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