diff --git a/07_Kalman_Filter_Math/Kalman_Filter_Math.ipynb b/07_Kalman_Filter_Math.ipynb similarity index 99% rename from 07_Kalman_Filter_Math/Kalman_Filter_Math.ipynb rename to 07_Kalman_Filter_Math.ipynb index 0291b5a..b4f60ca 100644 --- a/07_Kalman_Filter_Math/Kalman_Filter_Math.ipynb +++ b/07_Kalman_Filter_Math.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:f6eb3441648c1a743a0155aaa4a53c9703d22947e978a0eb7a6bce0eadc6a698" + "signature": "sha256:f8dcc12ca7ce84bf0c76ff9c37aab41847d1661cdde92d0fcb8b409ae6520cc6" }, "nbformat": 3, "nbformat_minor": 0, @@ -32,7 +32,7 @@ "from __future__ import division, print_function\n", "import matplotlib.pyplot as plt\n", "import sys\n", - "sys.path.insert(0, '../code') # allow us to format the book\n", + "sys.path.insert(0, './code') # allow us to format the book\n", "import book_format\n", "book_format.load_style()" ], @@ -261,7 +261,7 @@ "output_type": "pyout", "prompt_number": 1, "text": [ - "" + "" ] } ], @@ -633,7 +633,7 @@ " IEEE Trans. Automomatic Control, AC-23 (3): 395-404 (June 1978)\n", " \n", " [2] Robert Grover Brown. \"Introduction to Random Signals and Applied\n", - " Kalman Fitlering.\" Forth edition. John Wiley & Sons. p. 126-7. (2012)\n", + " Kalman Filtering.\" Forth edition. John Wiley & Sons. p. 126-7. (2012)\n", "\n" ] } @@ -739,7 +739,7 @@ "output_type": "display_data", "png": 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f/gpTI0O80cUZrZzkuJ96X9UnI+Mx7t2VVI+n9HaCicjCe1/sRFpmLhzkphjR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\n70CwAOA2hAoAALwHwQKAWxAqAADwLgQLAO2OUAE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"text": [ - "" + "" ] } ], @@ -1294,9 +1294,9 @@ "output_type": "pyout", "prompt_number": 13, "text": [ - "array([[ 1.56250000e-08, 7.81250000e-07, 2.08333333e-05],\n", - " [ 7.81250000e-07, 4.16666667e-05, 1.25000000e-03],\n", - " [ 2.08333333e-05, 1.25000000e-03, 5.00000000e-02]])" + "array([[ 0.00000002, 0.00000078, 0.00002083],\n", + " [ 0.00000078, 0.00004167, 0.00125 ],\n", + " [ 0.00002083, 0.00125 , 0.05 ]])" ] } ], diff --git a/07_Kalman_Filter_Math/README.md b/07_Kalman_Filter_Math/README.md deleted file mode 100644 index d041551..0000000 --- a/07_Kalman_Filter_Math/README.md +++ /dev/null @@ -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)