Python* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 5

em_gmm_dense_batch.py

1 # file: em_gmm_dense_batch.py
2 #===============================================================================
3 # Copyright 2014-2019 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 from daal.algorithms import em_gmm
23 from daal.data_management import FileDataSource, DataSourceIface
24 
25 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
26 if utils_folder not in sys.path:
27  sys.path.insert(0, utils_folder)
28 from utils import printNumericTable
29 
30 DAAL_PREFIX = os.path.join('..', 'data')
31 
32 # Input data set parameters
33 datasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'em_gmm.csv')
34 nComponents = 2
35 
36 if __name__ == "__main__":
37 
38  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
39  dataSource = FileDataSource(
40  datasetFileName,
41  DataSourceIface.doAllocateNumericTable,
42  DataSourceIface.doDictionaryFromContext
43  )
44  nFeatures = dataSource.getNumberOfColumns()
45 
46  # Retrieve the data from the input file
47  dataSource.loadDataBlock()
48 
49  # Create algorithm objects to initialize the EM algorithm for the GMM
50  # computing the number of components using the default method
51  initAlgorithm = em_gmm.init.Batch(nComponents)
52 
53  # Set an input data table for the initialization algorithm
54  initAlgorithm.input.set(em_gmm.init.data, dataSource.getNumericTable())
55 
56  # Compute initial values for the EM algorithm for the GMM with the default parameters
57  resultInit = initAlgorithm.compute()
58 
59  # Create algorithm objects for the EM algorithm for the GMM computing the number of components using the default method
60  algorithm = em_gmm.Batch(nComponents)
61 
62  # Set an input data table for the algorithm
63  algorithm.input.setTable(em_gmm.data, dataSource.getNumericTable())
64  algorithm.input.setValues(em_gmm.inputValues, resultInit)
65 
66  # Compute the results of the EM algorithm for the GMM with the default parameters
67  result = algorithm.compute()
68 
69  # Print the results
70  printNumericTable(result.getResult(em_gmm.weights), "Weights")
71  printNumericTable(result.getResult(em_gmm.means), "Means")
72  for i in range(nComponents):
73  printNumericTable(result.getCovariances(em_gmm.covariances, i), "Covariance")

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