com.caseystella.analytics.outlier.batch.rpca.RidgeRegression.java Source code

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Here is the source code for com.caseystella.analytics.outlier.batch.rpca.RidgeRegression.java

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/**
 * Copyright (C) 2016 Hurence (support@hurence.com)
 *
 * 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.caseystella.analytics.outlier.batch.rpca;

import org.apache.commons.math3.linear.MatrixUtils;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.linear.SingularValueDecomposition;

public class RidgeRegression {

    private RealMatrix X;
    private SingularValueDecomposition X_svd = null;
    private double[] Y;
    private double l2penalty;
    private double[] coefficients;
    private double[] standarderrors;

    private double[] fitted;
    private double[] residuals;

    public RidgeRegression(double[][] x, double[] y) {
        this.X = MatrixUtils.createRealMatrix(x);
        this.X_svd = null;
        this.Y = y;
        this.l2penalty = 0;
        this.coefficients = null;

        this.fitted = new double[y.length];
        this.residuals = new double[y.length];
    }

    public void updateCoefficients(double l2penalty) {
        if (this.X_svd == null) {
            this.X_svd = new SingularValueDecomposition(X);
        }
        RealMatrix V = this.X_svd.getV();
        double[] s = this.X_svd.getSingularValues();
        RealMatrix U = this.X_svd.getU();

        for (int i = 0; i < s.length; i++) {
            s[i] = s[i] / (s[i] * s[i] + l2penalty);
        }
        RealMatrix S = MatrixUtils.createRealDiagonalMatrix(s);

        RealMatrix Z = V.multiply(S).multiply(U.transpose());

        this.coefficients = Z.operate(this.Y);

        this.fitted = this.X.operate(this.coefficients);
        double errorVariance = 0;
        for (int i = 0; i < residuals.length; i++) {
            this.residuals[i] = this.Y[i] - this.fitted[i];
            errorVariance += this.residuals[i] * this.residuals[i];
        }
        errorVariance = errorVariance / (X.getRowDimension() - X.getColumnDimension());

        RealMatrix errorVarianceMatrix = MatrixUtils.createRealIdentityMatrix(this.Y.length)
                .scalarMultiply(errorVariance);
        RealMatrix coefficientsCovarianceMatrix = Z.multiply(errorVarianceMatrix).multiply(Z.transpose());
        this.standarderrors = getDiagonal(coefficientsCovarianceMatrix);
    }

    private double[] getDiagonal(RealMatrix X) {
        double[] diag = new double[X.getColumnDimension()];
        for (int i = 0; i < diag.length; i++) {
            diag[i] = X.getEntry(i, i);
        }
        return diag;
    }

    public double getL2penalty() {
        return l2penalty;
    }

    public void setL2penalty(double l2penalty) {
        this.l2penalty = l2penalty;
    }

    public double[] getCoefficients() {
        return coefficients;
    }

    public double[] getStandarderrors() {
        return standarderrors;
    }
}