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0035838 May 1, 2017
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Perceptron Binary Classification Learning Algorithm

Run demo dataset: Basic Naive Cycle PLA

>>> import numpy as np

>>> import FukuML.PLA as pla

>>> pla_bc = pla.BinaryClassifier()

>>> pla_bc.load_train_data()

>>> pla_bc.set_param()

>>> pla_bc.init_W()

>>> pla_bc.train()

>>> test_data = '0.97681 0.10723 0.64385 0.29556 1'

>>> prediction = pla_bc.prediction(test_data)

>>> future_data = '0.97681 0.10723 0.64385 0.29556'

>>> prediction = pla_bc.prediction(future_data, 'future_data')

Run demo dataset: Basic Naive Cycle PLA with Linear Regression Accelerator

>>> import numpy as np

>>> import FukuML.PLA as pla

>>> pla_bc = pla.BinaryClassifier()

>>> pla_bc.load_train_data()

>>> pla_bc.set_param()

>>> pla_bc.init_W('linear_regression_accelerator')

>>> pla_bc.train()

>>> test_data = '0.97681 0.10723 0.64385 0.29556 1'

>>> prediction = pla_bc.prediction(test_data)

>>> future_data = '0.97681 0.10723 0.64385 0.29556'

>>> prediction = pla_bc.prediction(future_data, 'future_data')

Run demo dataset: Random Cycle PLA

>>> import numpy as np

>>> import FukuML.PLA as pla

>>> pla_bc = pla.BinaryClassifier()

>>> pla_bc.load_train_data()

>>> pla_bc.set_param(loop_mode='random')

>>> pla_bc.init_W()

>>> pla_bc.train()

>>> test_data = '0.97681 0.10723 0.64385 0.29556 1'

>>> prediction = pla_bc.prediction(test_data)

>>> future_data = '0.97681 0.10723 0.64385 0.29556'

>>> prediction = pla_bc.prediction(future_data, 'future_data')

Run demo dataset: Random Cycle PLA alpha=0.5 step correction

>>> import numpy as np

>>> import FukuML.PLA as pla

>>> pla_bc = pla.BinaryClassifier()

>>> pla_bc.load_train_data()

>>> pla_bc.set_param(loop_mode='random', step_alpha=0.5)

>>> pla_bc.init_W()

>>> pla_bc.train()

>>> test_data = '0.97681 0.10723 0.64385 0.29556 1'

>>> prediction = pla_bc.prediction(test_data)

>>> future_data = '0.97681 0.10723 0.64385 0.29556'

>>> prediction = pla_bc.prediction(future_data, 'future_data')

Perceptron Multi Classification Learning Algorithm

Run demo dataset: One vs One

>>> import numpy as np

>>> import FukuML.PLA as pla

>>> pla_mc = pla.MultiClassifier()

>>> pla_mc.load_train_data()

>>> pla_mc.set_param()

>>> pla_mc.init_W()

>>> W = pla_mc.train()

>>> test_data = '0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0'

>>> prediction = pla_mc.prediction(test_data)

Run demo dataset: One vs One with Linear Regression Accelerator

>>> import numpy as np

>>> import FukuML.PLA as pla

>>> pla_mc = pla.MultiClassifier()

>>> pla_mc.load_train_data()

>>> pla_mc.set_param()

>>> pla_mc.init_W('linear_regression_accelerator')

>>> W = pla_mc.train()

>>> test_data = '0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0'

>>> prediction = pla_mc.prediction(test_data)

Pocket Perceptron Binary Classification Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.PocketPLA as pocket

>>> pocket_bc = pocket.BinaryClassifier()

>>> pocket_bc.load_train_data()

>>> pocket_bc.set_param(loop_mode='naive_cycle', step_alpha=1, updates=50)

>>> pocket_bc.init_W()

>>> W = pocket_bc.train()

>>> test_data = '0.62771 0.11513 0.82235 0.14493 -1'

>>> prediction = pocket_bc.prediction(test_data)

>>> future_data = '0.62771 0.11513 0.82235 0.14493'

>>> prediction = pocket_bc.prediction(future_data, 'future_data')

>>> pocket_bc.load_test_data()

>>> pocket_bc.calculate_avg_error(pocket_bc.test_X, pocket_bc.test_Y, W)

Run demo dataset with Linear Regression Accelerator

>>> import numpy as np

>>> import FukuML.PocketPLA as pocket

>>> pocket_bc = pocket.BinaryClassifier()

>>> pocket_bc.load_train_data()

>>> pocket_bc.set_param(loop_mode='naive_cycle', step_alpha=1, updates=50)

>>> pocket_bc.init_W('linear_regression_accelerator')

>>> W = pocket_bc.train()

>>> test_data = '0.62771 0.11513 0.82235 0.14493 -1'

>>> prediction = pocket_bc.prediction(test_data)

>>> future_data = '0.62771 0.11513 0.82235 0.14493'

>>> prediction = pocket_bc.prediction(future_data, 'future_data')

>>> pocket_bc.load_test_data()

>>> pocket_bc.calculate_avg_error(pocket_bc.test_X, pocket_bc.test_Y, W)

Pocket Perceptron Multi Classification Learning Algorithm

Run demo dataset: One vs One

>>> import numpy as np

>>> import FukuML.PocketPLA as pocket

>>> pocket_mc = pocket.MultiClassifier()

>>> pocket_mc.load_train_data()

