kmeans_dense_batch.py

Deprecation Notice: With the introduction of daal4py, a package that supersedes PyDAAL, Intel is deprecating PyDAAL and will discontinue support starting with Intel® DAAL 2021 and Intel® Distribution for Python 2021. Until then Intel will continue to provide compatible pyDAAL pip and conda packages for newer releases of Intel DAAL and make it available in open source. However, Intel will not add the new features of Intel DAAL to pyDAAL. Intel recommends developers switch to and use daal4py.

Note: To find daal4py examples, refer to daal4py documentation or browse github repository.

 # file: kmeans_dense_batch.py
 #===============================================================================
 # Copyright 2014-2019 Intel Corporation.
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 #===============================================================================
 
 ## <a name="DAAL-EXAMPLE-PY-KMEANS_DENSE_BATCH"></a>
 ## \example kmeans_dense_batch.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
 nIterations = 5
 
 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, nIterations, 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)
     printNumericTable(res.get(kmeans.centroids), "First 10 dimensions of centroids:", 20, 10)
     printNumericTable(res.get(kmeans.objectiveFunction), "Objective function value:")
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