#===============================================================================
# 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
#
# 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
# limitations under the License.
#===============================================================================

# 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

    def read_csv(f, c, 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, t=np.float64):
        return np.loadtxt(f, usecols=c, delimiter=',', ndmin=2)


def main(readcsv=read_csv, method='defaultDense'):
    infile = "./data/batch/lbfgs.csv"
    # Read the data, let's have 10 independent variables
    data = readcsv(infile, range(10))
    dep_data = readcsv(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)

    # 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.double
        )
    )
    print("\nResulting coefficients:\n", res.minimum)
    print("\nNumber of iterations performed:\n", res.nIterations[0][0])
    print('All looks good!')