/*
    * 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.linear;
  
  import java.util.ArrayList;
  import java.util.List;
  
  import org.hipparchus.complex.Complex;
  import org.hipparchus.complex.ComplexField;
  import org.hipparchus.exception.LocalizedCoreFormats;
  import org.hipparchus.exception.MathIllegalStateException;
  import org.hipparchus.exception.MathRuntimeException;
  import org.hipparchus.util.FastMath;
  import org.hipparchus.util.Precision;
  
  /**
   * Calculates the eigen decomposition of a non-symmetric real matrix.
   * <p>
   * The eigen decomposition of matrix A is a set of two matrices:
   * \(V\) and \(D\) such that \(A V = V D\) where $\(A\),
   * \(V\) and \(D\) are all \(m \times m\) matrices.
   * <p>
   * This class is similar in spirit to the {@code EigenvalueDecomposition}
   * class from the <a href="http://math.nist.gov/javanumerics/jama/">JAMA</a>
   * library, with the following changes:
   * <ul>
   *   <li>a {@link #getVInv() getVInv} method has been added,</li>
   *   <li>z {@link #getEigenvalue(int) getEigenvalue} method to pick up a
   *       single eigenvalue has been added,</li>
   *   <li>a {@link #getEigenvector(int) getEigenvector} method to pick up a
   *       single eigenvector has been added,</li>
   *   <li>a {@link #getDeterminant() getDeterminant} method has been added.</li>
   * </ul>
   * <p>
   * This class supports non-symmetric matrices, which have complex eigenvalues.
   * Support for symmetric matrices is provided by {@link EigenDecompositionSymmetric}.
   * </p>
   * <p>
   * As \(A\) is not symmetric, then the eigenvalue matrix \(D\) is block diagonal with
   * the real eigenvalues in 1-by-1 blocks and any complex eigenvalues, \(\lambda \pm i \mu\),
   * in 2-by-2 blocks:
   * </p>
   * <p>
   * \[
   *   \begin{bmatrix}
   *    \lambda &amp; \mu\\
   *    -\mu    &amp; \lambda
   *   \end{bmatrix}
   * \]
   * </p>
   * <p>
   * The columns of \(V\) represent the eigenvectors in the sense that \(A V = V D\),
   * i.e. {@code A.multiply(V)} equals {@code V.multiply(D)}.
   * The matrix \(V\) may be badly conditioned, or even singular, so the validity of the
   * equation \(A = V D V^{-1}\) depends upon the condition of \(V\).
   * </p>
   * <p>
   * This implementation is based on the paper by A. Drubrulle, R.S. Martin and
   * J.H. Wilkinson "The Implicit QL Algorithm" in Wilksinson and Reinsch (1971)
   * Handbook for automatic computation, vol. 2, Linear algebra, Springer-Verlag,
   * New-York.
   *
   * @see <a href="http://mathworld.wolfram.com/EigenDecomposition.html">MathWorld</a>
   * @see <a href="http://en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix">Wikipedia</a>
   * @since 3.0
   */
  public class EigenDecompositionNonSymmetric {
      /** Default epsilon value to use for internal epsilon **/
      public static final double DEFAULT_EPSILON = 1e-12;
      /** Internally used epsilon criteria. */
      private final double epsilon;
      /** eigenvalues. */
      private Complex[] eigenvalues;
      /** Eigenvectors. */
      private List<FieldVector<Complex>> eigenvectors;
      /** Cached value of \(V\). */
      private RealMatrix cachedV;
      /** Cached value of \(D\). */
      private RealMatrix cachedD;
     /** Cached value of \(V^{-1}\). */
     private RealMatrix cachedVInv;
 
     /** Calculates the eigen decomposition of the given real matrix.
      * @param matrix Matrix to decompose.
      * @throws MathIllegalStateException if the algorithm fails to converge.
      * @throws MathRuntimeException if the decomposition of a general matrix
      * results in a matrix with zero norm
      */
     public EigenDecompositionNonSymmetric(final RealMatrix matrix) {
         this(matrix, DEFAULT_EPSILON);
     }
 
