{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bayes Filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from filters import bayes\n",
    "from plots import plot_bayes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discrete Bayes Filter\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Belief vs. Prior\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x320 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "belief = [0, 0, 0.4, 0.6, 0, 0, 0, 0, 0, 0]\n",
    "prior = bayes.prior_with_jitter(belief, 2, 0.8, 0.1, 0.1)\n",
    "plot_bayes.plot_belief_prior(belief, prior, ylim=(0, 0.6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x320 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "belief = [0.05, 0.05, 0.05, 0.05, 0.55, 0.05, 0.05, 0.05, 0.05, 0.05]\n",
    "prior = bayes.prior_by_convol(belief, offset=1, kernel=[0.1, 0.8, 0.1])\n",
    "plot_bayes.plot_belief_prior(belief, prior, ylim=(0, 0.6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prior vs. Posterior\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x320 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = np.array([1, 1, 0, 0, 0, 0, 0, 0, 1, 0])\n",
    "prior = np.array([0.1] * 10)\n",
    "likelihood = bayes.likelihood(data, z=1, z_prob=0.75)\n",
    "posterior = bayes.posterior(likelihood, prior)\n",
    "plot_bayes.plot_prior_posterior(prior, posterior, ylim=(0, 0.6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x320 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kernel = (0.1, 0.8, 0.1)\n",
    "prior = bayes.prior_by_convol(posterior, 1, kernel)\n",
    "likelihood = bayes.likelihood(data, z=1, z_prob=0.75)\n",
    "posterior = bayes.posterior(likelihood, prior)\n",
    "plot_bayes.plot_prior_posterior(prior, posterior, ylim=(0, 0.6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x320 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prior = bayes.prior_by_convol(posterior, 1, kernel)\n",
    "likelihood = bayes.likelihood(data, z=0, z_prob=0.75)\n",
    "posterior = bayes.posterior(likelihood, prior)\n",
    "plot_bayes.plot_prior_posterior(prior, posterior, ylim=(0, 0.6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x320 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prior = bayes.prior_by_convol(posterior, 1, kernel)\n",
    "likelihood = bayes.likelihood(data, z=0, z_prob=0.75)\n",
    "posterior = bayes.posterior(likelihood, prior)\n",
    "plot_bayes.plot_prior_posterior(prior, posterior, ylim=(0, 0.6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulation\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "kaggle",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}