IGNITE-8907: [ML] Using vectors in featureExtractor
[ignite.git] / modules / ml / src / main / java / org / apache / ignite / ml / preprocessing / normalization / NormalizationTrainer.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.normalization;
19
20 import org.apache.ignite.ml.dataset.DatasetBuilder;
21 import org.apache.ignite.ml.math.Vector;
22 import org.apache.ignite.ml.math.functions.IgniteBiFunction;
23 import org.apache.ignite.ml.preprocessing.PreprocessingTrainer;
24
25 /**
26 * Trainer of the Normalization preprocessor.
27 *
28 * @param <K> Type of a key in {@code upstream} data.
29 * @param <V> Type of a value in {@code upstream} data.
30 */
31 public class NormalizationTrainer<K, V> implements PreprocessingTrainer<K, V, Vector, Vector> {
32 /** Normalization in L^p space. Must be greater than 0. Default value is 2. */
33 private int p = 2;
34
35 /** {@inheritDoc} */
36 @Override public NormalizationPreprocessor<K, V> fit(DatasetBuilder<K, V> datasetBuilder,
37 IgniteBiFunction<K, V, Vector> basePreprocessor) {
38 return new NormalizationPreprocessor<>(p, basePreprocessor);
39 }
40
41 /**
42 * Gets the degree of L space parameter value.
43 * @return The parameter value.
44 */
45 public double p() {
46 return p;
47 }
48
49 /**
50 * Sets the p parameter value. Must be greater than 0.
51 *
52 * @param p The given value.
53 * @return The Normalization trainer.
54 */
55 public NormalizationTrainer<K, V> withP(int p) {
56 assert p > 0;
57 this.p = p;
58 return this;
59 }
60 }