/*
    * 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
    *
    *      https://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.
   */
  
  /*
   * This is not the original file distributed by the Apache Software Foundation
   * It has been modified by the Hipparchus project
   */
  package org.hipparchus.stat.regression;
  
  import java.io.Serializable;
  import java.util.Arrays;
  
  import org.hipparchus.exception.MathIllegalArgumentException;
  import org.hipparchus.util.FastMath;
  import org.hipparchus.util.MathUtils;
  
  /**
   * Results of a Multiple Linear Regression model fit.
   *
   */
  public class RegressionResults implements Serializable {
  
      /** INDEX of Sum of Squared Errors */
      private static final int SSE_IDX = 0;
      /** INDEX of Sum of Squares of Model */
      private static final int SST_IDX = 1;
      /** INDEX of R-Squared of regression */
      private static final int RSQ_IDX = 2;
      /** INDEX of Mean Squared Error */
      private static final int MSE_IDX = 3;
      /** INDEX of Adjusted R Squared */
      private static final int ADJRSQ_IDX = 4;
      /** UID */
      private static final long serialVersionUID = 1l;
      /** regression slope parameters */
      private final double[] parameters;
      /** variance covariance matrix of parameters */
      private final double[][] varCovData;
      /** boolean flag for variance covariance matrix in symm compressed storage */
      private final boolean isSymmetricVCD;
      /** rank of the solution */
      @SuppressWarnings("unused")
      private final int rank;
      /** number of observations on which results are based */
      private final long nobs;
      /** boolean flag indicator of whether a constant was included*/
      private final boolean containsConstant;
      /** array storing global results, SSE, MSE, RSQ, adjRSQ */
      private final double[] globalFitInfo;
  
      /**
       *  Set the default constructor to private access
       *  to prevent inadvertent instantiation
       */
      @SuppressWarnings("unused")
      private RegressionResults() {
          this.parameters = null;
          this.varCovData = null;
          this.rank = -1;
          this.nobs = -1;
          this.containsConstant = false;
          this.isSymmetricVCD = false;
          this.globalFitInfo = null;
      }
  
      /**
       * Constructor for Regression Results.
       *
       * @param parameters a double array with the regression slope estimates
       * @param varcov the variance covariance matrix, stored either in a square matrix
       * or as a compressed
       * @param isSymmetricCompressed a flag which denotes that the variance covariance
       * matrix is in symmetric compressed format
       * @param nobs the number of observations of the regression estimation
       * @param rank the number of independent variables in the regression
       * @param sumy the sum of the independent variable
       * @param sumysq the sum of the squared independent variable
       * @param sse sum of squared errors
       * @param containsConstant true model has constant,  false model does not have constant
       * @param copyData if true a deep copy of all input data is made, if false only references
       * are copied and the RegressionResults become mutable
       */
      public RegressionResults(
              final double[] parameters, final double[][] varcov,
              final boolean isSymmetricCompressed,
             final long nobs, final int rank,
             final double sumy, final double sumysq, final double sse,
             final boolean containsConstant,
             final boolean copyData) {
         if (copyData) {
             this.parameters = parameters.clone();
             this.varCovData = new double[varcov.length][];
             for (int i = 0; i < varcov.length; i++) {
                 this.varCovData[i] = varcov[i].clone();
             }
         } else {
             this.parameters = parameters; // NOPMD - storing a reference to the array is controlled by a user-supplied parameter
             this.varCovData = varcov; // NOPMD - storing a reference to the array is controlled by a user-supplied parameter
         }
         this.isSymmetricVCD = isSymmetricCompressed;
         this.nobs = nobs;
         this.rank = rank;
         this.containsConstant = containsConstant;
         this.globalFitInfo = new double[5];
         Arrays.fill(this.globalFitInfo, Double.NaN);
 
         if (rank > 0) {
             this.globalFitInfo[SST_IDX] = containsConstant ?
                     (sumysq - sumy * sumy / nobs) : sumysq;
         }
 
         this.globalFitInfo[SSE_IDX] = sse;
         this.globalFitInfo[MSE_IDX] = this.globalFitInfo[SSE_IDX] / (nobs - rank);
         this.globalFitInfo[RSQ_IDX] = 1.0 - this.globalFitInfo[SSE_IDX] / this.globalFitInfo[SST_IDX];
 
         if (!containsConstant) {
             this.globalFitInfo[ADJRSQ_IDX] = 1.0 - (1.0 - this.globalFitInfo[RSQ_IDX]) * (((double) nobs) / (nobs - rank));
         } else {
             this.globalFitInfo[ADJRSQ_IDX] = 1.0 - (sse * (nobs - 1.0)) / (globalFitInfo[SST_IDX] * (nobs - rank));
         }
     }
 
