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38 lines
824 B
Python
38 lines
824 B
Python
# Sebastian Raschka 2014
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#
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# Sparsifying a matrix by Zeroing out all elements but the top k elements in a row.
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# The matrix could be a distance or similarity matrix (e.g., kernel matrix in kernel PCA),
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# where we are interested to keep the top k neighbors.
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import numpy as np
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print('Sparsify a matrix by zeroing all elements but the top 2 values in a row.\n')
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A = np.array([[1,2,3,4,5],[9,8,6,4,5],[3,1,7,8,9]])
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print('Before:\n%s\n' %A)
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k = 2 # keep top k neighbors
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for row in A:
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sort_idx = np.argsort(row)[::-1] # get indexes of sort order (high to low)
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for i in sort_idx[k:]:
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row[i]=0
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print('After:\n%s\n' %A)
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"""
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Sparsify a matrix by zeroing all elements but the top 2 values in a row.
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Before:
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[[1 2 3 4 5]
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[9 8 6 4 5]
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[3 1 7 8 9]]
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After:
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[[0 0 0 4 5]
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[9 8 0 0 0]
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[0 0 0 8 9]]
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""" |