# feature_matrix = [pos1, pos2, neg1, neg2]
# labels = [1, 1, -1, -1]
# p1.plot_2d_examples(feature_matrix, labels, 0, [0.25, 0.6])
loose_points = np.array([[-3, 4], [-2, 3], [2, 4], [4, 2], [-3, -2], [0, -2],
[3, -3]])
loose_labels = np.array([1, 1, 1, 1, -1, -1, -1])
average_theta, average_theta_0 = p1.averager(loose_points, loose_labels)
p1.plot_2d_examples(loose_points, loose_labels, average_theta_0, average_theta,
'Averager - loose points')
perceptron_theta, perceptron_theta_0 = p1.train_perceptron(
loose_points, loose_labels)
p1.plot_2d_examples(loose_points, loose_labels, perceptron_theta_0,
perceptron_theta, 'Perceptron - loose points')
pa_theta, pa_theta_0 = p1.train_passive_agressive(loose_points, loose_labels,
1000)
p1.plot_2d_examples(loose_points, loose_labels, pa_theta_0, pa_theta,
'Passive Agressive - loose points')
close_points = np.array([[-1, -1.25], [-1.5, -1], [1, 4], [1.5, 1.5], [4, 10],
[-1, -1]])
close_labels = np.array([-1, 1, 1, -1, 1, -1])
average_theta, average_theta_0 = p1.averager(close_points, close_labels)
p1.plot_2d_examples(close_points, close_labels, average_theta_0, average_theta,
loose_points = np.array([
[-3,4],
[-2,3],
[2,4],
[4,2],
[-3,-2],
[0,-2],
[3,-3]])
loose_labels = np.array([1,1,1,1,-1,-1,-1])
average_theta, average_theta_0 = p1.averager(loose_points, loose_labels)
p1.plot_2d_examples(loose_points, loose_labels, average_theta_0, average_theta, 'Averager - loose points')
perceptron_theta, perceptron_theta_0 = p1.train_perceptron(loose_points, loose_labels)
p1.plot_2d_examples(loose_points, loose_labels, perceptron_theta_0, perceptron_theta, 'Perceptron - loose points')
pa_theta, pa_theta_0 = p1.train_passive_agressive(loose_points, loose_labels, 1000)
p1.plot_2d_examples(loose_points, loose_labels, pa_theta_0, pa_theta, 'Passive Agressive - loose points')
close_points = np.array([
[-1,-1.25],
[-1.5, -1],
[1,4],
[1.5,1.5],
[4,10],
[-1,-1]])
close_labels = np.array([-1, 1, 1, -1, 1, -1])
######################
average_theta, average_theta_0 = p1.averager(feature_matrix, labels)
label_output = p1.perceptron_classify(feature_matrix, average_theta_0, average_theta)
correct = 0
for i in xrange(0, len(label_output)):
if(label_output[i] == labels[i]):
correct = correct + 1
percentage_correct = 100.0 * correct / len(label_output)
print("Averager gets " + str(percentage_correct) + "% correct (" + str(correct) + " out of " + str(len(label_output)) + ").")
######################
# PERCEPTRON
######################
perceptron_theta, perceptron_theta_0 = p1.train_perceptron(feature_matrix, labels)
label_output = p1.perceptron_classify(feature_matrix, perceptron_theta_0, perceptron_theta)
print 'Size', len(dictionary)
print 'T_0', perceptron_theta_0
correct = 0
for i in xrange(0, len(label_output)):
if(label_output[i] == labels[i]):
correct = correct + 1
percentage_correct = 100.0 * correct / len(label_output)
print("Perceptron gets " + str(percentage_correct) + "% correct (" + str(correct) + " out of " + str(len(label_output)) + ").")
######################
# PASSIVE-AGRESSIVE
