smile.feature.Normalizer Maven / Gradle / Ivy
/*******************************************************************************
* Copyright (c) 2010 Haifeng Li
*
* Licensed 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.
*******************************************************************************/
package smile.feature;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import smile.data.Attribute;
import smile.math.Math;
/**
* Normalize samples individually to unit norm. Each sample (i.e. each row of
* the data matrix) with at least one non zero component is rescaled
* independently of other samples so that its norm (L1 or L2) equals one.
*
* Scaling inputs to unit norms is a common operation for text
* classification or clustering for instance.
*
* @author Haifeng Li
*/
public class Normalizer extends FeatureTransform {
private static final Logger logger = LoggerFactory.getLogger(Normalizer.class);
/**
* The types of data scaling.
*/
public static enum Norm {
/**
* L1 vector norm.
*/
L1,
/**
* L2 vector norm.
*/
L2,
/**
* L-infinity vector norm. Maximum absolute value.
*/
Inf
}
/** The type of norm .*/
private Norm norm = Norm.L2;
/** Default constructor with L2 norm. */
public Normalizer() {
}
/**
* Constructor with L2 norm.
* @param copy If false, try to avoid a copy and do inplace scaling instead.
*/
public Normalizer(boolean copy) {
super(copy);
}
/**
* Constructor.
* @param norm The norm to use to normalize each non zero sample.
*/
public Normalizer(Norm norm) {
this.norm = norm;
}
/**
* Constructor.
* @param norm The norm to use to normalize each non zero sample.
* @param copy If false, try to avoid a copy and do inplace scaling instead.
*/
public Normalizer(Norm norm, boolean copy) {
super(copy);
this.norm = norm;
}
@Override
public void learn(Attribute[] attributes, double[][] data) {
logger.info("Normalizer is stateless and learn() does nothing.");
}
@Override
public double[] transform(double[] x) {
double scale;
switch (norm) {
case L1:
scale = Math.norm1(x);
break;
case L2:
scale = Math.norm2(x);
break;
case Inf:
scale = Math.normInf(x);
break;
default:
throw new IllegalStateException("Unknown type of norm: " + norm);
}
double[] y = copy ? new double[x.length] : x;
if (Math.isZero(scale)) {
if (y != x) {
System.arraycopy(x, 0, y, 0, x.length);
}
} else {
for (int i = 0; i < x.length; i++) {
y[i] = x[i] / scale;
}
}
return y;
}
@Override
public String toString() {
return "Normalizer()";
}
}