43 lines
1.0 KiB
Python
43 lines
1.0 KiB
Python
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#!/usr/bin/env python
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import numpy as np
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def normalizeRows(x):
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""" Row normalization function
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Implement a function that normalizes each row of a matrix to have
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unit length.
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"""
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N = x.shape[0]
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x /= np.sqrt(np.sum(x**2, axis=1)).reshape((N,1)) + 1e-30
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return x
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def softmax(x):
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"""Compute the softmax function for each row of the input x.
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It is crucial that this function is optimized for speed because
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it will be used frequently in later code.
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Arguments:
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x -- A D dimensional vector or N x D dimensional numpy matrix.
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Return:
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x -- You are allowed to modify x in-place
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"""
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orig_shape = x.shape
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if len(x.shape) > 1:
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# Matrix
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tmp = np.max(x, axis=1)
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x -= tmp.reshape((x.shape[0], 1))
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x = np.exp(x)
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tmp = np.sum(x, axis=1)
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x /= tmp.reshape((x.shape[0], 1))
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else:
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# Vector
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tmp = np.max(x)
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x -= tmp
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x = np.exp(x)
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tmp = np.sum(x)
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x /= tmp
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assert x.shape == orig_shape
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return x
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