def main():
training_data = rtrain.train()
# rtest.test(training_data)
# odds_ratio(9, 7, training_data)
# odds_ratio(9, 4, training_data)
# odds_ratio(3, 5, training_data)
# odds_ratio(3, 8, training_data)
for i in range(10):
odds_ratio(i, i, training_data)
def main(): training_data = rtrain.train() # rtest.test(training_data) # odds_ratio(9, 7, training_data) # odds_ratio(9, 4, training_data) # odds_ratio(3, 5, training_data) # odds_ratio(3, 8, training_data) for i in range(10): odds_ratio(i, i, training_data)
if cur_line[j] != ' ': singular_image[i].append(1) else: singular_image[i].append(0); probability_per_class = list() for i in range(10): probability_per_class.append(get_prob(i, singular_image, digit_matrices, 1)) max_value = max(probability_per_class) max_index = probability_per_class.index(max_value) digit_class = int(line) global num_per_class global correct_count global correct_per_class global confusion_matrix num_per_class[digit_class] +=1 confusion_matrix[digit_class][max_index] += 1 if max_index == digit_class: correct_count+=1 correct_per_class[max_index]+=1 # print (max_index, digit_class) if __name__ == '__main__': start = time.clock() test(rtrain.train(0)) print time.clock() - start
if cur_line[j] != ' ':
singular_image[i].append(1)
else:
singular_image[i].append(0)
probability_per_class = list()
for i in range(10):
probability_per_class.append(
get_prob(i, singular_image, digit_matrices, 1))
max_value = max(probability_per_class)
max_index = probability_per_class.index(max_value)
digit_class = int(line)
global num_per_class
global correct_count
global correct_per_class
global confusion_matrix
num_per_class[digit_class] += 1
confusion_matrix[digit_class][max_index] += 1
if max_index == digit_class:
correct_count += 1
correct_per_class[max_index] += 1
# print (max_index, digit_class)
if __name__ == '__main__':
start = time.clock()
test(rtrain.train(0))
print time.clock() - start