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December 30, 2015 22:09
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Simple language similarity with character n-grams
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{ | |
"metadata": { | |
"name": "" | |
}, | |
"nbformat": 3, | |
"nbformat_minor": 0, | |
"worksheets": [ | |
{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"from sklearn.feature_extraction.text import TfidfVectorizer" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 38 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"texts = [(u\"Personalmente, e credo condividiate la mia opinione, sento \"\n", | |
" u\"una dolorosa sensazione di d\u00e9j\u00e0 vu quando vedo queste immagini in televisione.\"),\n", | |
" (u\"Personal, \u015fi sunt sigur c\u0103 acest lucru este valabil pentru cei \"\n", | |
" u\"mai mul\u0163i dintre noi, imaginile transmise la televizor \u00eemi trezesc \"\n", | |
" u\"un dureros sentiment de d\u00e9j\u00e0 vu\"),\n", | |
" u\"An english sentence that shouldn't be too similar.\"]" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 39 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"vect = TfidfVectorizer(analyzer=\"char_wb\", # n-grams within word boundary\n", | |
" ngram_range=(3, 9),\n", | |
" lowercase=True,\n", | |
" use_idf=False)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 52 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"X = vect.fit_transform(texts)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 53 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"from sklearn.metrics import euclidean_distances" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 57 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"print(1 - euclidean_distances(X) / np.sqrt(2)) # I think this is cosine similarity" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"[[ 1. 0.10266664 0.02078989]\n", | |
" [ 0.10266664 1. 0.01606852]\n", | |
" [ 0.02078989 0.01606852 1. ]]\n" | |
] | |
} | |
], | |
"prompt_number": 65 | |
} | |
], | |
"metadata": {} | |
} | |
] | |
} |
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