>>> pocket_mc.set_param(loop_mode='naive_cycle', step_alpha=1, updates=50)

>>> pocket_mc.init_W()

>>> W = pocket_mc.train()

>>> test_data = '0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0'

>>> prediction = pocket_mc.prediction(test_data)

Run demo dataset: One vs One with Linear Regression Accelerator

>>> import numpy as np

>>> import FukuML.PocketPLA as pocket

>>> pocket_mc = pocket.MultiClassifier()

>>> pocket_mc.load_train_data()

>>> pocket_mc.set_param(loop_mode='naive_cycle', step_alpha=1, updates=50)

>>> pocket_mc.init_W('linear_regression_accelerator')

>>> W = pocket_mc.train()

>>> test_data = '0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0'

>>> prediction = pocket_mc.prediction(test_data)

Linear Regression Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.LinearRegression as linear_regression

>>> linear = linear_regression.LinearRegression()

>>> linear.load_train_data()

>>> linear.init_W()

>>> W = linear.train()

>>> test_data = '0.62771 0.11513 0.82235 0.14493 -1'

>>> prediction = linear.prediction(test_data)

>>> future_data = '0.62771 0.11513 0.82235 0.14493'

>>> prediction = linear.prediction(future_data, 'future_data')

>>> linear.load_test_data()

>>> linear.calculate_avg_error(linear.test_X, linear.test_Y, W)

Linear Regression Binary Classification Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.LinearRegression as linear_regression

>>> linear_bc = linear_regression.BinaryClassifier()

>>> linear_bc.load_train_data()

>>> linear_bc.init_W()

>>> W = linear_bc.train()

>>> test_data = '0.62771 0.11513 0.82235 0.14493 -1'

>>> prediction = linear_bc.prediction(test_data)

>>> future_data = '0.62771 0.11513 0.82235 0.14493'

>>> prediction = linear_bc.prediction(future_data, 'future_data')

>>> linear_bc.load_test_data()

>>> linear_bc.calculate_avg_error(linear_bc.test_X, linear_bc.test_Y, W)

Linear Regression Multi Classification Learning Algorithm

Run demo dataset: One vs One

>>> import numpy as np

>>> import FukuML.LinearRegression as linear_regression

>>> linear_mc = linear_regression.MultiClassifier()

>>> linear_mc.load_train_data()

>>> linear_mc.init_W()

>>> W = linear_mc.train()

>>> test_data = '0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0'

>>> prediction = linear_mc.prediction(test_data)

Logistic Regression Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.LogisticRegression as logistic_regression

>>> logistic = logistic_regression.LogisticRegression()

>>> logistic.load_train_data()

>>> logistic.set_param()

>>> logistic.init_W()

>>> W = logistic.train()

>>> test_data = '0.26502 0.5486 0.971 0.19333 0.12207 0.81528 0.46743 0.45889 0.31004 0.3307 0.43078 0.50661 0.57281 0.052715 0.50443 0.78686 0.20099 0.85909 0.26772 0.13751 1'

>>> prediction = logistic.prediction(test_data)

Run demo dataset with Linear Regression Accelerator

>>> import numpy as np
>>> import FukuML.LogisticRegression as logistic_regression
>>> logistic = logistic_regression.LinearRegression()
>>> logistic.load_train_data()
>>> logistic.set_param()
>>> logistic.init_W('linear_regression_accelerator')
>>> W = logistic.train()
>>> test_data = '0.26502 0.5486 0.971 0.19333 0.12207 0.81528 0.46743 0.45889 0.31004 0.3307 0.43078 0.50661 0.57281 0.052715 0.50443 0.78686 0.20099 0.85909 0.26772 0.13751 1'
>>> prediction = logistic.prediction(test_data)

Logistic Regression Binary Classification Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.LogisticRegression as logistic_regression

>>> logistic = logistic_regression.BinaryClassifier()

>>> logistic.load_train_data()

>>> logistic.set_param()

>>> logistic.init_W()

>>> W = logistic.train()

>>> test_data = '0.26502 0.5486 0.971 0.19333 0.12207 0.81528 0.46743 0.45889 0.31004 0.3307 0.43078 0.50661 0.57281 0.052715 0.50443 0.78686 0.20099 0.85909 0.26772 0.13751 1'

>>> prediction = logistic.prediction(test_data)

Run demo dataset with Linear Regression Accelerator

>>> import numpy as np

>>> import FukuML.LogisticRegression as logistic_regression

>>> logistic = logistic_regression.BinaryClassifier()

>>> logistic.load_train_data()

>>> logistic.set_param()

>>> logistic.init_W('linear_regression_accelerator')

>>> W = logistic.train()

>>> test_data = '0.26502 0.5486 0.971 0.19333 0.12207 0.81528 0.46743 0.45889 0.31004 0.3307 0.43078 0.50661 0.57281 0.052715 0.50443 0.78686 0.20099 0.85909 0.26772 0.13751 1'

>>> prediction = logistic.prediction(test_data)

Logistic Regression Multi Classification Learning Algorithm

Run demo dataset: One vs All

>>> import numpy as np

>>> import FukuML.LogisticRegression as logistic_regression

>>> logistic = logistic_regression.MultiClassifier()

>>> logistic.load_train_data()

>>> logistic.set_param()

>>> logistic.init_W()

>>> W = logistic.train()

>>> test_data = '0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0'

>>> prediction = logistic.prediction(test_data)

