#===============================================================================
# Copyright 2014 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 PCA 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

    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='svdDense'):
    dataFileName = "data/batch/pca_transform.csv"
    nComponents = 2

    # read data
    data = readcsv(dataFileName, range(3))

    # configure a PCA object and perform PCA
    pca_algo = d4p.pca(isDeterministic=True, resultsToCompute="mean|variance|eigenvalue")
    pca_res = pca_algo.compute(data)

    # Apply transform with whitening because means and eigenvalues are provided
    pcatrans_algo = d4p.pca_transform(nComponents=nComponents)
    pcatrans_res = pcatrans_algo.compute(data, pca_res.eigenvectors,
                                         pca_res.dataForTransform)
    # pca_transform_result objects provides transformedData

    return (pca_res, pcatrans_res)


if __name__ == "__main__":
    pca_res, pcatrans_res = main()

    # print PCA results
    print("\nEigenvalues:\n", pca_res.eigenvalues)
    print("\nEigenvectors:\n", pca_res.eigenvectors)
    print("\nEigenvalues kv:\n", pca_res.dataForTransform['eigenvalue'])
    print("\nMeans kv:\n", pca_res.dataForTransform['mean'])
    print("\nVariances kv:\n", pca_res.dataForTransform['variance'])
    # print results of tranform
    print("\nTransformed data:", pcatrans_res.transformedData)
    print('All looks good!')
