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

import os
import sys

import daal.algorithms.kmeans.init
from daal.algorithms import kmeans
from daal.data_management import FileDataSource, DataSourceIface

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

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

# Input data set parameters
datasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'kmeans_dense.csv')

# K-Means algorithm parameters
nClusters = 20

if __name__ == "__main__":

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

    # Retrieve the data from the input file
    dataSource.loadDataBlock()

    # Get initial clusters for the K-Means algorithm
    initAlg = kmeans.init.Batch(nClusters, method=kmeans.init.randomDense)

    initAlg.input.set(kmeans.init.data, dataSource.getNumericTable())

    res = initAlg.compute()
    centroidsResult = res.get(kmeans.init.centroids)

    # Create an algorithm object for the K-Means algorithm
    algorithm = kmeans.Batch(nClusters, 0, method=kmeans.lloydDense)

    algorithm.input.set(kmeans.data, dataSource.getNumericTable())
    algorithm.input.set(kmeans.inputCentroids, centroidsResult)

    res = algorithm.compute()

    # Print the clusterization results
    printNumericTable(res.get(kmeans.assignments), "First 10 cluster assignments:", 10)