     /** Calculates the eigen decomposition of the given real matrix.
      * @param matrix Matrix to decompose.
      * @param epsilon Epsilon used for internal tests (e.g. is singular, eigenvalue ratio, etc.)
      * @throws MathIllegalStateException if the algorithm fails to converge.
      * @throws MathRuntimeException if the decomposition of a general matrix
      * results in a matrix with zero norm
      */
     public EigenDecompositionNonSymmetric(final RealMatrix matrix, double epsilon)
         throws MathRuntimeException {
         this.epsilon = epsilon;
         findEigenVectorsFromSchur(transformToSchur(matrix));
     }
 
     /**
      * Gets the matrix V of the decomposition.
      * V is a matrix whose columns hold either the real or the
      * imaginary part of eigenvectors.
      *
      * @return the V matrix.
      */
     public RealMatrix getV() {
 
         if (cachedV == null) {
             final int m = eigenvectors.size();
             cachedV = MatrixUtils.createRealMatrix(m, m);
             for (int k = 0; k < m; ++k) {
                 final FieldVector<Complex> ek = eigenvectors.get(k);
                 if (eigenvalues[k].getImaginaryPart() >= 0) {
                     // either it is a real eigenvalue, or it is the first of two conjugate eigenvalues,
                     // we pick up the real part of the eigenvector
                     for (int l = 0; l < m; ++l) {
                         cachedV.setEntry(l, k, ek.getEntry(l).getRealPart());
                     }
                 } else {
                     // second of two conjugate eigenvalues,
                     // we pick up the imaginary part of the eigenvector
                     for (int l = 0; l < m; ++l) {
                         cachedV.setEntry(l, k, -ek.getEntry(l).getImaginaryPart());
                     }
                 }
             }
         }
 
         // return the cached matrix
         return cachedV;
 
     }
 
     /**
      * Gets the block diagonal matrix D of the decomposition.
      * D is a block diagonal matrix.
      * Real eigenvalues are on the diagonal while complex values are on
      * 2x2 blocks { {real +imaginary}, {-imaginary, real} }.
      *
      * @return the D matrix.
      */
     public RealMatrix getD() {
 
         if (cachedD == null) {
             // cache the matrix for subsequent calls
             cachedD = MatrixUtils.createRealMatrix(eigenvalues.length, eigenvalues.length);
             for (int i = 0; i < eigenvalues.length; ++i) {
                 cachedD.setEntry(i, i, eigenvalues[i].getRealPart());
                 if (Precision.compareTo(eigenvalues[i].getImaginaryPart(), 0.0, epsilon) > 0) {
                     cachedD.setEntry(i, i + 1, eigenvalues[i].getImaginaryPart());
                 } else if (Precision.compareTo(eigenvalues[i].getImaginaryPart(), 0.0, epsilon) < 0) {
                     cachedD.setEntry(i, i - 1, eigenvalues[i].getImaginaryPart());
                 }
             }
         }
         return cachedD;
     }
 
     /**
      * Get's the value for epsilon which is used for internal tests (e.g. is singular, eigenvalue ratio, etc.)
      *
      * @return the epsilon value.
      */
     public double getEpsilon() {
         return epsilon;
     }
 
     /**
      * Gets the inverse of the matrix V of the decomposition.
      *
      * @return the inverse of the V matrix.
      */
     public RealMatrix getVInv() {
 
         if (cachedVInv == null) {
             cachedVInv = MatrixUtils.inverse(getV(), epsilon);
         }
 
         // return the cached matrix
         return cachedVInv;
     }
 
     /**
      * Gets a copy of the eigenvalues of the original matrix.
      *
      * @return a copy of the eigenvalues of the original matrix.
      *
      * @see #getD()
      * @see #getEigenvalue(int)
      */
     public Complex[] getEigenvalues() {
         return eigenvalues.clone();
     }
 
     /**
      * Returns the i<sup>th</sup> eigenvalue of the original matrix.
      *
      * @param i index of the eigenvalue (counting from 0)
      * @return i<sup>th</sup> eigenvalue of the original matrix.
      *
      * @see #getD()
      * @see #getEigenvalues()
      */
     public Complex getEigenvalue(final int i) {
         return eigenvalues[i];
     }
 
