def train_with_normal_equation(self, feature_data, labels_data,
normalized_feature_data, normalized_labels):
print("Normal Equation: ")
# Normal equation
feature_data_bias = np.insert(normalized_feature_data, 0, 1, axis=1)
x_trans_x = np.dot(np.transpose(feature_data_bias), feature_data_bias)
norm_equation_theta = np.dot(
np.dot(np.linalg.inv(x_trans_x), np.transpose(feature_data_bias)),
normalized_labels)
print(norm_equation_theta)
self.linear_regression_learner.theta = norm_equation_theta
normalized_predictions = self.linear_regression_learner.predict(
normalized_feature_data)
predictions = np.around(
DataService.denormalize_predictions(labels_data,
normalized_predictions,
method='min-max'))
cost = self.linear_regression_learner.calculate_cost(
normalized_predictions, normalized_labels)
print("Final Norm Equation Normalized Cost: ", cost)
x, y, z = self.build_model_plot_data(feature_data, predictions)
PlotService.plot3d_line(
x,
y,
z,
labels=['Age', 'Weight', 'BP'],
title="Normal Equation Model: Blood Pressure for Age and Weight.")
PlotService.plot3d_scatter_compare(
feature_data,
labels_data,
predictions,
labels=['Age', 'Weight', 'BP'],
title="Normal Equation Actual vs Projected")
def train_with_gradient_descent(self, feature_data, labels_data,
normalized_feature_data,
normalized_labels):
self.linear_regression_learner.train(normalized_feature_data,
normalized_labels)
normalized_predictions = self.linear_regression_learner.predict(
normalized_feature_data)
predictions = np.around(
DataService.denormalize_predictions(labels_data,
normalized_predictions,
method='min-max'))
PlotService.plot_line(x=range(
1,
len(self.linear_regression_learner.cost_history) + 1),
y=self.linear_regression_learner.cost_history,
x_label="Iteration",
y_label="Training Cost",
title="Training Learning Curve")
x, y, z = self.build_model_plot_data(feature_data, predictions)
PlotService.plot3d_line(
x,
y,
z,
labels=['Age', 'Weight', 'BP'],
title="Linear Model: Blood Pressure for Age and Weight.")
PlotService.plot3d_scatter_compare(feature_data,
labels_data,
predictions,
labels=['Age', 'Weight', 'BP'],
title="Actual vs Projected")
cost = self.linear_regression_learner.calculate_cost(
normalized_predictions, normalized_labels)
print("Final Normalized Cost: ", cost)