aitech-eks-pub/cw/03a_tfidf_tasks_ODPOWIEDZI.ipynb
2021-03-24 12:38:00 +01:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"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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