import statistics

import time

import numpy as np

import p3_utils

from load_data import FaceData

from perceptron import PerceptronClassifier

if __name__ == '__main__':

iterations = 3

legalLabels = range(2)

face_data = FaceData("facedata")

classifier = PerceptronClassifier(legalLabels, iterations)

featureFunction = face_data.basic_feature_extractor

trainingDataList = list(map(featureFunction, face_data.face_train_imgs))

validationDataList = list(map(featureFunction, face_data.face_validation_imgs))

testDataList = list(map(featureFunction, face_data.face_test_imgs))

n_train = len(trainingDataList)

classifier.set_weights(range(2), FaceData.FACE_DATUM_WIDTH, FaceData.FACE_DATUM_HEIGHT)

# Conduct training and testing

percentages, runtimes = ([], [])

for n, n_samples in enumerate(range(n_train // 10, n_train + 1, n_train // 10)):

start_time = time.time()

print('Training with {}% of data'.format((n + 1) * 10))

idx = np.random.choice(n_train, n_samples, replace=False)

train_img_sample = np.array(trainingDataList)[idx].tolist()

train_labels_sample = np.array(face_data.face_train_labels)[idx].tolist()

errors = classifier.train(train_img_sample, train_labels_sample)

print('errors over 3 iterations', errors)

print('Validating...')

validation_guesses = classifier.classify(validationDataList)

correct = [validation_guesses[i] == face_data.face_validation_labels[i] for i in

range(len(face_data.face_validation_labels))].count(True)

print(str(correct), 'correct out of ', str(len(face_data.face_validation_labels)))

print('Testing...')

test_guesses = classifier.classify(testDataList)

correct = [test_guesses[i] == face_data.face_test_labels[i] for i in

range(len(face_data.face_test_labels))].count(True)

percentage = (100.0 * correct / len(face_data.face_test_labels))

print(str(correct), 'correct out of ', str(len(face_data.face_test_labels)), 'percentage ', percentage)

percentages.append(percentage)

runtime = time.time() - start_time

print("Runtime taken: ", runtime)

runtimes.append(runtime)

p3_utils.plot_line_graph(range(10, 101, 10), percentages, "Accuracy of perceptron for face data",

"Percentage of training data used", "Percentage accuracy obtained on test data")

p3_utils.plot_line_graph(range(10, 101, 10), runtimes, "Runtime of perceptron for face data",

"Percentage of training data used", "Runtime taken for trianing and testing")

print(statistics.stdev(percentages))