From 0b37a768de006188d3af3dc3cfbd4aa108870f05 Mon Sep 17 00:00:00 2001 From: Roger Labbe Date: Fri, 16 May 2014 14:16:51 -0700 Subject: [PATCH] Added link to book in nbviewer to top of each page. --- Gaussians.ipynb | 14 +- Introduction.ipynb | 6 +- Kalman_Filters.ipynb | 19 ++- Multidimensional_Kalman_Filters.ipynb | 232 ++------------------------ g-h_filter.ipynb | 14 +- histogram_filter.ipynb | 4 +- 6 files changed, 43 insertions(+), 246 deletions(-) diff --git a/Gaussians.ipynb b/Gaussians.ipynb index d967c55..5486ffc 100644 --- a/Gaussians.ipynb +++ b/Gaussians.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:effec8da00a64ca2176d7b2e8c409aebee9e9defbf38ad9d1908494be9694875" + "signature": "sha256:92b908cbe06445f741a756723f6dfbc9dff00066e4025d0ca2351b1c6fcab4dd" }, "nbformat": 3, "nbformat_minor": 0, @@ -12,9 +12,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#Gaussian Probabilities\n", - "\n", - "Before we begin, we need to set the book's style." + "
[Kalman Filters and Random Signals in Python](http://github.com/rlabbe/Kalman-Filters-and-Random-Signals-in-Python)
\n" ] }, { @@ -238,18 +236,20 @@ ], "metadata": {}, "output_type": "pyout", - "prompt_number": 8, + "prompt_number": 1, "text": [ - "" + "" ] } ], - "prompt_number": 8 + "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ + "#Gaussian Probabilities\n", + "\n", "### Introduction\n", "\n", "The last chapter ended by discussing some of the drawbacks of the Discrete Bayesian filter. For many tracking and filtering problems our desire is to have a filter that is *unimodal* and *continuous*. That is, we want to model our system using floating point math (continuous) and to have only one belief represented (unimodal). For example, we want to say an aircraft is at (12.34381, -95.54321,2389.5) where that is latitude, longitude, and altidue. We do not want our filter to tell us \"it might be at (1,65,78) or at (34,656,98)\" That doesn't match our physical intuition of how the world works, and as we discussed, it is prohibitively expensive to compute.\n", diff --git a/Introduction.ipynb b/Introduction.ipynb index ef1fb8e..f3ffb65 100644 --- a/Introduction.ipynb +++ b/Introduction.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:e0ecac7790474bde2b505f02a8ac90ba8b03bc7b61101e309b6e7ce83f8d7abc" + "signature": "sha256:3387e32099123fdcfb3ec05bae2e0ebcbd26fc18d196ebfde1c597a02240f178" }, "nbformat": 3, "nbformat_minor": 0, @@ -12,7 +12,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Before we begin, we need to set the book's style." + "
[Kalman Filters and Random Signals in Python](http://github.com/rlabbe/Kalman-Filters-and-Random-Signals-in-Python)
" ] }, { @@ -249,7 +249,7 @@ "metadata": {}, "source": [ "#Introduction\n", - "##### Version 0.1\n", + "##### Version 0.0\n", "\n", "testthis\n", "\n", diff --git a/Kalman_Filters.ipynb b/Kalman_Filters.ipynb index cc519d8..38c5cdf 100644 --- a/Kalman_Filters.ipynb +++ b/Kalman_Filters.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:f25d2a0f796f1111451e1f65bc09a5db2fa05d439022118e16d8410eb07e755f" + "signature": "sha256:0c2ad6510d01279f1acade52799c2ef6e47a17779f7f6cce2784faed828a9fdf" }, "nbformat": 3, "nbformat_minor": 0, @@ -12,8 +12,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Kalman Filters\n", - "Now that we understand the histogram filter and Gaussians we are prepared to implement a 1D Kalman filter. We will do this exactly as we did the histogram filter - rather than going into the theory we will just develop the code step by step. But first, let's set the book style." + "
[Kalman Filters and Random Signals in Python](http://github.com/rlabbe/Kalman-Filters-and-Random-Signals-in-Python)
" ] }, { @@ -237,13 +236,21 @@ ], "metadata": {}, "output_type": "pyout", - "prompt_number": 6, + "prompt_number": 1, "text": [ - "" + "" ] } ], - "prompt_number": 6 + "prompt_number": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Kalman Filters\n", + "Now that we understand the histogram filter and Gaussians we are prepared to implement a 1D Kalman filter. We will do this exactly as we did the histogram filter - rather than going into the theory we will just develop the code step by step. But first, let's set the book style." + ] }, { "cell_type": "markdown", diff --git a/Multidimensional_Kalman_Filters.ipynb b/Multidimensional_Kalman_Filters.ipynb index cc4fc6b..fb9d5df 100644 --- a/Multidimensional_Kalman_Filters.ipynb +++ b/Multidimensional_Kalman_Filters.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:a6b3d341e235ad0a59766c340223d8f6a8c7a2128056e004ae5a109129dd293a" + "signature": "sha256:93f2aa8528b9bbe96c77fd678d6f33b1dfde74509596240e46ec4c363f0db314" }, "nbformat": 3, "nbformat_minor": 0, @@ -12,9 +12,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "

