49mt/85jc4nU4URemw/amn\nnuKZZ57hueeeY+PGjSQnJzNnzhxaWlr6qMX9x0cffcQ999zDunXrWLlyJRaLhUsvvZT6+vr2faR+\np5aVlcXTTz/N1q1b2bx5M5dccgnz589n+/btgNSus9avX8+LL77I6NGjO/z7lfqdXmFhIRUVFe2P\nnTt3tm+T2p1eQ0MDM2bMQFEU3nvvPfbt28dzzz1HcnJy+z5Sw1PbvHlzh/feli1bUBSFBQsWAPD0\n009L7U7HFOfE5MmTzTvvvLPDc/n5+ebDDz/cRy0aGGJiYsxFixa1/90wDDM1NdV84okn2p9rbW01\nPR6P+Yc//KEvmtivtbS0mJqmme+8845pmlK/7oiPjzf/67/+S2rXSQ0NDWZeXp65atUq8+KLLzbv\nvfde0zTlvXcmjz32mDly5MiTbpPandnDDz9sXnDBBafcLjXsmoULF5pxcXFmMBiU2nWC9EyfA7Lk\nes8pKSmhsrKyQy0dDgczZ86UWp5EU1MThmEQFxcHSP26Qtd1/vKXvxAMBpk5c6bUrpPuvPNOvv71\nr3PRRRdhfumWHKnfmR06dIiMjAwGDx7MTTfdRElJCSC164zFixczefJkFixYQEpKCuPGjeP5559v\n3y417DzTNHnppZe45ZZbsNvtUrtOkDB9DsiS6z3n83pJLTvn/vvvZ9y4cUybNg2Q+nXGzp07iYmJ\nweFwcOedd/LXv/6VoUOHSu064cUXX+TQoUMsXLgQoMMQD6nf6U2dOpVFixbxwQcf8OKLL1JRUcH0\n6dOpq6uT2nXCoUOHeOGFFxgyZAjLli3j/vvv56GHHmoP1FLDzlu+fDmHDx/m3/7t3wCpXWf0yXLi\nQvSGfx5b/VX3wAMPsHbtWtasWdOp2kj92hQWFrJjxw4aGxv529/+xo033sg//vGP0x4jtYP9+/fz\nyCOPsGbNGjRNA9p6uMxOTBgl9YPLL7+8/euRI0cybdo0cnNzWbRoEVOmTDnlcVK7NoZhMHnyZH72\ns58BMGbMGA4ePMjzzz/P3XfffdpjpYYdvfjii0yePJlRo0adcV+pXRvpmT4HZMn1npOamgpw0lp+\nvk3A9773Pd544w1WrlxJTk5O+/NSvzOzWq0MHjyYcePG8cQTTzB16lSef/759n+rUruTW7duHTU1\nNYwYMQKr1YrVamX16tW88MIL2Gw2EhMTAalfZ7lcLkaMGEFRUZG89zohPT2d4cOHd3iusLCQI0eO\nAPKzr7Oqqqp466232nulQWrXGRKmzwFZcr3n5Obmkpqa2qGWwWCQNWvWSC0/c//997cH6YKCgg7b\npH5dp+s6hmFI7c7g2muvZdeuXWzfvp3t27ezbds2Jk6cyE033cS2bdvIz8+X+nVBMBhk7969pKWl\nyXuvE2bMmMG+ffs6PHfgwIH2zgSpYee88sorOBwObrrppvbnpHad0Lf3P351vPHGG6bNZjP/+Mc/\nmnv27DHvu+8+0+PxmEeOHOnrpvU7LS0t5tatW82tW7eaLpfL/MlPfmJu3bq1vVZPPfWUGRsba775\n5pvmzp07zQULFpgZGRlmS0tLH7e8733nO98xvV6vuXLlSrO8vLz98eXaSP1O7Yc//KH58ccfmyUl\nJeaOHTvMhx56yFRV1Vy2bJlpmlK7rrrooovMe+65p/3vUr9T+/73v29+9NFH5qFDh8z169ebV155\npRkbGys/9zpp48aNptVqNX/2s5+ZBw8eNP/617+asbGx5gsvvNC+z//f3t3iKAyEYQBOK2hPQAII\nMASJ4QA13KCiEm4CZyDhJlgQSDThNnyrIMvys5sRC+J5kkkm6Scmr3rFZCrD187ncwyHw7uXxyJk\n9xtl+h+t1+sYDAZRFEVMJpPY7/fvPtJH2u12kWVZZFkWeZ5f9/P5/DqzWCyi0+lEWZZRVVUcj8c3\nnvhz/MzsspbL5c2c/B6bzWbR7/ejKIpot9sxnU6vRfpCdn/3/Wm8C/k91jRNdLvdaLVa0ev1oq7r\nOJ1ONzOye22z2cR4PI6yLGM0GsVqtbqbkeFz2+028jyPw+Hw8LvsnvM7cQAASOTONAAAJFKmAQAg\nkTINAACJlGkAAEikTAMAQCJlGgAAEinTAACQSJkGAIBEyjQAACT6AtT1nVUJ3N0KAAAAAElFTkSu\nQmCC\n", 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3ITBQR3l1FXZOTngHRRLmd/a5lpEjRzJnzhzg7CeCn3/xFZ+uWEn6yWOcjkvh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\njblz52IwGPjyyy/p3bs3zz7+Jyrz8xv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PBbDbFDLTLFR6KzlwrJawvIMmzhNnDNNr1qxh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\n1ddy7LMtFKz/BH9LMwDjxo3joYceYv78+WjaqUOyoigSooUQQgjRIRK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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3840,7 +3797,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.3" + "version": "3.4.1" } }, "nbformat": 4, diff --git a/11-Extended-Kalman-Filters.ipynb b/11-Extended-Kalman-Filters.ipynb index 261a573..b642c91 100644 --- a/11-Extended-Kalman-Filters.ipynb +++ b/11-Extended-Kalman-Filters.ipynb @@ -749,29 +749,33 @@ "\n", "We start by importing the filter and creating it. There are 3 variables in `x` and only 1 measurement. At the same time we will create our radar simulator.\n", "\n", - " from filterpy.kalman import ExtendedKalmanFilter\n", + "```python\n", + "from filterpy.kalman import ExtendedKalmanFilter\n", + "\n", + "rk = ExtendedKalmanFilter(dim_x=3, dim_z=1)\n", + "radar = RadarSim(dt, pos=0., vel=100., alt=1000.)```\n", "\n", - " rk = ExtendedKalmanFilter(dim_x=3, dim_z=1)\n", - " radar = RadarSim(dt, pos=0., vel=100., alt=1000.)\n", - " \n", "We will initialize the filter near the airplane's actual position\n", "\n", - " rk.x = array([radar.pos, radar.vel-10, radar.alt+100])\n", - " \n", + "```python\n", + "rk.x = array([radar.pos, radar.vel-10, radar.alt+100])```\n", + "\n", "We assign the system matrix using the first term of the Taylor series expansion we computed above.\n", "\n", - " dt = 0.05\n", - " rk.F = eye(3) + array([[0, 1, 0],\n", - " [0, 0, 0],\n", - " [0, 0, 0]])*dt\n", - " \n", + "```python\n", + "dt = 0.05\n", + "rk.F = eye(3) + array([[0, 1, 0],\n", + " [0, 0, 0],\n", + " [0, 0, 0]])*dt```\n", + "\n", "After assigning reasonable values to $\\mathbf{R}$, $\\mathbf{Q}$, and $\\mathbf{P}$ we can run the filter with a simple loop\n", "\n", - " for i in range(int(20/dt)):\n", - " z = radar.get_range()\n", - " rk.update(array([z]), HJacobian_at, hx)\n", - " rk.predict()\n", - " \n", + "```python\n", + "for i in range(int(20/dt)):\n", + " z = radar.get_range()\n", + " rk.update(array([z]), HJacobian_at, hx)\n", + " rk.predict()```\n", + "\n", "Putting that all together along with some boilerplate code to save the results and plot them, we get" ] }, @@ -1448,7 +1452,8 @@ "\n", "First, `evalf` uses a dictionary to pass in the values you want to use. For example, if your matrix contains an x and y, you can write\n", "\n", - " M.evalf(subs={x:3, y:17})\n", + "```python\n", + " M.evalf(subs={x:3, y:17})```\n", " \n", "to evaluate the matrix for `x=3` and `y=17`. \n", "\n", @@ -1924,7 +1929,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.3" + "version": "3.4.1" } }, "nbformat": 4, diff --git a/12-Particle-Filters.ipynb b/12-Particle-Filters.ipynb index 469e790..d8df7ac 100644 --- a/12-Particle-Filters.ipynb +++ b/12-Particle-Filters.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { "collapsed": false }, @@ -254,7 +254,7 @@ "" ] }, - "execution_count": 1, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -315,16 +315,16 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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Njfxz8UTAZrOhoqIC0WgUdrsdgiDA6/VCo9HwyD+7Vl9fH9577z0AQGtra4oT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\n3nEJduTxwQAAAABJRU5ErkJggg==\n", 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Y/EhtUSoHWk7HLu4nka3zvVqVu9LZbKl9T8dmLklJ5B5y1Ik1Z7mRlUyNabbXXykDutgW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GAAAg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\nZ49WZvfu3aqiKOrx48enqpo5t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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -389,7 +389,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -398,7 +398,7 @@ "data": { "image/png": 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i54QJqDp1SkzHiwCKX30VPZs3w2az4cKFC9i7dy9effVVbNq0CS0tLejt7YXH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\ntNKdj4iIKBXaCxCee84fHAD+ij8gBgl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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -433,30 +433,28 @@ "\n", "Let's use it to compute $\\pi$ by computing the area of a circle. This is very easy. We will create a circle with radius 1, and bound it in a square. The means that the length of a side of the box will be 2, and hence the area of the box is 4. We will generate a set of uniformly distributed random points within the box, and count how many fall inside the circle. The area of the circle is then the area of the box times the ratio of points inside the circle over the total number number of points. Finally, we know that $A = \\pi r^2$, so $\\pi = A / r^2$.\n", "\n", - "We start by creating the points." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " N = 20000\n", - " ps = uniform(-1, 1, (N, 2))\n", + "We start by creating the points.\n", + "\n", + "```python\n", + "N = 20000\n", + "ps = uniform(-1, 1, (N, 2))```\n", "\n", "A point is inside the circle if it's distance from the center point (0, 0) is less than or equal to one. We can compute the distance by using `numpy.linalg.norm`. If you aren't familiar with this, the norm is the magnitude of a vector. Since vectors start at (0, 0) calling norm will compute the point's distance from the origin.\n", "\n", - " dist = np.linalg.norm(ps, axis=1)\n", + "```python\n", + "dist = np.linalg.norm(ps, axis=1)```\n", "\n", "Next we compute which of this distances fit the criteria with this code, which returns a bool array that contains `True` if it meets the condition\n", "\n", - " in_circle = dist <= 1\n", + "```python\n", + "in_circle = dist <= 1```\n", "\n", "All that is left is to count the points inside the circle, compute pi, and plot the results. I've put it all in one cell so you can experiment with alternative values for `N`." