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

datastructures_matrix.py

1 # file: datastructures_matrix.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 # ! Content:
18 # ! Python example of using matrix data structures
19 # !*****************************************************************************
20 
21 #
22 
23 
24 #
25 
26 import sys, os
27 import numpy as np
28 from daal.data_management import Matrix, BlockDescriptor, readOnly
29 
30 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
31 if utils_folder not in sys.path:
32  sys.path.insert(0, utils_folder)
33 from utils import printArray
34 
35 if __name__ == "__main__":
36 
37  print("Matrix numeric table example\n")
38 
39  nObservations = 10
40  nFeatures = 11
41  firstReadRow = 0
42  nRead = 5
43 
44  #Example of using a matrix
45  data = np.array([(0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1),
46  (1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2),
47  (2.0, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3),
48  (3.0, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4),
49  (4.0, 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5),
50  (5.0, 5.1, 5.2, 5.3, 5.4, 5.5, 5.6, 5.7, 5.8, 5.9, 1),
51  (6.0, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, 2),
52  (7.0, 7.1, 7.2, 7.3, 7.4, 7.5, 7.6, 7.7, 7.8, 7.9, 3),
53  (8.0, 8.1, 8.2, 8.3, 8.4, 8.5, 8.6, 8.7, 8.8, 8.9, 4),
54  (9.0, 9.1, 9.2, 9.3, 9.4, 9.5, 9.6, 9.7, 9.8, 9.9, 5)], dtype=np.float32)
55 
56  dataTable = Matrix(data, ntype=np.float32)
57 
58  block = BlockDescriptor()
59 
60  # Read a block of rows
61  dataTable.getBlockOfRows(firstReadRow, nRead, readOnly, block)
62  print(str(block.getNumberOfRows()) + " rows are read")
63  printArray(block.getArray(), nFeatures, nRead, block.getNumberOfColumns(), "Print 5 rows from matrix data array as float:")
64  dataTable.releaseBlockOfRows(block)
65 
66  readFeatureIdx = 2
67 
68  # Set new values in Matrix
69  npdt = dataTable.getArray()
70  npdt[firstReadRow, readFeatureIdx] = -1
71  npdt[firstReadRow + 1, readFeatureIdx] = -2
72  npdt[firstReadRow + 2, readFeatureIdx] = -3
73 
74  # Read a feature(column) and print it
75  dataTable.getBlockOfColumnValues(readFeatureIdx, firstReadRow, nObservations, readOnly, block)
76  printArray(block.getArray(), 1, block.getNumberOfRows(), block.getNumberOfColumns(), "Print the third feature of matrix data:")
77  dataTable.releaseBlockOfColumnValues(block)

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