/*******************************************************************************
 * 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 smile.math.Math;
import smile.data.Attribute;

/**
 * Standardizes numeric feature to 0 mean and unit variance.
 * Standardization makes an assumption that the data follows
 * a Gaussian distribution and are also not robust when outliers present.
 * A robust alternative is to subtract the median and divide by the IQR
 * by RobustStandardizer.
 *
 * @author Haifeng Li
 */
public class Standardizer extends FeatureTransform {
    /**
     * Mean or median.
     */
    double[] mu;
    /**
     * Standard deviation or IQR.
     */
    double[] std;

    /**
     * Constructor.
     */
    public Standardizer() {

    }

    /**
     * Constructor.
     * @param copy  If false, try to avoid a copy and do inplace scaling instead.
     */
    public Standardizer(boolean copy) {
        super(copy);
    }

    @Override
    public void learn(Attribute[] attributes, double[][] data) {
        mu = Math.colMeans(data);
        std = Math.colSds(data);

        for (int i = 0; i < std.length; i++) {
            if (attributes[i].getType() != Attribute.Type.NUMERIC) {
                mu[i] = Double.NaN;
            }

            if (Math.isZero(std[i])) {
                std[i] = 1.0;
            }
        }
    }

    @Override
    public double[] transform(double[] x) {
        if (x.length != mu.length) {
            throw new IllegalArgumentException(String.format("Invalid vector size %d, expected %d", x.length, mu.length));
        }

        double[] y = copy ? new double[x.length] : x;
        for (int i = 0; i < x.length; i++) {
            if (!Double.isNaN(mu[i])) {
                y[i] = (x[i] - mu[i]) / std[i];
            } else {
                y[i] = x[i];
            }
        }

        return y;
    }

    @Override
    public String toString() {
        StringBuilder sb = new StringBuilder();
        sb.append("Standardizer(");
        if (mu != null) {
            sb.append("\n");
            for (int i = 0; i < mu.length; i++) {
                sb.append(String.format("  [%.4f, %.4f]%n", mu[i], std[i]));
            }
        }
        sb.append(")");
        return sb.toString();
    }
}