def test_trim_spaces(self):
self.set_up_file_content(" 2,3 ,2 \n 2, 5, 3\n4 ,7 ,6")
actual = data_loader.parse_data("file_name")
self.assertListEqual([[2, 3], [2, 5], [4, 7]], actual[0])
self.assertListEqual([2, 3, 6], actual[1])
from pylearn import high_order_model, visualizer, data_loader
from pylearn.space_transform import full_polynomial_mapper
# Polynomial regression example
X, y = data_loader.parse_data('data/approx_data.txt')
model = high_order_model.PolynomialRegression()
model.learning_rate = 0.12
model.max_iterations = 300
mapper = full_polynomial_mapper(7, 2)
predict = model.fit(X, y, mapper)
visualizer.plot_1d_approximator_stats(model, X, y)
def test_complex_matrix_parse(self):
self.set_up_file_content("1,1,3,6,2\n2,5,6,2,3\n4,4,5,5,6")
actual = data_loader.parse_data("file_name")
self.assertListEqual([[1, 1, 3, 6], [2, 5, 6, 2], [4, 4, 5, 5]],
actual[0])
self.assertListEqual([2, 3, 6], actual[1])
from pylearn import high_order_model, linear_model, visualizer, data_loader
from pylearn.space_transform import full_polynomial_mapper
# Polynomial logistic regression example
X_lo, y_lo = data_loader.parse_data('data/logistic_non_linear.txt')
y_lo = [int(y) for y in y_lo]
model = high_order_model.PolynomialLogisticRegression()
model.learning_rate = 0.1
model.max_iterations = 400
mapper = full_polynomial_mapper(6, 2)
predict = model.fit(X_lo, y_lo, mapper)
visualizer.plot_2d_classifier_stats(model, X_lo, y_lo)
exit()
# Linear logistic regression example
X_lo, y_lo = data_loader.parse_data('data/logistic_linear.txt')
y_lo = [int(y) for y in y_lo]
model = linear_model.LogisticRegression()
predict = model.fit(X_lo, y_lo)
visualizer.plot_2d_classifier_stats(model, X_lo, y_lo)
def test_trim_spaces(self):
self.set_up_file_content(" 2,3 ,2 \n 2, 5, 3\n4 ,7 ,6")
actual = data_loader.parse_data("file_name")
self.assertListEqual([[2, 3], [2, 5], [4, 7]], actual[0])
self.assertListEqual([2, 3, 6], actual[1])
def test_complex_matrix_parse(self):
self.set_up_file_content("1,1,3,6,2\n2,5,6,2,3\n4,4,5,5,6")
actual = data_loader.parse_data("file_name")
self.assertListEqual([[1, 1, 3, 6], [2, 5, 6, 2], [4, 4, 5, 5]],
actual[0])
self.assertListEqual([2, 3, 6], actual[1])
from pylearn import high_order_model, linear_model, visualizer, data_loader
from pylearn.space_transform import full_polynomial_mapper
# Polynomial logistic regression example
X_lo, y_lo = data_loader.parse_data('data/logistic_non_linear.txt')
y_lo = [int(y) for y in y_lo]
model = high_order_model.PolynomialLogisticRegression()
model.learning_rate = 0.1
model.max_iterations = 400
mapper = full_polynomial_mapper(6, 2)
predict = model.fit(X_lo, y_lo, mapper)
visualizer.plot_2d_classifier_stats(model, X_lo, y_lo)
exit()
# Linear logistic regression example
X_lo, y_lo = data_loader.parse_data('data/logistic_linear.txt')
y_lo = [int(y) for y in y_lo]
model = linear_model.LogisticRegression()
predict = model.fit(X_lo, y_lo)
visualizer.plot_2d_classifier_stats(model, X_lo, y_lo)