def our_avg_run(avg_num_of_run,filename):
dataset = load_dataset(filename)
ptraining_error = []
perceptron_error = []
for i in range(avg_num_of_run):
(training_set, testing_set) = split_dataset(dataset, PROBABILITY_TRAINING_SET)
testing_set = dataset
if IS_VERBOSE:
print("training set size: %s testing set size: %s num instances: %s" %
(len(training_set), len(testing_set), len(dataset)))
(train_x, train_y) = split_attribute_and_label(training_set)
(test_x, test_y) = split_attribute_and_label(testing_set)
p = PerceptronClassifier(ETA, THRESHOLD, UPPER_BOUND, False)
p.fit(train_x, train_y)
t_result_list = p.predict(train_x)
ptraining_error.append(calculate_error(train_y, t_result_list))
result_list = p.predict(test_x)
perceptron_error.append(calculate_error(test_y, result_list))
print(p.weights)
return sum(perceptron_error) / len(perceptron_error) , sum(ptraining_error) / len(ptraining_error)
