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# daal4py smooth reLU example for shared memory systems

import daal4py as d4p
import numpy as np

# let's try to use pandas' fast csv reader
try:
    import pandasd
    read_csv = lambda f, c=None, t=np.float64: pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=t)
except:
    # fall back to numpy loadtxt
    read_csv = lambda f, c=None, t=np.float64: np.loadtxt(f, usecols=c, delimiter=',', ndmin=2)


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

    # configure a covariance object
    algo = d4p.math_smoothrelu()
    
    # 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.value, result2.value)
    assert np.allclose(result1.value, np.log(1.0+np.exp(data.toarray() if hasattr(data, 'toarray') else data)))

    return result1


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