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -473,7 +471,7 @@ "data": { "image/png": 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uKMqegRFyFFP8PRCNIBjfj5snT8jmy32DA1IFp/+b1VAA4hMGIF0dr6yswWm1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\nZmZOeNVoF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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -517,7 +515,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -526,7 +524,7 @@ "data": { "image/png": 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x8alwm4SEBMTExCArKwv37t1DbGwsYmJihPUrVqxAYGAgLl26hPj4eCxevBiB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\nZw+Ki4vx8ssvAwD27t2Lr7/+GpWVlSgvLx/R/+j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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -551,7 +549,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -560,7 +558,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -605,7 +603,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": { "collapsed": true }, @@ -645,16 +643,17 @@ "\n", "I cannot easily modify a class definition in Jupyter Notebook, so I will show you the method in text. The entire class is in pf_internal, and we will be importing when we run the filter. \n", "\n", - " def predict(self, u, std, dt=1.):\n", - " \"\"\" move according to control input u (heading change, velocity) \n", - " with noise std\"\"\"\n", + "```python\n", + "def predict(self, u, std, dt=1.):\n", + " \"\"\" move according to control input u (heading change, velocity) \n", + " with noise std\"\"\"\n", "\n", - " self.particles[:, 2] += u[0] + randn(self.N) * std[0]\n", - " self.particles[:, 2] %= 2 * np.pi\n", + " self.particles[:, 2] += u[0] + randn(self.N) * std[0]\n", + " self.particles[:, 2] %= 2 * np.pi\n", "\n", - " d = u[1]*dt + randn(self.N) * std[1]\n", - " self.particles[:, 0] += np.cos(self.particles[:, 2]) * d\n", - " self.particles[:, 1] += np.sin(self.particles[:, 2]) * d" + " d = u[1]*dt + randn(self.N) * std[1]\n", + " self.particles[:, 0] += np.cos(self.particles[:, 2]) * d\n", + " self.particles[:, 1] += np.sin(self.particles[:, 2]) * d```" ] }, { @@ -667,22 +666,23 @@ "\n", "Think back to the *Discrete Bayes* chapter. In that chapter we modeled positions in a hallway as discrete and uniformly spaced. We assigned a probability to each position. When a new measurement came in we multiplied the current probability of that position by the probability that the measurement matched that location. We implemented that like this:\n", "\n", - " def update(map_, belief, z, prob_correct):\n", - " scale = prob_correct / (1. - prob_correct)\n", - " for i, val in enumerate(map_):\n", - " if val == z:\n", - " belief[i] *= scale\n", - " normalize(belief)\n", + "```python\n", + "def update(map_, belief, z, prob_correct):\n", + " scale = prob_correct / (1. - prob_correct)\n", + " for i, val in enumerate(map_):\n", + " if val == z:\n", + " belief[i] *= scale\n", + " normalize(belief)```\n", "\n", "We will want to do the same thing with our particles. Each particle has a position. We can also assign it a weight or probability based on how well it matches the measurement. We will want to multiply that new probability into the current weight of the particle. If we then normalize the weights of the particles those that are closest to the robot will generally have a higher weight than ones far from the robot.\n", "\n", - " def update(self, z):\n", - " self.weights.fill(1.)\n", - " for i, landmark in enumerate(self.landmarks):\n", - " dist = np.linalg.norm(self.particles[:, 0:2] - landmark, axis=1)\n", - " self.weights *= scipy.stats.norm(dist, self.R).pdf(z[i])\n", - " self.weights /= sum(self.weights) # normalize\n", - "\n", + "```python\n", + "def update(self, z):\n", + " self.weights.fill(1.)\n", + " for i, landmark in enumerate(self.landmarks):\n", + " dist = np.linalg.norm(self.particles[:, 0:2] - landmark, axis=1)\n", + " self.weights *= scipy.stats.norm(dist, self.R).pdf(z[i])\n", + " self.weights /= sum(self.weights) # normalize```\n", "\n", "In the literature this part of the algorithm is called **Sequential Importance Sampling**, or SIS, and the equation for the weights is called the **importance density**. I will give it theoretical underpinnings in a following section. For now I hope that this makes intuitive sense. If we weight the particles according to how how they match the measurements they *probably* are a good sample for the probability distribution of the system after incorporating the measurements. Theory changes 'probably are a good sample' to 'are a good sample'. Different problems will need to tackle this step in slightly different ways.\n", "\n", @@ -722,15 +722,16 @@ "\n", "One way to accomplish this is to sample from the current particle set `N` times, making a new set of particles from the sample. For this to work the probability of selecting any given particle should be proportional to its weight. The easiest way to accomplish this is to use NumPy's `cumsum` function. `cumsum` computes the *cumulative* sum of an array. That is, element one is the sum of elements zero and one, element two is the sum of elements zero and one, etc. Then we generate random numbers in the range of 0.0 to 1.0 and do a binary search to find the weight that most closely matches that number. This algorithm is variously called **multinomial resampling** or **simple random resampling**. Here is my implementation:\n", "\n", - " def resample(self):\n", - " cumulative_sum = np.cumsum(self.weights)\n", - " cumulative_sum[-1] = 1. # avoid round-off error\n", - " indexes = np.searchsorted(cumulative_sum, random(self.N))\n", - " \n", - " # resample according to indexes\n", - " self.particles = self.particles[indexes]\n", - " self.weights = self.weights[indexes]\n", - " self.weights /= np.sum(self.weights) # normalize\n", + "```python\n", + "def resample(self):\n", + " cumulative_sum = np.cumsum(self.weights)\n", + " cumulative_sum[-1] = 1. # avoid round-off error\n", + " indexes = np.searchsorted(cumulative_sum, random(self.N))\n", + "\n", + " # resample according to indexes\n", + " self.particles = self.particles[indexes]\n", + " self.weights = self.weights[indexes]\n", + " self.weights /= np.sum(self.weights) # normalize```\n", " \n", "There are many resampling algorithms. Each has different properties with respect to which particles are sampled, execution time, and memory required. I will share a few of them in a later section.\n", "\n", @@ -740,9 +741,9 @@ "\n", "and we can implement this in Python with\n", "\n", - " def neff(self):\n", - " return 1. / np.sum(np.square(self.weights))\n", - "\n", + "```python\n", + "def neff(self):\n", + " return 1. / np.sum(np.square(self.weights))```\n", "\n", "If $\\hat{N}_{eff}$ falls below some threshold it is time to resample. A useful starting point is $\\frac{1}{2}N$, but this varies by problem. It is also possible for $\\hat{N}_{eff} = N$, which means the particle set has collapsed to one point (each has equal weight). It may not be theoretically pure, but if that happens I create a new distribution of particles in the hopes of generating particles with more diversity. If this happens to you often, you may need to increase the number of particles, or otherwise adjust your filter. We will talk more of this later." ] @@ -764,7 +765,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": { "collapsed": false }, @@ -776,7 +777,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "collapsed": false }, @@ -792,7 +793,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -857,14 +858,15 @@ "source": [ "Most of this code is devoted to initialization and plotting. The entirety of the particle filter processing consists of these lines:\n", "\n", - " # distance from robot to each landmark\n", - " zs = norm(landmarks - pos, axis=1) + randn(NL)*sensor_std_err\n", + "```python\n", + "# distance from robot to each landmark\n", + "zs = norm(landmarks - pos, axis=1) + randn(NL)*sensor_std_err\n", "\n", - " # move diagonally forward to (x+1, x+1)\n", - " pf.predict(u=(0.00, 1.414), std=(.2, .05))\n", - " pf.update(z=zs)\n", - " if pf.neff() < N/2:\n", - " pf.resample()\n", + "# move diagonally forward to (x+1, x+1)\n", + "pf.predict(u=(0.00, 1.414), std=(.2, .05))\n", + "pf.update(z=zs)\n", + "if pf.neff() < N/2:\n", + " pf.resample()```\n", "\n", "The first line takes advantage of `numpy.linalg.norm`, which computes the Euclidian distance of a vector. In other words, we compute the distance of the robot to each landmark, and add in sensor noise so that this is a realistic simulation. Uou wouldn't add noise if this was real sensor data, of course!