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

from os import environ
from os.path import join as jp

from daal.algorithms.pca import (
    Online_Float64SvdDense, data, eigenvalues, eigenvectors
)
from daal.data_management import (
    FileDataSource, DataSourceIface
)
import inspect, sys, os.path
utils_folder = os.path.realpath(os.path.abspath(jp(os.path.split(inspect.getfile(inspect.currentframe()))[0], "..")))
if utils_folder not in sys.path:
    sys.path.insert(0, utils_folder)
from utils import printNumericTable

DAAL_PREFIX = jp('..', 'data')

# Input data set parameters
nVectorsInBlock = 250
dataFileName = jp(DAAL_PREFIX, 'online', 'pca_normalized.csv')

if __name__ == "__main__":

    # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
    dataSource = FileDataSource(
        dataFileName, DataSourceIface.doAllocateNumericTable,
        DataSourceIface.doDictionaryFromContext
    )

    # Create an algorithm for principal component analysis using the SVD method
    algorithm = Online_Float64SvdDense()

    while(dataSource.loadDataBlock(nVectorsInBlock) == nVectorsInBlock):
        # Set the input data to the algorithm
        algorithm.input.setDataset(data, dataSource.getNumericTable())

        # Update PCA decomposition
        algorithm.compute()

    # Finalize computations
    result = algorithm.finalizeCompute()

    # Print the results
    printNumericTable(result.get(eigenvalues), "Eigenvalues:")
    printNumericTable(result.get(eigenvectors), "Eigenvectors:")