Run demo dataset: One vs All with Linear Regression Accelerator

>>> import numpy as np

>>> import FukuML.LogisticRegression as logistic_regression

>>> logistic = logistic_regression.MultiClassifier()

>>> logistic.load_train_data()

>>> logistic.set_param()

>>> logistic.init_W('linear_regression_accelerator')

>>> W = logistic.train()

>>> test_data = '0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0'

>>> prediction = logistic.prediction(test_data)

Run demo dataset: One vs One

>>> import numpy as np

>>> import FukuML.LogisticRegression as logistic_regression

>>> logistic = logistic_regression.MultiClassifier()

>>> logistic.load_train_data()

>>> logistic.set_param()

>>> logistic.init_W('normal', 'ovo')

>>> W = logistic.train()

>>> test_data = '0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0'

>>> prediction = logistic.prediction(test_data)

Run demo dataset: One vs One with Linear Regression Accelerator

>>> import numpy as np

>>> import FukuML.LogisticRegression as logistic_regression

>>> logistic = logistic_regression.MultiClassifier()

>>> logistic.load_train_data()

>>> logistic.set_param()

>>> logistic.init_W('linear_regression_accelerator', 'ovo')

>>> W = logistic.train()

>>> test_data = '0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0'

>>> prediction = logistic.prediction(test_data)

L2 Regularized Logistic Regression Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.L2RLogisticRegression as l2r_logistic_regression

>>> l2r_logistic = l2r_logistic_regression.L2RLogisticRegression()

>>> l2r_logistic.load_train_data()

>>> l2r_logistic.set_param()

>>> l2r_logistic.init_W()

>>> W = l2r_logistic.train()

>>> test_data = '0.26502 0.5486 0.971 0.19333 0.12207 0.81528 0.46743 0.45889 0.31004 0.3307 0.43078 0.50661 0.57281 0.052715 0.50443 0.78686 0.20099 0.85909 0.26772 0.13751 1'

>>> prediction = l2r_logistic.prediction(test_data)

L2 Regularized Logistic Regression Binary Classification Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.L2RLogisticRegression as l2r_logistic_regression

>>> l2r_logistic = l2r_logistic_regression.BinaryClassifier()

>>> l2r_logistic.load_train_data()

>>> l2r_logistic.set_param()

>>> l2r_logistic.init_W()

>>> W = l2r_logistic.train()

>>> test_data = '0.26502 0.5486 0.971 0.19333 0.12207 0.81528 0.46743 0.45889 0.31004 0.3307 0.43078 0.50661 0.57281 0.052715 0.50443 0.78686 0.20099 0.85909 0.26772 0.13751 1'

>>> prediction = l2r_logistic.prediction(test_data)

Ridge Regression Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.RidgeRegression as ridge_regression

>>> ridge = ridge_regression.RidgeRegression()

>>> ridge.load_train_data()

>>> ridge.set_param(lambda_p=pow(10, -3))

>>> ridge.init_W()

>>> W = ridge.train()

>>> test_data = '0.62771 0.11513 0.82235 0.14493 -1'

>>> prediction = ridge.prediction(test_data)

>>> future_data = '0.62771 0.11513 0.82235 0.14493'

>>> prediction = ridge.prediction(future_data, 'future_data')

>>> ridge.load_test_data()

>>> ridge.calculate_avg_error(ridge.test_X, ridge.test_Y, W)

Ridge Regression Binary Classification Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.RidgeRegression as ridge_regression

>>> ridge_bc = ridge_regression.BinaryClassifier()

>>> ridge_bc.load_train_data()

>>> ridge_bc.set_param(lambda_p=pow(10, -3))

>>> ridge_bc.init_W()

>>> W = ridge_bc.train()

>>> test_data = '0.402041 0.402048 -1'

>>> prediction = ridge_bc.prediction(test_data)

>>> future_data = '0.402041 0.402048'

>>> prediction = ridge_bc.prediction(future_data, 'future_data')

>>> ridge_bc.load_test_data()

>>> ridge_bc.calculate_avg_error(ridge_bc.test_X, ridge_bc.test_Y, W)

Ridge Regression Multi Classification Learning Algorithm

Run demo dataset: One vs One

>>> import numpy as np

>>> import FukuML.RidgeRegression as ridge_regression

>>> ridge_mc = ridge_regression.MultiClassifier()

>>> ridge_mc.load_train_data()

>>> ridge_mc.set_param(lambda_p=pow(10, -3))

>>> ridge_mc.init_W()

>>> W = ridge_mc.train()

>>> test_data = '0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0'

>>> prediction = ridge_mc.prediction(test_data)

Primal Hard Margin Support Vector Machine Binary Classification Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.SupportVectorMachine as svm

>>> svm_bc = svm.BinaryClassifier()

>>> svm_bc.load_train_data()

>>> svm_bc.set_param(svm_kernel='primal_hard_margin')

>>> svm_bc.init_W()

>>> W = svm_bc.train()

>>> test_data = '0.97681 0.10723 0.64385 0.29556 1'

>>> prediction = svm_bc.prediction(test_data)

>>> test_data = '0.97681 0.10723 0.64385 0.29556'

>>> prediction = svm_bc.prediction(future_data, 'future_data')

>>> svm_bc.load_test_data()

>>> svm_bc.calculate_avg_error(svm_bc.test_X, svm_bc.test_Y, W)