     /**
      * <p>Returns the parameter estimate for the regressor at the given index.</p>
      *
      * <p>A redundant regressor will have its redundancy flag set, as well as
      *  a parameters estimated equal to {@code Double.NaN}</p>
      *
      * @param index Index.
      * @return the parameters estimated for regressor at index.
      * @throws MathIllegalArgumentException if {@code index} is not in the interval
      * {@code [0, number of parameters)}.
      */
     public double getParameterEstimate(int index) throws MathIllegalArgumentException {
         if (parameters == null) {
             return Double.NaN;
         }
         MathUtils.checkRangeInclusive(index, 0, this.parameters.length - 1);
         return this.parameters[index];
     }
 
     /**
      * <p>Returns a copy of the regression parameters estimates.</p>
      *
      * <p>The parameter estimates are returned in the natural order of the data.</p>
      *
      * <p>A redundant regressor will have its redundancy flag set, as will
      *  a parameter estimate equal to {@code Double.NaN}.</p>
      *
      * @return array of parameter estimates, null if no estimation occurred
      */
     public double[] getParameterEstimates() {
         if (this.parameters == null) {
             return null; // NOPMD
         }
         return parameters.clone();
     }
 
     /**
      * Returns the <a href="http://www.xycoon.com/standerrorb(1).htm">standard
      * error of the parameter estimate at index</a>,
      * usually denoted s(b<sub>index</sub>).
      *
      * @param index Index.
      * @return the standard errors associated with parameters estimated at index.
      * @throws MathIllegalArgumentException if {@code index} is not in the interval
      * {@code [0, number of parameters)}.
      */
     public double getStdErrorOfEstimate(int index) throws MathIllegalArgumentException {
         if (parameters == null) {
             return Double.NaN;
         }
         MathUtils.checkRangeInclusive(index, 0, this.parameters.length - 1);
         double var = this.getVcvElement(index, index);
         if (!Double.isNaN(var) && var > Double.MIN_VALUE) {
             return FastMath.sqrt(var);
         }
         return Double.NaN;
     }
 
     /**
      * <p>Returns the <a href="http://www.xycoon.com/standerrorb(1).htm">standard
      * error of the parameter estimates</a>,
      * usually denoted s(b<sub>i</sub>).</p>
      *
      * <p>If there are problems with an ill conditioned design matrix then the regressor
      * which is redundant will be assigned <code>Double.NaN</code>. </p>
      *
      * @return an array standard errors associated with parameters estimates,
      *  null if no estimation occurred
      */
     public double[] getStdErrorOfEstimates() {
         if (parameters == null) {
             return null; // NOPMD
         }
         double[] se = new double[this.parameters.length];
         for (int i = 0; i < this.parameters.length; i++) {
             double var = this.getVcvElement(i, i);
             if (!Double.isNaN(var) && var > Double.MIN_VALUE) {
                 se[i] = FastMath.sqrt(var);
                 continue;
             }
             se[i] = Double.NaN;
         }
         return se;
     }
 
     /**
      * <p>Returns the covariance between regression parameters i and j.</p>
      *
      * <p>If there are problems with an ill conditioned design matrix then the covariance
      * which involves redundant columns will be assigned {@code Double.NaN}. </p>
      *
      * @param i {@code i}th regression parameter.
      * @param j {@code j}th regression parameter.
      * @return the covariance of the parameter estimates.
      * @throws MathIllegalArgumentException if {@code i} or {@code j} is not in the
      * interval {@code [0, number of parameters)}.
      */
     public double getCovarianceOfParameters(int i, int j) throws MathIllegalArgumentException {
         if (parameters == null) {
             return Double.NaN;
         }
         MathUtils.checkRangeInclusive(i, 0, this.parameters.length - 1);
         MathUtils.checkRangeInclusive(j, 0, this.parameters.length - 1);
         return this.getVcvElement(i, j);
     }
 
     /**
      * <p>Returns the number of parameters estimated in the model.</p>
      *
      * <p>This is the maximum number of regressors, some techniques may drop
      * redundant parameters</p>
      *
      * @return number of regressors, -1 if not estimated
      */
     public int getNumberOfParameters() {
         if (this.parameters == null) {
             return -1;
         }
         return this.parameters.length;
     }
 
