# file: minmax_dense_batch.py
#===============================================================================
# Copyright 2014-2019 Intel Corporation.
#
# This software and the related documents are Intel copyrighted  materials,  and
# your use of  them is  governed by the  express license  under which  they were
# provided to you (License).  Unless the License provides otherwise, you may not
# use, modify, copy, publish, distribute,  disclose or transmit this software or
# the related documents without Intel's prior written permission.
#
# This software and the related documents  are provided as  is,  with no express
# or implied  warranties,  other  than those  that are  expressly stated  in the
# License.
#===============================================================================

#
# !  Content:
# !    Python example of Min-max normalization algorithm.
# !*****************************************************************************

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

import os
import sys

import daal.algorithms.normalization.minmax as minmax
from daal.data_management import DataSourceIface, FileDataSource

utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
if utils_folder not in sys.path:
    sys.path.insert(0, utils_folder)
from utils import printNumericTable

# Input data set parameters
datasetName = os.path.join('..', 'data', 'batch', 'normalization.csv')

if __name__ == "__main__":

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

    data = dataSource.getNumericTable()

    # Create an algorithm
    algorithm = minmax.Batch(method=minmax.defaultDense)

    # Set lower and upper bounds for the algorithm
    algorithm.parameter.lowerBound = -1.0
    algorithm.parameter.upperBound =  1.0

    # Set an input object for the algorithm
    algorithm.input.set(minmax.data, data)

    # Compute Min-max normalization function
    res = algorithm.compute()

    printNumericTable(data, "First 10 rows of the input data:", 10)
    printNumericTable(res.get(minmax.normalizedData), "First 10 rows of the min-max normalization result:", 10)