Dual Hard Margin Support Vector Machine Binary Classification Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.SupportVectorMachine as svm

>>> svm_bc = svm.BinaryClassifier()

>>> svm_bc.load_train_data()

>>> svm_bc.set_param(svm_kernel='dual_hard_margin')

>>> svm_bc.init_W()

>>> W = svm_bc.train()

>>> test_data = '0.97681 0.10723 0.64385 0.29556 1'

>>> prediction = svm_bc.prediction(test_data)

>>> test_data = '0.97681 0.10723 0.64385 0.29556'

>>> prediction = svm_bc.prediction(future_data, 'future_data')

>>> svm_bc.load_test_data()

>>> svm_bc.calculate_avg_error(svm_bc.test_X, svm_bc.test_Y, W)

Polynomial Kernel Support Vector Machine Binary Classification Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.SupportVectorMachine as svm

>>> svm_bc = svm.BinaryClassifier()

>>> svm_bc.load_train_data()

>>> svm_bc.set_param(svm_kernel='polynomial_kernel', zeta=100, gamma=1, Q=3)

>>> svm_bc.init_W()

>>> W = svm_bc.train()

>>> test_data = '0.97681 0.10723 0.64385 0.29556 1'

>>> prediction = svm_bc.prediction(test_data)

>>> test_data = '0.97681 0.10723 0.64385 0.29556'

>>> prediction = svm_bc.prediction(future_data, 'future_data')

>>> svm_bc.load_test_data()

>>> svm_bc.calculate_avg_error(svm_bc.test_X, svm_bc.test_Y, W)

Gaussian Kernel Support Vector Machine Binary Classification Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.SupportVectorMachine as svm

>>> svm_bc = svm.BinaryClassifier()

>>> svm_bc.load_train_data()

>>> svm_bc.set_param(svm_kernel='gaussian_kernel', gamma=1)

>>> svm_bc.init_W()

>>> W = svm_bc.train()

>>> test_data = '0.97681 0.10723 0.64385 0.29556 1'

>>> prediction = svm_bc.prediction(test_data)

>>> test_data = '0.97681 0.10723 0.64385 0.29556'

>>> prediction = svm_bc.prediction(future_data, 'future_data')

>>> svm_bc.load_test_data()

>>> svm_bc.calculate_avg_error(svm_bc.test_X, svm_bc.test_Y, W)

Soft Polynomial Kernel Support Vector Machine Binary Classification Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.SupportVectorMachine as svm

>>> svm_bc = svm.BinaryClassifier()

>>> svm_bc.load_train_data()

>>> svm_bc.set_param(svm_kernel='soft_polynomial_kernel', zeta=100, gamma=1, Q=3, C=0.1)

>>> svm_bc.init_W()

>>> W = svm_bc.train()

>>> test_data = '0.97681 0.10723 0.64385 0.29556 1'

>>> prediction = svm_bc.prediction(test_data)

>>> test_data = '0.97681 0.10723 0.64385 0.29556'

>>> prediction = svm_bc.prediction(future_data, 'future_data')

>>> svm_bc.load_test_data()

>>> svm_bc.calculate_avg_error(svm_bc.test_X, svm_bc.test_Y, W)

Soft Gaussian Kernel Support Vector Machine Binary Classification Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.SupportVectorMachine as svm

>>> svm_bc = svm.BinaryClassifier()

>>> svm_bc.load_train_data()

>>> svm_bc.set_param(svm_kernel='soft_gaussian_kernel', gamma=1, C=0.1)

>>> svm_bc.init_W()

>>> W = svm_bc.train()

>>> test_data = '0.97681 0.10723 0.64385 0.29556 1'

>>> prediction = svm_bc.prediction(test_data)

>>> test_data = '0.97681 0.10723 0.64385 0.29556'

>>> prediction = svm_bc.prediction(future_data, 'future_data')

>>> svm_bc.load_test_data()

>>> svm_bc.calculate_avg_error(svm_bc.test_X, svm_bc.test_Y, W)

Probabilistic Support Vector Machine Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.ProbabilisticSVM as probabilistic_svm

>>> probabilistic = probabilistic_svm.ProbabilisticSVM()

>>> probabilistic.load_train_data()

>>> probabilistic.set_param()

>>> probabilistic.init_W()

>>> probabilistic.train()

>>> test_data = '0.26502 0.5486 0.971 0.19333 0.12207 0.81528 0.46743 0.45889 0.31004 0.3307 0.43078 0.50661 0.57281 0.052715 0.50443 0.78686 0.20099 0.85909 0.26772 0.13751 1'

>>> prediction = probabilistic.prediction(test_data)

Decision Stump Binary Classification Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.DecisionStump as decision_stump

>>> decision_stump = decision_stump.BinaryClassifier()

>>> decision_stump.load_train_data()

>>> decision_stump.set_param()

>>> decision_stump.init_W()

>>> decision_stump.train()

>>> test_data = '-8.451 7.694 -1.887 1.017 3.708 7.244 9.748 -2.362 -3.618 1'

>>> prediction = decision_stump.prediction(test_data)

AdaBoost Stump Binary Classification Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.AdaBoostStump as adaboost_stump

>>> adaboost_stump_bc = adaboost_stump.BinaryClassifier()

>>> adaboost_stump_bc.load_train_data()

>>> adaboost_stump_bc.set_param(run_t=10)

>>> adaboost_stump_bc.init_W()

>>> adaboost_stump_bc.train()

