Python* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 2

mse_dense_batch.py

1 # file: mse_dense_batch.py
2 #===============================================================================
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40 #===============================================================================
41 
42 #
43 # ! Content:
44 # ! Python example of the mean squared error objective function
45 # !*****************************************************************************
46 
47 
48 #
49 
50 
51 #
52 
53 import os
54 import sys
55 
56 import numpy as np
57 
58 import daal.algorithms.optimization_solver as optimization_solver
59 import daal.algorithms.optimization_solver.mse
60 
61 from daal.data_management import (
62  DataSourceIface, FileDataSource, HomogenNumericTable, MergedNumericTable, NumericTableIface
63 )
64 
65 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
66 if utils_folder not in sys.path:
67  sys.path.insert(0, utils_folder)
68 from utils import printNumericTable
69 
70 datasetFileName = os.path.join('..', 'data', 'batch', 'mse.csv')
71 nFeatures = 3
72 
73 argumentValue = np.array([[-1], [0.1], [0.15], [-0.5]], dtype=np.float64)
74 
75 if __name__ == "__main__":
76 
77  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
78  dataSource = FileDataSource(datasetFileName,
79  DataSourceIface.notAllocateNumericTable,
80  DataSourceIface.doDictionaryFromContext)
81 
82  # Create Numeric Tables for data and values for dependent variable
83  data = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
84  dependentVariables = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
85  mergedData = MergedNumericTable(data, dependentVariables)
86 
87  # Retrieve the data from the input file
88  dataSource.loadDataBlock(mergedData)
89 
90  nVectors = data.getNumberOfRows()
91 
92  # Create the MSE objective function objects to compute the MSE objective function result using the default method
93  mseObjectiveFunction = optimization_solver.mse.Batch(nVectors)
94 
95  # Set input objects for the MSE objective function
96  mseObjectiveFunction.input.set(optimization_solver.mse.data, data)
97  mseObjectiveFunction.input.set(optimization_solver.mse.dependentVariables, dependentVariables)
98  mseObjectiveFunction.input.set(optimization_solver.mse.argument, HomogenNumericTable(argumentValue))
99  mseObjectiveFunction.parameter.resultsToCompute = (
100  optimization_solver.objective_function.gradient |
101  optimization_solver.objective_function.value |
102  optimization_solver.objective_function.hessian
103  )
104 
105  # Compute the MSE objective function result
106  # Result class from optimization_solver.objective_function
107  res = mseObjectiveFunction.compute()
108 
109  # Print computed the MSE objective function result
110  printNumericTable(res.get(optimization_solver.objective_function.valueIdx),
111  "Value")
112  printNumericTable(res.get(optimization_solver.objective_function.gradientIdx),
113  "Gradient")
114  printNumericTable(res.get(optimization_solver.objective_function.hessianIdx),
115  "Hessian")

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