#===============================================================================
# Copyright 2014-2021 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
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# 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
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#===============================================================================

# daal4py normalization minmax example for shared memory systems

import daal4py as d4p
import numpy as np

# let's try to use pandas' fast csv reader
try:
    import pandas

    def read_csv(f, c=None, t=np.float64):
        return pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=t)
except ImportError:
    # fall back to numpy loadtxt
    def read_csv(f, c=None, t=np.float64):
        return np.loadtxt(f, usecols=c, delimiter=',', ndmin=2)


def main(readcsv=read_csv, method='defaultDense'):
    infile = "./data/batch/normalization.csv"

    # configure a covariance object
    algo = d4p.normalization_minmax(lowerBound=-1.0, upperBound=1.0)

    # let's provide a file directly, not a table/array
    result1 = algo.compute(infile)

    # We can also load the data ourselfs and provide the numpy array
    data = readcsv(infile)
    result2 = algo.compute(data)

    # covariance result objects provide correlation, covariance and mean
    assert np.allclose(result1.normalizedData, result2.normalizedData)

    return result1


if __name__ == "__main__":
    res = main()
    print("MinMax result (first 5 rows):\n", res.normalizedData[:5])
    print('All looks good!')