| # ------------------------------------------------------------- |
| # |
| # 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. |
| # |
| # ------------------------------------------------------------- |
| |
| import logging |
| |
| import numpy as np |
| from systemds.context import SystemDSContext |
| from systemds.operator.algorithm import l2svm |
| |
| # Set a seed |
| np.random.seed(0) |
| # Generate random features and labels in numpy |
| # This can easily be exchanged with a data set. |
| features = np.array(np.random.randint( |
| 100, size=10 * 10) + 1.01, dtype=np.double) |
| features.shape = (10, 10) |
| labels = np.zeros((10, 1)) |
| |
| # l2svm labels can only be 0 or 1 |
| for i in range(10): |
| if np.random.random() > 0.5: |
| labels[i][0] = 1 |
| |
| # compute our model |
| with SystemDSContext() as sds: |
| model = l2svm(sds.from_numpy(features), |
| sds.from_numpy(labels), verbose=False).compute() |
| logging.info(model) |
