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20 lines
900 B
Python
20 lines
900 B
Python
import numpy as np
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def findCosineDistance(source_representation, test_representation):
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a = np.matmul(np.transpose(source_representation), test_representation)
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b = np.sum(np.multiply(source_representation, source_representation))
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c = np.sum(np.multiply(test_representation, test_representation))
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return 1 - (a / (np.sqrt(b) * np.sqrt(c)))
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def findEuclideanDistance(source_representation, test_representation):
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euclidean_distance = source_representation - test_representation
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euclidean_distance = np.sum(np.multiply(euclidean_distance, euclidean_distance))
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euclidean_distance = np.sqrt(euclidean_distance)
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return euclidean_distance
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def l2_normalize(x):
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return x / np.sqrt(np.sum(np.multiply(x, x)))
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"""def l2_normalize(x, axis=-1, epsilon=1e-10):
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output = x / np.sqrt(np.maximum(np.sum(np.square(x), axis=axis, keepdims=True), epsilon))
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return output""" |