Ch03_3_9.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
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   "source": [
    "import numpy as np"
   ]
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    "# Instead of predicting just\n",
    "# wheather the team won or lost,\n",
    "# now we're also predicting\n",
    "# whether they are happy/sad AND\n",
    "# the percentage of the team that\n",
    "# is hurt. we are making this\n",
    "# prediction using only the\n",
    "# current win / loss record.\n",
    "\n",
    "\n",
    "weights = [[0.1, 0.1, -0.3], #hurt?\n",
    "           [0.1, 0.2, 0.0],  #win?\n",
    "           [0.0, 1.3, 0.1]]  #sad?\n",
    "\n",
    "def neural_network(input, weights):\n",
    "    \n",
    "    pred = vect_mat_mul(input,weights)\n",
    "    \n",
    "    return pred\n",
    "\n",
    "\n",
    "\n",
    "def w_sum(a,b):\n",
    "    assert(len(a) == len(b))\n",
    "    output = 0\n",
    "    for i in range(len(a)):\n",
    "        output += (a[i] * b[i])\n",
    "    \n",
    "    return output\n",
    "\n",
    "\n",
    "def vect_mat_mul(vect, matrix):\n",
    "    assert(len(vect) == len(matrix))\n",
    "    output = [0, 0, 0]\n",
    "    \n",
    "    for i in range(len(vect)):\n",
    "        output[i] = w_sum(vect, matrix[i])\n",
    "    \n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.555, 0.9800000000000001, 0.9650000000000001]\n"
     ]
    }
   ],
   "source": [
    "# This dataset is the current\n",
    "# status at the beginning of\n",
    "# each game for the first 4 games\n",
    "# in a season.\n",
    "\n",
    "# toes = current number of toes\n",
    "# wlrec = current games won (\n",
    "\n",
    "toes = [8.5, 9.5, 9.9, 9.0]\n",
    "wlrec = [0.65, 0.8, 0.8, 0.9]\n",
    "nfans = [1.2, 1.3, 0.5, 1.0]\n",
    "\n",
    "# Input corresponds to every entry\n",
    "# for the first game of the season.\n",
    "\n",
    "input = [toes[0], wlrec[0], nfans[0]]\n",
    "\n",
    "pred = neural_network(input, weights)\n",
    "\n",
    "print(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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