>>> test_data = '-9.706 1.392 6.562 -6.543 -1.980 -6.261 -6.067 1.254 -1.071 1'

>>> prediction = adaboost_stump_bc.prediction(test_data)

AdaBoost Decision Tree Binary Classification Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.AdaBoostDecisionTree as adaboost_decision_tree

>>> adaboost_decision_tree_c = adaboost_decision_tree.Classifier()

>>> adaboost_decision_tree_c.load_train_data()

>>> adaboost_decision_tree_c.set_param(run_t=10)

>>> adaboost_decision_tree_c.init_W()

>>> adaboost_decision_tree_c.train()

>>> test_data = '-9.706 1.392 6.562 -6.543 -1.980 -6.261 -6.067 1.254 -1.071 1'

>>> prediction = adaboost_decision_tree_c.prediction(test_data)

Gradient Boost Decision Tree Regression Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.GradionBoostDecisionTree as gradient_boost_decision_tree

>>> gradient_boost_decision_tree_r = gradient_boost_decision_tree.Classifier()

>>> gradient_boost_decision_tree_r.load_train_data()

>>> gradient_boost_decision_tree_r.set_param(run_t=10)

>>> gradient_boost_decision_tree_r.init_W()

>>> gradient_boost_decision_tree_r.train()

>>> test_data = '0.62771 0.11513 0.82235 0.14493 -1'

>>> prediction = gradient_boost_decision_tree_r.prediction(test_data)

Decision Tree Classification Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.DecisionTree as decision_tree

>>> decision_tree_c = decision_tree.CART()

>>> decision_tree_c.load_train_data()

>>> decision_tree_c.set_param(learn_type='classifier')

>>> decision_tree_c.init_W()

>>> decision_tree_c.train()

>>> test_data = '6.0 2.2 5.0 1.5 virginica'

>>> prediction = decision_tree_c.prediction(test_data)

Decision Tree Regression Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.DecisionTree as decision_tree

>>> input_train_data_file = os.path.join(os.path.join(os.getcwd(), os.path.dirname(__file__)), 'FukuML/dataset/linear_regression_train.dat')

>>> decision_tree_c = decision_tree.CART()

>>> decision_tree_c.load_train_data(input_train_data_file)

>>> decision_tree_c.set_param(learn_type='regression')

>>> decision_tree_c.init_W()

>>> decision_tree_c.train()

>>> test_data = '55.7 43 285'

>>> prediction = decision_tree_c.prediction(test_data)

Random Forest Classification Learning Algorithm

>>> import numpy as np

>>> import FukuML.RandomForest as random_forest

>>> random_forest_c = random_forest.RandomForest()

>>> random_forest_c.load_train_data()

>>> random_forest_c.set_param(learn_type='classifier')

>>> random_forest_c.init_W()

>>> random_forest_c.train()

>>> test_data = '0.94544 0.42842 0.79833 0.16244 -1'

>>> prediction = random_forest_c.prediction(test_data)

Random Forest Regression Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.RandomForest as random_forest

>>> input_train_data_file = os.path.join(os.path.join(os.getcwd(), os.path.dirname(__file__)), 'FukuML/dataset/linear_regression_train.dat')

>>> random_forest_r = random_forest.RandomForest()

>>> random_forest_r.load_train_data(input_train_data_file)

>>> random_forest_r.set_param(learn_type='regression')

>>> random_forest_r.init_W()

>>> random_forest_r.train()

>>> test_data = '55.7 43 285'

>>> prediction = random_forest_r.prediction(test_data)

Kernel Logistic Regression Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.KernelLogisticRegression as kernel_logistic_regression

>>> kernel_logistic = kernel_logistic_regression.KernelLogisticRegression()

>>> kernel_logistic.load_train_data()

>>> kernel_logistic.set_param()

>>> kernel_logistic.init_W()

>>> W = kernel_logistic.train()

>>> test_data = '0.26502 0.5486 0.971 0.19333 0.12207 0.81528 0.46743 0.45889 0.31004 0.3307 0.43078 0.50661 0.57281 0.052715 0.50443 0.78686 0.20099 0.85909 0.26772 0.13751 1'

>>> prediction = kernel_logistic.prediction(test_data)

Kernel Ridge Regression Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.KernelRidgeRegression as kernel_ridge_regression

>>> kernel_ridge = kernel_ridge_regression.KernelRidgeRegression()

>>> kernel_ridge.load_train_data()

>>> kernel_ridge.set_param(lambda_p=pow(10, -3), gamma=1, C=0.1)

>>> kernel_ridge.init_W()

>>> kernel_ridge.train()

>>> test_data = '0.62771 0.11513 0.82235 0.14493 -1'

>>> prediction = kernel_ridge.prediction(test_data)

Kernel Ridge Regression Binary Classification Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.KernelRidgeRegression as kernel_ridge_regression

>>> kernel_ridge_bc = kernel_ridge_regression.BinaryClassifier()

>>> kernel_ridge_bc.load_train_data()

>>> kernel_ridge.set_param(lambda_p=pow(10, -3), gamma=1, C=0.1)

>>> kernel_ridge.init_W()

>>> kernel_ridge.train()

>>> test_data = '0.62771 0.11513 0.82235 0.14493 -1'

>>> prediction = kernel_ridge.prediction(test_data)

Kernel Ridge Regression Multi Classification Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.KernelRidgeRegression as kernel_ridge_regression

