Python* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 3

kmeans_dense_batch.py

1 # file: kmeans_dense_batch.py
2 #===============================================================================
3 # Copyright 2014-2018 Intel Corporation.
4 #
5 # This software and the related documents are Intel copyrighted materials, and
6 # your use of them is governed by the express license under which they were
7 # provided to you (License). Unless the License provides otherwise, you may not
8 # use, modify, copy, publish, distribute, disclose or transmit this software or
9 # the related documents without Intel's prior written permission.
10 #
11 # This software and the related documents are provided as is, with no express
12 # or implied warranties, other than those that are expressly stated in the
13 # License.
14 #===============================================================================
15 
16 
17 
18 
19 import os
20 import sys
21 
22 import daal.algorithms.kmeans.init
23 from daal.algorithms import kmeans
24 from daal.data_management import FileDataSource, DataSourceIface
25 
26 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
27 if utils_folder not in sys.path:
28  sys.path.insert(0, utils_folder)
29 from utils import printNumericTable
30 
31 DAAL_PREFIX = os.path.join('..', 'data')
32 
33 # Input data set parameters
34 datasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'kmeans_dense.csv')
35 
36 # K-Means algorithm parameters
37 nClusters = 20
38 nIterations = 5
39 
40 if __name__ == "__main__":
41 
42  # Initialize FileDataSource to retrieve the input data from a .csv file
43  dataSource = FileDataSource(
44  datasetFileName,
45  DataSourceIface.doAllocateNumericTable,
46  DataSourceIface.doDictionaryFromContext
47  )
48 
49  # Retrieve the data from the input file
50  dataSource.loadDataBlock()
51 
52  # Get initial clusters for the K-Means algorithm
53  initAlg = kmeans.init.Batch(nClusters, method=kmeans.init.randomDense)
54 
55  initAlg.input.set(kmeans.init.data, dataSource.getNumericTable())
56 
57  res = initAlg.compute()
58  centroidsResult = res.get(kmeans.init.centroids)
59 
60  # Create an algorithm object for the K-Means algorithm
61  algorithm = kmeans.Batch(nClusters, nIterations, method=kmeans.lloydDense)
62 
63  algorithm.input.set(kmeans.data, dataSource.getNumericTable())
64  algorithm.input.set(kmeans.inputCentroids, centroidsResult)
65 
66  res = algorithm.compute()
67 
68  # Print the clusterization results
69  printNumericTable(res.get(kmeans.assignments), "First 10 cluster assignments:", 10)
70  printNumericTable(res.get(kmeans.centroids), "First 10 dimensions of centroids:", 20, 10)
71  printNumericTable(res.get(kmeans.objectiveFunction), "Objective function value:")

For more complete information about compiler optimizations, see our Optimization Notice.