Lesseon21-kalmanfilter.ipynb

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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5-final"
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  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.8.5 64-bit (conda)",
   "metadata": {
    "interpreter": {
     "hash": "0a5386b8f77d18fbfd5c36939b50bf5030c7167b49641951cd2ba498f46ad0ff"
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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update(mean1, var1, mean2, var2):\n",
    "    new_mean = float(var2 * mean1 + var1 * mean2) / (var1 + var2)\n",
    "    new_var = 1./(1./var1 + 1./var2)\n",
    "    return [new_mean, new_var]\n",
    "\n",
    "def predict(mean1, var1, mean2, var2):\n",
    "    new_mean = mean1 + mean2\n",
    "    new_var = var1 + var2\n",
    "    return [new_mean, new_var]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Update :[4.998001, 3.998401]\nPredict : [5.998001, 5.998401]\nUpdate :[5.999200, 2.399744]\nPredict : [6.999200, 4.399744]\nUpdate :[6.999619, 2.095180]\nPredict : [8.999619, 4.095180]\nUpdate :[8.999812, 2.023515]\nPredict : [9.999812, 4.023515]\nUpdate :[9.999906, 2.005862]\nPredict : [10.999906, 4.005862]\n"
     ]
    }
   ],
   "source": [
    "measurements = [5., 6., 7., 9., 10.]\n",
    "motion = [1., 1., 2., 1., 1.]\n",
    "measurement_sig = 4.\n",
    "motion_sig = 2.\n",
    "mu = 0.\n",
    "sig = 10000.\n",
    "\n",
    "#Please print out ONLY the final values of the mean\n",
    "#and the variance in a list [mu, sig]. \n",
    "\n",
    "# Insert code here\n",
    "for i in range(len(measurements)):\n",
    "    mu_hat, sig_hat = update(mu, sig, measurements[i], measurement_sig)\n",
    "    print(\"Update :[%f, %f]\" %(mu_hat, sig_hat) )\n",
    "\n",
    "    mu, sig = predict(mu_hat,sig_hat,+motion[i],motion_sig)\n",
    "    print(\"Predict : [%f, %f]\" %(mu, sig))\n",
    "\n",
    "\n",
    "# print [mu, sig]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}