com.clust4j.algo.preprocess.WeightTransformer.java Source code

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Here is the source code for com.clust4j.algo.preprocess.WeightTransformer.java

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/*******************************************************************************
 *    Copyright 2015, 2016 Taylor G Smith
 *
 *    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 com.clust4j.algo.preprocess;

import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.linear.Array2DRowRealMatrix;
import org.apache.commons.math3.linear.RealMatrix;

import com.clust4j.utils.MatUtils;
import com.clust4j.utils.VecUtils;

/**
 * The weight transformer takes a vector of weights as a constructor argument,
 * and applies the weights to incoming data multiplicatively column-wise. This
 * transformer behaves differently than others, in that the {@link #fit(RealMatrix)}
 * method does not change the state of the transformer, but merely allows it to
 * conform to the {@link PreProcessor} API
 * @author Taylor G Smith
 */
public class WeightTransformer extends Transformer {
    private static final long serialVersionUID = -4256256213984769852L;
    final static double Inf = Double.POSITIVE_INFINITY;
    final double[] weights;
    final int n;

    private WeightTransformer(WeightTransformer wt) {
        this.weights = VecUtils.copy(wt.weights);
        this.n = wt.n;
    }

    public WeightTransformer(double[] weights) {
        this.weights = VecUtils.copy(weights);
        this.n = weights.length;
    }

    @Override
    protected void checkFit() {
        ; // will always be fit, per constructor...
    }

    /**
     * Inverse transform the incoming data. If the corresponding weight is 0.0,
     * will coerce the column to positive infinity rather than NaN.
     */
    @Override
    public RealMatrix inverseTransform(RealMatrix data) {
        checkFit();

        final int m = data.getRowDimension();
        if (data.getColumnDimension() != n)
            throw new DimensionMismatchException(n, data.getColumnDimension());

        double[][] X = data.getData();
        double weight, val;
        for (int j = 0; j < n; j++) {
            weight = weights[j];

            for (int i = 0; i < m; i++) {
                // sometimes, weight can be 0.0 if the user is masochistic...
                val = X[i][j] / weight;
                X[i][j] = Double.isNaN(val) ? Inf : val;
            }
        }

        // assign -- already copied in getData()
        return new Array2DRowRealMatrix(X, false);
    }

    @Override
    public WeightTransformer copy() {
        return new WeightTransformer(this);
    }

    @Override
    public WeightTransformer fit(RealMatrix X) {
        synchronized (fitLock) {
            // Only enforce this to prevent accidental exceptions later if the user
            // tries a fit(X).transform(X) and later gets a dim mismatch...
            if (X.getColumnDimension() != n)
                throw new DimensionMismatchException(n, X.getColumnDimension());
            return this;
        }
    }

    @Override
    public RealMatrix transform(RealMatrix data) {
        return new Array2DRowRealMatrix(transform(data.getData()), false);
    }

    @Override
    public double[][] transform(double[][] data) {
        checkFit();
        MatUtils.checkDimsForUniformity(data);

        final int m = data.length;
        if (data[0].length != n)
            throw new DimensionMismatchException(n, data[0].length);

        double[][] X = new double[m][n];
        // mult to weight:
        for (int j = 0; j < n; j++) {
            for (int i = 0; i < m; i++) {
                X[i][j] = data[i][j] * weights[j];
            }
        }

        // assign
        return X;
    }

}