Multidimensional

\n", - "

Kalman Filters

\n", - "Before we begin, we need to set the book's style." + "
[Kalman Filters and Random Signals in Python](http://github.com/rlabbe/Kalman-Filters-and-Random-Signals-in-Python)
" ] }, { @@ -28,223 +26,15 @@ ], "language": "python", "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - "\n" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 25, - "text": [ - "" - ] - } - ], - "prompt_number": 25 + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Multidimensional

\n", + "

Kalman Filters

" + ] }, { "cell_type": "markdown", diff --git a/g-h_filter.ipynb b/g-h_filter.ipynb index aed3f9a..d352d7b 100644 --- a/g-h_filter.ipynb +++ b/g-h_filter.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:82faa9340e18d14b79321a236acd20d0ba6eba274151ae39fd0fa04f7496473a" + "signature": "sha256:96eeab1151a83e762e6d09fe5a29ed386267e49e5822fdb12c8694bd925f3d4b" }, "nbformat": 3, "nbformat_minor": 0, @@ -12,9 +12,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Building Intuition via Thought Experiments\n", - "\n", - "Before we begin, we need to set the book's style." + "
[Kalman Filters and Random Signals in Python](http://github.com/rlabbe/Kalman-Filters-and-Random-Signals-in-Python)
" ] }, { @@ -238,18 +236,20 @@ ], "metadata": {}, "output_type": "pyout", - "prompt_number": 16, + "prompt_number": 1, "text": [ - "" + "" ] } ], - "prompt_number": 16 + "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ + "### Building Intuition via Thought Experiments\n", + ".\n", "Imagine we live in a world without scales - the devices you stand on to weigh yourself. One day at work a coworker comes running up to you and announces her invention to you. After she explains, you eagerly stand on it and announce the results: \"172 lbs\". You are estatic - for the first time in your life you know what you weigh. More importantly, dollar signs dance in your eyes as you imagine selling this device to weight loss clinics across the world! This is fantastic!\n", "\n", "Another coworker hears the commotion and walks over to find out what has you so excited. You explain the invention and once again step onto the scale, and proudly proclaim the result: \"161 lbs.\" And then you hesitate, confused.\n", diff --git a/histogram_filter.ipynb b/histogram_filter.ipynb index 3ac8128..26a8c00 100644 --- a/histogram_filter.ipynb +++ b/histogram_filter.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:42bb8e5d1f668d44e5a23d83f81c99c39dd2677d0be5f61bf2fe32773e18d86f" + "signature": "sha256:1c4f69bdeaf7cb502d55b13766010a7c6c8c1f59067e8fc0b1973c381d435c6b" }, "nbformat": 3, "nbformat_minor": 0, @@ -12,7 +12,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Before we begin, we need to set the book's style." + "
[Kalman Filters and Random Signals in Python](http://github.com/rlabbe/Kalman-Filters-and-Random-Signals-in-Python)
" ] }, {