{
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   "source": [
    "def word_to_index(word):\n",
    "    vec = np.zeros(len(vocabulary))\n",
    "    if word in vocabulary:\n",
    "        idx = vocabulary.index(word)\n",
    "        vec[idx] = 1\n",
    "    else:\n",
    "        vec[-1] = 1\n",
    "    return vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tf(document):\n",
    "    document_vector = None\n",
    "    for word in document:\n",
    "        if document_vector is None:\n",
    "            document_vector = word_to_index(word)\n",
    "        else:\n",
    "            document_vector += word_to_index(word)\n",
    "    return document_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def similarity(query, document):\n",
    "    numerator = np.sum(query * document)\n",
    "    denominator = np.sqrt(np.sum(query*query)) * np.sqrt(np.sum(document*document)) \n",
    "    return numerator / denominator"
   ]
  }
 ],
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