diff --git a/JSL_notebook.ipynb b/JSL_notebook.ipynb index 5a54aba..42211b2 100644 --- a/JSL_notebook.ipynb +++ b/JSL_notebook.ipynb @@ -1,205 +1,495 @@ { + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "JSL-notebook.ipynb", + "provenance": [], + "authorship_tag": "ABX9TyOyouFLeOExeSvfdoPotWlr", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, + "source": [ + "!pip install --upgrade git+https://github.com/google/flax.git\n", + "!pip install --upgrade tensorflow-probability\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "rX9X_KFwEtoK", + "outputId": "89fbddbe-e2b5-4fc5-b190-2a30852625be" + }, + "execution_count": 5, "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" + "Collecting git+https://github.com/google/flax.git\n", + " Cloning https://github.com/google/flax.git to /tmp/pip-req-build-ihuwjxbb\n", + " Running command git clone -q https://github.com/google/flax.git /tmp/pip-req-build-ihuwjxbb\n", + "Requirement already satisfied: numpy>=1.12 in /usr/local/lib/python3.7/dist-packages (from flax==0.4.0) (1.21.5)\n", + "Requirement already satisfied: jax>=0.3 in /usr/local/lib/python3.7/dist-packages (from flax==0.4.0) (0.3.1)\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from flax==0.4.0) (3.2.2)\n", + "Requirement already satisfied: msgpack in /usr/local/lib/python3.7/dist-packages (from flax==0.4.0) (1.0.3)\n", + "Requirement already satisfied: optax in /usr/local/lib/python3.7/dist-packages (from flax==0.4.0) (0.1.1)\n", + "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from jax>=0.3->flax==0.4.0) (3.10.0.2)\n", + "Requirement already satisfied: scipy>=1.2.1 in /usr/local/lib/python3.7/dist-packages (from jax>=0.3->flax==0.4.0) (1.4.1)\n", + "Requirement already satisfied: opt-einsum in /usr/local/lib/python3.7/dist-packages (from jax>=0.3->flax==0.4.0) (3.3.0)\n", + "Requirement already satisfied: absl-py in /usr/local/lib/python3.7/dist-packages (from jax>=0.3->flax==0.4.0) (1.0.0)\n", + "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from absl-py->jax>=0.3->flax==0.4.0) (1.15.0)\n", + "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->flax==0.4.0) (2.8.2)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->flax==0.4.0) (3.0.7)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->flax==0.4.0) (1.4.0)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->flax==0.4.0) (0.11.0)\n", + "Requirement already satisfied: chex>=0.0.4 in /usr/local/lib/python3.7/dist-packages (from optax->flax==0.4.0) (0.1.1)\n", + "Requirement already satisfied: jaxlib>=0.1.37 in /usr/local/lib/python3.7/dist-packages (from optax->flax==0.4.0) (0.3.0+cuda11.cudnn805)\n", + "Requirement already satisfied: toolz>=0.9.0 in /usr/local/lib/python3.7/dist-packages (from chex>=0.0.4->optax->flax==0.4.0) (0.11.2)\n", + "Requirement already satisfied: dm-tree>=0.1.5 in /usr/local/lib/python3.7/dist-packages (from chex>=0.0.4->optax->flax==0.4.0) (0.1.6)\n", + "Requirement already satisfied: flatbuffers<3.0,>=1.12 in /usr/local/lib/python3.7/dist-packages (from jaxlib>=0.1.37->optax->flax==0.4.0) (2.0)\n", + "Requirement already satisfied: tensorflow-probability in /usr/local/lib/python3.7/dist-packages (0.16.0)\n", + "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow-probability) (1.21.5)\n", + "Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from tensorflow-probability) (4.4.2)\n", + "Requirement already satisfied: gast>=0.3.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow-probability) (0.5.3)\n", + "Requirement already satisfied: cloudpickle>=1.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow-probability) (1.3.0)\n", + "Requirement already satisfied: dm-tree in /usr/local/lib/python3.7/dist-packages (from tensorflow-probability) (0.1.6)\n", + "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow-probability) (1.15.0)\n", + "Requirement already satisfied: absl-py in /usr/local/lib/python3.7/dist-packages (from tensorflow-probability) (1.0.0)\n" ] } + ] + }, + { + "cell_type": "code", + "source": [ + "#!pip install git+git://github.com/blackjax-devs/blackjax.git\n", + "#!pip install git@github.com:blackjax-devs/blackjax.git\n", + "\n", + "#!pip install --upgrade git+https://github.com/blackjax-devs/blackjax.git\n", + "!pip install blackjax" ], + "metadata": { + "id": "zs54LQC9u9qL", + "outputId": "e573820f-bdd6-4a3e-ff74-dc895f8c9829", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting git+https://github.com/blackjax-devs/blackjax.git\n", + " Cloning https://github.com/blackjax-devs/blackjax.git to /tmp/pip-req-build-l2squsd6\n", + " Running command git clone -q https://github.com/blackjax-devs/blackjax.git /tmp/pip-req-build-l2squsd6\n" + ] + } + ] + }, + { + "cell_type": "code", "source": [ - "%load_ext autoreload\n", - "%autoreload " + "#!pip install git+git://github.com/deepmind/distrax.git.