Example usage for weka.core.matrix Matrix regression

Introduction

In this page you can find the example usage for weka.core.matrix Matrix regression.

Prototype

public LinearRegression regression(Matrix y, double ridge) 

Source Link

Document

Performs a (ridged) linear regression.

Usage

From source file:Classifier.supervised.LinearRegression.java

License:Open Source License

/**
 * Calculate a linear regression using the selected attributes
 *
 * @param selectedAttributes an array of booleans where each element
 * is true if the corresponding attribute should be included in the
 * regression.//from   w  w  w  .jav  a 2s . com
 * @return an array of coefficients for the linear regression model.
 * @throws Exception if an error occurred during the regression.
 */
protected double[] doRegression(boolean[] selectedAttributes) throws Exception {

    if (m_Debug) {
        System.out.print("doRegression(");
        for (int i = 0; i < selectedAttributes.length; i++) {
            System.out.print(" " + selectedAttributes[i]);
        }
        System.out.println(" )");
    }
    int numAttributes = 0;
    for (int i = 0; i < selectedAttributes.length; i++) {
        if (selectedAttributes[i]) {
            numAttributes++;
        }
    }

    // Check whether there are still attributes left
    Matrix independent = null, dependent = null;
    if (numAttributes > 0) {
        independent = new Matrix(m_TransformedData.numInstances(), numAttributes);
        dependent = new Matrix(m_TransformedData.numInstances(), 1);
        for (int i = 0; i < m_TransformedData.numInstances(); i++) {
            Instance inst = m_TransformedData.instance(i);
            double sqrt_weight = Math.sqrt(inst.weight());
            int column = 0;
            for (int j = 0; j < m_TransformedData.numAttributes(); j++) {
                if (j == m_ClassIndex) {
                    dependent.set(i, 0, inst.classValue() * sqrt_weight);
                } else {
                    if (selectedAttributes[j]) {
                        double value = inst.value(j) - m_Means[j];

                        // We only need to do this if we want to
                        // scale the input
                        if (!m_checksTurnedOff) {
                            value /= m_StdDevs[j];
                        }
                        independent.set(i, column, value * sqrt_weight);
                        column++;
                    }
                }
            }
        }
    }

    // Compute coefficients (note that we have to treat the
    // intercept separately so that it doesn't get affected
    // by the ridge constant.)
    double[] coefficients = new double[numAttributes + 1];
    if (numAttributes > 0) {
        double[] coeffsWithoutIntercept = independent.regression(dependent, m_Ridge).getCoefficients();
        System.arraycopy(coeffsWithoutIntercept, 0, coefficients, 0, numAttributes);
    }
    coefficients[numAttributes] = m_ClassMean;

    // Convert coefficients into original scale
    int column = 0;
    for (int i = 0; i < m_TransformedData.numAttributes(); i++) {
        if ((i != m_TransformedData.classIndex()) && (selectedAttributes[i])) {

            // We only need to do this if we have scaled the
            // input.
            if (!m_checksTurnedOff) {
                coefficients[column] /= m_StdDevs[i];
            }

            // We have centred the input
            coefficients[coefficients.length - 1] -= coefficients[column] * m_Means[i];
            column++;
        }
    }

    return coefficients;
}

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