     /**
      * Returns the number of observations added to the regression model.
      *
      * @return Number of observations, -1 if an error condition prevents estimation
      */
     public long getN() {
         return this.nobs;
     }
 
     /**
      * <p>Returns the sum of squared deviations of the y values about their mean.</p>
      *
      * <p>This is defined as SSTO
      * <a href="http://www.xycoon.com/SumOfSquares.htm">here</a>.</p>
      *
      * <p>If {@code n < 2}, this returns {@code Double.NaN}.</p>
      *
      * @return sum of squared deviations of y values
      */
     public double getTotalSumSquares() {
         return this.globalFitInfo[SST_IDX];
     }
 
     /**
      * <p>Returns the sum of squared deviations of the predicted y values about
      * their mean (which equals the mean of y).</p>
      *
      * <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>:</p><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>
      *
      * @return sum of squared deviations of predicted y values
      */
     public double getRegressionSumSquares() {
         return this.globalFitInfo[SST_IDX] - this.globalFitInfo[SSE_IDX];
     }
 
     /**
      * <p>Returns the <a href="http://www.xycoon.com/SumOfSquares.htm">
      * sum of squared errors</a> (SSE) associated with the regression
      * model.</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>:</p>
      * <ul>
      * <li>numberOfParameters data pairs
      * 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>
      *
      * @return sum of squared errors associated with the regression model
      */
     public double getErrorSumSquares() {
         return this.globalFitInfo[ SSE_IDX];
     }
 
     /**
      * <p>Returns the sum of squared errors divided by the degrees of freedom,
      * usually abbreviated MSE.</p>
      *
      * <p>If there are fewer than <strong>numberOfParameters + 1</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() {
         return this.globalFitInfo[ MSE_IDX];
     }
 
     /**
      * <p>Returns the <a href="http://www.xycoon.com/coefficient1.htm">
      * coefficient of multiple determination</a>,
      * usually denoted r-square.</p>
      *
      * <p><strong>Preconditions</strong>:</p>
      * <ul>
      * <li>At least numberOfParameters observations (with at least numberOfParameters 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} is
      * returned.
      * </li></ul>
      *
      * @return r-square, a double in the interval [0, 1]
      */
     public double getRSquared() {
         return this.globalFitInfo[ RSQ_IDX];
     }
 
     /**
      * <p>Returns the adjusted R-squared statistic, defined by the formula
      * \(
      * R_\mathrm{adj}^2 = 1 - \frac{\mathrm{SSR} (n - 1)}{\mathrm{SSTO} (n - p)}
      * \)
      * where SSR is the sum of squared residuals},
      * SSTO is the total sum of squares}, n is the number
      * of observations and p is the number of parameters estimated (including the intercept).</p>
      *
      * <p>If the regression is estimated without an intercept term, what is returned is</p><pre>
      * <code> 1 - (1 - {@link #getRSquared()} ) * (n / (n - p)) </code>
      * </pre>
      *
      * @return adjusted R-Squared statistic
      */
     public double getAdjustedRSquared() {
         return this.globalFitInfo[ ADJRSQ_IDX];
     }
 
     /**
      * Returns true if the regression model has been computed including an intercept.
      * In this case, the coefficient of the intercept is the first element of the
      * {@link #getParameterEstimates() parameter estimates}.
      * @return true if the model has an intercept term
      */
     public boolean hasIntercept() {
         return this.containsConstant;
     }
 
     /**
      * Gets the i-jth element of the variance-covariance matrix.
      *
      * @param i first variable index
      * @param j second variable index
      * @return the requested variance-covariance matrix entry
      */
     private double getVcvElement(int i, int j) {
         if (this.isSymmetricVCD) {
             if (this.varCovData.length > 1) {
                 //could be stored in upper or lower triangular
                 if (i == j) {
                     return varCovData[i][i];
                 } else if (i >= varCovData[j].length) {
                     return varCovData[i][j];
                 } else {
                     return varCovData[j][i];
                 }
             } else {//could be in single array
                 if (i > j) {
                     return varCovData[0][(i + 1) * i / 2 + j];
                 } else {
                     return varCovData[0][(j + 1) * j / 2 + i];
                 }
             }
         } else {
             return this.varCovData[i][j];
         }
     }
 }