def load_images_only(image_paths): images = [] print("Images:") for i, image_path in enumerate(image_paths): print("\t#{}: {}".format(i, image_path)) image, raw_image = preprocess(image_path) images.append(image) return images
import gym from main import stack_frames, preprocess from model import PolicyGradientAgent agent = PolicyGradientAgent(learning_rate=0.001, discount_factor=0.9, num_actions=6, chkpt_dir='tmp/checkpoint') agent.load_checkpoint() env = gym.make('SpaceInvaders-v0') observation = env.reset() observation = preprocess(observation) stack_size = 4 stacked_frames = None stacked_frames = stack_frames(stacked_frames, observation, stack_size) done = False while not done: env.render() action = agent.choose_action(stacked_frames) obseravtion, reward, done, info = env.step(action) observation = preprocess(observation) stacked_frames = stack_frames(stacked_frames, observation, stack_size) env.close()
def display_graph(self): self.algorithm = self.combobox_algs.get() self.mode = self.combobox_types.get() print(self.mode) print(self.algorithm) if self.mode == "Google": self.file_path = "googl.us.txt" elif self.mode == "Apple": self.file_path = "aapl.us.txt" elif self.mode == "Amazon": self.file_path = "amzn.us.txt" elif self.mode == "Coca Cola": self.file_path = "ko.us.txt" # df = load_file(self.file_path) df = pd.read_csv(self.file_path) # if self.algorithm == "SVM": # print("Svm izabran") # # self.y_train, self.y_val, self.y_predict = svm_prediction(df) # # new_df = preprocess(load_file(self.file_path)) # x_train, y_train, x_valid, y_valid, self.train, self.valid = train_valid_split(new_df) # self.y_predict = svm_prediction(df, x_train, y_train, x_valid, y_valid) # self.y_train = y_train # self.y_val = y_valid if self.algorithm == "Moving average": print("MA izabran") #NISAM TESTIRAO MA, TREBA MODIFIKOVATI new_df = preprocess(load_file(self.file_path)) x_train, y_train, x_valid, y_valid, self.train, self.valid = train_valid_split(new_df) self.y_predict = predict_ma(df) self.y_train = y_train self.y_val = y_valid elif self.algorithm == "KNN": print("KNN izabran") df = load_file(self.file_path) new_df = preprocess(df) x_train, y_train, x_valid, y_valid, self.train, self.valid = train_valid_split(new_df) self.y_predict = knn_predict(x_train, y_train, x_valid) self.y_train = y_train self.y_val = y_valid plt.plot(self.y_train) plt.plot(self.y_val) plt.plot(self.y_predict) plt.show() # plot_graph(self.train, self.valid, self.y_predict) elif self.algorithm == "Auto Arima": print("Auto Arima izabran") # UBACI OVDE POZIV AUTO ARIMA METODE df = load_file(self.file_path) new_df = preprocess(df) x_train, y_train, x_valid, y_valid, self.train, self.valid = train_valid_split(new_df) self.y_predict = auto_arima_predict(df) self.y_train = y_train self.y_val = y_valid elif self.algorithm == "Linear Regression": print("Linear Regression izabran") # UBACI OVDE POZIV LINEAR REGRESSION df = load_file(self.file_path) new_df = preprocess(df) x_train, y_train, x_valid, y_valid, self.train, self.valid = train_valid_split(new_df) self.y_predict = linearregression.run_regression(x_train, y_train, x_valid, y_valid) self.y_train = y_train self.y_val = y_valid plt.plot(self.y_train) plt.plot(self.y_val) plt.plot(self.y_predict) plt.show() elif self.algorithm == "Prophet": print("Prophet") df = load_file(self.file_path) new_df = preprocess(df) x_train, y_train, x_valid, y_valid, self.train, self.valid = train_valid_split(new_df) self.y_predict = prophet_predict(self.train, self.valid) self.y_train = y_train self.y_val = y_valid plt.plot(self.y_train) plt.plot(self.y_val) plt.plot(self.y_predict) plt.show() print(self.y_predict) print("Zavrsio obucavanje i predikciju") p1 = figure(x_axis_type="datetime", title="Stock Closing Prices") p1.grid.grid_line_alpha = 0.3 p1.xaxis.axis_label = 'Date' p1.yaxis.axis_label = 'Price' plot_dates = df['Date'] print(self.valid) plot_dates = plot_dates[-len(self.y_predict):] p1.line(plot_dates, self.valid['Close'], color='#A6CEE3', legend=self.mode) p1.line(plot_dates, self.y_predict, color='#B2DF8A', legend="Predicted "+self.mode) output_file("stocks.html", title="Stocks prediction") show(gridplot([[p1]], plot_width=500, plot_height=500))
def test_preprocess(self): doc = 'Preprocess this string and output array of tokens' assert preprocess(doc) == [ 'preprocess', 'string', 'output', 'array', 'token' ]
from main import preprocess, extract_contours, cleanAndRead windowname = "Live Video" cap = cv2.VideoCapture(0) t = 3 if cap.isOpened(): ret, frame = cap.read() else: ret = False while ret: ret, img = cap.read() #time.sleep(t) cv2.imshow(windowname, img) print("DETECTING PLATE . . .") #img = cv2.imread("testData/Final.JPG") threshold_img = preprocess(img) contours = extract_contours(threshold_img) #if len(contours)!=0: #print len(contours) #Test # cv2.drawContours(img, contours, -1, (0,255,0), 1) # cv2.imshow("Contours",img) # cv2.waitKey(0) cleanAndRead(img, contours) if cv2.waitKey(1) == 27: break cv2.destroyAllWindows() cap.release()
modelctrl = 1 #1 --> Ada, 0 --> SVM ################### if datactrl == "PC1": dataset_path = 'MDP csv/PC01.csv' if datactrl == "PC2": dataset_path = 'MDP csv/PC02.csv' if datactrl == "PC3": dataset_path = 'MDP csv/PC03.csv' if datactrl == "PC4": dataset_path = 'MDP csv/PC04.csv' if datactrl == "PC5": dataset_path = 'MDP csv/PC05.csv' print("dataset_path-->", dataset_path) return dataset_path feature_data, target_data = preprocess(dataset_path, datactrl) print('feature_data') print(feature_data) print(feature_data.shape) print('target_data') print(target_data) print(target_data.shape) discretize_data = discretize(feature_data) print('\n') print("*** Discretize Data ***") print(discretize_data) print(discretize_data.shape) concat_data = 0