daviddoji/Kalman-and-Bayesian-Filters-in-Python
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Updated for Python 3.6

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Roger Labbe 2020-10-13 14:24:32 -07:00
parent bf04f49fa7
commit 8302572e60
1 changed files with 12 additions and 17 deletions
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29
02-Discrete-Bayes.ipynb
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@ -20,7 +20,6 @@
"metadata": {},
"outputs": [],
"source": [
"from __future__ import division, print_function\n",
"%matplotlib inline"
]
},
@ -36,7 +35,7 @@
" <style>\n",
" .output_wrapper, .output {\n",
" height:auto !important;\n",
" max-height:100000px; \n",
" max-height:100000px;\n",
" }\n",
" .output_scroll {\n",
" box-shadow:none !important;\n",
@ -582,7 +581,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6a0f5d2b16e54121bd8a3b11957d0966",
"model_id": "69e1cf784fd743ddaeae41d5fa161216",
"version_major": 2,
"version_minor": 0
},
@ -758,7 +757,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5e1bbeaf87af4835869cd7a6676fa209",
"model_id": "b7f90f29b9394f82b4c64982c8eab901",
"version_major": 2,
"version_minor": 0
},
@ -783,7 +782,7 @@
"# make interactive plot\n",
"def show_prior(step):\n",
" book_plots.bar_plot(predict_beliefs[step-1])\n",
" plt.title('Step {}'.format(step))\n",
" plt.title(f'Step {step}')\n",
"\n",
"interact(show_prior, step=IntSlider(value=1, max=len(predict_beliefs)));"
]
@ -1247,7 +1246,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9f7338d5d95a4133ae3825b18020b9cd",
"model_id": "5e52dc4ac04b4244ad4357a4fd837902",
"version_major": 2,
"version_minor": 0
},
@ -1300,7 +1299,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ffb4353460494a15ab0c0e9299dde120",
"model_id": "562f60dcb79f4651953d34fd40c1cab7",
"version_major": 2,
"version_minor": 0
},
@ -1382,7 +1381,7 @@
" posterior = update(likelihood, prior)\n",
" prior = predict(posterior, 1, kernel)\n",
" plt.subplot(5, 2, i+1)\n",
" book_plots.bar_plot(posterior, ylim=(0, .4), title='step {}'.format(i+1))\n",
" book_plots.bar_plot(posterior, ylim=(0, .4), title=f'step {i+1}')\n",
" plt.tight_layout()"
]
},
@ -1515,13 +1514,9 @@
" index = np.argmax(posterior)\n",
"\n",
" if do_print:\n",
" print('''time {}: pos {}, sensed {}, '''\n",
" '''at position {}'''.format(\n",
" i, robot.pos, m, track[robot.pos]))\n",
"\n",
" print(''' estimated position is {}'''\n",
" ''' with confidence {:.4f}%:'''.format(\n",
" index, posterior[index]*100)) \n",
" print(f'time {i}: pos {robot.pos}, sensed {m}, at position {track[robot.pos]}')\n",
" conf = posterior[index] * 100\n",
" print(f' estimated position is {index} with confidence {conf:.4f}%:') \n",
"\n",
" book_plots.bar_plot(posterior)\n",
" if do_print:\n",
@ -1661,7 +1656,7 @@
" train_filter(148+i, kernel=[.1, .8, .1], \n",
" sensor_accuracy=.8,\n",
" move_distance=4, do_print=False)\n",
" plt.title ('iteration {}'.format(148+i))"
" plt.title (f'iteration {148 + i}')"
]
},
{
@ -1777,7 +1772,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
"version": "3.7.4"
},
"widgets": {
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