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:
 # 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 dense K-Means clustering with different initialization methods
 # !    in the batch processing mode
 # !*****************************************************************************
 ## \example
 import os
 import numpy as np
 from daal.algorithms import kmeans
 import daal.algorithms.kmeans.init
 from daal.data_management import HomogenNumericTable, FileDataSource, DataSource, BlockDescriptor, readOnly
 DAAL_PREFIX = os.path.join('..', 'data')
 # Input data set
 datasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'kmeans_init_dense.csv')
 # K-Means algorithm parameters
 nMaxIterations = 1000
 cAccuracyThreshold = 0.01
 nClusters = 20
 def getSingleValue(pTbl, ntype):
     block = BlockDescriptor(ntype=ntype)
     pTbl.getBlockOfRows(0, 1, readOnly, block)
     value = block.getArray().flatten()[0]
     return value
 def runKmeans(inputData, nClusters, method, methodName, oversamplingFactor = -1.0):
     # Get initial clusters for the K-Means algorithm
     init = kmeans.init.Batch(nClusters, fptype=np.float32, method=method)
     init.input.set(, inputData)
     if oversamplingFactor > 0:
         init.parameter.oversamplingFactor = oversamplingFactor
     if method == kmeans.init.parallelPlusDense:
         print("K-means init parameters: method = " + methodName + ", oversamplingFactor = "
               + str(init.parameter.oversamplingFactor) + ", nRounds = " + str(init.parameter.nRounds))
         print("K-means init parameters: method = " + methodName)
     centroids = init.compute().get(kmeans.init.centroids)
     # Create an algorithm object for the K-Means algorithm
     algorithm = kmeans.Batch(nClusters, nMaxIterations)
     algorithm.input.set(, inputData)
     algorithm.input.set(kmeans.inputCentroids, centroids)
     algorithm.parameter.accuracyThreshold = cAccuracyThreshold
     print("K-means algorithm parameters: maxIterations = " + str(algorithm.parameter.maxIterations)
           + ", accuracyThreshold = " + str(algorithm.parameter.accuracyThreshold))
     res = algorithm.compute()
     # Print the results
     goalFunc = getSingleValue(res.get(kmeans.objectiveFunction), ntype=np.float32)
     nIterations = getSingleValue(res.get(kmeans.nIterations), ntype=np.intc)
     print("K-means algorithm results: Objective function value = " + str(goalFunc*1e-6)
           + "*1E+6, number of iterations = " + str(nIterations) + "\n")
 if __name__ == "__main__":
     # Initialize FileDataSource to retrieve the input data from a .csv file
     inputData = HomogenNumericTable(ntype=np.float32)
     dataSource = FileDataSource(datasetFileName,
     # Retrieve the data from the input file
     runKmeans(inputData, nClusters, kmeans.init.deterministicDense, "deterministicDense")
     runKmeans(inputData, nClusters, kmeans.init.randomDense, "randomDense")
     runKmeans(inputData, nClusters, kmeans.init.plusPlusDense, "plusPlusDense")
     runKmeans(inputData, nClusters, kmeans.init.parallelPlusDense, "parallelPlusDense", 0.5)
     runKmeans(inputData, nClusters, kmeans.init.parallelPlusDense, "parallelPlusDense", 2.0)
For more complete information about compiler optimizations, see our Optimization Notice.
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