\n", + "!pip install distrax" + ], + "metadata": { + "id": "ARlJdPVrvllp", + "outputId": "b9e87cfe-d06c-4dd3-eeca-01e3eb39814b", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting distrax\n", + " Downloading distrax-0.1.1-py3-none-any.whl (243 kB)\n", + "\u001b[?25l\r\u001b[K |█▍ | 10 kB 24.7 MB/s eta 0:00:01\r\u001b[K |██▊ | 20 kB 27.2 MB/s eta 0:00:01\r\u001b[K |████ | 30 kB 32.4 MB/s eta 0:00:01\r\u001b[K |█████▍ | 40 kB 16.4 MB/s eta 0:00:01\r\u001b[K |██████▊ | 51 kB 12.2 MB/s eta 0:00:01\r\u001b[K |████████ | 61 kB 14.0 MB/s eta 0:00:01\r\u001b[K |█████████▍ | 71 kB 12.6 MB/s eta 0:00:01\r\u001b[K |██████████▉ | 81 kB 13.3 MB/s eta 0:00:01\r\u001b[K |████████████▏ | 92 kB 14.3 MB/s eta 0:00:01\r\u001b[K |█████████████▌ | 102 kB 14.0 MB/s eta 0:00:01\r\u001b[K |██████████████▉ | 112 kB 14.0 MB/s eta 0:00:01\r\u001b[K |████████████████▏ | 122 kB 14.0 MB/s eta 0:00:01\r\u001b[K |█████████████████▌ | 133 kB 14.0 MB/s eta 0:00:01\r\u001b[K |██████████████████▉ | 143 kB 14.0 MB/s eta 0:00:01\r\u001b[K |████████████████████▎ | 153 kB 14.0 MB/s eta 0:00:01\r\u001b[K |█████████████████████▋ | 163 kB 14.0 MB/s eta 0:00:01\r\u001b[K |███████████████████████ | 174 kB 14.0 MB/s eta 0:00:01\r\u001b[K |████████████████████████▎ | 184 kB 14.0 MB/s eta 0:00:01\r\u001b[K |█████████████████████████▋ | 194 kB 14.0 MB/s eta 0:00:01\r\u001b[K |███████████████████████████ | 204 kB 14.0 MB/s eta 0:00:01\r\u001b[K |████████████████████████████▎ | 215 kB 14.0 MB/s eta 0:00:01\r\u001b[K |█████████████████████████████▋ | 225 kB 14.0 MB/s eta 0:00:01\r\u001b[K |███████████████████████████████ | 235 kB 14.0 MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 243 kB 14.0 MB/s \n", + "\u001b[?25hRequirement already satisfied: absl-py>=0.9.0 in /usr/local/lib/python3.7/dist-packages (from distrax) (1.0.0)\n", + "Requirement already satisfied: tensorflow-probability>=0.15.0 in /usr/local/lib/python3.7/dist-packages (from distrax) (0.16.0)\n", + "Requirement already satisfied: jax>=0.1.55 in /usr/local/lib/python3.7/dist-packages (from distrax) (0.3.1)\n", + "Requirement already satisfied: chex>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from distrax) (0.1.1)\n", + "Requirement already satisfied: jaxlib>=0.1.67 in /usr/local/lib/python3.7/dist-packages (from distrax) (0.3.0+cuda11.cudnn805)\n", + "Requirement already satisfied: numpy>=1.18.0 in /usr/local/lib/python3.7/dist-packages (from distrax) (1.21.5)\n", + "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from absl-py>=0.9.0->distrax) (1.15.0)\n", + "Requirement already satisfied: toolz>=0.9.0 in /usr/local/lib/python3.7/dist-packages (from chex>=0.0.7->distrax) (0.11.2)\n", + "Requirement already satisfied: dm-tree>=0.1.5 in /usr/local/lib/python3.7/dist-packages (from chex>=0.0.7->distrax) (0.1.6)\n", + "Requirement already satisfied: opt-einsum in /usr/local/lib/python3.7/dist-packages (from jax>=0.1.55->distrax) (3.3.0)\n", + "Requirement already satisfied: scipy>=1.2.1 in /usr/local/lib/python3.7/dist-packages (from jax>=0.1.55->distrax) (1.4.1)\n", + "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from jax>=0.1.55->distrax) (3.10.0.2)\n", + "Requirement already satisfied: flatbuffers<3.0,>=1.12 in /usr/local/lib/python3.7/dist-packages (from jaxlib>=0.1.67->distrax) (2.0)\n", + "Requirement already satisfied: gast>=0.3.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow-probability>=0.15.0->distrax) (0.5.3)\n", + "Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from tensorflow-probability>=0.15.0->distrax) (4.4.2)\n", + "Requirement already satisfied: cloudpickle>=1.