def test_gradient_descent(samples, test_data, scikit_pred):
reg = LinearRegressor(samples, labels)
gr = GradientDescentRunner(reg.get_gradient(), len(samples[0]) + 1)
_, weights = gr.run_once()
reg.weights = weights
p = reg.predict(test_data)
print "Prediction from gradient descent: ", p
def test_lets_just_look_at_the_outputs(self):
gr = GradientDescentRunner(self.logreg.get_gradient(), len(self.samples[0]) + 1,
self.logreg.get_error_function(), alpha=1e-8, max_iter=300)
_, weights = gr.run_once()
self.logreg.weights = weights
predictions = [self.logreg.get_probability(d) for d in self.train_dataset]
import pprint
pprint.pprint(zip(predictions, self.train_labels))
def test_lets_just_look_at_the_outputs(self):
gr = GradientDescentRunner(self.logreg.get_gradient(),
len(self.samples[0]) + 1,
self.logreg.get_error_function(),
alpha=1e-8,
max_iter=300)
_, weights = gr.run_once()
self.logreg.weights = weights
predictions = [
self.logreg.get_probability(d) for d in self.train_dataset
]
import pprint
pprint.pprint(zip(predictions, self.train_labels))