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Copy pathtest_util.py
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59 lines (51 loc) · 2.08 KB
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# Copyright 2016 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
from numpy.random import RandomState
from brainiak.fcma.util import compute_correlation
# specify the random state to fix the random numbers
prng = RandomState(1234567890)
def test_correlation_computation():
row1 = 5
col = 10
row2 = 6
mat1 = prng.rand(row1, col).astype(np.float32)
mat2 = prng.rand(row2, col).astype(np.float32)
corr = compute_correlation(mat1, mat1)
expected_corr = np.corrcoef(mat1)
assert np.allclose(corr, expected_corr, atol=1e-5), (
"high performance correlation computation does not provide correct "
"correlation results within the same set")
corr = compute_correlation(mat1, mat2)
mat = np.concatenate((mat1, mat2), axis=0)
expected_corr = np.corrcoef(mat)[0:row1, row1:]
assert np.allclose(corr, expected_corr, atol=1e-5), (
"high performance correlation computation does not provide correct "
"correlation results between two sets")
def test_correlation_nans():
row1 = 5
col = 10
row2 = 6
mat1 = prng.rand(row1, col).astype(np.float32)
mat2 = prng.rand(row2, col).astype(np.float32)
mat1[0, 0] = np.nan
corr = compute_correlation(mat1, mat2, return_nans=False)
assert np.all(corr == 0, axis=1)[0]
assert np.sum(corr == 0) == row2
corr = compute_correlation(mat1, mat2, return_nans=True)
assert np.all(np.isnan(corr), axis=1)[0]
assert np.sum(np.isnan(corr)) == row2
if __name__ == '__main__':
test_correlation_computation()
test_correlation_nans()