\n", "\n", @@ -879,7 +881,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": { "collapsed": false }, @@ -895,7 +897,7 @@ "data": { "image/png": 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56evogyRLECXR9JHCQcCv7+/XctcLiqM8PDyM8fFxDA8PY3h4GK2trUSfu729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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -931,7 +933,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "collapsed": false, "scrolled": true @@ -948,7 +950,7 @@ "data": { "image/png": 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1Lta4TC5xXV/DWgqQjTV75+1leUmAR8ignIqNyFpR1U1Do+5r9GNPScUsWeYE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i8n2q7rbI2DHsan2mJbbLHKV5in84nhAYAJhurp4Sl1+CK60ZfzS9GPlc8QjJC00jQycy0S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PlAYGnL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\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eTxGjveoqkmnTR1tt4E23IXzy2HKIAppsp8UpbptbPH/wPDbtRmwxd2/2o8UjvOZeQz/0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\n6yxumhsUUSFa3s8tntv6TE4qOED1q6w+9INJWr3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oQBsOQFSAuqthGyucUwtLFIZelc8F\n2MhOp2/37LeeS0NoZpZkqG2PzCgnpve+cM4XMBZ1IS1MbskxjaIxDeIJ3Yt1tb43P/nBLdrioFKf\nT/SFKXCWnqEIC9qoVSib4qbeoDIVdRicEeqD6cgPUhwDnBv58h6lR7gpb4hapGLM8hkeZA8AR9ZU\nQRBgW2+JF981SKNUWpZwoPjWvhtTtZUUMp3rADu4XTCyNItnuMgvAEvOKfN0jqqt5H7GQSyIyj6l\nia85DmNszAZ1VcN2FmEYIksyKa4VlPCTy6ZErGNBWLfNFju7E+9ffg++z6PC0Q0bJy/iTUvdF17E\neeNUSmESTnBb3JKiPM7EMo27QXxQ1UpLFHEURNJRSWNKEeN74CPi1lpxxmExcxiEQtdg4VwYhFR4\nww2UFD1QUoChG+IXUIvg/qFyf476PrJFVyALslHKGn8W/g6igDp7bdsibmPkIfFR8ygntbkdDq1R\nQJ9h1a1GXR0umsIwhDUUbhSHsRxI/ecRCtJ18j8DF5d88BeNSP/+SURr+UV2QXqYIEbVVYSE9Zzq\no4RceU70iSBkSZiINRnHy+8LnBiZvm1uCZSot2jaBs8dPYeyK8XNpqgLBHWAVbOS596f/4cExpGO\n0CiaB9Za0eWw8Pg0OxUhL6/RghoGhxFd//v3wR1+xktTSjE6jQjo0NC4zMnfu+0obKq1LV7bvAbX\nDeLti/yC+OUeCs0Fwiyeyf7mO/T4hRUHUamgLzb0VO7LslzKPHzj7g2yf1SElL/v7H34RvQN2a8P\nrSn791dj0JpE3WDP19hG1g4fheV7xgLBdU0di4fbhzAdddsc+q5O383OkgxBEIC9wnftDieTE1hn\nB+GZIl/uaUjfKXfOl8USpjVCJSrqYnADC2PqDLpihE77dIzr4hpNQ8DDdXGNi/xioD71oBHUEOwF\nDPTUSEdoNFk/8mFIuq5mK3+fRIncw5PsBA93D3Gzu6G5G0U4SU9GFCPTGgEE+Tvlebs1W7EA9p/z\n/e/RH3yQiEMCSmIVS7EqXt3Mq+ecAATYquEwMKqZ+hAmpu6WpiSmAfIRgFjUhVwPGxusCqJ2RVkk\nwKFvnbisl4gVJbXu2h2myRQdOjzteMeoGlBArvMhFlIRGrHfxi6aQvz8uMXC0YwK1HK8LW9xMb2A\nDaxMtLItibunFSpbIXAU7ZwnOYluugZH4dFIpd5o8liFBdqgFSsijnzkh5NPvIwo50kO7UgUEsex\ncICTIKENWdFiPwtmwq1jdwy2R7otbmmz7a1/GE33PQbzMMfDzUPZWLb1VlACnvCHUNwkSEYF9TSZ\nincwtzp9fixTWwCIVR9PetMZsQzkxYwLP2PJWmxbbWkyZgvEQYxNtSFjcq8A8+10fOpJEiaCxmzq\nDRVBIGT/Ir8grmbfujOdEV9R9pTl954lMyijgHYQNrJzCFu3ZXGGdbMmxTjIEvAIR9KqPFSEsGfp\nttnCGCqe5+kcaZjKgsXWX60l/pVpDZbFEpNwaE/53wc/4MzDO8/PsUgWuKvvcJaeiVPHPJ33Xwmh\nCEeTI9nwoiCSAxGjqQBGdlj0FwMtgA+S02QKNqHn6+EijulIoQ4l/AAd7vHbzrNzTKOpcOgjTS1q\nfrbEA1NRyhNz9DfVBmVbIosyVF01CohhWg0cDvJBpZsCz57JOdyUNzhJT3BX3ZG1UBqLZVPd1iIw\nAQZe4bbZigjYog/T6UJcTC8wTabymRjVzxMSJKVBSoVljz5xlLV4AQf0Hiw84tS3/XnALWC/kDhU\nMO+jnBIlGwwuO7wWLMsllsVSUNzX16/jKDnCqlrhtrjFc/PnpDvCSLMPHviWXP7cTYIEJ5MTiYIO\nVDDw0Xs+PRdd+xza/SAdKUYdPac3htC7LuyEF+3z5H2E3sKSULbbSIHno8t8KPcPGHzABCBUHziI\nzWPTkRD0rriTAooLaQ7C2O9OStve+/tQh4IuMrASasoY4JCmKKRURH8cErSKgNIrcJcVHYA39QZN\n12A+IctMtg211qK2VGQ93D4UYWhpS0zURJws+D41mgJaNtVG1uDABUh1OgIymP7k79N8vxn13Zmd\nIK5X6ytopVFa2o/PsjNCxaOMhPt7n5ufAz9P4Gp7BWVJALpxG6Ic9ILqpm7IlSiE6AH8wUg/08ny\nJKfDXk8pGQkuWajbO6zkUT6Kkm9VO4SCeEFnu4bCOHZmJxxbBQWnHGpboyjJk906+k4WwUKuLQkT\nrIoVIkSIJz0QZyEoMPNxgbFAf39N4OeC9StREME4Q/fG68rx+5qOCtdJOEESJcQ1dxBa3rahol+1\nanh+HL2O0krANu7c8zPgUzp8qgfTaP2uuX/9+3Q1pvhYWNL1GKK1HGfHuJxejjUegFDnphFRapk6\nwnQPoC/8VSSBN/56yYcCBsQu80sS6JsSR5MjqTWedrwjhbOfaFWYYtRmPeS1aOyQ2qK0GuzqNEH+\ngQrE23dV9grZmOx/uB3BXCT+whgF4YcGGERfzpF5tx9ewMrxoinAtlc8pslUWnryoHnITR7lKOtS\nLMkY8eLPzI4QnBomfMBgWPjzKMf19pqs8CbH2BlqV/C1M+rMv7c/fLRExFVQ2Bo66dVtjXWzhnYa\nRVVAaSW2LcxjLtoCq3qFUJEQIU9yhJbU7RxgEQUR2m4Ig6g78mqOdQyrrCjHeR4wz5zRGea18qGB\nT6MhQjRdIy07jvfkxZ8jNH3eOttkmYBQ70AFFNDSv8+22eJyeolVucLV9grzZI6b3Y1YhvF3BAx8\naQ7hYZ5bHucUTw0l0cDcEmLkk0V2TdvQxuuMePj6iD/PmzzJgYRoAZ0lGzYWbHDhEeiAirleXMkH\nUPbjrrtaeMoKCvOE/Ct9r1M2kw91KOI70xo5rbNFTxqmsiCyiI0pVj6/jduKdVtL4I/YdBni/yut\nSHAX5qgMISYn6YmIFk07pHtx8SWHa++6edGMwkgoMVDDhlY0Q7egdYNlU2ADJDoRV5kRMoWCOmAa\nknjJCZ3W2SHZr59rC03uPieTEylseL7Es1gODYwszdO50BSAsS/5Y9PcvGscaTA8UaJ1FkVbjDYj\nifCGQ2npu3CdQ9VWOM6OCVFVAWbxIIbj+SPggWuxmCzw13d/LT6w0ITsWmeljckHPgkmiIeih8dI\nmKv7Q52HMGmQ0HKRLdC2LRrT4OSIhFnLgnQp0iHbQ34FJbNjGz5/7fOpItfba9lPooCKV6awtZbm\nLBeAK0Pr3W15K2sxF6b7bjk35Y3YqG4NqfnrthZnCr4WdtjYH34rnvU762qNNEpRoBiBO5GiwuiZ\no2ckgZQ1CIyyGmuwqlZYlkuaz2GEeTpHHMYIEIz3hCBB4QpMwol44HMBIgmMHkXqUfFIfp9RfqZt\ncBu8NKWEguRJjkW6gLW0B8j6531udqpoWqKenGQnI8qBhYVqlQS7TOMpaSk6g+OMtD1Md/C99/m+\n31UUl+5CN5pH/JkY1eV1jPdpnietawUsc4rWH3aTMiBBHtO+TrIT6oSrBCokQeMiXQh9iFF1Xh/K\ntsRJekIFa9ugCQhY8+kDcBhpQbjL7dPWfLSdU0SVUkKdZKoj04ScclIwNl0zelaghnujNNUufjdq\nv5O035HwqR7bZiv2kdzt58wDdv1Sml6bQUcWaQP0/TcdpUpvo608U1pp0eLw88i5CftUmEhRF5lR\nZaZncO0mVKWWDmuvr14HQEnXzxw/g2fjZw8+t4fGO+bjzBt6pCPcVXf3JtUIWUmm5KsMJciZ0lRQ\ndV0HC4usJSSA29YKiniUbYNwEsIqi/PpkJTDpzmeNGxPk0Ypqo42ZkbdGJ18bf0alCM0ge2RgAH9\nYhHMIV4itzubrpGF2i/yOBP90O/zQjiNCRnkU1LTNZK6x2mF+0XzIXsqPl2XpiSxY0S88l29w6Pd\nI7oG5WBh8fzR8/I6y2IJKGBVrxBpivtlO7JlSa0qPmA4R5SSqqukAGm6Bmk4JJy9WbyJtm2lTc5F\nJrfPmYriu0YI4hMQ8jELZoLWx+FAk4DpD12akvQASACLL16S61Yhio6EUjCQA53pjCCFLIhkZCWO\n6P0iTSgzh0s4R44IAHC7I8cNjq9NQlKiX22vZP7zoSEOqaXPnqhhGApNxi882DfZT0RkpEAp4jTO\n4hmSMMHl7FKeJX4dnnPMP+WRBAkQj1tyta0xUSSkMDDkFWrv+7T6845dHB5uH6KxDdIgRdmWOE6P\nSTOgKLCAqSemIxTfdIY2C+Y9ukFgBgyHA06klOvWCVpN7gqJo05QoIMR2ijtxb5dx8+7n/oWBREy\nZHDKwRgj9mj8OzwYFYs0Ic/cyt4PjfEH3x9/YzKNOSjU2kdm+foYzWQnE2MJhWeUmjnCfkGulEKs\nYtS2Fn62hUU0iajFvzd88IA7Q8pRcmLryH5PRHM9j1yoTX3qoHBB+/RP/ll+vkXw1G9WfACONG2G\nJqDClL+TaTzFTu0w1VP5fMzL5UOMc046RH4hzd+DPz9ZsMoBQ/xscFoZ0ymCIMDr3evC59yZnXj0\nNq4ZueUkSMiTGncwraFWvmsxC2cjcad0Jby2+iH/4p3ZYVNvMAkmWNfk6T6fzCVqOg5inE4IvLmY\nXgwuRd4zua239JxHJBKHAxW21mKSTEYFMReEVllZw/zX4yJklsxoHdWJCFObllB69uSGBVEd4wzT\n6RTrek1zxdRIE6I/Ma2OgSO/6IpDsvfkItTB0dpltmRP1neDHByCIEAQBKjaCo+KRyhMgTzM8Wbx\nJi5nl+L9y/abSlO3e2Qx5hV7/F35a6VzZE22mFCIl9JKAtM0NOqyRoRIuqE7sxO7tZ3b4SK/GHHs\n/fupFImqK1sR99ZaSb40ltYZ9j9m3jbXSbxHsZEA68T8NSdLMpjWiMCOC0amlG3KDWpDHfQwCDFT\ns0GkrBPZjzl1MdShHA79jh//mdfJ/SKan808zMXrmdetVbdC5zrMo7nsrZNwQnOtJuenNElRtZXQ\nk4AhJyEKqBYybZ9AaD3AwXMmicIIi2hB1LB26AL5HRW+9rIpkSe5UG85z+JpxztSOHP0qFJkucWi\nhvlkjsvZ5UGfyW28pz6NEvH5YwSdN9nj6BhFU+A8P0fnOsySGcHtdrD74QX2rrqjVrQKqfgIIiwm\ni3ut0+vtNXGcAnrw2HHDwo7SfTg/nb8waWdNBp72IQ6beNZ2g6k6MBS+3LbgTYKFN1k4ZKw75+T0\nNDpx7Z0AoyCSVnmnO9ncipo4WAbU4jGtkc2Qi/tNTclzaZTitrzF+ewcUJR4yPeP7/Gm3GBjNnh+\n/rwsQnVbIwgCXO2usCpW8h7Pzp/Ftt6itrW0iHdmJxN2P4lQNl30qYR94chtK0FE9eDIwOlxPPh7\nYJGncooifdGjCWYnCBOfxpk6YqzBPCT1vnDdvcEUHDbX58Wav89NvaHWYZyPXtt3WImiiBZo73vc\nmV0/2SEcW6EagXhulakoJKcPD9gvjplbzv/OrwcM1AtemP2FmO9j57rHHhD9e2usIX591BcbXYO2\nbWG1FXcFCxIthV2IIAykaN4XVonoz7sOgJ7LuiWxWtM1ZHXZb3LbZituJstqieOQeNataxGpaMQz\nT4JE0sayIINRZpTM5lslsePOrtvJ3PeHX/TuI6R87T49zC+i0GGEzHKHhgVtraVYeGNJzAn0vPUw\nE0SKQxwsLB7tHiENUuRJjtvqVpBxhKSo9wXFfC/SKEWHbuAA91SqpmvkwMVJoAAkrKUxDf5y+5dC\nNattLSEfMi+8gCV/82XUTRA9PaTURQFpNbg48F0JTDdOOvU58izCPORVzI4FXPhz16VoCtSmFm/g\no+gIoSaeur+WcoxxGISj116kC6zKFQVmPMYtgue2/1n4unxvaAUlB8o4iPHq+lUsEupyFK7AQi/k\nPh4KaBB00DYIArJ1FSu3eCgcp/EQfYwW98JO9kcUDO1u/rNWmgqMHuRhIXAcUrrm9e5aAnz8QA5e\nd4w1An4sqyXysO8MhxGmakq5CUGGZbvE5fRSaAHsenRX3MF2hGYXbQHbWSx3S/lO/MwAn1rwVkMo\nhQAWkwWud9eSzbCtyd2B9R2NaSS1b57M5UB2PDkW6qF0GIPpvfe+yC+kA+cf0pbVcuiU+1xcDB3r\nWTIbF7F7Y58KwQDELJlJZ/AoOUJhCin4JWCn76CKS5hH7wFwr8vtz19jDWbBTPYCnuv+YV/SVEEU\nURbKb2tClbOE3GistXAgICd3uXQHuFvfhq04rdRtPe4e7Nn9Lcslpq5/7vp76juQFG2B48kxVjV5\nP1tLoue3Uw2/I4XzzlA7JQ5jlG2Jtm3FvYLFS/sWY4t0MQRotFTkzpM51s0aR8mRTCy+8Y0ms+zF\nZIGyLaGNlgcOIEToanNFJ+T+faaWUrL41MsOF9wW2tZbCV5h9wzmTjHlgU/+LOzjibVrdpLuxoWZ\nX3jwZrpvqeWfwuuultRB3pD9AtK38dq3ttl/eHyhJLcrGtu3X3QEo8bG9H5xyu2+OIwlxU1phReO\nX5BW/cMdiTAa0+ClRy+RwE3R+6+KFdbFGkqR4Tpa4K68Q2UqdK6DzrQsdD6C9J3f8Z0AgK985SuD\nurwvSs+n56MFhTfL0Qm/v2+jjVvTxlJ1FZQj8/Xj7FgKq0k4kULZdGQ9mAQJ5sEcu2aHF+YvoLak\n0vUTpvg6fNSNC43WtmL+H6iADmrWwNTU7lORkmu/p+pXkIKJeYRcIHAKGh8cDtktLgtKfKtU7+7S\nz6cRl8+2JPLsRVtxRALRUIXCkd/3/r43FCEWN+0NZpMZrCVqjIWVBS5qI0EZfRszX3nOn/MQp5RF\nest6KVSYOq7xnpP3kNPLHp2KNQEJyDeYiyte5M+n54J+3ZQ3CFSAdbXGulnL2sH39+HuId13U2MJ\nSoE71BVwzqFA8djkKS6impZEXftcPy6w5GBk6f4wasQFuLFGOKNckGulkQUZkijB+fQcz8+fF9X+\n/vXwveY5epqeYlWuhOcJjIVH/qGGv4uypk24UAUmkwkCFcj6eVPejNwXWEzHayAfoljIwwgdMFBa\npL3az2X2Tg7bUBL6+Pr5PvL98+eTHGyCYTPlz+LrL5qOQk2SiA7TkY4kJprb3twN4TnPOgMLK1Qo\n/3Dpo7uHDp2M+J+kJ9iZHe6qu4F6BAcbEyJ8OjklGlcPCMi98r6X4/QYq2qFY31MCaNxjosZOVKN\nLD/VQFnhueSntPHrja5dY5R8mcUZvnb7NShLnd6t29IzURNg8uzRs6MgFX/w97G1Wyy3S7EWU1oh\n03s85GSImbduCFqJAuqgtF178BmKgmhEa+OOE3ebfc0Dz2l+tpRSItzfmR1gQJqW/l4fJ8cog3I0\nn/m9faCOn02mpO7vYUEQoKwoHfG2uoV2mnRaipyq0igVzRfv+Ycs80ZzodeKsIbFL9ivtleC0t6U\nNyN++CJdoLMdYvSiaAUpQPfR831KzLJcSgf+jc0bdNjor8+n4hZNQUYHLDatGxSqkPvEByMuftmC\njl9rW29HwT+TcAILK9aS3JHd36v2dWCmMyIKBmivWFZL2MpiU2wQhZGYWDzteEcK51CTHUxZUdHM\nBaU/0X0UwUd8AMjNuMMdzsIz4U6Kd2o/6V+YvyARq4kePHSB4WeYA8MTixX1zG8GIB6dFhauc9jY\njUwe0xqxmms6sr3hL475SzflDbY1kfw73VFrba+o3W93HxrT+IBfosdXAjCiIez/O58A/fvKNjtR\nEOG5+XN4bfUapnqKNEmRJUMaWBRSIAwn6szTOQXTmB0SJINReB8hzklaRVsgUhHe3L2Jo/QID7cP\ncVfcEYcuVAhUQOIcR4LPTU3ilH/ygX+CznUIFKmc/+i//BEA4OWXX0Ycx3j333s3/uTP/kQW+fe/\n//2wzuLPvvxnotBl1xRfsMB0CYBO/rwgXk4vqc3H7XI3tBK5fVsZsofjE35tahEV+Zt013WUwOdA\nceKtQVEVKJsSZ9mZBNjkUT4o66PpqIXN8890RhZz5xzyMJfN27R9WEEvSAstce/2kSKe66Y1kpQW\nabouFah78850xOWGoiLaGktF6N7C5/88XzcP9gGfhBPRDLBCehIR3aM0pRzUgIFPx5uP33XZpzTw\ns1u2VKxxF8t/9vaviTdO/n4jFYnqPLJEmeFFNVLRyKaQC0Cgj1OeLGRt4fQ9vytgOoO78g6TcILT\n7BQW9rHoYtVWcs/KhjyN73n76l4/EVAnoG5rsdeDggiZR8MNzzjPn33up9+elAKmR/eTgPzfZ3Fv\n96jdPfX51mwpZc32/HoVSReJw6n2NyZTmxHSeEgQuW22QITRtfDP8vyMgxgqJlS2MEQveaN+Y3hN\nhZFTj2yYHhhxaPNnZyYGc4w1uJxdSlLZttmSQLpPOUTUH2J7UTU7urB4+h66e+B58Yd0YpotAgRE\nHeq7orfVLdIwlY4jd7b8QozvaxImEgfPHS/5zntOMR8coYiXG4cx+dgfGCOueDL+HEVTYBqShZfW\nGrnKhV7oH3j9ec/XyV7GnF5adiQWrtt6JFxn+gm/HmsdeJ3PVIa74g51Q2hjNh13Kbmo42f14e4h\noaqGfOYZHeYDMKPagQpoTreFCP6X1RKX4SW9sBpCQ3ifDVRAegFroFvas/cpgr4oOAkSFHVBrjeu\nxabciDWq6x6PJHOH+63qB38/4a580dC+bDR1riJFnVQGfUJNdDqmNPn1kFPjTsl+EcqFPdOyAh3c\n86M3nRnWzJ6DfFfeIdYxjvNjlKZEGqRy0DIBHZp89xfuyiilJK30UJfncV2Tx/0dd4L0VGMZkKHE\n+fQcbdkevL+HxjtSOEMBx+kx3ty+ida2OJocIQxDCXG4hwr61AMM4rZFuhB3CI6qZl4fK/k720nb\nZH8s0oWcBEMdEuKKCJtqg8YSojBREylonpk9I6KjKIikOLgr7sSLNgqIJ8mDg0vYho5TnKT9vEdJ\n8cf+InOoJccpjFwM+ocD4P4JkIUcPgrKEz0OY3zb/NtEXDFP56MFnx/Suq2FYx1pQmZfX7+ONExR\nqAJ31Z0sClmSkUBCKazKlXzeVb3CXM3RKSqYwyBEGBFvjbmaqv/f1/7qa3jm6Bn8xE/8BF566SU4\n5/C1v/oa3v3Mu7FeD3ZyURThZ//Pn8Vvf+a3AQA/9r//GLTS+MM/+kMAwA/84A8AAH79139d7pXf\nwmMuPRy188uuJL/I3v5wnw9lrEGKVF4LAG6rW/G+vqvvkMYpKlPBGIPT/BRFRxy8PCJRIce+suDO\nKUdCN5DgJA9zQX7ZvitxCW7LW6GQoAVOJifkCpIcjdp2PvXiantFXHPboGornExORkU5I0nO0SFS\nBQrTkBYrrbS4evDn5Y3FF1BK6yxocTG7kIIvizJ0tsMkpudgZ3bSHXGgaHNnnfDIuQsCDAjqiBca\nE3eS7cQ615HNHegAcyiBzd/4T9NTLIslEp2MnDzYpcIfcsjGIFbe1Tvh1PuH8da2FOHbx+526HCm\nzuS1R92lHt320XZGkPIol2AYn29ZtRXpMEyFwhR4kD8Q3QdfH9MmoiCCVVai1fcPVL6DAxdTrWul\nwGottT/LpiT3jSQTdTw7t7AbTt2Rf+rLy5cpPEgROnt5dDm6d3yoZtcBOQQBgo5x54OtKAEMThV7\ngEccxmjqRorDIAigtSZRXZjCaCM6hCe15nlwUaEjygjgg9NiskBpSuH2traFMQZvNm8i0hF21U5C\nKJxzRDcJI9jGvuU6f2hM4yn0TOMKpIWwoSVxm6I5rJQaIWv+wY2BA6YMLrKB5yucXWsOCqqfBN7s\no9D+4OdOKUKdTWtgtBl3D7qh28B/ZjcpLroYGAAG5xUGG+BAdLS+s8dzgUEqDQ0Wau9Tybgb6uBQ\nmAJVVyFAIJS0pmuw3RBgkEc57qo7XG+vcZwco+5qAvimuRRWwvVXPYrdO+4wiMR1CNOFOJyF15g8\nIbDw4fahiLKNM5Kim8apJLb6yD47AHHn6a0oNVw/7HuFa2jUhmiTAIEDSZgM62b/XutmDWNon1EB\nOX49DpTa73LzNRzqMMCR7mvbbjFVU5TNYDvL/96oBjfljeyR/FlZYMi5FM45NK4Rv2WmNh1E4A/M\n20OHOdGvBKEU+THGbiZvNd4ZcSBIHDhP5kTL0ArHyfFIzX8IhR2hxf2H4w/G4h9GOW6qG2zKjbxm\nh04cGWRD7lFURkjyOBcl767YiQ0dc1ABiDqVF/kkTAQFnE+osOC2mNh59aiQIH/e4i/0E08Y6Y+n\nQSi4KOZJzIsKIwF8rUw/YYspthpjX9xtsyX1qyYKTbktR0Ed03gqJ2tGqN7YvIF1uUbbtSTAzM8x\nT+Z4s3wT03BKG1lAXtxMJ8jiDLmhGOM8yXGcHsNZh851+NTHPwUAeOW/v0LFs6fO/43f+I1hDjk3\nKpoBwBiD3/r0b8mff/PTvzn696/91dcAAL/16d9CFNG9fPffezf+9Mt/Cjhqs3Iy42/+xm8iUhG+\n8AdfwCf+5Sfo3gbAulnj1dWrgoxmMZnhT0NqFS+LJU7yE3S2g3IKy+2SvJYT4t7OFKVlzeKZeKUC\nxEvmQn7nCLGMMWyM3EmZxlOsyhWUU5I6CQs82j2SDScKBvoDo+H+ckPI/AAAIABJREFURst2jL5A\nw0eSiqYQ1IXYA066Lvy9++EG4sXeuySwJVOkIuSTHBEIsbfKyjyXlps3zxlNACCCSZ57+0mXfL2L\ndEGIY1kgjENULdFl0jgdU54wLOKiG+ht5/iwznzvKIhoQQ8JxeNulhxA+wNzXffWUgry/F1vroXH\n37oWuabCsGorOSj4ATi+5yrfBxbGMupWd7VwipOAPHmnCXGOuXgePQfdwP2dxtMhKOoAdYs5jozY\nKihxgjCtkYTBIAjEt3W/2Jom9JyvihUu8gtx+EjDVFB8PhCwT73vIvJ242z5+9p3W2EQxHQGk2Ai\n6/M9tOstQmKe5r2Z78mHiNbS/ZtEE+JI96DOvpUigKcuUAHaa85n5yjqAstyiVkyE6CE95P9e+cj\n6nzY4MH2hVxQ84Hc57e/lW7hSdd6U96QwF3RXnySnGBrttJtEd5sH8wUKTrQMOIY6hC7difuDdyJ\nK00ph5RJNBFgTUOPaAtNS0DO88fPE/WQ6U979yYOY6KCtIoEiH1BdlffCejxpqPDUKQjdIoO/CFC\nKNdTHXubQC6M9wEoNCAApLfJY2S87moCllSER+UjEk52kGuIdQwNjTRKEQcx7R1xhmkyFdAsCRIs\nu6Ug79t6e5ACw0mLTCfyRai8xsWK+NQWFu+dvndUXHOnqEQp75UECZweKKn+mr3f5T5E6eHOJ5sj\nnExOBFA4m54R+t7rmuIolgM0P8NJmMh3ygFJoQ5HyZaRjrCslqNO1luBlHyY48HAbBSMNUdCk32K\n8cTC+ROf+AR+93d/Fy+99BKSJMEHPvABfOITn8B3fMd3PPZ3ZtEMsYrFfsRZQpz45ORHPvpIMyMW\n3CbkdhRvpPwFms4gQIBFtpBwg0T1RujNWkjgxhmcpqfYqq2IGxg141O5FO8HuLHAYNnlOyrwpNKd\nRq1rGEcIlbMOrzWv4UH+ANN4KlHdAHmR+pzMJ53s/b/nBWG/uBhZQNlBgAbgHnd6V9MkZF5v21Eb\nug1a+Vz3DjMOgujXbU3UhbQjvmh6Tve0L6avd9eYRBM54c4nc0yiCc6n5wCAqw0hK3/wf/+BcMAB\njDa8/9kx2jx7QcJfvfRXIiT8sf/jx9DaFr/wiV/A737md+VnP/EvP0GIktlhFs/waPsIAPDc8XP4\nxuobpMK3LbmUBInwEvk9pwml6G2qDaGJcS5ziCkexhpxhMiSIXWQuayqoXYUu1A4RT7XWZjhzd2b\nVFB1LVb1SuhCJ+mJJCsChKJw92V/sQGGecWCEGfpJL/IFuL+wcIN42ixUZY6CcznZv58YUhklQWZ\nbAqLdIGX3EuAI6sk/9DIhz9J9+wFRLxBMY9va7YSGw4MfLR5MkcSJSLW8v1D7yEi/dyfJeTIwu4m\nvmjpMr9EZSocJUfUBegaCdTJ4xyzZEYc2P5/1pHd4SScUHtWU5uTvcizOLvHI2eayqFCzjpLlI92\nENPx4YIR+AiR+PHyM8kiQqed0Nu2HfE5mX7jU2DYMnBndkhC8nPdmR12O/qz1qTp4Hhufy30iyxf\nvJlFGWk53MCrZDeIaTIV60H+fRl7Gy4jSDxHRgWdtx5z6MSu3uF2d0sCrcmcNC56Ie/DwALf80O0\nI2BA2qy14nLAVoIsEDUdRXbfVrdQlgRXO7PDs7Nn6bChFR3qvOKenzGm2z3N4I6hg0OIEDfVDdHJ\nets35ncz5W7f2ouBEv/AyIV3EiVDHLSG0NieBqU7NC6nl2T159Gh8igXb2w+xDMq2DqiAORxLoe0\nyxlFo3Mn5La8xSye4Wp3hVCFSNre/SYkZHLftxmg39vW9Flvihsp3P25O0/mFKIRzVC2Je6qO4Qq\nFPvMUIcoLNGzsjBDFmUyb/m9oyCCCpXQTADIQSnXOVRCmhOlKMbZOYdABcjSjEwLohkuJhfUV9VK\n0HKliDrK3ZZAB9BWo6qI0tXZDvNwPjw7jvjBnSbB5rve9S4S8tZUE7WKHIc608nvW2fxXPocOtvh\nNDmlLkFrEasYpjHoVO8k5YhnnkT9umnpM2lLWQKst3GO3CeajgpZPlweh8eSk+AcgWPa0toY6hBH\nwRFm6Yx+p49tZ+cwgOa4hkZniLZpLD0/gQtwFBwNaxAiuS/WWXp/A6ztGoGie88WpACwa3fCk4cD\nXEDPjA/WNG2DLMhQuvKxrIXHjScWzp///Ofxsz/7s/j+7/9+WGvx0Y9+FD/8wz+Mv/iLv8BisTj4\nO4yG8iYlQosDJxjmAgEQjhm3CX1hk1IKucslgptbE3zaA8YxlFuzRdM06NoOLUistTM7NHWDHXYS\nKmIsJRT5J3s/NtZvbzN/aESnUGTNY7TBXXNHaUddg7vqTtrKjHTdFDdyqvM3OH/sFwL7wy9yJWVK\nq3tJib67BKfxMFLIkbqsbH6rkUd0z1fVSk7fHTrkGcX4ZkmGJEjwwvwFXAWDchUaeG7+nLzOIqOE\nw/MLSnT64I9+EEop/Pa//e0nXsPf1Hjx374IAPj3n/n3o7//qZ/6Kfzap35NWtlc+LItTutaaKcR\n6hDX5TWcdTjOjhGGIRbRQpLEsijDdDKV+c8tN18gwvOMra3ggDc2bwxhOJYSv5RSiHWMwpACuO1a\n1Jq+0852UMEgjuHN2zgjSKdfhPgbzZsFIS3TeAqjqeXFgSuAJ7rxFMjogBoULLJIF+IL3nSNJCnC\nkQMGF9G+IIc3AC5CAx0IRapAMTjVALL5+ge+RCdQkRpZWvrtf59/zmgFP0dsX8nrD/8eh2nw51CK\nqDemJX/fMAipi9LVmILWg9KUQpuIVIRsQsWybzPHg//bP2gDHg2oR/YVlBxWmOfLXHeOk/VdJuRQ\nEM/EJmwaT8Uj1zmHJZYCNnAkPYcScLGTaAoV4g1wZ3YS276/RrFndBiE2JgNYIEddsKt9r3HS1MK\ndYjnoKxFvv6gb8P7665/b6bRFG0wHLySMEEapII0112NXA0uRfsdDTmo7H0f/D1cTi+J1x5mI6TP\ndCQ6LRpyIHh+9jy+vvo6AIhX+nF6jGk8vWfbJd7bwSAQe1JhyrSRk+xEwCHu1AEQVHXEb3YGU020\nMwY6+NDLvGbnHLQevPJ9ehcP6TphLKI8NPj955M5Hu0eEV+Wf74v1gMdjOYof+ZYx2jQCM0Bagh5\nmsbEjWcXK61IQNsGZI3oHzqjsPcj7qluKqCC1hcb+y4Lz8+fF02Ohqa1NKe1tDGUABkFEaW0MvDU\ngx0TTKRLyN9fYai7m+kM89l81C39Zo7JZPLkH/o7Pib4m/uMzjnsCuryyjzpRbrLklxNOF/iaccT\nC+ff//3fH/35M5/5DObzOf74j/8YP/IjP/L4X+xPfkVT0AIcL0YbCzAsZq1tB6RGHQ4LiIIh0IG5\nhdt6C9v11jpxJBHLpusRYOdgjSWuapiKc0YSkIcubzSHvDZ5EfXbNWzg7z9M02SKoi6wczvkST5S\nYXMLVStNAidFLb9YU4tmiXG7wV90uBCQe7CHiHMbkRWx/D7Sktj7eU5X4g02VPfbLPvof5YQMtM1\nHY4nx1QwJ9SiZf6haY20kV6YvyA0mX2u9jSmJMIv/ukXpVAJdYhP/uonBVHh9vM3e3z2330WX/zj\nL0Iphf/0xf+EMKRwAGcHhW/dEkfqeHIMB0I05ukcm3ojlnSMuvJIgkT8qA9tXGxtNQko3UkpBdUp\nrKs18jhHGIY4mhzBOuI/WpDKXEHJASnVqbihsHvHvrK5qAu0XYu76k5cDXyBLAtCz/Nz+RysZE6D\nFKWjAwQs+VbPJjPEQSzIwa7ZYedowy67ElmYjYJT+LWW5VLSsXgeRjoixLsPySiaYmT/xwdYpkpU\nbUWpYG7gyvnFArujWGslmTCLqMBj5wcAgMYooWpZUQKfA61Z8wnx/zmsZWd2UE4JWs8UtGk8HYn/\noCA0kHW9hgMJU7mQAjzagaXrgCb6F1tFXZfXWEwWEnhxryPVu1A450Q4yusGQEX4qltJTO3J5AR1\nRGtFqAjZzRPi4G/rLXb1jlrHvZXZXXGHTnXUWt2jBnAhHgcxVuWKBLH52WDj5qVVAhDxETA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IoIF/X7Gp6iLVCYAutqjRbkaZ6GKXWpHe2FRVuIBqO1Lf1ckI3Ajm+Nv3tDQQ3z3BmkAa25YRRK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\n3G6FoGxK3H96P81v4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A4a3Qi3WJJ2s63YLEui87nGxabeYBbNkDhbFYpnX3g2Hl4/TAmi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0JKBwSxUYdGDFbrevZDvv5DABIATV2tS42l8JXrKxjZAFufIYzpfVfiVQjFWzQqqoomE8Vd23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\n/D/PjUIVaI/acl+zkr7XicEJ2Ja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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -968,8 +970,9 @@ "\n", "Another approach is to be smarter about generating the initial particle cloud. Suppose we know that the robot is near (0,0). This is not exact, as the simulation actually places the robot at (1,1), but it is close. If instead of creating a uniform cloud of particles over the entire map we made a normally distributed cloud near (0, 0) there is a much greater chance of the particles matching the robot's position. The particle filter code includes the method `create_gaussian_particles()`; feel free to alter the code above to use this function as in the code snippet below. However, we will be using this in the next section to help with a different problem, so feel free to wait.