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow-probability>=0.15.0->distrax) (1.3.0)\n", + "Installing collected packages: distrax\n", + "Successfully installed distrax-0.1.1\n" + ] + } ] }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], "source": [ - "import jax.numpy as jnp\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n" + "\n", + "import tensorflow as tf\n", + "import tensorflow_probability as tfp\n" + ], + "metadata": { + "id": "ye9LqR2JE-bD" + }, + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "!git clone https://github.com/probml/JSL.git\n", + "#!pip install git+https://github.com/probml/jsl\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "asDC0nQvFXee", + "outputId": "d65cebdd-d399-48de-cf44-1dc13e2f6c0b" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Cloning into 'JSL'...\n", + "remote: Enumerating objects: 1464, done.\u001b[K\n", + "remote: Counting objects: 100% (1409/1409), done.\u001b[K\n", + "remote: Compressing objects: 100% (960/960), done.\u001b[K\n", + "remote: Total 1464 (delta 876), reused 925 (delta 429), pack-reused 55\u001b[K\n", + "Receiving objects: 100% (1464/1464), 3.01 MiB | 12.24 MiB/s, done.\n", + "Resolving deltas: 100% (895/895), done.\n" + ] + } ] }, { "cell_type": "code", - "execution_count": 2, + "source": [ + "%cd /content/JSL/\n", + "!pip install -e ." + ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 218 + "height": 590 }, - "id": "_b8lNuou9RaT", - "outputId": "7165d3db-0250-479b-83cc-77157beb6e56" + "id": "RHRJ6MA-I6c9", + "outputId": "2874c232-39e4-4c55-b04a-212a10bd82e0" }, + "execution_count": 9, "outputs": [ { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content/JSL\n", + "Obtaining file:///content/JSL\n", + "Requirement already satisfied: chex in /usr/local/lib/python3.7/dist-packages (from jsl==0.0.0) (0.1.1)\n", + "Collecting dataclasses\n", + " Downloading dataclasses-0.6-py3-none-any.whl (14 kB)\n", + "Requirement already satisfied: jaxlib in /usr/local/lib/python3.7/dist-packages (from jsl==0.0.0) (0.3.0+cuda11.cudnn805)\n", + "Requirement already satisfied: jax in /usr/local/lib/python3.7/dist-packages (from jsl==0.0.0) (0.3.1)\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from jsl==0.0.0) (3.2.2)\n", + "Requirement already satisfied: tensorflow_probability in /usr/local/lib/python3.7/dist-packages (from jsl==0.0.0) (0.16.0)\n", + "Requirement already satisfied: dm-tree>=0.1.5 in /usr/local/lib/python3.7/dist-packages (from chex->jsl==0.0.0) (0.1.6)\n", + "Requirement already satisfied: numpy>=1.18.0 in /usr/local/lib/python3.7/dist-packages (from chex->jsl==0.0.0) (1.21.5)\n", + "Requirement already satisfied: toolz>=0.9.0 in /usr/local/lib/python3.7/dist-packages (from chex->jsl==0.0.0) (0.11.2)\n", + "Requirement already satisfied: absl-py>=0.9.0 in /usr/local/lib/python3.7/dist-packages (from chex->jsl==0.0.0) (1.0.0)\n", + "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from absl-py>=0.9.0->chex->jsl==0.0.0) (1.15.0)\n", + "Requirement already satisfied: scipy>=1.2.1 in /usr/local/lib/python3.7/dist-packages (from jax->jsl==0.0.0) (1.4.1)\n", + "Requirement already satisfied: opt-einsum in /usr/local/lib/python3.7/dist-packages (from jax->jsl==0.0.0) (3.3.0)\n", + "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from jax->jsl==0.0.0) (3.10.0.2)\n", + "Requirement already satisfied: flatbuffers<3.0,>=1.12 in /usr/local/lib/python3.7/dist-packages (from jaxlib->jsl==0.0.0) (2.0)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->jsl==0.0.0) (1.4.0)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->jsl==0.0.0) (3.0.7)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->jsl==0.0.0) (0.11.0)\n", + "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->jsl==0.0.0) (2.8.2)\n", + "Requirement already satisfied: cloudpickle>=1.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow_probability->jsl==0.0.0) (1.3.0)\n", + "Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from tensorflow_probability->jsl==0.0.0) (4.4.2)\n", + "Requirement already satisfied: gast>=0.3.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow_probability->jsl==0.0.0) (0.5.3)\n", + "Installing collected packages: dataclasses, jsl\n", + " Running setup.py develop for jsl\n", + "Successfully installed dataclasses-0.6 jsl-0.0.0\n" + ] + }, + { + "output_type": "display_data", "data": { - "text/plain": [ - "'/Users/kpmurphy/github/JSL'" - ] + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "dataclasses" + ] + } + } }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" + "metadata": {} } - ], - "source": [ - "\n", - "%pwd\n" ] }, { "cell_type": "code", + "source": [ + "!pwd" + ], + "metadata": { + "id": "OmuXLXtHwRaH", + "outputId": "6478494f-fcdb-425e-fd51-0fca226b1c53", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content/JSL\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!ls" + ], "metadata": { - "id": "CWEhPo9YJuut" + "id": "Sq2dul_2wd83", + "outputId": "6fc8c1c8-5505-47ea-b4b9-9050b30da961", + "colab": { + "base_uri": "https://localhost:8080/" + } }, + "execution_count": 17, "outputs": [ { + "output_type": "stream", "name": "stdout", + "text": [ + "environment.yml jsl.egg-info\t LICENSE\tsetup.py\n", + "jsl\t\t JSL_notebook.ipynb README.md\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "%run /content/JSL/jsl/demos/kf_tracking.py\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 813 + }, + "id": "YhvXkCeiI4aX", + "outputId": "d6f7b479-b2b7-459f-efb2-2d2b455c4bb9" + }, + "execution_count": 14, + "outputs": [ + { "output_type": "stream", + "name": "stdout", "text": [ - "L2-filter: 37198.8789\n", - "L2-smooth: 52081.6406\n", - "{'kalman-tracking-truth':
, 'kalman-tracking-filtered':
, 'kalman-tracking-smoothed':
}\n" + "L2-filter: 3.2481\n", + "L2-smooth: 2.0450\n" ] }, { + "output_type": "display_data", "data": { - "image/png": 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", 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\n" }, "metadata": { "needs_background": "light" - }, - "output_type": "display_data" + } }, { + "output_type": "display_data", "data": { - "image/png": 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1UlFQQPrtYyj8+msCO3cm9qWlBEVFuZ2WOUkncg/kxSKyR0Q2e8UeFpFMEfnWeVzhNe9+EUkVke0icqlX/DInlioiU73i3UVknRN/VUTsGrbG+IiKwkLSx46jICWFwE6d6Jq8hGA7T8UnnUjLIBm4rJp4kqr2cx7vAohIb2AYcIazzgsiEiAiAcAc4HKgN3CjsyzAk862fgnsB0bUZYeMMY2joriYjPETKFi3jsDISGKTlxDctavbaZlaOm4xUNVPgX0nuL0hwDJVLVbVn4BUoL/zSFXVH1W1BFgGDBERAX4PLHfWXwpcfXK7YIxpbBUlJWRMuINDX3xBQMeOxC5NJsRO+PRpdRkzmCAiG51upMqzSaKAdK9lMpxYTfGOwAFVLTsiXi0RGS0iKSKSYpecMMYdWlJC5sRJHPrsMwLCw+mavISQ005zOy1TR7UtBnOBXwD9gCxgRn0ldCyqukBV41Q1LtKOXTam0WlpKZl3303+v/9NQLt2xCYvIaRHD7fTMvWgVtcmUtXdldMishD4l/M0E4jxWjTaiVFDPAdoLyKBTuvAe3ljTBOiZWVk3nMvB1d+RIu2bYldspjQ0093Oy1TT2rVMhCRzl5PrwEqjzR6GxgmIiEi0h3oAXwJfAX0cI4cCsYzyPy2eq41/Qkw1Fk/HnirNjkZYxqOlpWx874pHPzgA1q0aUPsiy8S2rv38Vc0PuO4LQMReQUYBESISAYwHRgkIv0ABX4GbgdQ1S0i8hqwFSgDxqtqubOdCcAHQACwWFW3OC8xBVgmIo8B3wAv1tfOGWPqTsvL2TltGnnvvkuLsDBiFy2k5Zl93E7L1DPx1RvBxMXFaUpKittpGOPXtKKCrAf+RO6KFUirVsQuWkirs85yOy1TByKyXlXjjozbGcjGmGppRQW7pj/sKQQtWxI7f54VAj9mxcAYcxRVZdef/8yB119HQkOJmTuXVuec43ZapgFZMTDGHEZV2f2XxznwyjIkOJiYF+YQNnCA22mZBmbFwBhTRVXZ8+RT7P/735GgIKLnzCbsd79zOy3TCKwYGGMATyHIfvZZ9iUnQ1AQUbOeo/X557udlmkkVgyMMQBkz5pFzsJFEBhI9Mwk2lx0kdspmUZkxcAYQ/acOeTMnQcBAUTNmEGbiy92OyXTyKwYGNPM5CxaxKG166qe7503n73PzwYRop5+iraXDnYxO+MWKwbGNDOhfc4kc/JkDq1dR86LL5I9cyYAHUePou0VVxx7ZeO3anWhOmOM7wobOICopCTSx45FCwsB6DByJKdMnuxyZsZN1jIwphkqTk2tKgStBw2i0z13u5yRcZsVA2Oamf3LlrH7sccACLvgAgo3bDhsDME0T1YMjGlG9r/+OrsefgSA8FtuIXbBfKKSkqrGEEzzZcXAmGbiwIo32fXQdADCb7qRU//0APDfMYSizZvcTM+4zAaQjWkGct95h6xp00CVU+69h44jRhw2P2zgALv+UDNnLQNj/Fzeu++yc8pUUCXyzjuPKgTGgBUDY/xa3vsfkHnvfVBRQcSECUSMud3tlEwTddxiICKLRWSPiGz2ij0tIv9PRDaKyAoRae/Eu4lIoYh86zzmea1ztohsEpFUEZklIuLEO4jIShH53vkZ3gD7aUyzc3DVKjLvuQfKy+k4dgwR48e5nZJpwk6kZZAMXHZEbCXQR1X7At8B93vN+0FV+zmPMV7xucAooIfzqNzmVGCVqvYAVjnPjTF1cPCTT8i4czKUldFx1EgiJ07E+f5lTLWOWwxU9VNg3xGxD1W1zHm6Fog+1jZEpDPQVlXXquemyy8BVzuzhwBLnemlXnFjTC3kf/YZmRMnQWkpHRISiLzrLisE5rjqY8xgOPCe1/PuIvKNiPxHRCovhh4FZHgtk+HEADqpapYzvQvoVA85GdMs5X/+ORnjJ6ClpYTfeiunTLnPCoE5IXU6tFREHgDKgJedUBYQq6o5InI28KaInHGi21NVFRE9xuuNBkYDxMbG1j5xY/zQobXryBg3Hi0pof2Nw+g07X4rBOaE1bplICIJwJXAzU7XD6parKo5zvR64AegJ5DJ4V1J0U4MYLfTjVTZnbSnptdU1QWqGqeqcZGRkbVN3Ri/U/DVV54LzxUX0/5//5dTH3zQCoE5KbUqBiJyGXAfcJWqFnjFI0UkwJk+Dc9A8Y9ON1CeiAx0jiK6DXjLWe1tIN6ZjveKG2NOQMHX35B2+xi0sJB2117LqY88jLSwo8bNyTluN5GIvAIMAiJEJAOYjufooRBgpfPtY61z5NAFwKMiUgpUAGNUtXLweRyeI5Na4hljqBxneAJ4TURGADuA6+tlz4xpBgo3bCB91Ci0oIB2Q66i858ftUJgakWcHh6fExcXpykpKW6nYYxrCjdtJi0xkYr8fNpecQVdnn4KCQhwOy3TxInIelWNOzJuXyGM8UFFW7eSNmIEFfn5tLn0Uro89aQVAlMnVgyM8TFF27eTljicirw82lzyB6KeeRoJtGtOmrqxYmCMDyn67jvSEhIpz82l9UUXETVjBhIU5HZaxg9YMTDGRxT/8ANpicMp37+fsAvOJ+q5mUhwsNtpGT9hxcAYH1D800/sSEigPCeHsN/9jujnn6eFFQJTj6wYGNPElaSlebqGsvfSasAAoufMpkVIiNtpGT9jxcCYJqwkI4Md8QmU7d5Nq7g4Yua+QIuWLd1Oy/ghKwbGNFGlmZmk3RZPWVYWLc86i5j582jRqpXbaRk/ZcXAmCaodNcudiQkUrpzJy1//WtiFsynRViY22kZP2bFwJgmpnT3HnbEx1Oank5onz7ELFpIQOvWbqdl/JwVA2OakLLsbNISEijdkUZI717EvriIgDZt3E7LNANWDIxpIspyctiRmEjJTz8RcvrpxL74IgHt2rmdlmkmrBgY0wSU7d9PWuJwSlJ/IKTHL4ldspjA8HC30zLNiBUDY1xWfuAAacNHUPzddwSfdhqxS5YQ2KGD22mZZsaKgTEuKs/LI23ESIq3bSO4Wzdik5cQGBHhdlqmGbJiYIxLyg8eJG3kKIq2bCEoNpbYpckEnXKK22mZZsqKgTEuKM8/RPqo0RRt3EhQdDRdlyYT1KmT22mZZsyKgTGNrOLQIdJvv53Cb78lsEtnYpOTCerc2e20TDN3QsVARBaLyB4R2ewV6yAiK0Xke+dnuBMXEZklIqkislFEzvJaJ95Z/nsRifeKny0im5x1ZolzY2Vj/E1FYSHpY8dRuH49gaeeStelSwmOjnI7LWNOuGWQDFx2RGwqsEpVewCrnOcAlwM9nMdoYC54igcwHRgA9AemVxYQZ5lRXusd+VrG+LyKoiLSx42j4MsvCYyMpGvyEoJjYtxOyxjgBIuBqn4K7DsiPARY6kwvBa72ir+kHmuB9iLSGbgUWKmq+1R1P7ASuMyZ11ZV16qqAi95bcsYv1BRXEzG+AkUrFlLQEQEsUuXEtytm9tpGVOlLmMGnVQ1y5neBVSOfkUB6V7LZTixY8UzqokfRURGi0iKiKRkZ2fXIXVjGk9FSQkZEydy6PPPCejQga7JSwg5rbvbaRlzmHoZQHa+0Wt9bOs4r7NAVeNUNS4yMrKhX86YWslZtIhDa9cBoCUlZN45mUP/+RQJDSU2eQkhv/ylyxkac7S6FIPdThcPzs89TjwT8O4IjXZix4pHVxM3xieF9jmTzMmTyf/8czLvvof8jz8GETpNu5/Qnj3dTs+YatWlGLwNVB4RFA+85RW/zTmqaCCQ63QnfQAMFpFwZ+B4MPCBMy9PRAY6RxHd5rUtY3xO2MABdJnxDOljxnJw5UpPIZg+nfDrr3c7NWNqFHgiC4nIK8AgIEJEMvAcFfQE8JqIjAB2AJW/6e8CVwCpQAGQCKCq+0Tkz8BXznKPqmrloPQ4PEcstQTecx7G+CQtLyf3nyugtBSAdtdeS4dhN7iclTHHJp7uft8TFxenKSkpbqdhzGG0vJysaQ+Q+5ancdvu2mvI/+TfRCUlETZwgMvZGQMisl5V446M2xnIxtQTragg66GHqgpBp2nT6PL440QlJZE5eXLVoLIxTZEVA2PqgVZUsOvhR8h9458QGMgpU6fS4bZbAc8YQlRSEkWbN7mcpTE1O6ExA2NMzVSV3Y89xoHXXkNCQoiZN5ew3/72sGXCBg6wbiLTpFnLwJg6UFV2//Wv7P/HK0hwMNFz5hxVCIzxBVYMjKklVWXPU0+z/6W/IUFBRD8/i9bnnet2WsbUihUDY2pBVcl+Nol9S5ZAYCBRzz1H6wsvdDstY2rNioExtbD3+efJWbgQAgKIenYGbX5/kdspGVMnVgyMOUnZL7zA3hfmegrBjGdoO3iw2ykZU2dWDIw5CXvnL2DvrOehRQu6PPkkbS+zW28Y/2DFwJgTlPPiYrKTkkCELn99nHZX/tHtlIypN1YMjDkB+5YuZc/TTwPQ+bE/027IEJczMqZ+WTEw5jj2vfwyu//6BACnPvII7a+7zuWMjKl/VgyMOYb9y15l958fA6DTQw8SfoNdhtr4JysGxtTgwPLl7Hr4YcBz0bkON93kbkLGNCArBsZU48CKN8l68CEATrnvvqqLzhnjr6wYGHOE3Hf+Rda0aaBK5N130XF4otspGdPgrBgY4yXv3XfZOWWKpxBMmkjEqFFup2RMo6h1MRCR00XkW69HnojcKSIPi0imV/wKr3XuF5FUEdkuIpd6xS9zYqkiMrWuO2VMbeR98CGZ994HFRVEjBtHxNixbqdkTKOp9f0MVHU70A9ARAKATGAFnnseJ6nqM97Li0hvYBhwBtAF+EhEejqz5wCXABnAVyLytqpurW1uxpysg6tWkXn33VBeTsfRo4m4Y4LbKRnTqOrr5jYXAz+o6g4RqWmZIcAyVS0GfhKRVKC/My9VVX8EEJFlzrJWDEyjOPjJJ2TcORnKyugwfDiRk+/kGL/Hxvil+hozGAa84vV8gohsFJHFIhLuxKKAdK9lMpxYTXFjGlz+Z5+ROXESlJbSIf42Trn3HisEplmqczEQkWDgKuB1JzQX+AWeLqQsYEZdX8PrtUaLSIqIpGRnZ9fXZk0zdeiLL8gYPwEtLSX85ps5ZepUKwSm2aqPlsHlwNequhtAVXerarmqVgAL+W9XUCYQ47VetBOrKX4UVV2gqnGqGhcZGVkPqZvm6tC6L0kfNx4tKaH9DTfQ6U8PWCEwzVp9FIMb8eoiEpHOXvOuATY7028Dw0QkRES6Az2AL4GvgB4i0t1pZQxzljWmQRSkpJA+ZgxaVES7oddx6vSHrBCYZq9OA8giEobnKKDbvcJPiUg/QIGfK+ep6hYReQ3PwHAZMF5Vy53tTAA+AAKAxaq6pS55GVOTgq+/IW307WhhIe2uvprOjz6KtLDTbYwRVXU7h1qJi4vTlJQUt9MwTVjOokWE9jmTsIEDACjcsIEd8QloURFt/+d/6PLEX5GAAJezNKZxich6VY07Mm5fiYzfCu1zJpmTJ3No7ToKN21mR0IiWlREqwH96fLXx60QGOOlvs4zMKbJCRs4gKikJDImTqSiqAhKSmgZF0fsokVIoP3qG+PNWgbGrwWEt6eiuBhKSgjq1o2ui19EgoLcTsuYJseKgfFbxd9/z46bb4HiYoJiY6nIzaXg62/cTsuYJsmKgfFLxT/+yM8330JFfj6hffpw2jtvE5WUVDWGYIw5nBUD43dKfv6ZtPgEKvLyCD2jN13//jdahIRUjSEUbd7kdorGNDk2imb8SklaGjviEyjLzqZV//7EzJ9Hi9DQqvlhAwdUHWpqjPkvaxkYv1GSkeEpBLt30zLubGLmzaVFy5Zup2WMT7BiYPxC6c6dpMUnUJaVRcvf/IaYefNp0aqV22kZ4zOsGBifV7prFzviEyjNzCS0b19iFi4goHWY22kZ41OsGBifVrp7D2nxCZSmpxN6xhnELlpIQOvWbqdljM+xYmB8VtnevaQlJlKyYwchvXoR++IiAtq2dTstY3ySFQPjk8r27WNHQgIlP/5ISM+exC5+kYD27d1OyxifZcXA+Jyy/ftJS0ikJPUHgn/5C2KXLCYwPPz4KxpjamTFwPiU8gMHSBs+guLvviP4tNPompxMYMeObqdljM+zYmB8RnleHmkjRlK8bRvBXbsSm7yEwIgIt9Myxi9YMTA+oTw/n7RRoyjasoWgmBhilyYTdMopbqdljN+wYmCavPL8Q6SPGk3Rho0ERUXRdWkyQaee6nZaxviVOhcDEflZRDaJyLcikuLEOojIShH53vkZ7sRFRGaJSKqIbBSRs7y2E+8s/72IxNc1L+MfKgoKSB9zO4XffENgl87ELl1KUJcubqdljN+pr5bBRaraz+u+mlOBVaraA1jlPAe4HOjhPEYDc8FTPIDpwACgPzC9soCY5quisJD0seMoTFlPYKdOdE1OJjg6yu20jPFLDdVNNARY6kwvBa72ir+kHmuB9iLSGbgUWKmq+1R1P7ASuKyBcjM+oKKoiIzx4ylYt47AyEi6Lk0mODbW7bSM8Vv1UQwU+FBE1ovIaCfWSVWznOldQCdnOgpI91o3w4nVFD+MiIwWkRQRScnOzq6H1E1TkbNoUdVNZyqKi8m4YyKHvliDtGpF7NJkgrt1czdBY/xcfRSD81T1LDxdQONF5ALvmaqqeApGnanqAlWNU9W4yMjI+tikaSJC+5xJ5uTJ5K9eTebESRz67DMQ4dQHphFy2mlup2eM36vzzW1UNdP5uUdEVuDp898tIp1VNcvpBtrjLJ4JxHitHu3EMoFBR8T/XdfcjO8IGziALs88TfqYsVBa6ikEjz5K++uuczs1Y5qFOrUMRCRMRNpUTgODgc3A20DlEUHxwFvO9NvAbc5RRQOBXKc76QNgsIiEOwPHg52YaSa0tJQDr77mKQRA+6FDCf/foS5nZUzzUdeWQSdghYhUbusfqvq+iHwFvCYiI4AdwPXO8u8CVwCpQAGQCKCq+0Tkz8BXznKPquq+OuZmfISWlbFzyhQOfvghiNDu2ms5+NFHtP3jH+0WlcY0EvF06fueuLg4TUlJcTsNU0daXs7OqfeT9847AHR66EE63HQTh9auI3PyZKKSkqwgGFOPRGS912kAVewMZOMaragg608PkvfOO0hQEJ3+9AAdbroJ8IwhRCUlUbR5k8tZGtM81HkA2Zja0IoKdk2fTu6KFUjLlsQuXECruMO/rIQNHGCtAmMaibUMTKNTVXY9+igHXl+OhIYSM2/eUYXAGNO4rBiYRqWq7H7sLxxY9ioSEkLM3BcIG9Df7bSMafasGJhGo6rseeJJ9r/8MhIURPTs2YT99rdup2WMwYqBaSSqyp5nnmHf0qUQFET07Odpff55bqdljHFYMTANTlXJnvkc+15cDIGBRD83k9YXXuh2WsYYL1YMTIPbO3sOOfPnQ0AAUc/OoM3vf+92SsaYI1gxMA1q77x57J0zB1q0IOqZp2k7eLDbKRljqmHFwDSYvQsXkj3zOWjRgi5PPknbyy93OyVjTA2sGJh64X0/AoCcJclkz3gWgM6P/4V2/3OlW6kZY06AFQNTLyrvR3Bo7Tr2vfQ39jz5JAAdRgyn/dVXu5ucMea47HIUpl5UXksofdw4tKAAgPCEBDrde6/LmRljToS1DEy9KUnbUVUIws4/j1OnTnE5I2PMibJiYOrFgTf+ya6HpgMQdu65FG3ectgYgjGmabNiYOos9623yHrgAQDa33ADsS8uIiopqWoMwRjT9FkxMHWS+6//Y+f90wBoN3QonR95GLD7ERjja2pdDEQkRkQ+EZGtIrJFRCY58YdFJFNEvnUeV3itc7+IpIrIdhG51Ct+mRNLFZGpddsl01jy3n+fnVOmQEUFERPvoMtjfz5sftjAAXQcOdKl7IwxJ6MuRxOVAXer6tci0gZYLyIrnXlJqvqM98Ii0hsYBpwBdAE+EpGezuw5wCVABvCViLytqlvrkJtpYHkrV5J59z1QXk7EuLFEjhvndkrGmDqodTFQ1Swgy5k+KCLbgKhjrDIEWKaqxcBPIpIKVF7IPlVVfwQQkWXOslYMmqiDH39C5l13Q3k5HUeNIuKOO9xOyRhTR/UyZiAi3YDfAJWjhRNEZKOILBaRcCcWBaR7rZbhxGqKmyYo/z//IXPSJCgtpcPw4UTeNRkRcTstY0wd1bkYiEhr4A3gTlXNA+YCvwD64Wk5zKjra3i91mgRSRGRlOzs7PrarDlB+as/J+OOiWhpKeG33cop995jhcAYP1GnYiAiQXgKwcuq+k8AVd2tquWqWgEs5L9dQZlAjNfq0U6spvhRVHWBqsapalxkZGRdUjcnwPt6Q4fWrCFj/Hi0pISW/frR6f77rRAY40fqcjSRAC8C21T1Wa94Z6/FrgE2O9NvA8NEJEREugM9gC+Br4AeItJdRILxDDK/Xdu8TP2pvN5QTvJS0seOQ4uLkZAQIiZNtEJgjJ+py9FE5wK3AptE5FsnNg24UUT6AQr8DNwOoKpbROQ1PAPDZcB4VS0HEJEJwAdAALBYVbfUIS9TT8IGDiBi7Fh2P/44ABIcTPS8ubS2+xYb43fqcjTRaqC6r4fvHmOdvwB/qSb+7rHWM+4o+OYbsmfOrHreYfhwKwTG+Ck7A9lUq3DjRtJHjaaioAAJDqbjmDEcePVVu7yEMX7KioE5SuGWLaSNHEVFfj4SFET0vLmccucku96QMX7MioE5TNG2baQNH0FFXh7BPXp4xgh+9zvArjdkjD+zm9uYKkXbvyMtcTgVubm0vugiop+biQQHH7ZM2MABhA0c4FKGxpiGYi2DZsz7PILi1FTSEhMpP3CAoO7diaqmEBhj/JcVg2as8jyCAyveZEdCIuX79kFgIKdOm0YLKwTGNCvWTdSMhQ0cQKepU9k5dSqoQmAgMXNfoPX557mdmjGmkVnLoBkrSU9nT1KSpxAAHRMTaH3++S5nZYxxgxWDZqokI5Md8fGU7doFgYF0HDmSA8vfsMNGjWmmrBg0E96DxaVZWaQlJFC2MwtatCB69mxOueduO4/AmGbMikEzUTlYnPve++yIT6A0IwNEiJh4B20GXQjYeQTGNGeiTn+xr4mLi9OUlBS30/Apue+/z8677oaKCggIIPr5WbT5/e/dTssY04hEZL2qxh0Zt5ZBM1G2dy97n5/tKQRAh/jbrBAYY6pYMfBT3mMEZfv2kZaYSMkPP4AIHRITyV3xpo0NGGOqWDHwU5VjBHkffURa4nCKv08FIGL8eDpNuc8Gi40xh7Fi4GcqWwRhAwfQ+bHHyJx0J8XbtwMQMWE8kRPGAzZYbIw5nJ2B7CcmfTSJ1m98RHkL5Zo5yupb+3HtF2VQXg5A68GDiZww4bB17KJzxphK1jLwcbm5uZy59Mz/FoIvlHfOEQYv+paizZ7bT7eMO5vCr76yLiFjTI2aTDEQkctEZLuIpIrIVLfz8RXnvXkeU18tJ/JAhacQ9BduWK0Eeg4a4ucI6Pb3v9sYgTHmmJpEMRCRAGAOcDnQG7hRRHq7m1XTN+mjSQBs6g6XfAMxfxzKzZ8HVBWC/BDocAiemZtgYwTGmGNqEsUA6A+kquqPqloCLAOGuJxTk/dx5scA/F//ADrdey+5y5dDWRkALcLDqWgBK34nnL9gXdWgcseRI91M2RjTRDWVYhAFpHs9z3BihxGR0SKSIiIp2dnZjZacL4gYMRxp2xYACQtD8BSCa75QVvxOrEVgjDmmplIMToiqLlDVOFWNi4yMdDudJiXr4UfQvDwCunRBCwpoc+mlVYUgoEKsRWCMOaamUgwygRiv59FOzBzD76M8l5P445fl7Fu2jPbDhtHz41Wcct997Fu2jDW/goAKoeh/L3M5U2NMU9ckLlQnIoHAd8DFeIrAV8BNqrqlpnXsQnUeZy49k6mvlrOpu2fsoNIfvyznzJ/giRsC2BRvXUTGGI+aLlTXJE46U9UyEZkAfAAEAIuPVQjMf62+ejXncfRtKv+vfwD/198z3xhjjqepdBOhqu+qak9V/YWq/sXtfHxFu3bt2BS/qarLqNLgmMFsit9Eu3btXMrMGONLmkTLwNTdc394zu0UjDE+rMm0DIwxxrjHioExxhgrBsYYY5rIoaW1ISLZwI46biYC2FsP6TQltk++wfbJN/jjPnVV1aPO2vXZYlAfRCSluuNtfZntk2+wffIN/rhPNbFuImOMMVYMjDHGWDFY4HYCDcD2yTfYPvkGf9ynajXrMQNjjDEezb1lYIwxBisGxhhj8PNiICIPi0imiHzrPK7wmne/iKSKyHYRudQrfpkTSxWRqV7x7iKyzom/KiLBjb0/x1NT7k2RiPwsIpuczyXFiXUQkZUi8r3zM9yJi4jMcvZro4ic5bWdeGf570Uk3oX9WCwie0Rks1es3vZDRM523qdUZ11xYX98+u9IRGJE5BMR2SoiW0RkkhP32c+pQaiq3z6Ah4F7qon3BjYAIUB34Ac8l84OcKZPA4KdZXo767wGDHOm5wFj3d6/I/apxtyb4gP4GYg4IvYUMNWZngo86UxfAbwHCDAQWOfEOwA/Oj/DnenwRt6PC4CzgM0NsR/Al86y4qx7uQv749N/R0Bn4Cxnug2ee6f09uXPqSEeft0yOIYhwDJVLVbVn4BUoL/zSFXVH1W1BFgGDHGq/O+B5c76S4GrGz/tY6o2d5dzOllD8Ly3cPh7PAR4ST3WAu1FpDNwKbBSVfep6n5gJdCot3VT1U+BfUeE62U/nHltVXWtev7jvEQD/97VsD818Ym/I1XNUtWvnemDwDY891j32c+pITSHYjDBaeotrmwG4vlFSPdaJsOJ1RTvCBxQ1bIj4k1JTbk3VQp8KCLrRWS0E+ukqlnO9C6gkzN9sp+X2+prP6Kc6SPjbvCLvyMR6Qb8BliHf35OtebzxUBEPhKRzdU8hgBzgV8A/YAsYIabuZrDnKeqZwGXA+NF5ALvmc43LJ8/7tlP9sMv/o5EpDXwBnCnquZ5z/OTz6lOfP7mNqr6hxNZTkQWAv9ynmYCMV6zo50YNcRz8DQVA51vNd7LNxXH2qcmR1UznZ97RGQFnq6F3SLSWVWznKb3HmfxmvYtExh0RPzfDZz6iaiv/ch0po9cvlGp6u7KaV/9OxKRIDyF4GVV/acT9qvPqa58vmVwLM4HXOkaoPIIibeBYSISIiLdgR54BoC+Ano4RzwEA8OAt51vDZ8AQ53144G3GmMfTkK1ubucU7VEJExE2lROA4PxfDZv43lv4fD3+G3gNucoj4FArtO8/wAYLCLhTtfFYCfmtnrZD2denogMdPrbb8OF3ztf/zty3rsXgW2q+qzXLL/6nOrM7RHshnwAfwM2ARvxfMCdveY9gOeIh+14jfzjOZLgO2feA17x0/D8oqcCrwMhbu9fNftbbe5N7eG8lxucx5bKXPH0Ka8Cvgc+Ajo4cQHmOPu1CYjz2tZw5zNJBRJd2JdX8HSdlOLpKx5Rn/sBxOH55/sDMBvnqgGNvD8+/XcEnIenC2gj8K3zuMKXP6eGeNjlKIwxxvh3N5ExxpgTY8XAGGOMFQNjjDFWDIwxxmDFwBhjDFYMjDHGYMXAGGMM8P8BiPVtObCfAVMAAAAASUVORK5CYII=", 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\n" }, "metadata": { "needs_background": "light" - }, - "output_type": "display_data" + } }, { + "output_type": "display_data", "data": { - "image/png": 