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# daal4py cholesky 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
    read_csv = lambda f, c, 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, t=np.float64: np.loadtxt(f, usecols=c, delimiter=',', ndmin=2)


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

    # configure a cholesky object
    algo = d4p.cholesky()
    
    # let's provide a file directly, not a table/array
    return algo.compute(infile)
    # cholesky result objects provide choleskyFactor


if __name__ == "__main__":
    result = main()
    print("\nFactor:\n", result.choleskyFactor)
    print('All looks good!')
    np.savetxt('c.csv', result.choleskyFactor, delimiter=',')
