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# daal4py LBFGS (limited memory Broyden-Fletcher-Goldfarb-Shanno) example for shared memory systems
# using Mean Squared Error objective function

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: pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=np.float64).values
except:
    # fall back to numpy loadtxt
    read_csv = lambda f, c: np.loadtxt(f, usecols=c, delimiter=',', ndmin=2)


def main():
    infile   = "./data/batch/lbfgs.csv"
    # Read the data, let's have 10 independent variables
    data     = read_csv(infile, range(10))
    dep_data = read_csv(infile, range(10,11))
    nVectors = data.shape[0]

    # configure a MSE object
    mse_algo = d4p.optimization_solver_mse(nVectors)
    mse_algo.setup(data, dep_data, None)

    # configure an LBFGS object
    sls = np.array([[1.0e-4]], dtype=np.double)
    niters = 1000
    lbfgs_algo = d4p.optimization_solver_lbfgs(mse_algo,
                                               stepLengthSequence=sls,
                                               nIterations=niters)

    # finally do the computation
    inp = np.array([[100]]*11, dtype=np.double)
    res = lbfgs_algo.compute(inp)

    # The LBFGS result provides minimum and nIterations
    assert res.minimum.shape == inp.shape and res.nIterations[0][0] <= niters

    return res


if __name__ == "__main__":
    res = main()
    print("\nExpected coefficients:\n", np.array([[11], [1], [2], [3], [4], [5], [6], [7], [8], [9], [10]], dtype=np.float64))
    print("\nResulting coefficients:\n", res.minimum)
    print("\nNumber of iterations performed:\n", res.nIterations[0][0])
    print('All looks good!')
