Python* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 1

kmeans_csr_batch_assign.py

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40 
41 ## <a name="DAAL-EXAMPLE-PY-KMEANS_CSR_BATCH_ASSIGN"></a>
42 ## \example kmeans_csr_batch_assign.py
43 
44 import os
45 import sys
46 
47 import daal.algorithms.kmeans.init
48 from daal.algorithms import kmeans
49 
50 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
51 if utils_folder not in sys.path:
52  sys.path.insert(0, utils_folder)
53 from utils import printNumericTable, createSparseTable
54 
55 DAAL_PREFIX = os.path.join('..', 'data')
56 
57 # Input data set parameters
58 datasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'kmeans_csr.csv')
59 
60 # K-Means algorithm parameters
61 nClusters = 20
62 
63 if __name__ == "__main__":
64 
65  # Retrieve the data from the input file
66  dataTable = createSparseTable(datasetFileName)
67 
68  # Get initial clusters for the K-Means algorithm
69  init = kmeans.init.Batch(nClusters, method=kmeans.init.randomDense)
70 
71  init.input.set(kmeans.init.data, dataTable)
72  res = init.compute()
73 
74  centroids = res.get(kmeans.init.centroids)
75 
76  # Create an algorithm object for the K-Means algorithm
77  algorithm = kmeans.Batch(nClusters, 0, method=kmeans.lloydCSR)
78 
79  algorithm.input.set(kmeans.data, dataTable)
80  algorithm.input.set(kmeans.inputCentroids, centroids)
81 
82  res = algorithm.compute()
83 
84  # Print the clusterization results
85  printNumericTable(res.get(kmeans.assignments), "First 10 cluster assignments:", 10)

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