# file: cov_csr_online.py
#===============================================================================
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## <a name="DAAL-EXAMPLE-PY-COVARIANCE_CSR_ONLINE"></a>
## \example cov_csr_online.py

import os
import sys

from daal.algorithms import covariance

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, createSparseTable

DAAL_PREFIX = os.path.join('..', 'data')

# Input data set parameters
nBlocks = 4
datasetFileNames = [
    os.path.join(DAAL_PREFIX, 'online', 'covcormoments_csr_1.csv'),
    os.path.join(DAAL_PREFIX, 'online', 'covcormoments_csr_2.csv'),
    os.path.join(DAAL_PREFIX, 'online', 'covcormoments_csr_3.csv'),
    os.path.join(DAAL_PREFIX, 'online', 'covcormoments_csr_4.csv'),
]

if __name__ == "__main__":

    # Create algorithm objects for covariance matrix computing in online mode using default method
    algorithm = covariance.Online()

    for i in range(nBlocks):
        dataTable = createSparseTable(datasetFileNames[i])

        # Set input arguments of the algorithm
        algorithm.input.set(covariance.data, dataTable)

        # Compute partial covariance estimates
        algorithm.compute()

    # Finalize online result and get computed covariance
    res = algorithm.finalizeCompute()

    printNumericTable(res.get(covariance.covariance), "Covariance matrix (upper left square 10*10) :", 10, 10)
    printNumericTable(res.get(covariance.mean),       "Mean vector:", 1, 10)
