daviddoji/Kalman-and-Bayesian-Filters-in-Python
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Merge pull request #256 from belm0/g_h_filter_example

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improve naming in ch 1 "predict_using_gain_guess" example

I (Roger Labbe) need to make further modifications to the function as explained above.
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Roger Labbe 2018-11-02 14:17:09 -07:00 committed by GitHub
commit 034f2b809a
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2 changed files with 22 additions and 22 deletions
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42
01-g-h-filter.ipynb
View File

@ -583,18 +583,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
"previous: 160.00, prediction: 161.00 estimate 159.80\n",
"previous: 159.80, prediction: 160.80 estimate 162.16\n",
"previous: 162.16, prediction: 163.16 estimate 162.02\n",
"previous: 162.02, prediction: 163.02 estimate 161.77\n",
"previous: 161.77, prediction: 162.77 estimate 162.50\n",
"previous: 162.50, prediction: 163.50 estimate 163.94\n",
"previous: 163.94, prediction: 164.94 estimate 166.80\n",
"previous: 166.80, prediction: 167.80 estimate 167.64\n",
"previous: 167.64, prediction: 168.64 estimate 167.75\n",
"previous: 167.75, prediction: 168.75 estimate 169.65\n",
"previous: 169.65, prediction: 170.65 estimate 170.87\n",
"previous: 170.87, prediction: 171.87 estimate 172.16\n"
"previous estimate: 160.00, prediction: 161.00, estimate 159.80\n",
"previous estimate: 159.80, prediction: 160.80, estimate 162.16\n",
"previous estimate: 162.16, prediction: 163.16, estimate 162.02\n",
"previous estimate: 162.02, prediction: 163.02, estimate 161.77\n",
"previous estimate: 161.77, prediction: 162.77, estimate 162.50\n",
"previous estimate: 162.50, prediction: 163.50, estimate 163.94\n",
"previous estimate: 163.94, prediction: 164.94, estimate 166.80\n",
"previous estimate: 166.80, prediction: 167.80, estimate 167.64\n",
"previous estimate: 167.64, prediction: 168.64, estimate 167.75\n",
"previous estimate: 167.75, prediction: 168.75, estimate 169.65\n",
"previous estimate: 169.65, prediction: 170.65, estimate 170.87\n",
"previous estimate: 170.87, prediction: 171.87, estimate 172.16\n"
]
}
],
@ -608,30 +608,30 @@
"time_step = 1.0 # day\n",
"scale_factor = 4.0/10\n",
"\n",
"def predict_using_gain_guess(weight, gain_rate, do_print=False): \n",
"def predict_using_gain_guess(estimate, gain_rate, do_print=False): \n",
" # store the filtered results\n",
" estimates, predictions = [weight], []\n",
" estimates, predictions = [estimate], []\n",
"\n",
" # most filter literature uses 'z' for measurements\n",
" for z in weights: \n",
" # predict new position\n",
" prediction = weight + gain_rate * time_step\n",
" prediction = estimate + gain_rate * time_step\n",
"\n",
" # update filter \n",
" weight = prediction + scale_factor * (z - prediction)\n",
" estimate = prediction + scale_factor * (z - prediction)\n",
"\n",
" # save\n",
" estimates.append(weight)\n",
" estimates.append(estimate)\n",
" predictions.append(prediction)\n",
" if do_print:\n",
" gh.print_results(estimates, prediction, weight)\n",
" gh.print_results(estimates, prediction, estimate)\n",
"\n",
" return estimates, predictions\n",
"\n",
"initial_guess = 160.\n",
"initial_estimate = 160.\n",
"\n",
"estimates, predictions = predict_using_gain_guess(\n",
" weight=initial_guess, gain_rate=1, do_print=True) "
" estimate=initial_estimate, gain_rate=1, do_print=True) "
]
},
{
@ -690,7 +690,7 @@
}
],
"source": [
"e, p = predict_using_gain_guess(initial_guess, -1.)\n",
"e, p = predict_using_gain_guess(initial_estimate, -1.)\n",
"gh.plot_gh_results(weights, e, p)"
]
},

2
kf_book/gh_internal.py
View File

@ -191,7 +191,7 @@ def plot_gh_results(weights, estimates, predictions, time_step=0):
def print_results(estimates, prediction, weight):
print('previous: {:.2f}, prediction: {:.2f} estimate {:.2f}'.format(
print('previous estimate: {:.2f}, prediction: {:.2f}, estimate {:.2f}'.format(
estimates[-2], prediction, weight))