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# Sebastian Raschka 2014

#

# Sparsifying a matrix by Zeroing out all elements but the top k elements in a row.

# The matrix could be a distance or similarity matrix (e.g., kernel matrix in kernel PCA),

# where we are interested to keep the top k neighbors.

import numpy as np

print('Sparsify a matrix by zeroing all elements but the top 2 values in a row.\n')

A = np.array([[1,2,3,4,5],[9,8,6,4,5],[3,1,7,8,9]])

print('Before:\n%s\n' %A)

k = 2 # keep top k neighbors

for row in A:

sort_idx = np.argsort(row)[::-1] # get indexes of sort order (high to low)

for i in sort_idx[k:]:

row[i]=0

print('After:\n%s\n' %A)

"""

Sparsify a matrix by zeroing all elements but the top 2 values in a row.

Before:

[[1 2 3 4 5]

[9 8 6 4 5]

[3 1 7 8 9]]

After:

[[0 0 0 4 5]

[9 8 0 0 0]

[0 0 0 8 9]]

"""