from data_loader import load_sensor_files
from sklearn.decomposition import PCA
from numpy import array
from random import sample
from numpy.linalg import norm
num_samples = 100000
data_path = "/home/willist/Documents/dnn/data/"
training_set = load_sensor_files(data_path, num_samples=num_samples, shared=False)
pca = PCA(n_components=5)
pca.fit(training_set)
num_test_samples = len(training_set) / 10
test_samples = sample(training_set, num_test_samples)
mean_error = 0.0
for sample in test_samples:
s_compr = pca.transform(sample)
s_decompr = pca.inverse_transform(s_compr)
# print sample
# print s_compr
# print s_decompr
mean_error += norm(sample - s_decompr)
print mean_error / num_test_samples
from data_loader import load_sensor_files
from sklearn.linear_model import LinearRegression
import numpy as np
data_path = "/home/willist/Documents/dnn/data/labeled/"
training_set, training_labels, test_set, test_labels = load_sensor_files(data_path, shared=False)
regr = LinearRegression()
regr.fit(training_set, training_labels)
predicted_labels = regr.predict(test_set)
print ("Residual sum of squares: %.5f" % np.mean(np.sum((predicted_labels - test_labels) ** 2, axis=1)))
"Fine Tune Epochs: " + str(fine_tune_epochs) + "\n" + \
"Fine Tune Supervised: " + str(fine_tune_supervised) + "\n" + \
"Hidden Layer Sizes: " + str(hidden_layer_sizes) + "\n" + \
"Corruption Levels: " + str(corruption_levels) + "\n" + \
"Pretraining Learning Rates: " + str(pretraining_learning_rates) + "\n" + \
"Tied Weights: " + str(tied_weights) + "\n" + \
"Sigmoid Compressions: " + str(sigmoid_compressions) + "\n" + \
"Sigmoid Reconstructions: " + str(sigmoid_reconstructions) + "\n" + \
"Supervised Sigmoid Activation: " + str(supervised_sigmoid_activation) + "\n"
print_flush(description)
training_set, training_labels, test_set, test_labels = load_sensor_files(training_path, testing_path,
history_length=history_length,
num_training_samples=num_training_samples,
num_training_samples_per_file=num_training_samples_per_file,
num_test_samples=num_test_samples,
num_test_samples_per_file=num_test_samples_per_file,
feature_indexes=feature_indexes,
label_indexes=label_indexes)
# compute number of minibatches for training, validation and testing
n_train_batches = training_set.get_value(borrow=True).shape[0]
n_train_batches /= batch_size
# numpy random generator
# start-snippet-3
numpy_rng = random.RandomState(89677)
print_flush("... building the model")
# construct the stacked denoising autoencoder class