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# daal4py SVD example for streaming on 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, s=0, n=None, t=np.float64: pandas.read_csv(f, usecols=c, delimiter=',', header=None, skiprows=s, nrows=n, dtype=t)
except:
    # fall back to numpy genfromtxt
    def read_csv(f, c, s=0, n=np.iinfo(np.int64).max):
        a = np.genfromtxt(f, usecols=c, delimiter=',', skip_header=s, max_rows=n)
        if a.shape[0] == 0:
            raise Exception("done")
        if a.ndim == 1:
            return a[:, np.newaxis]
        return a


def main(readcsv=read_csv, method='defaultDense'):
    infiles = ["./data/distributed/svd_{}.csv".format(i) for i in range(1,5)]

    # configure a SVD object
    algo = d4p.svd(streaming=True)
    
    # let's provide files directly, not a tables/arrays
    # Feed file by file
    for infile in infiles:
        algo.compute(infile)

    # All files are done, now finalize the computation
    result= algo.finalize()

    # SVD result objects provide leftSingularMatrix, rightSingularMatrix and singularValues
    return result


if __name__ == "__main__":
    result = main()
    print("\nSingular values:\n", result.singularValues)
    print("\nRight orthogonal matrix V:\n", result.rightSingularMatrix)
    print("\nLeft orthogonal matrix U (first 10 rows):\n", result.leftSingularMatrix[0:10])
    print('All looks good!')
