Added UKF/EKF comparison.
Had to adjust some examples and rerun notebooks in several places to account for some code chantes that I made.
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Roger Labbe committed
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@@ -19,7 +19,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"execution_count": 1,
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"metadata": {
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"collapsed": false
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},
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@@ -29,8 +29,8 @@
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"output_type": "stream",
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"text": [
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"50000\n",
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"3.00761397\n",
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"1.99265941012\n"
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"3.00043200502\n",
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"2.01072423406\n"
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]
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}
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],
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@@ -59,7 +59,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 64,
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"execution_count": 2,
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@@ -68,7 +68,7 @@
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"data": {
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truncated
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"text/plain": [
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},
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"metadata": {},
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@@ -99,7 +99,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"execution_count": 3,
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"metadata": {
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"collapsed": false,
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"scrolled": true
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@@ -107,9 +107,9 @@
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"outputs": [
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{
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"data": {
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"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x9bf1588>"
|
||||
"<matplotlib.figure.Figure at 0x46db518>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -130,16 +130,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x861ca90>"
|
||||
"<matplotlib.figure.Figure at 0x7fd8b00>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -160,16 +160,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x9a4e780>"
|
||||
"<matplotlib.figure.Figure at 0x82595f8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -190,16 +190,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x830f6d8>"
|
||||
"<matplotlib.figure.Figure at 0x851f860>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -208,6 +208,7 @@
|
||||
],
|
||||
"source": [
|
||||
"plt.hist(data, bins=200, normed=True, histtype='step')\n",
|
||||
"norm = stats.norm(mean, std)\n",
|
||||
"plt.plot(xs, norm.pdf(xs), color='k', lw=2)\n",
|
||||
"plt.show()"
|
||||
]
|
||||
@@ -221,16 +222,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 44,
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8531710>"
|
||||
"<matplotlib.figure.Figure at 0x7a13710>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -259,16 +260,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 93,
|
||||
"execution_count": 8,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0xaf517f0>"
|
||||
"<matplotlib.figure.Figure at 0x859ba90>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -278,8 +279,8 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"mean = 14.01\n",
|
||||
"std = 2.80\n"
|
||||
"mean = 13.96\n",
|
||||
"std = 2.81\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -323,16 +324,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 99,
|
||||
"execution_count": 9,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0xb0126d8>"
|
||||
"<matplotlib.figure.Figure at 0x95bb7f0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -342,7 +343,7 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"mean = -1.60\n",
|
||||
"mean = -1.57\n",
|
||||
"std = 2.10\n"
|
||||
]
|
||||
}
|
||||
@@ -378,7 +379,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 75,
|
||||
"execution_count": 10,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -389,7 +390,7 @@
|
||||
"-1.5*sin(x/3)*sin(1.5*x + 2.1) + cos(x/3)*cos(1.5*x + 2.1)/3 - 1.6"
|
||||
]
|
||||
},
|
||||
"execution_count": 75,
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -411,7 +412,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 95,
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -422,7 +423,7 @@
|
||||
"-1.66528051815545"
|
||||
]
|
||||
},
|
||||
"execution_count": 95,
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -441,30 +442,20 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 112,
|
||||
"execution_count": 12,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0xb0c47b8>"
|
||||
"<matplotlib.figure.Figure at 0xaa5bac8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"-2.33139272541763"
|
||||
]
|
||||
},
|
||||
"execution_count": 112,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
@@ -472,8 +463,7 @@
|
||||
"plot_normal(xs, f2(mean), abs(float(m)*std), color='k', lw=3, label='EKF')\n",
|
||||
"plot_normal(xs, d_t.mean(), d_t.std(), color='r', lw=3, label='MC')\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()\n",
|
||||
"m*stdb"
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
Reference in new issue
Block a user