/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You 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 org.apache.commons.math.stat.regression;
import java.io.Serializable;

import org.apache.commons.math.MathException;
import org.apache.commons.math.MathRuntimeException;
import org.apache.commons.math.distribution.TDistribution;
import org.apache.commons.math.distribution.TDistributionImpl;
import org.apache.commons.math.exception.util.LocalizedFormats;
import org.apache.commons.math.util.FastMath;

/**
 * Estimates an ordinary least squares regression model
 * with one independent variable.
 * <p>
 * <code> y = intercept + slope * x  </code></p>
 * <p>
 * Standard errors for <code>intercept</code> and <code>slope</code> are
 * available as well as ANOVA, r-square and Pearson's r statistics.</p>
 * <p>
 * Observations (x,y pairs) can be added to the model one at a time or they
 * can be provided in a 2-dimensional array.  The observations are not stored
 * in memory, so there is no limit to the number of observations that can be
 * added to the model.</p>
 * <p>
 * <strong>Usage Notes</strong>: <ul>
 * <li> When there are fewer than two observations in the model, or when
 * there is no variation in the x values (i.e. all x values are the same)
 * all statistics return <code>NaN</code>. At least two observations with
 * different x coordinates are requred to estimate a bivariate regression
 * model.
 * </li>
 * <li> getters for the statistics always compute values based on the current
 * set of observations -- i.e., you can get statistics, then add more data
 * and get updated statistics without using a new instance.  There is no
 * "compute" method that updates all statistics.  Each of the getters performs
 * the necessary computations to return the requested statistic.</li>
 * </ul></p>
 *
 * @version $Revision: 1042336 $ $Date: 2010-12-05 13:40:48 +0100 (dim. 05 déc. 2010) $
 */
public class SimpleRegression implements Serializable {

    /** Serializable version identifier */
    private static final long serialVersionUID = -3004689053607543335L;

    /** the distribution used to compute inference statistics. */
    private TDistribution distribution;

    /** sum of x values */
    private double sumX = 0d;

    /** total variation in x (sum of squared deviations from xbar) */
    private double sumXX = 0d;

    /** sum of y values */
    private double sumY = 0d;

    /** total variation in y (sum of squared deviations from ybar) */
    private double sumYY = 0d;

    /** sum of products */
    private double sumXY = 0d;

    /** number of observations */
    private long n = 0;

    /** mean of accumulated x values, used in updating formulas */
    private double xbar = 0;

    /** mean of accumulated y values, used in updating formulas */
    private double ybar = 0;

    // ---------------------Public methods--------------------------------------

    /**
     * Create an empty SimpleRegression instance
     */
    public SimpleRegression() {
        this(new TDistributionImpl(1.0));
    }

    /**
     * Create an empty SimpleRegression using the given distribution object to
     * compute inference statistics.
     * @param t the distribution used to compute inference statistics.
     * @since 1.2
     * @deprecated in 2.2 (to be removed in 3.0). Please use the {@link
     * #SimpleRegression(int) other constructor} instead.
     */
    @Deprecated
    public SimpleRegression(TDistribution t) {
        super();
        setDistribution(t);
    }

    /**
     * Create an empty SimpleRegression.
     *
     * @param degrees Number of degrees of freedom of the distribution
     * used to compute inference statistics.
     * @since 2.2
     */
    public SimpleRegression(int degrees) {
        setDistribution(new TDistributionImpl(degrees));
    }

    /**
     * Adds the observation (x,y) to the regression data set.
     * <p>
     * Uses updating formulas for means and sums of squares defined in
     * "Algorithms for Computing the Sample Variance: Analysis and
     * Recommendations", Chan, T.F., Golub, G.H., and LeVeque, R.J.
     * 1983, American Statistician, vol. 37, pp. 242-247, referenced in
     * Weisberg, S. "Applied Linear Regression". 2nd Ed. 1985.</p>
     *
     *
     * @param x independent variable value
     * @param y dependent variable value
     */
    public void addData(double x, double y) {
        if (n == 0) {
            xbar = x;
            ybar = y;
        } else {
            double dx = x - xbar;
            double dy = y - ybar;
            sumXX += dx * dx * (double) n / (n + 1d);
            sumYY += dy * dy * (double) n / (n + 1d);
            sumXY += dx * dy * (double) n / (n + 1d);
            xbar += dx / (n + 1.0);
            ybar += dy / (n + 1.0);
        }
        sumX += x;
        sumY += y;
        n++;

        if (n > 2) {
            distribution.setDegreesOfFreedom(n - 2);
        }
    }


