def createInitialDatasets(): print "Creating data sets" t0 = time.time() datasetDict = {} dataFile = h5py.File(dataFileName, 'r') mapCoord['x1'] = len(dataFile['bed'][:][0]) mapCoord['y1'] = len(dataFile['bed'][:]) mapCoord['proj_x1'] = dataFile['x'][:][-1] mapCoord['proj_y1'] = dataFile['y'][:][-1] surfaceX = Dataset('surfaceGradX') surfaceY = Dataset('surfaceGradY') datasetDict['surfaceGradX'] = surfaceX datasetDict['surfaceGradY'] = surfaceY velocity = Dataset('velocity') datasetDict['velocity'] = velocity smb = Dataset('smb') datasetDict['smb'] = smb bed = Dataset('bed') datasetDict['bed'] = bed surface = Dataset('surface') datasetDict['surface'] = surface thickness = Dataset('thickness') datasetDict['thickness'] = thickness t2m = Dataset('t2m') datasetDict['t2m'] = t2m datasetDict['x'] = Dataset('x') datasetDict['y'] = Dataset('y') dataFile.close() print "Loaded all data sets in ", time.time() - t0, " seconds" return datasetDict
def test3(): d = sentences() literals = [] literals.append([d['not_a'], d['c']]) literals.append([d['not_b'], d['c']]) literals.append([d['a'], d['b']]) clauses = create_clauses(literals) not_observable_fact = [d['not_c']] not_observable_clause = Clause(not_observable_fact) clauses.append(not_observable_clause) dataset = Dataset(clauses) return dataset
def createInitialDataSets(): print "Creating data sets" t0 = time.time() datasetDict = {} """ Read in dimensions of bed at point of comment x = 1670, y = 2991 read in last values of x and y and map to the object map """ dataFile = h5py.File(dataFileName, 'r') map['x1'] = len(dataFile['bed'][:][0]) map['y1'] = len(dataFile['bed'][:]) map['proj_x1'] = dataFile['x'][:][-1] map['proj_y1'] = dataFile['y'][:][-1] velocity = Dataset('velocity', greenPlotPen) datasetDict['velocity'] = velocity smb = Dataset('smb', redPlotPen) datasetDict['smb'] = smb bed = Dataset('bed', bluePlotPen) datasetDict['bed'] = bed surface = Dataset('surface', greyPlotPen) datasetDict['surface'] = surface thickness = Dataset('thickness', orangePlotPen) datasetDict['thickness'] = thickness t2m = Dataset('t2m', tealPlotPen) datasetDict['t2m'] = t2m dataFile.close() print "Loaded all data sets in ", time.time() - t0, " seconds" return datasetDict
def __init__(self): super(DatasetGui, self).__init__() self.setWindowTitle("Pointing Gesture Recognition - Dataset recording") # Retrieve all settings self.settings = Settings() # Load sounds self.countdownSound = QtMultimedia.QSound( self.settings.getResourceFolder() + "countdown.wav") self.countdownEndedSound = QtMultimedia.QSound( self.settings.getResourceFolder() + "countdown-ended.wav") # Get the context and initialise it self.context = Context() self.context.init() # Create the depth generator to get the depth map of the scene self.depth = DepthGenerator() self.depth.create(self.context) self.depth.set_resolution_preset(RES_VGA) self.depth.fps = 30 # Create the image generator to get an RGB image of the scene self.image = ImageGenerator() self.image.create(self.context) self.image.set_resolution_preset(RES_VGA) self.image.fps = 30 # Create the user generator to detect skeletons self.user = UserGenerator() self.user.create(self.context) # Initialise the skeleton tracking skeleton.init(self.user) # Start generating self.context.start_generating_all() print "Starting to detect users.." # Create a new dataset item self.data = Dataset() # Create a timer for an eventual countdown before recording the data self.countdownTimer = QtCore.QTimer() self.countdownRemaining = 10 self.countdownTimer.setInterval(1000) self.countdownTimer.setSingleShot(True) self.countdownTimer.timeout.connect(self.recordCountdown) # Create a timer to eventually record data for a heat map self.heatmapRunning = False self.heatmapTimer = QtCore.QTimer() self.heatmapTimer.setInterval(10) self.heatmapTimer.setSingleShot(True) self.heatmapTimer.timeout.connect(self.recordHeatmap) # Create the global layout self.layout = QtWidgets.QVBoxLayout(self) # Create custom widgets to hold sensor's images self.depthImage = SensorWidget() self.depthImage.setGeometry(10, 10, 640, 480) # Add these custom widgets to the global layout self.layout.addWidget(self.depthImage) # Hold the label indicating the number of dataset taken self.numberLabel = QtWidgets.QLabel() self.updateDatasetNumberLabel() # Create the acquisition form elements self.createAcquisitionForm() # Register a dialog window to prompt the target position self.dialogWindow = DatasetDialog(self) # Allow to save the data when the right distance is reached self.recordIfReady = False # Create and launch a timer to update the images self.timerScreen = QtCore.QTimer() self.timerScreen.setInterval(30) self.timerScreen.setSingleShot(True) self.timerScreen.timeout.connect(self.updateImage) self.timerScreen.start()
from classes import Dataset from classes import Classifier from classes import Classifier_random from classes import Generation from classes import First_Generation import time import pickle t = time.time() data_1 = '\\flowers\\daisy' data_2 = '\\flowers\\tulip' data = Dataset(data_1, data_2) #with open('test_dataset.pickle', 'rb') as handle: # data = pickle.load(handle) print('---Data Loaded(' + str(round(time.time() - t, 2)) + 's)---\n') t = time.time() g = First_Generation( 500) #The number of individuals in all the generations is the same g.evaluate(data) print(f'Génération 1 ({round(time.time()-t,2)}s)') print(g) for i in range(2, 100): t = time.time() g = g.make_new_gen(150, mutation_rate=0.005) g.evaluate(data) print(f'\nGénération {i} ({round(time.time()-t,2)}s)') print(g)
def remove_duplicate_clauses(dataset): new_clauses = list(set(dataset.clauses)) new_dataset = Dataset(new_clauses) return new_dataset
if not os.path.isdir(name_exp + "/models"): os.mkdir(name_exp + "/models") #CUDA use_cuda = torch.cuda.is_available() device = torch.device("cuda:0" if use_cuda else "cpu") torch.backends.cudnn.benchmark = True params = {'batch_size': 64, 'shuffle': True, 'num_workers': 6} #Dataset partition = create_partition_dict(list_utt_id, []) spk_id_att_labels = txt_2_dict(att_labels_txt) labels = create_labels_dict(list_utt_id, [], spk_id_att_labels) #Generators training_set = Dataset(partition['train'], labels, data_file, prob_file) generator = data.DataLoader(training_set, **params) # Layer dimension input_dim = 512 latent_dim = 128 input_dim_discrim = latent_dim hidden_dim_discrim = 128 model_ae = Autoencoder(input_dim, latent_dim) optimizer_ae = torch.optim.SGD(model_ae.parameters(), lr=0.0001, momentum=0.9) model_ae.to(device)