Moved chapter 4 as part of reorg
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Roger Labbe committed
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4 files changed
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-26
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@@ -1,7 +1,7 @@
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{
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"metadata": {
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"name": "",
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"signature": "sha256:0e132ac8cc355b74191dd1be26d43c2cb06906e475027ce68a2bc08a6fab0984"
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"signature": "sha256:66e678fa200bb5f5cf2b969a860179e3c9d92699768f7e8146e5160a54619519"
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},
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"nbformat": 3,
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"nbformat_minor": 0,
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@@ -32,7 +32,7 @@
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"from __future__ import division, print_function\n",
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"import matplotlib.pyplot as plt\n",
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"import sys\n",
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"sys.path.insert(0, '../code') # allow us to format the book\n",
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"sys.path.insert(0, './code') # allow us to format the book\n",
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"import book_format\n",
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"book_format.load_style()"
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],
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@@ -261,7 +261,7 @@
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"output_type": "pyout",
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@@ -302,7 +302,7 @@
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@@ -348,7 +348,7 @@
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@@ -425,7 +425,7 @@
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||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fa7c9c539b0>"
|
||||
"<matplotlib.figure.Figure at 0x7f9fe6c91e48>"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -556,7 +556,7 @@
|
||||
"output_type": "display_data",
|
||||
"png": 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|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fa7c9a9f080>"
|
||||
"<matplotlib.figure.Figure at 0x7f9fe6add940>"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -598,7 +598,7 @@
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fa7c9c5e780>"
|
||||
"<matplotlib.figure.Figure at 0x7f9fe6e08c50>"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -654,7 +654,7 @@
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fa7c9cd9f60>"
|
||||
"<matplotlib.figure.Figure at 0x7f9fe6d01f60>"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -674,7 +674,7 @@
|
||||
"source": [
|
||||
"Finally, if you are reading this in an IPython Notebook, here is an animation of a Gaussian. First, the mean is being shifted to the right. Then the mean is centered at $\\mu=5$ and the variance is modified.\n",
|
||||
"\n",
|
||||
"<img src='gaussian_animate.gif'>"
|
||||
"<img src='animations/04_gaussian_animate.gif'>"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -788,9 +788,9 @@
|
||||
"output_type": "stream",
|
||||
"stream": "stdout",
|
||||
"text": [
|
||||
"[-0.91921644 4.58775212 1.05856827 3.83479323 6.01848015 -1.41305258\n",
|
||||
" -1.97691685 -3.00926484 6.59031405 -1.99993401 5.14010913 4.05641238\n",
|
||||
" -1.57482631 2.70415239 0.49846418]\n"
|
||||
"[-0.56643549 5.55377853 3.20392736 -2.46707904 5.05869208 6.0326174\n",
|
||||
" 1.80130981 1.41284223 3.22560866 5.97999268 4.22365687 5.92671618\n",
|
||||
" -6.25729187 6.92417721 -2.47483372]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -896,7 +896,7 @@
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fa7d23cb518>"
|
||||
"<matplotlib.figure.Figure at 0x7f9ff81a0320>"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -950,9 +950,9 @@
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fa7c9e35eb8>"
|
||||
"<matplotlib.figure.Figure at 0x7f9fe6e30cc0>"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1004,9 +1004,9 @@
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"png": "iVBORw0KGgoAAAANSUhEUgAAAyIAAAGNCAYAAADtmMVMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fa7c9d815f8>"
|
||||
"<matplotlib.figure.Figure at 0x7f9fe6e400f0>"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1,2 +0,0 @@
|
||||
You may read this book online via nbviewer by using this link:
|
||||
[*Read Online Now*](http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/table_of_contents.ipynb)
|
||||
File renamed without changes.
@@ -1,7 +1,7 @@
|
||||
{
|
||||
"metadata": {
|
||||
"name": "",
|
||||
"signature": "sha256:c9bc1ab3d4c4d98f1a764e1c04e56097cb0fd31b74ccc69ddffcc67d4d96ef4d"
|
||||
"signature": "sha256:aada3223e5395f37eadc453245d1072d753bcb8ccf236d890bad5a586a4ed406"
|
||||
},
|
||||
"nbformat": 3,
|
||||
"nbformat_minor": 0,
|
||||
@@ -20,7 +20,7 @@
|
||||
"# use same formatting as rest of book so that the plots are\n",
|
||||
"# consistant with that look and feel.\n",
|
||||
"import book_format\n",
|
||||
"book_format.load_style()"
|
||||
"book_format.load_style(directory='..')"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
@@ -247,7 +247,7 @@
|
||||
"output_type": "pyout",
|
||||
"prompt_number": 1,
|
||||
"text": [
|
||||
"<IPython.core.display.HTML at 0x7f1f0bb83f60>"
|
||||
"<IPython.core.display.HTML at 0x7fde2b0ceb38>"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -285,8 +285,7 @@
|
||||
" plt.cla()\n",
|
||||
" plt_g(mu,sigma)\n",
|
||||
"\n",
|
||||
"animate('gaussian_animate.gif', ganimate, frames=80, interval=50)\n",
|
||||
"animate??"
|
||||
"animate('04_gaussian_animate.gif', ganimate, frames=80, interval=50)"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
@@ -296,7 +295,7 @@
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7f1ee8230438>"
|
||||
"<matplotlib.figure.Figure at 0x7fde0e233e80>"
|
||||
]
|
||||
}
|
||||
],
|
||||
Reference in new issue
Block a user