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# Copyright 2014-2019 Intel Corporation
# All Rights Reserved.
# This software is licensed under the Apache License, Version 2.0 (the
# "License"), the following terms apply:
# You may not use this file except in compliance with the License. You may
# obtain a copy of the License at
# 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.
# daal4py correlation distance example for shared memory systems
import daal4py as d4p
import numpy as np
import os
# let's try to use pandas' fast csv reader
import pandas
read_csv = lambda f, c, t=np.float64: pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=t)
# fall back to numpy loadtxt
read_csv = lambda f, c, t=np.float64: np.loadtxt(f, usecols=c, delimiter=',', ndmin=2)
def main(readcsv=read_csv, method='defaultDense'):
data = readcsv(os.path.join('data', 'batch', 'distance.csv'), range(10))
# Create algorithm to compute correlation distance (no parameters)
algorithm = d4p.correlation_distance()
# Computed correlation distance with file or numpy array
res1 = algorithm.compute(os.path.join('data', 'batch', 'distance.csv'))
res2 = algorithm.compute(data)
assert np.allclose(res1.correlationDistance, res2.correlationDistance)
return res1
if __name__ == "__main__":
res = main()
print("\nCorrelation distance (first 15 rows/columns):\n", res.correlationDistance[0:15,0:15])
print("All looks good!")
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