8 examples of 'cosine similarity calculator' in Python

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10def cosine_similarity(x, y):
11 numerator = sum(a * b for a, b in zip(x, y))
12 denominator = square_rooted(x) * square_rooted(y)
13 try:
14 return numerator / float(denominator)
15 except ZeroDivisionError:
16 return 0.0
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47def computeCosineDistance(vector1, vector2):
48 return 1 - spatial.distance.cosine(vector1, vector2)
81def test_cosine_identical(self):
82 cosine = CosineTextSimilarity(self.ilist)
83 cosine_sim = cosine(self.ilist[0], self.ilist[0])
84 self.assertAlmostEqual(cosine_sim, 1, places=5)
307def CosineSimilarity(v1, v2):
308 """ Implements the Cosine similarity metric.
309 This is the recommended metric in the LaSSI paper
310
311 **Arguments**:
312
313 - two vectors (sequences of bit ids)
314
315 **Returns**: a float.
316
317 **Notes**
318
319 - the vectors must be sorted
320
321 >>> print('%.3f'%CosineSimilarity( (1,2,3,4,10), (2,4,6) ))
322 0.516
323 >>> print('%.3f'%CosineSimilarity( (1,2,2,3,4), (2,2,4,5,6) ))
324 0.714
325 >>> print('%.3f'%CosineSimilarity( (1,2,2,3,4), (1,2,2,3,4) ))
326 1.000
327 >>> print('%.3f'%CosineSimilarity( (1,2,2,3,4), (5,6,7) ))
328 0.000
329 >>> print('%.3f'%CosineSimilarity( (1,2,2,3,4), () ))
330 0.000
331
332 """
333 d1 = Dot(v1, v1)
334 d2 = Dot(v2, v2)
335 denom = math.sqrt(d1 * d2)
336 if not denom:
337 res = 0.0
338 else:
339 numer = Dot(v1, v2)
340 res = numer / denom
341 return res
158def cosine_distance(s1, s2, k):
159 """Compute the cosine difference of the strings as kmer vectors
160 """
161 vec1, vec2 = to_kmer_vector(s1, s2, k)
162
163 intersection = set(vec1.keys()) & set(vec2.keys())
164 numerator = sum([vec1[x] * vec2[x] for x in intersection])
165
166 sum1 = sum([vec1[x] ** 2 for x in vec1.keys()])
167 sum2 = sum([vec2[x] ** 2 for x in vec2.keys()])
168 denominator = math.sqrt(sum1) * math.sqrt(sum2)
169 if not denominator:
170 return 0.0
171 else:
172 return float(numerator) / denominator
22def cos_simi(vector1,vector2):
23 dot_product = 0.0
24 normA = 0.0
25 normB = 0.0
26
27 for a,b in zip(vector1,vector2):
28 dot_product += a*b
29 normA += a**2
30 normB += b**2
31
32
33 if normA == 0.0 or normB==0.0:
34 return None
35 else:
36 return dot_product / ((normA*normB)**0.5)
48def cosSim(v1, v2):
49 N = dot(v1, v2)
50 D = dist((0,0), v1)*dist((0,0), v2)
51
52 if D <= 0.01: D = 0.01
53
54 return N/D
478def cosine_similarity(v1, v2):
479 """Cosine similarity [-1, 1].
480
481 Parameters
482 ----------
483 v1, v2 : Tensor
484 Tensor with the same shape [batch_size, n_feature].
485
486 References
487 ----------
488 - `Wiki <https://en.wikipedia.org/wiki/Cosine_similarity>`__.
489
490 """
491
492 return tf.reduce_sum(tf.multiply(v1, v2), 1) / \
493 (tf.sqrt(tf.reduce_sum(tf.multiply(v1, v1), 1)) *
494 tf.sqrt(tf.reduce_sum(tf.multiply(v2, v2), 1)))

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