| # ------------------------------------------------------------- |
| # |
| # 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. |
| # |
| # ------------------------------------------------------------- |
| |
| from systemds.context import SystemDSContext |
| from systemds.matrix import Matrix |
| from systemds.operator.algorithm import multiLogReg, multiLogRegPredict |
| from systemds.examples.tutorials.mnist import DataManager |
| |
| d = DataManager() |
| |
| with SystemDSContext() as sds: |
| # Train Data |
| X = Matrix(sds, d.get_train_data().reshape((60000, 28*28))) |
| Y = Matrix(sds, d.get_train_labels()) + 1.0 |
| bias = multiLogReg(X, Y, tol= 0.0001, verbose= False) |
| # Test data |
| Xt = Matrix(sds, d.get_test_data().reshape((10000, 28*28))) |
| Yt = Matrix(sds, d.get_test_labels()) + 1.0 |
| [_, _, acc] = multiLogRegPredict(Xt, bias, Yt).compute() |
| |
| print(acc) |