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", 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\n" }, "metadata": { "needs_background": "light" + } + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ] }, - "output_type": "display_data" + "metadata": {} } - ], - "source": [ - "import jsl # need to import module to reload automatically\n", - "import jsl.demos\n", - "\n", - "from jsl.demos import kf_tracking as demo\n", - "\n", - "figures = demo.main()\n", - "print(figures)" ] }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "source": [ + "from jsl.demos import kf_tracking as demo\n", + "figures = demo.main()\n", + "#print(figures)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 813 + }, + "id": "RMKqBKfDFbcp", + "outputId": "57cf142f-1c09-40d2-c503-f44996aa60ef" + }, + "execution_count": 10, "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:absl:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", "text": [ - "LDS(A=DeviceArray([[0., 0., 0., 0., 0.],\n", - " [0., 0., 0., 0., 0.],\n", - " [0., 0., 0., 0., 0.],\n", - " [0., 0., 0., 0., 0.],\n", - " [0., 0., 0., 0., 0.]], dtype=float32), C=DeviceArray([[0., 0., 0., 0., 0.],\n", - " [0., 0., 0., 0., 0.],\n", - " [0., 0., 0., 0., 0.]], dtype=float32), Q=DeviceArray([[0., 0., 0., 0., 0.],\n", - " [0., 0., 0., 0., 0.],\n", - " [0., 0., 0., 0., 0.],\n", - " [0., 0., 0., 0., 0.],\n", - " [0., 0., 0., 0., 0.]], dtype=float32), R=DeviceArray([[0., 0., 0.],\n", - " [0., 0., 0.],\n", - " [0., 0., 0.]], dtype=float32), mu=DeviceArray([0., 0., 0., 0., 0.], dtype=float32), Sigma=DeviceArray([[0., 0., 0., 0., 0.],\n", - " [0., 0., 0., 0., 0.],\n", - " [0., 0., 0., 0., 0.],\n", - " [0., 0., 0., 0., 0.],\n", - " [0., 0., 0., 0., 0.]], dtype=float32), state_offset=None, obs_offset=None, nstates=5, nobs=3)\n", - "[0. 0. 0. 0. 0.]\n", - "[0. 0. 0.]\n" + "L2-filter: 3.2481\n", + "L2-smooth: 2.0450\n" ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" 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\n" 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\n" + }, + "metadata": { + "needs_background": "light" + } } - ], + ] + }, + { + "cell_type": "code", "source": [ - "from jsl.lds.kalman_filter import LDS, smooth, filter\n", "\n", - "nstates, nobs = 5, 3\n", - "A = jnp.zeros((nstates, nstates))\n", - "Q = jnp.zeros((nstates, nstates))\n", - "C = jnp.zeros((nobs, nstates))\n", - "R = jnp.zeros((nobs, nobs))\n", - "mu = jnp.zeros((nstates,))\n", - "Sigma = jnp.zeros((nstates, nstates))\n", - "lds = LDS(A, C, Q, R, mu, Sigma) # obs_offset = jnp.ones((nobs,)))\n", - "print(lds)\n", - "print(lds.get_state_offset_of(5))\n", - "print(lds.get_obs_offset_of(5))" + "\n", + "from jsl.demos import hmm_casino as demo\n", + "figures = demo.main()\n", + "print(figures)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 218 + }, + "id": "_b8lNuou9RaT", + "outputId": "7165d3db-0250-479b-83cc-77157beb6e56" + }, + "execution_count": 19, + "outputs": [ + { + "output_type": "error", + "ename": "AttributeError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mjsl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdemos\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mhmm_casino\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mdemo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mfigures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdemo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigures\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: module 'jsl.demos.hmm_casino' has no attribute 'main'" + ] + } ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "colab": { - "authorship_tag": "ABX9TyOyouFLeOExeSvfdoPotWlr", - "include_colab_link": true, - "name": "JSL-notebook.ipynb", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 + "source": [ + "" + ], + "metadata": { + "id": "CWEhPo9YJuut" }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.2" + "execution_count": null, + "outputs": [] } - }, - "nbformat": 4, - "nbformat_minor": 0 -} + ] +} \ No newline at end of file diff --git a/jsl/demos/kf_tracking.py b/jsl/demos/kf_tracking.py index 2605d20..94ef70f 100644 --- a/jsl/demos/kf_tracking.py +++ b/jsl/demos/kf_tracking.py @@ -101,7 +101,6 @@ def main(): Sigma0 = jnp.eye(state_size) * 1.0 lds_instance = LDS(A, C, Q, R, mu0, Sigma0) - result = sample_filter_smooth(key, lds_instance, timesteps) l2_filter = jnp.linalg.norm(result["z_hist"][:, :2] - result["mu_hist"][:, :2], 2) diff --git a/jsl/lds/kalman_filter.py b/jsl/lds/kalman_filter.py index 5ad4890..4982ec3 100644 --- a/jsl/lds/kalman_filter.py +++ b/jsl/lds/kalman_filter.py @@ -1,5 +1,5 @@ # Jax implementation of a Linear Dynamical System -# Author: Gerardo Durán-Martín (@gerdm), Aleyna Kara(@karalleyna), Kevin Murphy (@murphyk) +# Author: Gerardo Durán-Martín (@gerdm), Aleyna Kara(@karalleyna) from jax import config config.update("jax_default_matmul_precision", "float32") @@ -9,14 +9,13 @@ from jax.random import multivariate_normal, split from jax.scipy.linalg import solve from jax import tree_map, lax, vmap -from dataclasses import dataclass, field +from dataclasses import dataclass from functools import partial from typing import Union, Callable from tensorflow_probability.substrates import jax as tfp tfd = tfp.distributions -ArrayOrFn = Union[chex.Array, Callable] @dataclass class LDS: @@ -27,42 +26,35 @@ class LDS: the model parameters are known. The LDS evolves as follows: - x_t = A x_t-1 + w_t; w_t ~ N(state_offset, Q) - y_t = C x_t + v_t; v_t ~ N(obs_offset, R) + x_t = A x_t-1 + w_t; w_t ~ N(0, Q) + y_t = C x_t + v_t; v_t ~ N(0, R) with initial state x_0 ~ N(mu, Sigma) Parameters ---------- A: array(state_size, state_size) - Transition matrix or function that depends on time + Transition matrix C: array(observation_size, state_size) Constant observation matrix or function that depends on time Q: array(state_size, state_size) - Transition covariance matrix or function that depends on time + Transition covariance matrix R: array(observation_size, observation_size) - Observation covariance or function that depends on time + Observation covariance mu: array(state_size) Mean of initial configuration Sigma: array(state_size, state_size) or 0 Covariance of initial configuration. If value is set to zero, the initial state will be completely determined by mu0 - """ - A: ArrayOrFn - C: ArrayOrFn - Q: ArrayOrFn - R: ArrayOrFn + A: chex.Array + C: Union[chex.Array, Callable] + Q: chex.Array + R: chex.Array mu: chex.Array Sigma: chex.Array - state_offset: ArrayOrFn = None - obs_offset: ArrayOrFn = None - - nstates: int = field(init=False) - nobs: int = field(init=False) - def get_trans_mat_of(self, t: int): if callable(self.A): @@ -88,25 +80,6 @@ def get_observation_noise_of(self, t: int): else: return self.R - def get_state_offset_of(self, t: int): - if self.state_offset is None: - return jnp.zeros((self.nstates)) - elif callable(self.state_offset): - return self.state_offset(t) - else: - return self.state_offset - - def get_obs_offset_of(self, t: int): - if self.obs_offset is None: - return jnp.zeros((self.nobs)) - elif callable(self.obs_offset): - return self.obs_offset(t) - else: - return self.obs_offset - - def __post_init__(self): - self.nobs, self.nstates = self.C.shape - def sample(self, key: chex.PRNGKey, @@ -143,28 +116,22 @@ def sample(self, # Generate all future noise terms zeros_state = jnp.zeros(state_size) - Q = self.get_system_noise_of(0) # assumed static - R = self.get_observation_noise_of(0) # assumed static - + R = self.get_observation_noise_of(0) - #observation_size = timesteps if isinstance(R, int) else R.shape[0] - observation_size = R.shape[0] + observation_size = timesteps if isinstance(R, int) else R.shape[0] zeros_obs = jnp.zeros(observation_size) - system_noise = multivariate_normal(key_system_noise, zeros_state, Q, (timesteps, n_samples)) + system_noise = multivariate_normal(key_system_noise, zeros_state, + self.get_system_noise_of(0), (timesteps, n_samples)) obs_noise = multivariate_normal(key_obs_noise, zeros_obs, R, (timesteps, n_samples)) - # observation at time t=0 obs_t = jnp.einsum("ij,sj->si", self.get_obs_mat_of(0), state_t) + obs_noise[0] def sample_step(state, inps): system_noise_t, obs_noise_t, t = inps A = self.get_trans_mat_of(t) - C = self.get_obs_mat_of(t) state_new = state @ A.T + system_noise_t - #state_new = state_new + self.get_state_offset_of(t) - obs_new = state_new @ C.T + obs_noise_t - #obs_new = obs_new + self.get_obs_offset_of(t) + obs_new = state_new @ self.get_obs_mat_of(t).T + obs_noise_t return state_new, (state_new, obs_new) timesteps = jnp.arange(1, timesteps) @@ -191,6 +158,7 @@ def kalman_step(state, obs, params): Sigma_cond = A @ Sigma @ A.T + Q # \mu_{t |t-1} and xn|{n-1} + mu_cond = A @ mu mu_cond = mu_cond + params.get_state_offset_of(t)