62 lines
2 KiB
Python
62 lines
2 KiB
Python
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"""Tests for _sketches.py."""
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from __future__ import division, print_function, absolute_import
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import numpy as np
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from scipy.linalg import clarkson_woodruff_transform
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from numpy.testing import assert_
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def make_random_dense_gaussian_matrix(n_rows, n_columns, mu=0, sigma=0.01):
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"""
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Make some random data with Gaussian distributed values
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"""
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np.random.seed(142352345)
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res = np.random.normal(mu, sigma, n_rows*n_columns)
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return np.reshape(res, (n_rows, n_columns))
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class TestClarksonWoodruffTransform(object):
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"""
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Testing the Clarkson Woodruff Transform
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"""
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# Big dense matrix dimensions
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n_matrix_rows = 2000
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n_matrix_columns = 100
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# Sketch matrix dimensions
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n_sketch_rows = 100
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# Error threshold
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threshold = 0.1
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dense_big_matrix = make_random_dense_gaussian_matrix(n_matrix_rows,
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n_matrix_columns)
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def test_sketch_dimensions(self):
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sketch = clarkson_woodruff_transform(self.dense_big_matrix,
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self.n_sketch_rows)
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assert_(sketch.shape == (self.n_sketch_rows,
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self.dense_big_matrix.shape[1]))
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def test_sketch_rows_norm(self):
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# Given the probabilistic nature of the sketches
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# we run the 'test' multiple times and check that
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# we pass all/almost all the tries
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n_errors = 0
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seeds = [1755490010, 934377150, 1391612830, 1752708722, 2008891431,
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1302443994, 1521083269, 1501189312, 1126232505, 1533465685]
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for seed_ in seeds:
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sketch = clarkson_woodruff_transform(self.dense_big_matrix,
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self.n_sketch_rows, seed_)
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# We could use other norms (like L2)
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err = np.linalg.norm(self.dense_big_matrix) - np.linalg.norm(sketch)
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if err > self.threshold:
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n_errors += 1
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assert_(n_errors == 0)
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