def read_landsat(): array = [] f = open("database/landsat.txt", "r") for line in f.read().splitlines(): lista = line.split(" ") classe = lista.pop(len(lista) - 1) parameters = [float(i) for i in lista] array.append(Sample(parameters, classe)) bd = Dataset("landsat", array) return bd
def read_database(name, index): array = [] f = open("database/" + name + ".txt", "r") for line in f.read().splitlines(): lista = line.split(",") classe = lista.pop(index) parameters = [float(i) for i in lista] array.append(Sample(parameters, classe)) bd = Dataset(name, array) return bd
def read_yeast(): array = [] f = open("database/yeast.data", "r") for line in f.read().splitlines(): lista = line.split() classe = lista.pop(len(lista) - 1) lista.pop(0) parameters = [float(i) for i in lista] array.append(Sample(parameters, classe)) bd = Dataset("yeast", array) return bd
def read_xls_database(name): df = pd.read_excel("database/" + name + ".xls") array = [] nparray = df[1:].as_matrix() for k in nparray: infos = k classe = infos[-1] parameters = [float(i) for i in infos[:-1]] array.append(Sample(parameters, classe)) data_base = Dataset(name, array) return data_base
def cluster_centers_pcm(self, numero_de_centros, vizinhos): parameters = np.transpose( np.array([k.parameters for k in self.initial_dataset.samples])) cntr, u, u0, d, jm, p, fpc = fuzzy.cluster.cmeans( parameters, numero_de_centros, 2.0, 0.005, 2000) new_samples = [] local_dataset = self.dataset for k in cntr: amostras = self.get_k1_closest(k, vizinhos) for sample_pega in amostras: new_samples.append(sample_pega) self.clustered_dataset = Dataset("Clustered", new_samples)
# Add the cost and accuracy to summary tf.summary.scalar('loss', cost) tf.summary.scalar('accuracy', accuracy) # Merge all summaries together merged_summary = tf.summary.merge_all() path = "../dqn" #The path to save our model to. num_epochs = 100 batch_size=32 saver = tf.train.Saver() with tf.Session() as sess: dset=Dataset() image_count=dset.getNumExamples() # Initialize all variables sess.run(tf.global_variables_initializer()) print('Loading Model...') ckpt = tf.train.get_checkpoint_state(path) saver.restore(sess,ckpt.model_checkpoint_path) # Loop over number of epochs for epoch in range(num_epochs): start_time = time.time() train_accuracy = 0 for batch in range(0, int(image_count/batch_size)): # Run the optimizer using this batch of training data. dataBatch=dset.next_batch(batch_size)
def split_train_test(dataset, i): test = dataset[i] train_dataset = Dataset( "Treino", array_of_samples_to_db([dataset[:i] + dataset[i + 1:]])) test_dataset = Dataset("Teste", test) return test_dataset, train_dataset
else: print("Loading configuration from file %s" % inputfile) print() inputParser = InputParser(inputfile) dimensions = inputParser.get_dimensions() clusters = inputParser.get_clusters() export_name = inputParser.get_export_name() print("Dimensions = %s" % dimensions) print("Cluster count = %s" % len(clusters)) for cluster in clusters: print(" - %s" % cluster) print() dataset = Dataset(dimensions, clusters, export_name) print("Generating random values for clusters...") dataset.generate_values() print("Balancing clusters... (this may take a while)") dataset.balance_clusters() print() filewriter = FileWriter(dataset) print("Writing SOMToolbox files...") filewriter.export_for_somtoolbox() if 1 <= dimensions <= 2 and not quiet_mode: print() print("Input has <= 2 dimensions - showing plot") plotter = Plotter(dataset) plotter.plot()
# using teacher forcing dec_input = tf.expand_dims(targ[:, t], 1) batch_loss = (loss / int(targ.shape[1])) variables = encoder.trainable_variables + decoder.trainable_variables gradients = tape.gradient(loss, variables) optimizer.apply_gradients(zip(gradients, variables)) return batch_loss EPOCHS = 5 steps_per_epoch = 5 dataset = Dataset() for epoch in range(EPOCHS): start = time.time() enc_hidden = encoder.initialize_hidden_state() enc_cell = encoder.initialize_cell_state() total_loss = 0 for (batch, (inp, targ)) in enumerate(dataset(steps_per_epoch)): batch_loss = train_step(inp, targ, enc_hidden, enc_cell) total_loss += batch_loss if batch % 1 == 0: print('Epoch {} Batch {} Loss {:.4f}'.format( epoch + 1, batch, batch_loss.numpy()))