\n", "\n", - " pf = RobotLocalizationParticleFilter(N, 20, 20, landmarks, sensor_std_err)\n", - " pf.create_gaussian_particles(mean=(0, 0), var=(10,10))\n" + "```python\n", + "pf = RobotLocalizationParticleFilter(N, 20, 20, landmarks, sensor_std_err)\n", + "pf.create_gaussian_particles(mean=(0, 0), var=(10,10))```" ] }, { @@ -983,7 +986,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": { "collapsed": false }, @@ -999,7 +1002,7 @@ "data": { "image/png": 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Jaed/r6OkQ2/ckxIqa2xJ+Z6pCUnrSmaFsB6mbJXJR/OE9BA3Dm8ck3xuNbfY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qLWLeGC2rRa1ZI+PNTNx4ZB8th5d52HioJv310nXRVy7RbxF3hEq3IgJAf4aMP6MOMMNNPmeh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\no9QKrE2LQ/FDo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mJukanoFLOx2TvzVRPJynJxWejsYnK7epuIO6IO1f2J485q29n1s3SM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\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SK2tWYrByEHnkEbGdJEP1lfVYWROe8YfPXl61HMurlsOGjY3xjYG6XxGpUEFaLJNJ9/Q+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\nDWMUxqOe2wgrwRjjinbIB3t7z5w540+Sx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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1022,7 +1025,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": { "collapsed": false }, @@ -1038,7 +1041,7 @@ "data": { "image/png": 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bao/RmWta5ToQxRkZGcHk5CSOHTtmWmE6Ojpa1nJ6+816LARBEEZQssVNp9M4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\n28jSNbs8MvIIC9UFWTUHOLd0jnwzT7VTBQvGw+Moi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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1103,12 +1106,13 @@ "\n", "When you formulate a particle filter algorithm you will have to implement this step depending on the particulars of your situation. For robot localization the best distribution to use for $q(x)$ is the particle distribution from the `predict()` step of the filter. Let's look at the code again:\n", "\n", - " def update(self, z):\n", - " self.weights.fill(1.)\n", - " for i, landmark in enumerate(self.landmarks):\n", - " dist = np.linalg.norm(self.particles[:, 0:2] - landmark, axis=1)\n", - " self.weights *= scipy.stats.norm(dist, self.R).pdf(z[i])\n", - " self.weights /= sum(self.weights) # normalize\n", + "```python\n", + "def update(self, z):\n", + " self.weights.fill(1.)\n", + " for i, landmark in enumerate(self.landmarks):\n", + " dist = np.linalg.norm(self.particles[:, 0:2] - landmark, axis=1)\n", + " self.weights *= scipy.stats.norm(dist, self.R).pdf(z[i])\n", + " self.weights /= sum(self.weights) # normalize```\n", " \n", "The reason for `self.weights.fill(1.)` might have confused you; it confused me the first time I saw it. In all the Bayesian filters up to this chapter we started with the probability distribution created by the `predict` step, and this appears to discard that information by setting all of the weights to 1. Well, we are discarding the weights, but we do not discard the particles. That is a direct result of applying importance sampling - we draw from the known distribution, but weight by the unknown distribution. \n", "\n", @@ -1131,12 +1135,13 @@ "\n", "FilterPy implements several of the popular algorithms. FilterPy doesn't know how your particle filter is implemented, so it doesn't make sense to have the resample algorithm generate the new samples. Instead, the algorithms create an ndarray containing the indexes of the weights and particles that are chosen; your class or code needs to perform the resampling step. For example, the robot localization class implements this with\n", "\n", - " def resample_from_index(self, indexes):\n", - " assert len(indexes) == self.N\n", + "```python\n", + "def resample_from_index(self, indexes):\n", + " assert len(indexes) == self.N\n", "\n", - " self.particles = self.particles[indexes]\n", - " self.weights = self.weights[indexes]\n", - " self.weights /= np.sum(self.weights)\n", + " self.particles = self.particles[indexes]\n", + " self.weights = self.weights[indexes]\n", + " self.weights /= np.sum(self.weights)```\n", "\n", "\n", "### Multinomial Resampling\n", @@ -1146,7 +1151,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": { "collapsed": false }, @@ -1162,7 +1167,7 @@ "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAacAAABACAYAAAC+2bgWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOP8CSdsd9j7kQ7EAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1184,17 +1189,18 @@ "\n", "This is very easy to code using NumPy's ufunc support. `searchsorted` is NumPy's binary search algorithm. If you provide is with an array of search values it will return an array of answers; one answer for each search value. \n", "\n", - " def multinomal_resample(weights):\n", - " cumulative_sum = np.cumsum(weights)\n", - " cumulative_sum[-1] = 1. # avoid round-off errors\n", - " return np.searchsorted(cumulative_sum, random(len(weights)))\n", + "```python\n", + "def multinomal_resample(weights):\n", + " cumulative_sum = np.cumsum(weights)\n", + " cumulative_sum[-1] = 1. # avoid round-off errors\n", + " return np.searchsorted(cumulative_sum, random(len(weights)))```\n", " \n", "Here is an example:" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": { "collapsed": false }, @@ -1203,7 +1209,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1221,10 +1227,10 @@ "source": [ "This is an O(n log(n)) algorithm. That is not terrible, but there are O(n) resampling algorithms with better properties with respect to the uniformity of the samples. I show it to you first merely because you can understand the other algorithms as variations on this one. There is a faster implementation of this algorithm that uses the inverse of the CDF of the distribution, but since we will not be using this much, if ever, I won't cover it. You can search on the internet if you are interested.\n", "\n", - "\n", "You may import this from FilterPy using\n", "\n", - " from filterpy.monte_carlo import multinomal_resample" + "```python\n", + "from filterpy.monte_carlo import multinomal_resample```" ] }, { @@ -1237,28 +1243,28 @@ "\n", "However, this does not make *N* selections. To select the rest, we take the *residual*: the weights minus the integer part, which leaves the fractional part of the number. We then use a simpler sampling scheme such as multinomial, to select the rest of the particles based on the residual. In the example above the scaled weight was 3.6, so the residual will be 0.6 (3.6 - int(3.6)). This residual is very large so the particle will be likely to be sampled again. This is reasonable because the larger the residual the larger the error in the round off, and thus the particle was relatively under sampled in the integer step.