| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, |
| # software distributed under the License is distributed on an |
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| # KIND, either express or implied. See the License for the |
| # specific language governing permissions and limitations |
| # under the License. |
| |
| # pylint: disable=missing-docstring |
| from __future__ import print_function |
| |
| import os |
| import numpy as np |
| from sklearn.datasets import fetch_mldata |
| |
| |
| def get_mnist(): |
| np.random.seed(1234) # set seed for deterministic ordering |
| data_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__))) |
| data_path = os.path.join(data_path, '../../data') |
| mnist = fetch_mldata('MNIST original', data_home=data_path) |
| p = np.random.permutation(mnist.data.shape[0]) |
| X = mnist.data[p].astype(np.float32)*0.02 |
| Y = mnist.target[p] |
| return X, Y |
