| from __future__ import print_function |
| import numpy |
| import os |
| import ssl |
| |
| |
| def load_mnist(training_num=50000): |
| data_path = os.path.join(os.path.dirname(os.path.realpath('__file__')), 'mnist.npz') |
| if not os.path.isfile(data_path): |
| from six.moves import urllib |
| origin = ( |
| 'https://github.com/sxjscience/mxnet/raw/master/example/bayesian-methods/mnist.npz' |
| ) |
| print('Downloading data from %s to %s' % (origin, data_path)) |
| context = ssl._create_unverified_context() |
| urllib.request.urlretrieve(origin, data_path, context=context) |
| print('Done!') |
| dat = numpy.load(data_path) |
| X = (dat['X'][:training_num] / 126.0).astype('float32') |
| Y = dat['Y'][:training_num] |
| X_test = (dat['X_test'] / 126.0).astype('float32') |
| Y_test = dat['Y_test'] |
| Y = Y.reshape((Y.shape[0],)) |
| Y_test = Y_test.reshape((Y_test.shape[0],)) |
| return X, Y, X_test, Y_test |
| |
| |
| def load_toy(): |
| training_data = numpy.loadtxt('toy_data_train.txt') |
| testing_data = numpy.loadtxt('toy_data_test_whole.txt') |
| X = training_data[:, 0].reshape((training_data.shape[0], 1)) |
| Y = training_data[:, 1].reshape((training_data.shape[0], 1)) |
| X_test = testing_data[:, 0].reshape((testing_data.shape[0], 1)) |
| Y_test = testing_data[:, 1].reshape((testing_data.shape[0], 1)) |
| return X, Y, X_test, Y_test |
| |
| |
| def load_synthetic(theta1, theta2, sigmax, num=20): |
| flag = numpy.random.randint(0, 2, (num,)) |
| X = flag * numpy.random.normal(theta1, sigmax, (num,)) \ |
| + (1.0 - flag) * numpy.random.normal(theta1 + theta2, sigmax, (num,)) |
| return X |