\n", "\n", + "```python\n", + "def residual_resample(weights):\n", + " N = len(weights)\n", + " indexes = np.zeros(N, 'i')\n", "\n", - " def residual_resample(weights):\n", - " N = len(weights)\n", - " indexes = np.zeros(N, 'i')\n", - "\n", - " # take int(N*w) copies of each weight\n", - " num_copies = (N*np.asarray(weights)).astype(int)\n", - " k = 0\n", - " for i in range(N):\n", - " for _ in range(num_copies[i]): # make n copies\n", - " indexes[k] = i\n", - " k += 1\n", + " # take int(N*w) copies of each weight\n", + " num_copies = (N*np.asarray(weights)).astype(int)\n", + " k = 0\n", + " for i in range(N):\n", + " for _ in range(num_copies[i]): # make n copies\n", + " indexes[k] = i\n", + " k += 1\n", "\n", "\n", - " # use multinormial resample on the residual to fill up the rest.\n", - " residual = w - num_copies # get fractional part\n", - " residual /= sum(residual) # normalize\n", - " cumulative_sum = np.cumsum(residual)\n", - " cumulative_sum[-1] = 1. # avoid round-off errors: ensures sum is exactly one\n", - " indexes[k:N] = np.searchsorted(cumulative_sum, random(N-k))\n", + " # use multinormial resample on the residual to fill up the rest.\n", + " residual = w - num_copies # get fractional part\n", + " residual /= sum(residual) # normalize\n", + " cumulative_sum = np.cumsum(residual)\n", + " cumulative_sum[-1] = 1. # ensures sum is exactly one\n", + " indexes[k:N] = np.searchsorted(cumulative_sum, random(N-k))\n", "\n", - " return indexes\n", + " return indexes```\n", "\n", "You may be tempted to replace the inner for loop with a slice `indexes[k:k + num_copies[i]] = i`, but very short slices are comparatively slow, and the for loop usually runs faster.\n", "\n", @@ -1267,7 +1273,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": { "collapsed": false }, @@ -1276,7 +1282,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1294,7 +1300,8 @@ "source": [ "You may import this from FilterPy using\n", "\n", - " from filterpy.monte_carlo import residual_resample" + "```python\n", + " from filterpy.monte_carlo import residual_resample```" ] }, { @@ -1310,7 +1317,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": { "collapsed": false }, @@ -1319,7 +1326,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1337,25 +1344,27 @@ "source": [ "The code to perform the stratification is quite straightforward. \n", "\n", - " def stratified_resample(weights):\n", - " N = len(weights)\n", - " # make N subdivisions, and chose a random position within each one\n", - " positions = (random(N) + range(N)) / N\n", + "```python\n", + "def stratified_resample(weights):\n", + " N = len(weights)\n", + " # make N subdivisions, and chose a random position within each one\n", + " positions = (random(N) + range(N)) / N\n", "\n", - " indexes = np.zeros(N, 'i')\n", - " cumulative_sum = np.cumsum(weights)\n", - " i, j = 0, 0\n", - " while i < N:\n", - " if positions[i] < cumulative_sum[j]:\n", - " indexes[i] = j\n", - " i += 1\n", - " else:\n", - " j += 1\n", - " return indexes\n", + " indexes = np.zeros(N, 'i')\n", + " cumulative_sum = np.cumsum(weights)\n", + " i, j = 0, 0\n", + " while i < N:\n", + " if positions[i] < cumulative_sum[j]:\n", + " indexes[i] = j\n", + " i += 1\n", + " else:\n", + " j += 1\n", + " return indexes```\n", "\n", "Import it from FilterPy with\n", "\n", - " from filterpy.monte_carlo import stratified_resample" + "```python\n", + "from filterpy.monte_carlo import stratified_resample```" ] }, { @@ -1369,7 +1378,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": { "collapsed": false }, @@ -1378,7 +1387,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1396,26 +1405,28 @@ "source": [ "The code couldn't be simpler.\n", "\n", - " def systematic_resample(weights):\n", - " N = len(weights)\n", + "```python\n", + "def systematic_resample(weights):\n", + " N = len(weights)\n", "\n", - " # make N subdivisions, choose positions with a consistent random offset\n", - " positions = (np.arange(N) + random()) / N\n", + " # make N subdivisions, choose positions with a consistent random offset\n", + " positions = (np.arange(N) + random()) / N\n", "\n", - " indexes = np.zeros(N, 'i')\n", - " cumulative_sum = np.cumsum(weights)\n", - " i, j = 0, 0\n", - " while i < N:\n", - " if positions[i] < cumulative_sum[j]:\n", - " indexes[i] = j\n", - " i += 1\n", - " else:\n", - " j += 1\n", - " return indexes\n", + " indexes = np.zeros(N, 'i')\n", + " cumulative_sum = np.cumsum(weights)\n", + " i, j = 0, 0\n", + " while i < N:\n", + " if positions[i] < cumulative_sum[j]:\n", + " indexes[i] = j\n", + " i += 1\n", + " else:\n", + " j += 1\n", + " return indexes```\n", " \n", "Import from FilterPy with\n", "\n", - " from filterpy.monte_carlo import systematic_resample" + "```python\n", + " from filterpy.monte_carlo import systematic_resample```" ] }, { @@ -1429,7 +1440,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": { "collapsed": false, "scrolled": true @@ -1439,7 +1450,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1524,16 +1535,6 @@ "\n", "\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] } ], "metadata": { @@ -1552,7 +1553,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.3" + "version": "3.4.1" } }, "nbformat": 4, diff --git a/13-Smoothing.ipynb b/13-Smoothing.ipynb index fcad42a..1190d32 100644 --- a/13-Smoothing.ipynb +++ b/13-Smoothing.ipynb @@ -483,20 +483,21 @@ "\n", "As always, the hardest part of the implementation is correctly accounting for the subscripts. A basic implementation without comments or error checking would be:\n", "\n", - " def rts_smoother(Xs, Ps, F, Q):\n", - " n, dim_x, _ = Xs.shape\n", - " \n", - " # smoother gain\n", - " K = zeros((n,dim_x, dim_x))\n", - " x, P = Xs.copy(), Ps.copy()\n", + "```python\n", + "def rts_smoother(Xs, Ps, F, Q):\n", + " n, dim_x, _ = Xs.shape\n", "\n", - " for k in range(n-2,-1,-1):\n", - " P_pred = dot(F, P[k]).dot(F.T) + Q\n", + " # smoother gain\n", + " K = zeros((n,dim_x, dim_x))\n", + " x, P = Xs.copy(), Ps.copy()\n", "\n", - " K[k] = dot(P[k], F.T).dot(inv(P_pred))\n", - " x[k] += dot(K[k], x[k+1] - dot(F, x[k]))\n", - " P[k] += dot(K[k], P[k+1] - P_pred).dot(K[k].T)\n", - " return (x, P, K)\n", + " for k in range(n-2,-1,-1):\n", + " P_pred = dot(F, P[k]).dot(F.T) + Q\n", + "\n", + " K[k] = dot(P[k], F.T).dot(inv(P_pred))\n", + " x[k] += dot(K[k], x[k+1] - dot(F, x[k]))\n", + " P[k] += dot(K[k], P[k+1] - P_pred).dot(K[k].T)\n", + " return (x, P, K)```\n", " \n", "This implementation mirrors the implementation provided in FilterPy. It assumes that the Kalman filter is being run externally in batch mode, and the results of the state and covariances are passed in via the `Xs` and `Ps` variable.\n", "\n", @@ -841,7 +842,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.3" + "version": "3.4.1" } }, "nbformat": 4, diff --git a/14-Adaptive-Filtering.ipynb b/14-Adaptive-Filtering.ipynb index 15da4e9..a82c2ad 100644 --- a/14-Adaptive-Filtering.ipynb +++ b/14-Adaptive-Filtering.ipynb @@ -533,20 +533,21 @@ "source": [ "To be clear about the dimension reduction, if we wanted to track both *x* and *y* we would have written\n", "\n", - " cvfilter = KalmanFilter(dim_x = 4, dim_z=2)\n", - " cvfilter.x = array([0., 0., 0., 0.])\n", - " cvfilter.P *= 300\n", - " cvfilter.R *= 1\n", - " cvfilter.F = array([[1, dt, 0, 0],\n", - " [0, 1, 0, 0],\n", - " [0, 0, 1, dt],\n", - " [0, 0, 0, 1]], dtype=float)\n", - " cvfilter.H = array([[1, 0, 0, 0],\n", - " [0, 0, 1, 0]], dtype=float)\n", + "```python\n", + "cvfilter = KalmanFilter(dim_x = 4, dim_z=2)\n", + "cvfilter.x = array([0., 0., 0., 0.])\n", + "cvfilter.P *= 300\n", + "cvfilter.R *= 1\n", + "cvfilter.F = array([[1, dt, 0, 0],\n", + " [0, 1, 0, 0],\n", + " [0, 0, 1, dt],\n", + " [0, 0, 0, 1]], dtype=float)\n", + "cvfilter.H = array([[1, 0, 0, 0],\n", + " [0, 0, 1, 0]], dtype=float)\n", "\n", - " q = Q_discrete_white_noise(dim=2, dt=dt, var=0.020)\n", - " cvfilter.Q[0:2, 0:2] = q\n", - " cvfilter.Q[2:4, 2:4] = q" + "q = Q_discrete_white_noise(dim=2, dt=dt, var=0.020)\n", + "cvfilter.Q[0:2, 0:2] = q\n", + "cvfilter.Q[2:4, 2:4] = q```" ] }, { @@ -891,11 +892,11 @@ "\n", "That may seem like a subtle distinction, but from the plots you see it is not. The amount of deviation in the left plot when the maneuver starts is small, but the deviation in the right tells a very different story. If the tracked object was moving according to the process model the residual plot should bounce around 0.0. This is because the measurements will be obeying the equation \n", "\n", - "$$measurement = process\\_model(t) + noise(t)$$.\n", + "$$\\mathtt{measurement} = \\mathtt{process\\_model}(t) + \\mathtt{noise}(t)$$.