    /**
     * Removes the observation (x,y) from the regression data set.
     * <p>
     * Mirrors the addData method.  This method permits the use of
     * SimpleRegression instances in streaming mode where the regression
     * is applied to a sliding "window" of observations, however the caller is
     * responsible for maintaining the set of observations in the window.</p>
     *
     * The method has no effect if there are no points of data (i.e. n=0)
     *
     * @param x independent variable value
     * @param y dependent variable value
     */
    public void removeData(double x, double y) {
        if (n > 0) {
            double dx = x - xbar;
            double dy = y - ybar;
            sumXX -= dx * dx * (double) n / (n - 1d);
            sumYY -= dy * dy * (double) n / (n - 1d);
            sumXY -= dx * dy * (double) n / (n - 1d);
            xbar -= dx / (n - 1.0);
            ybar -= dy / (n - 1.0);
            sumX -= x;
            sumY -= y;
            n--;

            if (n > 2) {
                distribution.setDegreesOfFreedom(n - 2);
            }
        }
    }

    /**
     * Adds the observations represented by the elements in
     * <code>data</code>.
     * <p>
     * <code>(data[0][0],data[0][1])</code> will be the first observation, then
     * <code>(data[1][0],data[1][1])</code>, etc.</p>
     * <p>
     * This method does not replace data that has already been added.  The
     * observations represented by <code>data</code> are added to the existing
     * dataset.</p>
     * <p>
     * To replace all data, use <code>clear()</code> before adding the new
     * data.</p>
     *
     * @param data array of observations to be added
     */
    public void addData(double[][] data) {
        for (int i = 0; i < data.length; i++) {
            addData(data[i][0], data[i][1]);
        }
    }


    /**
     * Removes observations represented by the elements in <code>data</code>.
      * <p>
     * If the array is larger than the current n, only the first n elements are
     * processed.  This method permits the use of SimpleRegression instances in
     * streaming mode where the regression is applied to a sliding "window" of
     * observations, however the caller is responsible for maintaining the set
     * of observations in the window.</p>
     * <p>
     * To remove all data, use <code>clear()</code>.</p>
     *
     * @param data array of observations to be removed
     */
    public void removeData(double[][] data) {
        for (int i = 0; i < data.length && n > 0; i++) {
            removeData(data[i][0], data[i][1]);
        }
    }

    /**
     * Clears all data from the model.
     */
    public void clear() {
        sumX = 0d;
        sumXX = 0d;
        sumY = 0d;
        sumYY = 0d;
        sumXY = 0d;
        n = 0;
    }

    /**
     * Returns the number of observations that have been added to the model.
     *
     * @return n number of observations that have been added.
     */
    public long getN() {
        return n;
    }

    /**
     * Returns the "predicted" <code>y</code> value associated with the
     * supplied <code>x</code> value,  based on the data that has been
     * added to the model when this method is activated.
     * <p>
     * <code> predict(x) = intercept + slope * x </code></p>
     * <p>
     * <strong>Preconditions</strong>: <ul>
     * <li>At least two observations (with at least two different x values)
     * must have been added before invoking this method. If this method is
     * invoked before a model can be estimated, <code>Double,NaN</code> is
     * returned.
     * </li></ul></p>
     *
     * @param x input <code>x</code> value
     * @return predicted <code>y</code> value
     */
    public double predict(double x) {
        double b1 = getSlope();
        return getIntercept(b1) + b1 * x;
    }

