8 examples of 'cosine similarity calculator' in Python

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def cosine_similarity(x, y):
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    numerator = sum(a * b for a, b in zip(x, y))
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    denominator = square_rooted(x) * square_rooted(y)
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    try:
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        return numerator / float(denominator)
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    except ZeroDivisionError:
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        return 0.0

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def computeCosineDistance(vector1, vector2):
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    return 1 - spatial.distance.cosine(vector1, vector2)

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def test_cosine_identical(self):
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    cosine = CosineTextSimilarity(self.ilist)
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    cosine_sim = cosine(self.ilist[0], self.ilist[0])
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    self.assertAlmostEqual(cosine_sim, 1, places=5)

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def CosineSimilarity(v1, v2):
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  """ Implements the Cosine similarity metric.
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   This is the recommended metric in the LaSSI paper
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  **Arguments**:
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    - two vectors (sequences of bit ids)
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  **Returns**: a float.
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  **Notes**
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    - the vectors must be sorted
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  >>> print('%.3f'%CosineSimilarity( (1,2,3,4,10), (2,4,6) ))
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  0.516
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  >>> print('%.3f'%CosineSimilarity( (1,2,2,3,4), (2,2,4,5,6) ))
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  0.714
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  >>> print('%.3f'%CosineSimilarity( (1,2,2,3,4), (1,2,2,3,4) ))
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  1.000
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  >>> print('%.3f'%CosineSimilarity( (1,2,2,3,4), (5,6,7) ))
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  0.000
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  >>> print('%.3f'%CosineSimilarity( (1,2,2,3,4), () ))
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  0.000
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  """
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  d1 = Dot(v1, v1)
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  d2 = Dot(v2, v2)
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  denom = math.sqrt(d1 * d2)
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  if not denom:
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    res = 0.0
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  else:
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    numer = Dot(v1, v2)
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    res = numer / denom
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  return res

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def cosine_distance(s1, s2, k):
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    """Compute the cosine difference of the strings as kmer vectors
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    """
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    vec1, vec2 = to_kmer_vector(s1, s2, k)
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    intersection = set(vec1.keys()) & set(vec2.keys())
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    numerator = sum([vec1[x] * vec2[x] for x in intersection])
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    sum1 = sum([vec1[x] ** 2 for x in vec1.keys()])
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    sum2 = sum([vec2[x] ** 2 for x in vec2.keys()])
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    denominator = math.sqrt(sum1) * math.sqrt(sum2)
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    if not denominator:
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        return 0.0
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    else:
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        return float(numerator) / denominator

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def cos_simi(vector1,vector2):  
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	dot_product = 0.0  
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	normA = 0.0  
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	normB = 0.0
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	for a,b in zip(vector1,vector2):  
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		dot_product += a*b  
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		normA += a**2  
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		normB += b**2  
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	if normA == 0.0 or normB==0.0:  
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		return None  
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	else:  
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		return dot_product / ((normA*normB)**0.5)

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def cosSim(v1, v2):
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	N = dot(v1, v2)
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	D = dist((0,0), v1)*dist((0,0), v2)
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	if D <= 0.01: D = 0.01
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	return N/D

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def cosine_similarity(v1, v2):
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    """Cosine similarity [-1, 1].
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    Parameters
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    ----------
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    v1, v2 : Tensor
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        Tensor with the same shape [batch_size, n_feature].
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    References
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    ----------
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    - `Wiki `__.
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    """
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    return tf.reduce_sum(tf.multiply(v1, v2), 1) / \
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        (tf.sqrt(tf.reduce_sum(tf.multiply(v1, v1), 1)) *
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         tf.sqrt(tf.reduce_sum(tf.multiply(v2, v2), 1)))