\n", "\n", "Once the target starts maneuvering the predictions of the target behavior will not match the behavior as the equation will be \n", "\n", - "$$measurement = process\\_model(t) + maneuver\\_delta(t) + noise(t)$$.\n", + "$$\\mathtt{measurement} = \\mathtt{process\\_model}(t) + \\mathtt{maneuver\\_delta}(t) + \\mathtt{noise}(t)$$.\n", "\n", "Therefore if the residuals diverge from a mean of 0.0 we know that a maneuver has commenced.\n", "\n", @@ -1677,7 +1678,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.3" + "version": "3.4.1" } }, "nbformat": 4, diff --git a/Appendix-E-Ensemble-Kalman-Filters.ipynb b/Appendix-E-Ensemble-Kalman-Filters.ipynb index 4f8d02c..016ba1b 100644 --- a/Appendix-E-Ensemble-Kalman-Filters.ipynb +++ b/Appendix-E-Ensemble-Kalman-Filters.ipynb @@ -360,8 +360,9 @@ "\n", "This just says to select the sigma points from the filter's initial mean and covariance. In code this might look like\n", "\n", - " N = 1000\n", - " sigmas = multivariate_normal(mean=x, cov=P, size=N)" + "```python\n", + "N = 1000\n", + "sigmas = multivariate_normal(mean=x, cov=P, size=N)```" ] }, { @@ -382,26 +383,18 @@ "\\end{aligned}\n", "$$\n", "\n", - "That is short and sweet, but perhaps not entirely clear. The first line passes all of the sigma points through a use supplied state transition function and then adds some noise distributed according to the $\\mathbf{Q}$ matrix. In Python we might write" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " for i, s in enumerate(sigmas):\n", - " sigmas[i] = fx(x=s, dt=0.1, u=0.)\n", + "That is short and sweet, but perhaps not entirely clear. The first line passes all of the sigma points through a use supplied state transition function and then adds some noise distributed according to the $\\mathbf{Q}$ matrix. In Python we might write\n", + "\n", + "```python\n", + "for i, s in enumerate(sigmas):\n", + " sigmas[i] = fx(x=s, dt=0.1, u=0.)\n", + "\n", + "sigmas += multivariate_normal(x, Q, N)```\n", "\n", - " sigmas += multivariate_normal(x, Q, N)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ "The second line computes the mean from the sigmas. In Python we will take advantage of `numpy.mean` to do this very concisely and quickly.\n", "\n", - " x = np.mean(sigmas, axis=0)" + "```python\n", + "x = np.mean(sigmas, axis=0)```" ] }, { @@ -414,10 +407,11 @@ "\n", "$\\boldsymbol\\chi-\\mathbf{x}^-$ is a one dimensional vector, so we will use `numpy.outer` to compute the $[\\boldsymbol\\chi-\\mathbf{x}^-][\\boldsymbol\\chi-\\mathbf{x}^-]^\\mathsf{T}$ term. In Python we might write\n", "\n", + "```python\n", " P = 0\n", " for s in sigmas:\n", " P += outer(s-x, s-x)\n", - " P = P / (N-1)" + " P = P / (N-1)```" ] }, { @@ -460,7 +454,8 @@ "\n", "just passes the sigma points through the measurement function $h$. We name the resulting points $\\chi_h$ to distinguish them from the sigma points. In Python we could write this as\n", "\n", - " sigmas_h = h(sigmas, u)" + "```python\n", + "sigmas_h = h(sigmas, u)```" ] }, { @@ -473,7 +468,8 @@ "\n", "In Python we can compute that with\n", "\n", - " z_mean = np.mean(sigmas_h, axis=0)\n", + "```python\n", + "z_mean = np.mean(sigmas_h, axis=0)```\n", " \n", "Now that we have the mean of the measurement sigmas we can compute the covariance for every measurement sigma point, and the *cross variance* for the measurement sigma points vs the sigma points. That is expressed by these two equations\n", "\n", @@ -485,16 +481,17 @@ "\n", "We can express this in Python with\n", "\n", - " P_zz = 0\n", - " for sigma in sigmas_h:\n", - " s = sigma - z_mean\n", - " P_zz += outer(s, s)\n", - " P_zz = P_zz / (N-1) + R\n", + "```python\n", + "P_zz = 0\n", + "for sigma in sigmas_h:\n", + " s = sigma - z_mean\n", + " P_zz += outer(s, s)\n", + "P_zz = P_zz / (N-1) + R\n", "\n", - " P_xz = 0\n", - " for i in range(N):\n", - " P_xz += outer(self.sigmas[i] - self.x, sigmas_h[i] - z_mean)\n", - " P_xz /= N-1" + "P_xz = 0\n", + "for i in range(N):\n", + " P_xz += outer(self.sigmas[i] - self.x, sigmas_h[i] - z_mean)\n", + "P_xz /= N-1```" ] }, { @@ -503,9 +500,10 @@ "source": [ "Computation of the Kalman gain is straightforward $\\mathbf{K} = \\mathbf{P}_{xz} \\mathbf{P}_{zz}^{-1}$.\n", "\n", - "In Python this is the trivial\n", + "In Python this is\n", "\n", - " K = np.dot(P_xz, inv(P_zz))\n", + "```python\n", + "K = np.dot(P_xz, inv(P_zz))```\n", "\n", "Next, we update the sigma points with\n", "\n", @@ -513,15 +511,17 @@ "\n", "Here $\\mathbf{v}_R$ is the perturbation that we add to the sigmas. In Python we can implement this with\n", "\n", - " v_r = multivariate_normal([0]*dim_z, R, N)\n", - " for i in range(N):\n", - " sigmas[i] += dot(K, z + v_r[i] - sigmas_h[i])\n", + "```python\n", + "v_r = multivariate_normal([0]*dim_z, R, N)\n", + "for i in range(N):\n", + " sigmas[i] += dot(K, z + v_r[i] - sigmas_h[i])```\n", "\n", "\n", "Our final step is recompute the filter's mean and covariance.\n", "\n", - " x = np.mean(sigmas, axis=0)\n", - " P = self.P - dot3(K, P_zz, K.T)" + "```python\n", + " x = np.mean(sigmas, axis=0)\n", + " P = self.P - dot3(K, P_zz, K.T)```" ] }, { @@ -549,12 +549,13 @@ "\n", "The EnKF is designed for nonlinear problems, so instead of using matrices to implement the state transition and measurement functions you will need to supply Python functions. For this problem they can be written as:\n", "\n", - " def hx(x):\n", - " return np.array([x[0]])\n", + "```python\n", + "def hx(x):\n", + " return np.array([x[0]])\n", + "\n", + "def fx(x, dt):\n", + " return np.dot(F, x)```\n", "\n", - " def fx(x, dt):\n", - " return np.dot(F, x)\n", - " \n", "One final thing: the EnKF code, like the UKF code, uses a single dimension for $\\mathbf{x}$, not a two dimensional column matrix as used by the linear kalman filter code.\n", "\n", "Without further ado, here is the code." @@ -738,7 +739,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.3" + "version": "3.4.1" } }, "nbformat": 4, diff --git a/Interactions.ipynb b/Interactions.ipynb index 676153f..f3cdd7c 100644 --- a/Interactions.ipynb +++ b/Interactions.ipynb @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "collapsed": false, "scrolled": true @@ -61,9 +61,9 @@ "outputs": [ { "data": { 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atCnmzp2LZ555pvIXYSCXxdUIyanEx8fjjTfewFdffYW8/EtmDRo0CC+++CLG\njx9v3MUiakFiYiJWrFiBFStW4PLlywCAJk2aYPbs2XjuuefQsGFDgyskZ8MAJ6d0/vx5vP/++1ix\nYgVSUlIAAEFBQZg0aRImT56Mvn37QlxgXeqUlBRs3rwZa9aswcaNG5Gbf4GHTp064amnnsJjjz2G\nRnXg0mBUNQxwcmq3b9/GypUrsWTJEsTHxxc8HxISgsmTJ+Phhx9Gjx49TDV9LikpCRs2bMDatWux\nffv2gpOaPDw8MGHCBDz99NMYMmSIS/yBoppVowEuIg8DWACgE4A+SqnD5WzHAKcKKaVw6NAhrF69\nGl9++WXBEAOgz/YcNmwYhg8fjhEjRqB9+/YGVlpaSkoK9u3bh927d2PPnj04ePBgQU9bRDBw4EBM\nmDABkydP5tK7VCk1HeCdAOQBWAbgZQY4OUJubi6ioqKwevVqbNmyBQkJCcVeb9u2LXr37o3w8HCE\nh4ejR48eaNWqVa30aJOTkxEXF4fY2FjExsZi//79iI6OLhjPBwB3d3cMGzYMEyZMQEREBEObqqxW\nhlBEZBcY4FQDlFI4ffo0duzYgZ07d+L777/HzZs3S23XtGlTdO7cGa1bty7VGjduDB8fH/j4+MDb\n27vMA6W5ubm4c+cOUlJScPny5VLt3LlziI2NxcWLF0t9rYeHB/r06YMhQ4Zg8ODB6N+/P/z8/Grk\n+0F1CwOcXEpubi7i4uIQExODI0eOFNwmJSXZ/R5eXl5o0KABlFLIyclBdna23Ytv1a9fH126dEFY\nWBjCwsLQu3dv9OvXD97e3lX9JxGVy54A97jLG0QCKOsz4Dyl1AZ7C1mwYEHBfYvFAovFYu+XEhVw\nd3dH9+7d0b17d0ydOhWA7qVfvHgRZ86cQUJCQrGWmJiI5ORkpKWlIS0tDenp6cjMzERmZmax9xUR\neHt7w8fHB4GBgQgKCkJQUBCCg4MRFBSE1q1bIywsDCEhIS491ZGMZbVaYbVaK/U17IFTnaGUwp07\nd3Dnzh24u7vDw8MDHh4e8PLy4qwQcjrV7oFXZl8Oeh+iGmPraXPIg1xFlSfYisjvRSQBwG8BbBKR\nLY4ri4iI7oYn8hAROSGuRkhE5MIY4EREJsUAJyIyKQY4EZFJMcCJiEyKAU5EZFIMcCIik2KAExGZ\nFAOciMikGOBERCbFACciMikGOBGRSTHAiYhMigFORGRSDHAiIpNigBMRmRQDnIjIpBjgREQmxQAn\nIjIpBjh45aAiAAAFpElEQVQRkUkxwImITIoBTkRkUgxwIiKTYoATEZkUA5yIyKQY4EREJsUAJyIy\nKQY4EZFJMcCJiEyKAU5EZFIMcCIik2KAExGZVJUDXET+ISLHRSRGRNaJiJ8jCyMioopVpwe+HUCY\nUqoHgFMAXnNMSUREZI8qB7hSKlIplZf/8ACAVo4piYiI7OGoMfDHAWx20HsREZEdPCp6UUQiAQSW\n8dI8pdSG/G1eB5CllPqiBuojIqJyVBjgSqmRFb0uItMAjAEwvKLtFixYUHDfYrHAYrHYWx8RUZ1g\ntVphtVor9TWilKrSzkRkNIB/AhiilLpewXaqqvsgIqqrRARKKalwm2oEeDyAegBu5D/1g1JqZhnb\nMcCJiCqpRgO8EkUwwImIKsmeAOeZmEREJsUAJyIyKQY4EZFJMcCJiEyKAU5EZFIMcCIik2KAExGZ\nFAOciMikGOBERCbFACciMikGOBGRSTHAiYhMigFORGRSDHAiIpNigBMRmRQDnIjIpBjgREQmxQAn\nIjIpBjgRkUkxwImITIoBns9qtRpdgl3MUKcZagRYp6OxztrHAM9nlh+qGeo0Q40A63Q01ln7GOBE\nRCbFACciMilRStXsDkRqdgdERC5KKSUVvV7jAU5ERDWDQyhERCbFACciMqlaC3AReVlE8kSkaW3t\nszJE5E0RiRGRIyKyU0RaG11TWUTkHyJyPL/WdSLiZ3RNZRGRh0UkTkRyReQ+o+spSURGi8gJEYkX\nkVeMrqcsIvKxiPwqIkeNrqUiItJaRHbl/7xjReR5o2sqSUTqi8iB/N/vYyLyd6NrqoiIuItItIhs\nqGi7Wgnw/DAcCeC/tbG/KnpbKdVDKRUOYD2A+UYXVI7tAMKUUj0AnALwmsH1lOcogN8D2GN0ISWJ\niDuA9wGMBtAFwBQR6WxsVWX6N3SNzi4bwEtKqTAAvwXwjLN9P5VSGQCG5v9+dwcwVEQGGlxWRV4A\ncAxAhQcpa6sH/v8AzK2lfVWJUup2kYe+AK4bVUtFlFKRSqm8/IcHALQysp7yKKVOKKVOGV1HOfoC\nOK2UOq+UygawBsB4g2sqRSkVBeCm0XXcjVLqilLqSP79VADHAQQbW1VpSqn0/Lv1ALgDuGFgOeUS\nkVYAxgD4EECFs1BqPMBFZDyARKXULzW9r+oSkUUicgHAowDeMroeOzwOYLPRRZhQSwAJRR4n5j9H\n1SQi7QD0hO5cOBURcRORIwB+BbBLKXXM6JrKsRjAHAB5d9vQwxF7E5FIAIFlvPQ69Ef8UUU3d8Q+\nq6KCOucppTYopV4H8LqIvAr9TXysVgvMd7c687d5HUCWUuqLWi2uCHvqdFKcO1sDRMQXwNcAXsjv\niTuV/E+u4fnHjbaJiEUpZTW4rGJE5AEAV5VS0SJiudv2DglwpdTIcorpCiAEQIyIAPrj/s8i0lcp\nddUR+66M8uoswxcwsGd7tzpFZBr0R6zhtVJQOSrx/XQ2FwEUPUjdGroXTlUkIp4A1gL4TCm13uh6\nKqKUShaRTQB6A7AaXE5J/QGME5ExAOoDaCQinyqlHilr4xodQlFKxSqlWiilQpRSIdC/JPcZEd53\nIyKhRR6OBxBtVC0VEZHR0B+vxucfmDEDwz51leMnAKEi0k5E6gGYBOA7g2syLdG9s48AHFNKvWN0\nPWUREX8RaZx/vwH0pAqn+x1XSs1TSrXOz8vJAL4vL7yB2p8H7swfXf8uIkfzx8gsAF42uJ7yLIE+\nyBqZP83oX0YXVBYR+b2IJEDPStgkIluMrslGKZUD4FkA26CP9H+plDpubFWlichqAPsBdBSRBBEx\nZEjPDgMA/B/omR3R+c3ZZs8EAfg+//f7AIANSqmdBtdkjwozk6fSExGZFM/EJCIyKQY4EZFJMcCJ\niEyKAU5EZFIMcCIik2KAExGZFAOciMikGOBERCb1/wEbhM1o5h+khwAAAABJRU5ErkJggg==\n", 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -196,7 +196,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.3" + "version": "3.4.1" } }, "nbformat": 4, diff --git a/code/nonlinear_plots.py b/code/nonlinear_plots.py index 9d3a465..c759768 100644 --- a/code/nonlinear_plots.py +++ b/code/nonlinear_plots.py @@ -238,7 +238,7 @@ def plot_monte_carlo_mean(xs, ys, f, mean_fx, label, plot_colormap=True): plt.scatter(fxs, fys, marker='.', alpha=0.02, color='k') plt.scatter(mean_fx[0], mean_fx[1], - marker='v', s=300, c='r', label='Linearized Mean') + marker='v', s=300, c='r', label=label) plt.scatter(computed_mean_x, computed_mean_y, marker='*',s=120, c='r', label='Computed Mean') diff --git a/code/ukf_internal.py b/code/ukf_internal.py index bd15b18..a699363 100644 --- a/code/ukf_internal.py +++ b/code/ukf_internal.py @@ -11,7 +11,9 @@ from filterpy.stats import plot_covariance_ellipse import matplotlib.pyplot as plt from matplotlib.patches import Ellipse,Arrow import math +from math import cos, sin, atan2, pi import numpy as np +from numpy.random import randn def _sigma_points(mean, sigma, kappa): sigma1 = mean + math.sqrt((1+kappa)*sigma) @@ -148,6 +150,13 @@ def show_3_sigma_points(): plt.plot(xs, ys) plt.show() +def _plot_sigmas(s, w, alpha=0.5, **kwargs): + min_w = min(abs(w)) + scale_factor = 100 / min_w + return plt.scatter(s[:, 0], s[:, 1], s=abs(w)*scale_factor, + alpha=alpha, **kwargs) + + def show_sigma_selections(): ax=plt.gca() ax.axes.get_xaxis().set_visible(False) @@ -156,22 +165,25 @@ def show_sigma_selections(): x = np.array([2, 5]) P = np.array([[3, 1.1], [1.1, 4]]) - points = MerweScaledSigmaPoints(2, .05, 2., 1.) + points = MerweScaledSigmaPoints(2, .09, 2., 1.) sigmas = points.sigma_points(x, P) - plot_covariance_ellipse(x, P, facecolor='b', alpha=0.6, variance=[.5]) - plt.scatter(sigmas[:,0], sigmas[:, 1], c='k', s=50) + Wm, Wc = points.weights() + plot_covariance_ellipse(x, P, facecolor='b', alpha=.3, variance=[.5]) + _plot_sigmas(sigmas, Wc, alpha=1.0, facecolor='k') x = np.array([5, 5]) - points = MerweScaledSigmaPoints(2, .15, 2., 1.) + points = MerweScaledSigmaPoints(2, .15, 1., .15) sigmas = points.sigma_points(x, P) - plot_covariance_ellipse(x, P, facecolor='b', alpha=0.6, variance=[.5]) - plt.scatter(sigmas[:,0], sigmas[:, 1], c='k', s=50) + Wm, Wc = points.weights() + plot_covariance_ellipse(x, P, facecolor='b', alpha=0.3, variance=[.5]) + _plot_sigmas(sigmas, Wc, alpha=1.0, facecolor='k') x = np.array([8, 5]) - points = MerweScaledSigmaPoints(2, .4, 2., 1.) + points = MerweScaledSigmaPoints(2, .2, 3., 10) sigmas = points.sigma_points(x, P) - plot_covariance_ellipse(x, P, facecolor='b', alpha=0.6, variance=[.5]) - plt.scatter(sigmas[:,0], sigmas[:, 1], c='k', s=50) + Wm, Wc = points.weights() + plot_covariance_ellipse(x, P, facecolor='b', alpha=0.3, variance=[.5]) + _plot_sigmas(sigmas, Wc, alpha=1.0, facecolor='k') plt.axis('equal') plt.xlim(0,10); plt.ylim(0,10) @@ -292,6 +304,105 @@ def plot_rts_output(xs, Ms, t): print('Difference in position in meters:', xs[-6:-1, 0] - Ms[-6:-1, 0]) +def plot_scatter_of_bearing_error(): + d = 100 + xs, ys = [], [] + + for i in range (3000): + a = math.radians(30) + randn() * math.radians(1) + xs.append(d*math.cos(a)) + ys.append(d*math.sin(a)) + plt.scatter(xs, ys) + + +def plot_scatter_moving_target(): + pos = np.array([5., 5.]) + for i in range(5): + pos += (0.5, 1.) + actual_angle = math.atan2(pos[1], pos[0]) + d = math.sqrt(pos[0]**2 + pos[1]**2) + + xs, ys = [], [] + for i in range (100): + a = actual_angle + randn() * math.radians(1) + xs.append(d*math.cos(a)) + ys.append(d*math.sin(a)) + plt.scatter(xs, ys) + + plt.axis('equal') + plt.plot([5.5, pos[0]], [6, pos[1]], c='g', linestyle='--') + + +def _isct(pa, pb, alpha, beta): + """ Returns the (x, y) intersections of points pa and pb + given the bearing ba for point pa and bearing bb for + point pb. + """ + + B = [pb[0] - pa[0], pb[1] - pa[1]] + AB = math.sqrt((pa[0] - pb[0])**2 + (pa[1] - pb[1])**2) + ab = atan2(B[1], B[0]) + a = alpha - ab + b = pi - beta - ab + p = pi - b - a + + AP = (sin(b) / sin(p)) * AB + x = cos(alpha) * AP + pa[0] + y = sin(alpha) * AP + pa[1] + return x, y + + +def _plot_iscts(pos, sa, sb, N=4): + for i in range(N): + pos += (0.5, 1.) + actual_angle_a = math.atan2(pos[1] - sa[1], pos[0] - sa[0]) + actual_angle_b = math.atan2(pos[1] - sb[1], pos[0] - sb[0]) + + da = math.sqrt((sa[0] - pos[0])**2 + (sa[1] - pos[1])**2) + db = math.sqrt((sb[0] - pos[0])**2 + (sb[1] - pos[1])**2) + + xs, ys, xs_a, xs_b, ys_a, ys_b = [], [], [], [], [], [] + + for i in range (300): + a_a = actual_angle_a + randn() * math.radians(1) + a_b = actual_angle_b + randn() * math.radians(1) + + x,y = _isct(sa, sb, a_a, a_b) + xs.append(x) + ys.append(y) + + xs_a.append(da*math.cos(a_a) + sa[0]) + ys_a.append(da*math.sin(a_a) + sa[1]) + + xs_b.append(db*math.cos(a_b) + sb[0]) + ys_b.append(db*math.sin(a_b) + sb[1]) + + plt.scatter(xs, ys, c='r', marker='.') + plt.scatter(xs_a, ys_a) + plt.scatter(xs_b, ys_b) + plt.gca().set_aspect('equal') + plt.show() + +def plot_iscts_two_sensors(): + pos = np.array([4., 4,]) + sa = [0., 2.] + sb = [8., 2.] + + plt.scatter(*sa, s=100) + plt.scatter(*sb, s=100) + _plot_iscts(pos, sa, sb, N=4) + + +def plot_iscts_two_sensors_changed_sensors(): + sa = [3, 4] + sb = [3, 7] + pos= np.array([3., 3.]) + + plt.scatter(*sa, s=100) + plt.scatter(*sb, s=100) + _plot_iscts(pos, sa, sb, N=5) + + if __name__ == '__main__': #show_2d_transform() diff --git a/pdf/clean_book b/pdf/clean_book index f35beee..3bdd8d4 100644 --- a/pdf/clean_book +++ b/pdf/clean_book @@ -9,6 +9,8 @@ rm --f Kalman_and_Bayesian_Filters_in_Python.ipynb rm --f Kalman_and_Bayesian_Filters_in_Python.toc rm --f Kalman_and_Bayesian_Filters_in_Python.tex rm --f short.ipynb +rm --f chapter.ipynb +rm --f chapter.pdf rmdir ./*_files/ 2> /dev/null diff --git a/pdf/clean_book.bat b/pdf/clean_book.bat index 3d6fdf8..a24bded 100644 --- a/pdf/clean_book.bat +++ b/pdf/clean_book.bat @@ -5,8 +5,12 @@ rm --f *.toc rm --f *.aux rm --f *.log rm --f *.out - rm --f book.ipynb rm --f book.toc rm --f book.tex + +rm --f chapter.ipynb +rm --f chapter.pdf + rmdir /S /Q book_files +rmdir /S /Q chapter_files diff --git a/pdf/make_chapter.bat b/pdf/make_chapter.bat new file mode 100644 index 0000000..5748258 --- /dev/null +++ b/pdf/make_chapter.bat @@ -0,0 +1,5 @@ +cp %1 chapter.ipynb +ipython nbconvert --to latex chapter.ipynb +ipython to_pdf.py chapter.tex + + diff --git a/pdf/to_pdf.py b/pdf/to_pdf.py index c235790..6497d71 100644 --- a/pdf/to_pdf.py +++ b/pdf/to_pdf.py @@ -1,6 +1,12 @@ import IPython.nbconvert.exporters.pdf as pdf +import sys -f = open('book.tex', 'r', encoding="iso-8859-1") +if len(sys.argv) == 2: + name = sys.argv[1] +else: + name = 'book.tex' + +f = open(name, 'r', encoding="iso-8859-1") filedata = f.read() f.close() @@ -11,5 +17,5 @@ f.write(newdata) f.close() p = pdf.PDFExporter() -p.run_latex('book.tex') +p.run_latex(name)