def main(argv):
train_set, test_set = data.readFile(argv[1])
index, _data = getData(train_set)
n_estimators = np.arange(0.1, 0.5, 0.01)
for n_estimator in n_estimators:
model = training(_data, argv[2], n_estimator)
testing(model, test_set)
def refreshLevelData():
objects.lvl_names, objects.times, objects.player_pos, objects.beatTimes = data.readFile('level data.txt')
objects.levelButtonList = []
objects.timeList = []
for lvl in range(len(objects.lvl_names)):
objects.levelButtonList.append(button.textButton('%s' %objects.lvl_names[lvl], 40, 0, -50))
objects.timeList.append(button.textButton('%s s' %float(objects.times[lvl]), 30, 0, -50))
objects.beatList.append(button.textButton('%s s' %float(objects.beatTimes[lvl]), 30, 0, -50))
def main(argv):
training_set, testing_set = data.readFile(argv[1])
training_X, training_y = features_labels(training_set)
testing_X, testing_y = features_labels(testing_set)
model, error = svm(training_X, training_y, testing_X, testing_y)
print(error)
print()
model, error = gbr(training_X, training_y, testing_X, testing_y)
print(error)
print()
model, error = rfr(training_X, training_y, testing_X, testing_y)
print(error)
print()
model, error = mlr(training_X, training_y, testing_X, testing_y)
print(error)
print()
def main():
global args, max_length
args = parser.parse_args()
if args.eval:
if not os.path.exists(args.output_dir):
print("Output directory do not exists")
exit(0)
try:
model = EncoderDecoder().load(args.output_dir)
print("Model loaded successfully")
except:
print("The trained model could not be loaded...")
exit()
test_pairs = readFile(args.test_file)
outputs = model.evaluatePairs(test_pairs, rand=False, char=args.char)
writeToFile(outputs, os.path.join(args.output_dir, "output.pkl"))
reference = []
hypothesis = []
for (hyp, ref) in outputs:
if args.char or args.char_bleu:
reference.append([list(ref)])
hypothesis.append(list(hyp))
else:
reference.append([ref.split(" ")])
hypothesis.append(hyp.split(" "))
bleu_score = compute_bleu(reference, hypothesis)
print("Bleu Score: " + str(bleu_score))
print(
model.evaluateAndShowAttention(
"L'anglais n'est pas facile pour nous.", char=args.char))
print(
model.evaluateAndShowAttention(
"J'ai dit que l'anglais est facile.", char=args.char))
print(
model.evaluateAndShowAttention(
"Je n'ai pas dit que l'anglais est une langue facile.",
char=args.char))
print(
model.evaluateAndShowAttention("Je fais un blocage sur l'anglais.",
char=args.char))
else:
input_lang, output_lang, pairs = prepareData(args.train_file)
print(random.choice(pairs))
if args.char:
model = EncoderDecoder(args.hidden_size, input_lang.n_chars,
output_lang.n_chars, args.drop, args.tfr,
args.max_length, args.lr, args.simple,
args.bidirectional, args.dot, False, 1)
else:
model = EncoderDecoder(args.hidden_size, input_lang.n_words,
output_lang.n_words, args.drop, args.tfr,
args.max_length, args.lr, args.simple,
args.bidirectional, args.dot, args.multi,
args.num_layers)
model.trainIters(pairs,
input_lang,
output_lang,
args.n_iters,
print_every=args.print_every,
plot_every=args.plot_every,
char=args.char)
model.save(args.output_dir)
model.evaluatePairs(pairs, char=args.char)
import pygame
import button, data, funcs
from constants import *
pygame.font.init()
blockList = []
bulletList = []
enemyList = []
decalList = []
health_battery = []
barList = []
mainButtonList = [button.textButton('Choose Level', 20, WIN_WIDTH/2, (WIN_HEIGHT/2)-40),
button.textButton('Instructions', 20, WIN_WIDTH/2, (WIN_HEIGHT/2)),
button.textButton('Quit', 20, WIN_WIDTH/2, (WIN_HEIGHT/2)+40)]
lvl_names, times, player_pos, beatTimes = data.readFile('level data.txt')
levelButtonList = []
timeList = []
beatList = []
for level in range(len(lvl_names)):
levelButtonList.append(button.textButton('%s' %lvl_names[level], 40, 0, -50))
timeList.append(button.textButton('%s s' %float(times[level]), 30, 0, -50))
beatList.append(button.textButton('%s s' %float(beatTimes[level]), 30, 0, -50))
pageButtonList = [button.textButton('<=', 30, 50, WIN_HEIGHT-50),
button.textButton('main', 20, WIN_WIDTH/2, WIN_HEIGHT-50),
button.textButton('=>', 30, WIN_WIDTH-50, WIN_HEIGHT-50)]
characterButtonList = []
import sys import data import models import matplotlib.pyplot as plt training_set, testing_set = data.readFile(sys.argv[1]) training_X, training_Y = models.features_labels(training_set) testing_X, testing_Y = models.features_labels(testing_set) model, err = models.gbr(training_X, training_Y, testing_X, testing_Y) prediction = model.predict(testing_X) diff = [testing_Y[i] - prediction[i] for i in range(len(testing_Y))] plt.plot(testing_Y, diff, 'bo') plt.axhline(0, color='red') plt.savefig(r"figure_1.png")