>>> kernel_ridge_mc = kernel_ridge_regression.MultiClassifier()

>>> kernel_ridge_mc.load_train_data()

>>> kernel_ridge_mc.set_param(lambda_p=pow(10, -3), gamma=1, C=0.1)

>>> kernel_ridge_mc.init_W()

>>> kernel_ridge_mc.train()

>>> test_data = '0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0'

>>> prediction = kernel_ridge_mc.prediction(test_data)

Least Squares Support Vector Machine Binary Classification Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.LeastSquaresSVM as least_squares_svm

>>> least_squares_svm = least_squares_svm.BinaryClassifier()

>>> least_squares_svm.load_train_data()

>>> least_squares_svm.set_param(lambda_p=pow(10, -3), gamma=1, C=0.1)

>>> least_squares_svm.init_W()

>>> least_squares_svm.train()

>>> test_data = '0.62771 0.11513 0.82235 0.14493 -1'

>>> prediction = least_squares_svm.prediction(test_data)

Least Squares Support Vector Machine Multi Classification Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.LeastSquaresSVM as least_squares_svm

>>> least_squares_mc = least_squares_svm.MultiClassifier()

>>> least_squares_mc.load_train_data()

>>> least_squares_mc.set_param(lambda_p=pow(10, -3), gamma=1, C=0.1)

>>> least_squares_mc.init_W()

>>> least_squares_mc.train()

>>> test_data = '0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0'

>>> prediction = least_squares_mc.prediction(test_data)

Soft Gaussian Kernel Support Vector Machine Multi Classification Learning Algorithm

Run demo dataset: One vs One

>>> import numpy as np

>>> import FukuML.SupportVectorMachine as svm

>>> svm_mc = svm.MultiClassifier()

>>> svm_mc.load_train_data()

>>> svm_mc.set_param(svm_kernel='soft_gaussian_kernel', gamma=1, C=1)

>>> svm_mc.init_W()

>>> svm_mc.train()

>>> test_data = '0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0'

>>> prediction = svm_mc.prediction(test_data)

Support Vector Regression Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.SupportVectorRegression as svr

>>> sv_regression = svr.SupportVectorRegression()

>>> sv_regression.load_train_data()

>>> sv_regression.set_param(gamma=1, C=1, epsilon=0.1)

>>> sv_regression.init_W()

>>> sv_regression.train()

>>> test_data = '0.62771 0.11513 0.82235 0.14493 -1'

>>> prediction = sv_regression.prediction(test_data)

Neural Network Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.NeuralNetwork as nn

>>> neural_network = nn.NeuralNetwork()

>>> neural_network.load_train_data()

>>> neural_network.set_param(network_structure=[8, 3, 1], w_range_high=0.1, w_range_low=-0.1, feed_mode='stochastic', step_eta=0.01, updates=50000)

>>> neural_network.init_W()

>>> neural_network.train()

>>> test_data = '0.135592 0.0317051 -1'

>>> prediction = neural_network.prediction(test_data)

Neural Network Binary Classification Learning Algorithm

Run demo dataset

>>> import numpy as np

>>> import FukuML.NeuralNetwork as nn

>>> neural_network = nn.BinaryClassifier()

>>> neural_network.load_train_data()

>>> neural_network.set_param(network_structure=[8, 4, 1], w_range_high=0.1, w_range_low=-0.1, feed_mode='stochastic', step_eta=0.01, updates=50000)

>>> neural_network.init_W()

>>> neural_network.train()

>>> test_data = '0.135592 0.0317051 -1'

>>> prediction = neural_network.prediction(test_data)

Polynomial Feature Transform

>>> import numpy as np

>>> import FukuML.PLA as pla

>>> pla_bc = pla.BinaryClassifier()

>>> pla_bc.load_train_data()

>>> pla_bc.set_feature_transform('polynomial', 2)

Legendre Feature Transform

>>> import numpy as np

>>> import FukuML.PLA as pla

>>> pla_bc = pla.BinaryClassifier()

>>> pla_bc.load_train_data()

>>> pla_bc.set_feature_transform('legendre', 2)

10 Fold Cross Validation

>>> cross_validator = utility.CrossValidator()

>>> pla_mc = pla.MultiClassifier()

>>> pla_mc.load_train_data()

>>> pla_mc.set_param()

>>> pocket_mc = pocket.MultiClassifier()

>>> pocket_mc.load_train_data()

>>> pocket_mc.set_param()

>>> cross_validator.add_model(pla_mc)

>>> cross_validator.add_model(pocket_mc)

>>> avg_errors = cross_validator.excute()

>>> best_model = cross_validator.get_best_model()

Uniform Blending for Classification

>>> input_train_data_file = os.path.join(os.path.join(os.getcwd(), os.path.dirname(__file__)), 'FukuML/dataset/pocket_pla_binary_train.dat')

>>> input_test_data_file = os.path.join(os.path.join(os.getcwd(), os.path.dirname(__file__)), 'FukuML/dataset/pocket_pla_binary_test.dat')

>>> uniform_blending_classifier = blending.UniformBlendingClassifier()

>>> pla_bc = pla.BinaryClassifier()

>>> pla_bc.load_train_data(input_train_data_file)

>>> pla_bc.load_test_data(input_test_data_file)

>>> pla_bc.set_param()

>>> pla_bc.init_W()

>>> pla_bc.train()

>>> print("PLA 平均錯誤值(Eout):")