    /**
     * Returns the intercept of the estimated regression line.
     * <p>
     * The least squares estimate of the intercept is computed using the
     * <a href="http://www.xycoon.com/estimation4.htm">normal equations</a>.
     * The intercept is sometimes denoted b0.</p>
     * <p>
     * <strong>Preconditions</strong>: <ul>
     * <li>At least two observations (with at least two different x values)
     * must have been added before invoking this method. If this method is
     * invoked before a model can be estimated, <code>Double,NaN</code> is
     * returned.
     * </li></ul></p>
     *
     * @return the intercept of the regression line
     */
    public double getIntercept() {
        return getIntercept(getSlope());
    }

    /**
    * Returns the slope of the estimated regression line.
    * <p>
    * The least squares estimate of the slope is computed using the
    * <a href="http://www.xycoon.com/estimation4.htm">normal equations</a>.
    * The slope is sometimes denoted b1.</p>
    * <p>
    * <strong>Preconditions</strong>: <ul>
    * <li>At least two observations (with at least two different x values)
    * must have been added before invoking this method. If this method is
    * invoked before a model can be estimated, <code>Double.NaN</code> is
    * returned.
    * </li></ul></p>
    *
    * @return the slope of the regression line
    */
    public double getSlope() {
        if (n < 2) {
            return Double.NaN; //not enough data
        }
        if (FastMath.abs(sumXX) < 10 * Double.MIN_VALUE) {
            return Double.NaN; //not enough variation in x
        }
        return sumXY / sumXX;
    }

    /**
     * Returns the <a href="http://www.xycoon.com/SumOfSquares.htm">
     * sum of squared errors</a> (SSE) associated with the regression
     * model.
     * <p>
     * The sum is computed using the computational formula</p>
     * <p>
     * <code>SSE = SYY - (SXY * SXY / SXX)</code></p>
     * <p>
     * where <code>SYY</code> is the sum of the squared deviations of the y
     * values about their mean, <code>SXX</code> is similarly defined and
     * <code>SXY</code> is the sum of the products of x and y mean deviations.
     * </p><p>
     * The sums are accumulated using the updating algorithm referenced in
     * {@link #addData}.</p>
     * <p>
     * The return value is constrained to be non-negative - i.e., if due to
     * rounding errors the computational formula returns a negative result,
     * 0 is returned.</p>
     * <p>
     * <strong>Preconditions</strong>: <ul>
     * <li>At least two observations (with at least two different x values)
     * must have been added before invoking this method. If this method is
     * invoked before a model can be estimated, <code>Double,NaN</code> is
     * returned.
     * </li></ul></p>
     *
     * @return sum of squared errors associated with the regression model
     */
    public double getSumSquaredErrors() {
        return FastMath.max(0d, sumYY - sumXY * sumXY / sumXX);
    }

    /**
     * Returns the sum of squared deviations of the y values about their mean.
     * <p>
     * This is defined as SSTO
     * <a href="http://www.xycoon.com/SumOfSquares.htm">here</a>.</p>
     * <p>
     * If <code>n < 2</code>, this returns <code>Double.NaN</code>.</p>
     *
     * @return sum of squared deviations of y values
     */
    public double getTotalSumSquares() {
        if (n < 2) {
            return Double.NaN;
        }
        return sumYY;
    }

    /**
     * Returns the sum of squared deviations of the x values about their mean.
     *
     * If <code>n < 2</code>, this returns <code>Double.NaN</code>.</p>
     *
     * @return sum of squared deviations of x values
     */
    public double getXSumSquares() {
        if (n < 2) {
            return Double.NaN;
        }
        return sumXX;
    }

    /**
     * Returns the sum of crossproducts, x<sub>i</sub>*y<sub>i</sub>.
     *
     * @return sum of cross products
     */
    public double getSumOfCrossProducts() {
        return sumXY;
    }

