IGNITE-8907: [ML] Using vectors in featureExtractor
[ignite.git] / modules / ml / src / test / java / org / apache / ignite / ml / preprocessing / minmaxscaling / MinMaxScalerPreprocessorTest.java
1 /*
2 * Licensed to the Apache Software Foundation (ASF) under one or more
3 * contributor license agreements. See the NOTICE file distributed with
4 * this work for additional information regarding copyright ownership.
5 * The ASF licenses this file to You under the Apache License, Version 2.0
6 * (the "License"); you may not use this file except in compliance with
7 * the License. You may obtain a copy of the License at
8 *
9 * http://www.apache.org/licenses/LICENSE-2.0
10 *
11 * Unless required by applicable law or agreed to in writing, software
12 * distributed under the License is distributed on an "AS IS" BASIS,
13 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 * See the License for the specific language governing permissions and
15 * limitations under the License.
16 */
17
18 package org.apache.ignite.ml.preprocessing.minmaxscaling;
19
20 import org.apache.ignite.ml.math.Vector;
21 import org.apache.ignite.ml.math.VectorUtils;
22 import org.junit.Test;
23
24 import static org.junit.Assert.assertArrayEquals;
25
26 /**
27 * Tests for {@link MinMaxScalerPreprocessor}.
28 */
29 public class MinMaxScalerPreprocessorTest {
30 /** Tests {@code apply()} method. */
31 @Test
32 public void testApply() {
33 double[][] data = new double[][]{
34 {2., 4., 1.},
35 {1., 8., 22.},
36 {4., 10., 100.},
37 {0., 22., 300.}
38 };
39
40 MinMaxScalerPreprocessor<Integer, Vector> preprocessor = new MinMaxScalerPreprocessor<>(
41 new double[] {0, 4, 1},
42 new double[] {4, 22, 300},
43 (k, v) -> v
44 );
45
46 double[][] standardData = new double[][]{
47 {2. / 4, (4. - 4.) / 18., 0.},
48 {1. / 4, (8. - 4.) / 18., (22. - 1.) / 299.},
49 {1., (10. - 4.) / 18., (100. - 1.) / 299.},
50 {0., (22. - 4.) / 18., (300. - 1.) / 299.}
51 };
52
53 for (int i = 0; i < data.length; i++)
54 assertArrayEquals(standardData[i], preprocessor.apply(i, VectorUtils.of(data[i])).asArray(), 1e-8);
55 }
56 }