# file: pca_transform_dense_batch.py
#===============================================================================
# Copyright 2014-2018 Intel Corporation
# All Rights Reserved.
#
# If this  software was obtained  under the  Intel Simplified  Software License,
# the following terms apply:
#
# The source code,  information  and material  ("Material") contained  herein is
# owned by Intel Corporation or its  suppliers or licensors,  and  title to such
# Material remains with Intel  Corporation or its  suppliers or  licensors.  The
# Material  contains  proprietary  information  of  Intel or  its suppliers  and
# licensors.  The Material is protected by  worldwide copyright  laws and treaty
# provisions.  No part  of  the  Material   may  be  used,  copied,  reproduced,
# modified, published,  uploaded, posted, transmitted,  distributed or disclosed
# in any way without Intel's prior express written permission.  No license under
# any patent,  copyright or other  intellectual property rights  in the Material
# is granted to  or  conferred  upon  you,  either   expressly,  by implication,
# inducement,  estoppel  or  otherwise.  Any  license   under such  intellectual
# property rights must be express and approved by Intel in writing.
#
# Unless otherwise agreed by Intel in writing,  you may not remove or alter this
# notice or  any  other  notice   embedded  in  Materials  by  Intel  or Intel's
# suppliers or licensors in any way.
#
#
# If this  software  was obtained  under the  Apache License,  Version  2.0 (the
# "License"), the following terms apply:
#
# 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.
#===============================================================================

#
# !  Content:
# !    Python example of PCA transformation algorithm.
# !*****************************************************************************

#
## <a name="DAAL-EXAMPLE-PY-PCA_TRANSFORM_DENSE_BATCH"></a>
## \example pca_transform_dense_batch.py
#

import os
import sys
import numpy as np

import daal.algorithms.pca as pca
import daal.algorithms.pca.transform as pca_transform
from daal.data_management import DataSourceIface, FileDataSource

utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))))
if utils_folder not in sys.path:
    sys.path.insert(0, utils_folder)
from utils import printNumericTable
from daal.data_management import NumericTable
# Input data set parameters
datasetName = os.path.join('..', 'data', 'batch', 'pca_transform.csv')

if __name__ == "__main__":

    # Retrieve the input data
    dataSource = FileDataSource(datasetName,
                                DataSourceIface.doAllocateNumericTable,
                                DataSourceIface.doDictionaryFromContext)
    dataSource.loadDataBlock()
    data = dataSource.getNumericTable()

    # Create an algorithm
    algorithm = pca.Batch(fptype=np.float64,method=pca.svdDense)

    # Set the algorithm input data
    algorithm.input.setDataset(pca.data, data)

    # Set the algorithm normalization parameters (mean and variance)
    # to be exported for transform and whitening parameter (eigenvalue)
    # If whitening is not required eigenvalues should be removed
    # The eigenvalues would be calculated in pca.eigenvalues table of result
    # but would not be passed to dataForTranform collection
    # algorithm.paramter.resultsToCompute = (pca.mean | pca.variance | pca.eigenvalue)

    algorithm.parameter.resultsToCompute = pca.mean | pca.variance | pca.eigenvalue;

    # Compute PCA
    res = algorithm.compute()
    # Output basis, eigenvalues and mean values
    printNumericTable(res.get(pca.eigenvalues), "Eigenvalues:")
    printNumericTable(res.get(pca.eigenvectors), "Eigenvectors:")

    eigenvaluesT = res.get(pca.eigenvalues)
    printNumericTable(eigenvaluesT, "Eigenvalues kv:")

    meansT = res.get(pca.means)
    printNumericTable(meansT, "Means kv:")

    #eigenvaluesT = res.getCollection(pca.eigenvalue)
    variancesT = res.get(pca.variances)
    printNumericTable(variancesT, "Variances kv:")

    # Create an algorithm
    tralgorithm = pca_transform.Batch(fptype=np.float64)

    # Set lower and upper bounds for the algorithm
    tralgorithm.parameter.nComponents = 2

    # Set an input object for the algorithm
    tralgorithm.input.setTable(pca_transform.data, data)

    # Set an input object for the eigenvectors
    tralgorithm.input.setTable(pca_transform.eigenvectors, res.get(pca.eigenvectors))

    # Set an input object for the eigenvectors
    tralgorithm.input.setCollection(pca_transform.dataForTransform, res.getCollection(pca.dataForTransform))

    # Compute PCA transformation function
    trres = tralgorithm.compute()

    printNumericTable(trres.get(pca.transform.transformedData), "Transformed data:");
    #printNumericTable(data, "First rows of the input data:", 4)
    #printNumericTable(trres.get(pca_transform.transformedData), "First rows of the min-max normalization result:", 4)