    /**
     * Returns the sum of squared deviations of the predicted y values about
     * their mean (which equals the mean of y).
     * <p>
     * This is usually abbreviated SSR or SSM.  It is defined as SSM
     * <a href="http://www.xycoon.com/SumOfSquares.htm">here</a></p>
     * <p>
     * <strong>Preconditions</strong>: <ul>
     * <li>At least two observations (with at least two different x values)
     * must have been added before invoking this method. If this method is
     * invoked before a model can be estimated, <code>Double.NaN</code> is
     * returned.
     * </li></ul></p>
     *
     * @return sum of squared deviations of predicted y values
     */
    public double getRegressionSumSquares() {
        return getRegressionSumSquares(getSlope());
    }

    /**
     * Returns the sum of squared errors divided by the degrees of freedom,
     * usually abbreviated MSE.
     * <p>
     * If there are fewer than <strong>three</strong> data pairs in the model,
     * or if there is no variation in <code>x</code>, this returns
     * <code>Double.NaN</code>.</p>
     *
     * @return sum of squared deviations of y values
     */
    public double getMeanSquareError() {
        if (n < 3) {
            return Double.NaN;
        }
        return getSumSquaredErrors() / (n - 2);
    }

    /**
     * Returns <a href="http://mathworld.wolfram.com/CorrelationCoefficient.html">
     * Pearson's product moment correlation coefficient</a>,
     * usually denoted r.
     * <p>
     * <strong>Preconditions</strong>: <ul>
     * <li>At least two observations (with at least two different x values)
     * must have been added before invoking this method. If this method is
     * invoked before a model can be estimated, <code>Double,NaN</code> is
     * returned.
     * </li></ul></p>
     *
     * @return Pearson's r
     */
    public double getR() {
        double b1 = getSlope();
        double result = FastMath.sqrt(getRSquare());
        if (b1 < 0) {
            result = -result;
        }
        return result;
    }

    /**
     * Returns the <a href="http://www.xycoon.com/coefficient1.htm">
     * coefficient of determination</a>,
     * usually denoted r-square.
     * <p>
     * <strong>Preconditions</strong>: <ul>
     * <li>At least two observations (with at least two different x values)
     * must have been added before invoking this method. If this method is
     * invoked before a model can be estimated, <code>Double,NaN</code> is
     * returned.
     * </li></ul></p>
     *
     * @return r-square
     */
    public double getRSquare() {
        double ssto = getTotalSumSquares();
        return (ssto - getSumSquaredErrors()) / ssto;
    }

    /**
     * Returns the <a href="http://www.xycoon.com/standarderrorb0.htm">
     * standard error of the intercept estimate</a>,
     * usually denoted s(b0).
     * <p>
     * If there are fewer that <strong>three</strong> observations in the
     * model, or if there is no variation in x, this returns
     * <code>Double.NaN</code>.</p>
     *
     * @return standard error associated with intercept estimate
     */
    public double getInterceptStdErr() {
        return FastMath.sqrt(
            getMeanSquareError() * ((1d / (double) n) + (xbar * xbar) / sumXX));
    }

    /**
     * Returns the <a href="http://www.xycoon.com/standerrorb(1).htm">standard
     * error of the slope estimate</a>,
     * usually denoted s(b1).
     * <p>
     * If there are fewer that <strong>three</strong> data pairs in the model,
     * or if there is no variation in x, this returns <code>Double.NaN</code>.
     * </p>
     *
     * @return standard error associated with slope estimate
     */
    public double getSlopeStdErr() {
        return FastMath.sqrt(getMeanSquareError() / sumXX);
    }

    /**
     * Returns the half-width of a 95% confidence interval for the slope
     * estimate.
     * <p>
     * The 95% confidence interval is</p>
     * <p>
     * <code>(getSlope() - getSlopeConfidenceInterval(),
     * getSlope() + getSlopeConfidenceInterval())</code></p>
     * <p>
     * If there are fewer that <strong>three</strong> observations in the
     * model, or if there is no variation in x, this returns
     * <code>Double.NaN</code>.</p>
     * <p>
     * <strong>Usage Note</strong>:<br>
     * The validity of this statistic depends on the assumption that the
     * observations included in the model are drawn from a
     * <a href="http://mathworld.wolfram.com/BivariateNormalDistribution.html">
     * Bivariate Normal Distribution</a>.</p>
     *
     * @return half-width of 95% confidence interval for the slope estimate
     * @throws MathException if the confidence interval can not be computed.
     */
    public double getSlopeConfidenceInterval() throws MathException {
        return getSlopeConfidenceInterval(0.05d);
    }

