Python* API Reference for Intel® Data Analytics Acceleration Library 2020 Update 1

pca_cor_csr_batch.py

1 # file: pca_cor_csr_batch.py
2 #===============================================================================
3 # Copyright 2014-2020 Intel Corporation
4 #
5 # Licensed under the Apache License, Version 2.0 (the "License");
6 # you may not use this file except in compliance with the License.
7 # You may obtain a copy of the License at
8 #
9 # http://www.apache.org/licenses/LICENSE-2.0
10 #
11 # Unless required by applicable law or agreed to in writing, software
12 # distributed under the License is distributed on an "AS IS" BASIS,
13 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 # See the License for the specific language governing permissions and
15 # limitations under the License.
16 #===============================================================================
17 
18 
19 
20 
21 import os
22 import sys
23 
24 import numpy as np
25 
26 from daal.algorithms import covariance
27 from daal.algorithms import pca
28 
29 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
30 if utils_folder not in sys.path:
31  sys.path.insert(0, utils_folder)
32 from utils import printNumericTable, createSparseTable
33 
34 DAAL_PREFIX = os.path.join('..', 'data')
35 
36 # Input data set parameters
37 dataFileName = os.path.join(DAAL_PREFIX, 'batch', 'covcormoments_csr.csv')
38 
39 if __name__ == "__main__":
40 
41  # Read data from a file and create a numeric table to store input data
42  dataTable = createSparseTable(dataFileName)
43 
44  # Create an algorithm for principal component analysis using the correlation method
45  algorithm = pca.Batch(fptype=np.float64, method=pca.correlationDense)
46 
47  # Use covariance algorithm for sparse data inside the PCA algorithm
48  algorithm.parameter.covariance = covariance.Batch(fptype=np.float64, method=covariance.fastCSR)
49 
50  # Set the algorithm input data
51  algorithm.input.setDataset(pca.data, dataTable)
52  algorithm.parameter.resultsToCompute = pca.mean | pca.variance | pca.eigenvalue;
53  algorithm.parameter.isDeterministic = True;
54  # Compute results of the PCA algorithm
55  result = algorithm.compute()
56 
57  # Print the results
58  printNumericTable(result.get(pca.eigenvalues), "Eigenvalues:")
59  printNumericTable(result.get(pca.eigenvectors), "Eigenvectors:")
60  printNumericTable(result.get(pca.means), "Means:")
61  printNumericTable(result.get(pca.variances), "Variances:")

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