/*
* Apache License
* Version 2.0, January 2004
* http://www.apache.org/licenses/
*
* Copyright 2013 Aurelian Tutuianu
* Copyright 2014 Aurelian Tutuianu
* Copyright 2015 Aurelian Tutuianu
* Copyright 2016 Aurelian Tutuianu
*
* 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 rapaio.data.filter.var;
import rapaio.core.stat.Mean;
import rapaio.core.stat.Variance;
import rapaio.data.Var;
/**
* Created by <a href="mailto:padreati@yahoo.com">Aurelian Tutuianu</a> at 1/30/15.
*/
public class VFStandardize extends AbstractVF {
private static final long serialVersionUID = -2817341319523250499L;
private double mean;
private double sd;
public VFStandardize() {
this(Double.NaN, Double.NaN);
}
public VFStandardize(double mean) {
this(mean, Double.NaN);
}
public VFStandardize(double mean, double sd) {
this.mean = mean;
this.sd = sd;
}
@Override
public void fit(Var... vars) {
checkSingleVar(vars);
if (Double.isNaN(mean)) {
mean = Mean.from(vars[0]).value();
}
if (Double.isNaN(sd)) {
sd = Variance.from(vars[0]).sdValue();
}
}
@Override
public Var apply(Var... vars) {
checkSingleVar(vars);
if (!vars[0].type().isNumeric()) {
return vars[0];
}
if(Math.abs(sd)<1e-20)
return vars[0];
return vars[0].stream().transValue(x -> (x - mean) / sd).toMappedVar();
}
}