def draw(self): for i in self.points: plot(i) if (self.divided): self.boundary.drawDivision() self.northEast.draw() self.northWest.draw() self.southEast.draw() self.southWest.draw()
def ppo_test(): env = gym.make('Pendulum-v0') ppo = PPO(3, 1, 2) ppo.ep_max = 500 for ep in range(ppo.ep_max): s = env.reset() buffer_s, buffer_a, buffer_r = [], [], [] ep_r = 0 for t in range(300): # in one episode env.render() a = ppo.get_action(s) s_, r, done, _ = env.step(a) s_ = np.squeeze(s_) buffer_s.append(s) buffer_a.append(a) buffer_r.append((r + 2) / 2) # normalize reward, find to be useful s = s_ ep_r += r if (t + 1) % ppo.batch_size == 0 or t == 300 - 1: v_s_ = ppo.get_value(s_) discounted_r = [] for r in buffer_r[::-1]: v_s_ = r + 0.9 * v_s_ discounted_r.append(v_s_) discounted_r.reverse() bs, ba, br = np.vstack(buffer_s), np.vstack( buffer_a), np.vstack( discounted_r) #np.array(discounted_r)[:,np.newaxis] buffer_s, buffer_a, buffer_r = [], [], [] ppo.train(bs, ba, br, ep) ep_r_all.append(ep_r) print(ep, ' ', ep_r) plot(ppo.a_loss) plot(ppo.c_loss) plot(ep_r_all)
def drawMove(move, port): fileName = getfile(move) plot(fileName, port)
X.append(data["X1"]) X.append(data["X2"]) # Now array X has two rows, first row contains all the numbers for X1 and the # second row contains all the numbers for X2 but I want an output in which # the number of rows is equal to the number of data and each row contains # one X1 and X2 so I got the transpose of array X Xt = np.transpose(X) return np.array(Xt), data["Label"].values X, y = read_data('dataset.csv') plot(X, y) # I want to split the data to training and test data but before doing it I shuffle the data to get a different # training and test data every time I run the code so the result I get may differ each time. X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=True) # # Make a perceptron and train it # p = perceptron.Perceptron() # p.fit(X_train, y_train) # # border(X, p) # # # Now I want to test the trained perceptron
print("training...") seller_train_data = [] buyer_train_data = [] for i in tqdm(range(NEG_TRAIN_DATA)): world = SCML2020World( **SCML2020World.generate( agent_types=[MyLearnNegotiationAgent, DecentralizingAgent,], n_steps=40, n_processes=2, ), construct_graphs=True ) SCML2020World.cancelled_contracts = cancelled_contracts world.run() plot(world) seller_data, buyer_data = get_train_data(world) seller_train_data += seller_data buyer_train_data += buyer_data seller_train_features, seller_train_tags = split_features_tags(seller_train_data) seller_train_features = [ torch.from_numpy(feature).float() for feature in seller_train_features ] seller_train_tags = [torch.from_numpy(tag).float() for tag in seller_train_tags] all_seller_data = list(zip(seller_train_features, seller_train_tags)) shuffle(all_seller_data) train_seller_data = all_seller_data[: -int(len(all_seller_data) * NEG_VALIDATION_SPLIT)] test_seller_data = all_seller_data[-int(len(all_seller_data) * NEG_VALIDATION_SPLIT) :]
from draw import plot import argparse parser = argparse.ArgumentParser( description='This is a basic gcode sender. http://crcibernetica.com') parser.add_argument('-p', '--port', help='Input USB port', required=True) parser.add_argument('-f', '--file', help='Gcode file name', required=True) args = parser.parse_args() port = args.port #plot("1.g",port) #plot("2.g",port) #plot("3.g",port) #plot("4.g",port) #plot("5.g",port) #plot("6.g",port) #plot("7.g",port) #plot("8.g",port) plot(args.file, args.port)
def clicked(x, y): qt.insertDraw([x, y]) if qt.contains([x, y]): plot([x, y]) print("You clicked : ", x, ", ", y)
from quadTree import quadTree from draw import Rect, win, head, plot import random import turtle def clicked(x, y): qt.insertDraw([x, y]) if qt.contains([x, y]): plot([x, y]) print("You clicked : ", x, ", ", y) boundary = Rect([0, 0], 250, 250) boundary.draw() qt = quadTree(boundary, 4) for _ in [0] * 600: rand_x = random.uniform(-250, 251) rand_y = random.uniform(-250, 251) qt.insertDraw([rand_x, rand_y]) plot([rand_x, rand_y]) win.onclick(clicked) qt.draw() win.mainloop()
import sys import draw if len(sys.argv) > 4: def f(x): return eval(sys.argv[1]) draw.plot(f, float(sys.argv[2]), float(sys.argv[3]), float(sys.argv[4])) else: print('Bad arguments')