>>> print(pla_bc.calculate_avg_error(pla_bc.test_X, pla_bc.test_Y, pla_bc.W))

>>> pocket_bc = pocket.BinaryClassifier()

>>> pocket_bc.load_train_data(input_train_data_file)

>>> pocket_bc.load_test_data(input_test_data_file)

>>> pocket_bc.set_param()

>>> pocket_bc.init_W()

>>> pocket_bc.train()

>>> print("Pocket 平均錯誤值(Eout):")

>>> print(pocket_bc.calculate_avg_error(pocket_bc.test_X, pocket_bc.test_Y, pocket_bc.W))

>>> linear_bc = linear_regression.BinaryClassifier()

>>> linear_bc.load_train_data(input_train_data_file)

>>> linear_bc.load_test_data(input_test_data_file)

>>> linear_bc.set_param()

>>> linear_bc.init_W()

>>> linear_bc.train()

>>> print("Linear 平均錯誤值(Eout):")

>>> print(linear_bc.calculate_avg_error(linear_bc.test_X, linear_bc.test_Y, linear_bc.W))

>>> uniform_blending_classifier.add_model(pla_bc)

>>> uniform_blending_classifier.add_model(pocket_bc)

>>> uniform_blending_classifier.add_model(linear_bc)

>>> test_data = '0.32368 0.61439 0.42097 0.025626 -1'

>>> prediction = uniform_blending_classifier.prediction(test_data)

>>> print("測試資料 x:")

>>> print(prediction['input_data_x'])

>>> print("測試資料 y:")

>>> print(prediction['input_data_y'])

>>> print("預測結果:")

>>> print(prediction['prediction'])

>>> print("平均錯誤率(Ein):")

>>> print(uniform_blending_classifier.calculate_avg_error(input_train_data_file))

>>> print("平均錯誤率(Eout):")

>>> print(uniform_blending_classifier.calculate_avg_error(input_test_data_file))

Linear Blending for Classification

>>> input_train_data_file = os.path.join(os.path.join(os.getcwd(), os.path.dirname(__file__)), 'FukuML/dataset/pocket_pla_binary_train.dat')

>>> input_test_data_file = os.path.join(os.path.join(os.getcwd(), os.path.dirname(__file__)), 'FukuML/dataset/pocket_pla_binary_test.dat')

>>> linear_blending_classifier = blending.LinearBlendingClassifier()

>>> pla_bc = pla.BinaryClassifier()

>>> pla_bc.load_train_data(input_train_data_file)

>>> pla_bc.load_test_data(input_test_data_file)

>>> pla_bc.set_param()

>>> pla_bc.init_W()

>>> pla_bc.train()

>>> print("PLA 平均錯誤值(Eout):")

>>> print(pla_bc.calculate_avg_error(pla_bc.test_X, pla_bc.test_Y, pla_bc.W))

>>> pocket_bc = pocket.BinaryClassifier()

>>> pocket_bc.load_train_data(input_train_data_file)

>>> pocket_bc.load_test_data(input_test_data_file)

>>> pocket_bc.set_param()

>>> pocket_bc.init_W()

>>> pocket_bc.train()

>>> print("Pocket 平均錯誤值(Eout):")

>>> print(pocket_bc.calculate_avg_error(pocket_bc.test_X, pocket_bc.test_Y, pocket_bc.W))

>>> linear_bc = linear_regression.BinaryClassifier()

>>> linear_bc.load_train_data(input_train_data_file)

>>> linear_bc.load_test_data(input_test_data_file)

>>> linear_bc.set_param()

>>> linear_bc.init_W()

>>> linear_bc.train()

>>> print("Linear 平均錯誤值(Eout):")

>>> print(linear_bc.calculate_avg_error(linear_bc.test_X, linear_bc.test_Y, linear_bc.W))

>>> linear_blending_classifier.add_model(pla_bc)

>>> linear_blending_classifier.add_model(pocket_bc)

>>> linear_blending_classifier.add_model(linear_bc)

>>> linear_blending_classifier.train()

>>> test_data = '0.32368 0.61439 0.42097 0.025626 -1'

>>> prediction = linear_blending_classifier.prediction(test_data)

>>> print("測試資料 x:")

>>> print(prediction['input_data_x'])

>>> print("測試資料 y:")

>>> print(prediction['input_data_y'])

>>> print("預測結果:")

>>> print(prediction['prediction'])

>>> print("平均錯誤率(Ein):")

>>> print(linear_blending_classifier.calculate_avg_error(input_train_data_file))

>>> print("平均錯誤率(Eout):")

>>> print(linear_blending_classifier.calculate_avg_error(input_test_data_file))

>>> print('-'*70)

Uniform Blending for Regression

>>> input_train_data_file = os.path.join(os.path.join(os.getcwd(), os.path.dirname(__file__)), 'FukuML/dataset/pocket_pla_binary_train.dat')

>>> input_test_data_file = os.path.join(os.path.join(os.getcwd(), os.path.dirname(__file__)), 'FukuML/dataset/pocket_pla_binary_test.dat')

>>> uniform_blending_regression = blending.UniformBlendingRegression()

>>> linear1 = linear_regression.LinearRegression()

>>> linear1.load_train_data(input_train_data_file)

>>> linear1.load_test_data(input_test_data_file)

>>> linear1.set_param()

>>> linear1.init_W()

