"""

===================================================

Introduction to Machine Learning (67577)

===================================================

Running script for Ex4.

Author:

Date: May, 2018

"""

import numpy as np

from adaboost import AdaBoost

from ex4_tools import decision_boundaries, h_opt, DecisionStump

from matplotlib.pyplot import *

def Q3(): # AdaBoost

# TODO - implement this function

return

def Q4(): # decision trees

# TODO - implement this function

return

def Q5(): # spam data

# TODO - implement this function

return

def to_ndarray(list_):

return np.array(list_).astype(np.float64)

def read_all_data(file_name):

return to_ndarray(data), to_ndarray(labels)

def get_data(test_size):

all_data, all_labels = read_all_data('spam.data')

def _get_file_path(name):

return 'ex4files/SynData/{}.txt'.format(name)

def _load_data(name):

return np.loadtxt(_get_file_path('X_' + name)), np.loadtxt(_get_file_path('y_' + name))

if __name__ == '__main__':

X_train, y_train = _load_data('train')

X_val, y_val = _load_data('val')

T_values = range(5, 200, 5)

validation_error = []

training_error = []

for t in T_values:

ada_boost = AdaBoost(DecisionStump, t)

ada_boost.train(X_train, y_train)

validation_error.append(ada_boost.error(X_val, y_val))

training_error.append(ada_boost.error(X_train, y_train))

training_error_plot, = plot(T_values, training_error, linestyle='--', label='training_error')

validation_error_plot, = plot(T_values, validation_error, linestyle='--', label='validation_error')

legend(handles=[training_error_plot, validation_error_plot])

title('training and validation error vs T values')

xlabel('T values')

ylabel('training and validation error')

savefig('adaboost.png')

clf()

# T_values = {1, 5, 10, 100, 200}

# T_values = {100}

#

# for t in T_values:

# ada_boost = AdaBoost(DecisionStump, t)

# ada_boost.train(X_train, y_train)

# decision_boundaries(ada_boost, X_train, y_train)