Example #1
0
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
######################
pa_theta, pa_theta_0 = p1.train_passive_agressive(feature_matrix, labels, 50)
label_output = p1.perceptron_classify(feature_matrix, pa_theta_0, pa_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("Passive-agressive gets " + str(percentage_correct) + "% correct (" + str(correct) + " out of " + str(len(label_output)) + ").")

# ######################
# # CROSS VALIDATION
# ######################
K = 40
print 'K =', K
Example #2
0
                [-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, 'Averager - close points')
Example #3
0
import numpy as np
import project1_code as p1

######################
# INITIALIZE
######################
adjectives = p1.extract_set('adjectives.txt')
dictionary = p1.extract_dictionary('train-tweet.txt')
train_labels = p1.read_vector_file('train-answer.txt')
train_feature_matrix = p1.extract_feature_vectors_with_keywords('train-tweet.txt', dictionary, adjectives)
test_feature_matrix = p1.extract_feature_vectors_with_keywords('test-tweet.txt', dictionary, adjectives)

######################
# TRAIN
######################
pa_theta, pa_theta_0 = p1.train_passive_agressive(train_feature_matrix, train_labels, 1000)

######################
# CLASSIFY
######################
label_output = p1.perceptron_classify(test_feature_matrix, pa_theta_0, pa_theta)

print train_feature_matrix.shape
print test_feature_matrix.shape
p1.write_label_answer(label_output, 'tweet_labels.txt')