    /**
     * Returns the half-width of a (100-100*alpha)% confidence interval for
     * the slope estimate.
     * <p>
     * The (100-100*alpha)% confidence interval is </p>
     * <p>
     * <code>(getSlope() - getSlopeConfidenceInterval(),
     * getSlope() + getSlopeConfidenceInterval())</code></p>
     * <p>
     * To request, for example, a 99% confidence interval, use
     * <code>alpha = .01</code></p>
     * <p>
     * <strong>Usage Note</strong>:<br>
     * The validity of this statistic depends on the assumption that the
     * observations included in the model are drawn from a
     * <a href="http://mathworld.wolfram.com/BivariateNormalDistribution.html">
     * Bivariate Normal Distribution</a>.</p>
     * <p>
     * <strong> Preconditions:</strong><ul>
     * <li>If there are fewer that <strong>three</strong> observations in the
     * model, or if there is no variation in x, this returns
     * <code>Double.NaN</code>.
     * </li>
     * <li><code>(0 < alpha < 1)</code>; otherwise an
     * <code>IllegalArgumentException</code> is thrown.
     * </li></ul></p>
     *
     * @param alpha the desired significance level
     * @return half-width of 95% confidence interval for the slope estimate
     * @throws MathException if the confidence interval can not be computed.
     */
    public double getSlopeConfidenceInterval(double alpha)
        throws MathException {
        if (alpha >= 1 || alpha <= 0) {
            throw MathRuntimeException.createIllegalArgumentException(
                  LocalizedFormats.OUT_OF_BOUND_SIGNIFICANCE_LEVEL,
                  alpha, 0.0, 1.0);
        }
        return getSlopeStdErr() *
            distribution.inverseCumulativeProbability(1d - alpha / 2d);
    }

    /**
     * Returns the significance level of the slope (equiv) correlation.
     * <p>
     * Specifically, the returned value is the smallest <code>alpha</code>
     * such that the slope confidence interval with significance level
     * equal to <code>alpha</code> does not include <code>0</code>.
     * On regression output, this is often denoted <code>Prob(|t| > 0)</code>
     * </p><p>
     * <strong>Usage Note</strong>:<br>
     * The validity of this statistic depends on the assumption that the
     * observations included in the model are drawn from a
     * <a href="http://mathworld.wolfram.com/BivariateNormalDistribution.html">
     * Bivariate Normal Distribution</a>.</p>
     * <p>
     * If there are fewer that <strong>three</strong> observations in the
     * model, or if there is no variation in x, this returns
     * <code>Double.NaN</code>.</p>
     *
     * @return significance level for slope/correlation
     * @throws MathException if the significance level can not be computed.
     */
    public double getSignificance() throws MathException {
        return 2d * (1.0 - distribution.cumulativeProbability(
                    FastMath.abs(getSlope()) / getSlopeStdErr()));
    }

    // ---------------------Private methods-----------------------------------

    /**
    * Returns the intercept of the estimated regression line, given the slope.
    * <p>
    * Will return <code>NaN</code> if slope is <code>NaN</code>.</p>
    *
    * @param slope current slope
    * @return the intercept of the regression line
    */
    private double getIntercept(double slope) {
        return (sumY - slope * sumX) / n;
    }

    /**
     * Computes SSR from b1.
     *
     * @param slope regression slope estimate
     * @return sum of squared deviations of predicted y values
     */
    private double getRegressionSumSquares(double slope) {
        return slope * slope * sumXX;
    }

    /**
     * Modify the distribution used to compute inference statistics.
     * @param value the new distribution
     * @since 1.2
     * @deprecated in 2.2 (to be removed in 3.0).
     */
    @Deprecated
    public void setDistribution(TDistribution value) {
        distribution = value;

        // modify degrees of freedom
        if (n > 2) {
            distribution.setDegreesOfFreedom(n - 2);
        }
    }
}