>>> linear1.train_X, linear1.train_Y = utility.DatasetLoader.bootstrap_bagging( linear1.train_X, linear1.train_Y, linear1.data_num)

>>> linear1.train()

>>> print("Linear 1 平均錯誤值(Eout):")

>>> print(linear1.calculate_avg_error(linear1.test_X, linear1.test_Y, linear1.W))

>>> linear2 = linear_regression.LinearRegression()

>>> linear2.load_train_data(input_train_data_file)

>>> linear2.load_test_data(input_test_data_file)

>>> linear2.set_param()

>>> linear2.init_W()

>>> linear2.train_X, linear2.train_Y = utility.DatasetLoader.bootstrap_bagging(linear2.train_X, linear2.train_Y, linear2.data_num)

>>> linear2.train()

>>> print("Linear 2 平均錯誤值(Eout):")

>>> print(linear2.calculate_avg_error(linear2.test_X, linear2.test_Y, linear2.W))

>>> linear3 = linear_regression.LinearRegression()

>>> linear3.load_train_data(input_train_data_file)

>>> linear3.load_test_data(input_test_data_file)

>>> linear3.set_param()

>>> linear3.init_W()

>>> linear3.train_X, linear1.train_Y = utility.DatasetLoader.bootstrap_bagging(inear3.train_X, linear3.train_Y, linear3.data_num)

>>> linear3.train()

>>> print("Linear 3 平均錯誤值(Eout):")

>>> print(linear3.calculate_avg_error(linear3.test_X, linear3.test_Y, linear3.W))

>>> uniform_blending_regression.add_model(linear1)

>>> uniform_blending_regression.add_model(linear2)

>>> uniform_blending_regression.add_model(linear3)

>>> test_data = '0.32368 0.61439 0.42097 0.025626 -1'

>>> prediction = uniform_blending_regression.prediction(test_data)

>>> print("測試資料 x:")

>>> print(prediction['input_data_x'])

>>> print("測試資料 y:")

>>> print(prediction['input_data_y'])

>>> print("預測結果:")

>>> print(prediction['prediction'])

>>> print("平均錯誤值(Ein):")

>>> print(uniform_blending_regression.calculate_avg_error(input_train_data_file))

>>> print("平均錯誤值(Eout):")

>>> print(uniform_blending_regression.calculate_avg_error(input_test_data_file))

>>> print('-'*70)

Linear Blending for Regression

>>> input_train_data_file = os.path.join(os.path.join(os.getcwd(), os.path.dirname(__file__)), 'FukuML/dataset/pocket_pla_binary_train.dat')
>>> input_test_data_file = os.path.join(os.path.join(os.getcwd(), os.path.dirname(__file__)), 'FukuML/dataset/pocket_pla_binary_test.dat')
>>> linear_blending_regression = blending.LinearBlendingRegression()
>>> linear1 = linear_regression.LinearRegression()
>>> linear1.load_train_data(input_train_data_file)
>>> linear1.load_test_data(input_test_data_file)
>>> linear1.set_param()
>>> linear1.init_W()
>>> linear1.train_X, linear1.train_Y = utility.DatasetLoader.bootstrap_bagging(linear1.train_X, linear1.train_Y, linear1.data_num)
>>> linear1.train()
>>> print("Linear 1 平均錯誤值(Eout):")
>>> print(linear1.calculate_avg_error(linear1.test_X, linear1.test_Y, linear1.W))
>>> linear2 = linear_regression.LinearRegression()
>>> linear2.load_train_data(input_train_data_file)
>>> linear2.load_test_data(input_test_data_file)
>>> linear2.set_param()
>>> linear2.init_W()
>>> linear2.train_X, linear2.train_Y = utility.DatasetLoader.bootstrap_bagging(linear2.train_X, linear2.train_Y, linear2.data_num)
>>> linear2.train()
>>> print("Linear 2 平均錯誤值(Eout):")
>>> print(linear2.calculate_avg_error(linear2.test_X, linear2.test_Y, linear2.W))
>>> linear3 = linear_regression.LinearRegression()
>>> linear3.load_train_data(input_train_data_file)
>>> linear3.load_test_data(input_test_data_file)
>>> linear3.set_param()
>>> linear3.init_W()
>>> linear3.train_X, linear1.train_Y = utility.DatasetLoader.bootstrap_bagging(linear3.train_X, linear3.train_Y, linear3.data_num)
>>> linear3.train()
>>> print("Linear 3 平均錯誤值(Eout):")
>>> print(linear3.calculate_avg_error(linear3.test_X, linear3.test_Y, linear3.W))
>>> linear_blending_regression.add_model(linear1)
>>> linear_blending_regression.add_model(linear2)
>>> linear_blending_regression.add_model(linear3)
>>> linear_blending_regression.train()
>>> test_data = '0.32368 0.61439 0.42097 0.025626 -1'
>>> prediction = linear_blending_regression.prediction(test_data)
>>> print("測試資料 x:")
>>> print(prediction['input_data_x'])
>>> print("測試資料 y:")
>>> print(prediction['input_data_y'])
>>> print("預測結果:")
>>> print(prediction['prediction'])
>>> print("平均錯誤值(Ein):")
>>> print(linear_blending_regression.calculate_avg_error(input_train_data_file))
>>> print("平均錯誤值(Eout):")
>>> print(linear_blending_regression.calculate_avg_error(input_test_data_file))
>>> print('-'*70)