     /**
      * Gets a copy of the i<sup>th</sup> eigenvector of the original matrix.
      * <p>
      * Note that if the the i<sup>th</sup> is complex this method will throw
      * an exception.
      * </p>
      * @param i Index of the eigenvector (counting from 0).
      * @return a copy of the i<sup>th</sup> eigenvector of the original matrix.
      * @see #getD()
      */
     public FieldVector<Complex> getEigenvector(final int i) {
         return eigenvectors.get(i).copy();
     }
 
     /**
      * Computes the determinant of the matrix.
      *
      * @return the determinant of the matrix.
      */
     public Complex getDeterminant() {
         Complex determinant = Complex.ONE;
         for (int i = 0; i < eigenvalues.length; ++i) {
             determinant = determinant.multiply(eigenvalues[i]);
         }
         return determinant;
     }
 
     /**
      * Transforms the matrix to Schur form and calculates the eigenvalues.
      *
      * @param matrix Matrix to transform.
      * @return the {@link SchurTransformer Schur transform} for this matrix
      */
     private SchurTransformer transformToSchur(final RealMatrix matrix) {
         final SchurTransformer schurTransform = new SchurTransformer(matrix, epsilon);
         final double[][] matT = schurTransform.getT().getData();
         final double norm = matrix.getNorm1();
 
         eigenvalues = new Complex[matT.length];
 
         int i = 0;
         while (i < eigenvalues.length) {
             if (i == (eigenvalues.length - 1) ||
                 Precision.equals(matT[i + 1][i], 0.0, norm * epsilon)) {
                 eigenvalues[i] = new Complex(matT[i][i]);
                 i++;
             } else {
                 final double x   = matT[i + 1][i + 1];
                 final double p   = 0.5 * (matT[i][i] - x);
                 final double z   = FastMath.sqrt(FastMath.abs(p * p + matT[i + 1][i] * matT[i][i + 1]));
                 eigenvalues[i++] = new Complex(x + p, +z);
                 eigenvalues[i++] = new Complex(x + p, -z);
             }
         }
         return schurTransform;
     }
 
     /**
      * Performs a division of two complex numbers.
      *
      * @param xr real part of the first number
      * @param xi imaginary part of the first number
      * @param yr real part of the second number
      * @param yi imaginary part of the second number
      * @return result of the complex division
      */
     private Complex cdiv(final double xr, final double xi,
                          final double yr, final double yi) {
         return new Complex(xr, xi).divide(new Complex(yr, yi));
     }
 
     /**
      * Find eigenvectors from a matrix transformed to Schur form.
      *
      * @param schur the schur transformation of the matrix
      * @throws MathRuntimeException if the Schur form has a norm of zero
      */
     private void findEigenVectorsFromSchur(final SchurTransformer schur)
         throws MathRuntimeException {
         final double[][] matrixT = schur.getT().getData();
         final double[][] matrixP = schur.getP().getData();
 
         final int n = matrixT.length;
 
         // compute matrix norm
         double norm = 0.0;
         for (int i = 0; i < n; i++) {
            for (int j = FastMath.max(i - 1, 0); j < n; j++) {
                norm += FastMath.abs(matrixT[i][j]);
            }
         }
 
         // we can not handle a matrix with zero norm
         if (norm == 0.0) {
            throw new MathRuntimeException(LocalizedCoreFormats.ZERO_NORM);
         }
 
         // Backsubstitute to find vectors of upper triangular form
 
         double r = 0.0;
         double s = 0.0;
         double z = 0.0;
 
         for (int idx = n - 1; idx >= 0; idx--) {
             double p = eigenvalues[idx].getRealPart();
             double q = eigenvalues[idx].getImaginaryPart();
 
             if (Precision.equals(q, 0.0)) {
                 // Real vector
                 int l = idx;
                 matrixT[idx][idx] = 1.0;
                 for (int i = idx - 1; i >= 0; i--) {
                     double w = matrixT[i][i] - p;
                     r = 0.0;
                     for (int j = l; j <= idx; j++) {
                         r += matrixT[i][j] * matrixT[j][idx];
                     }
                     if (Precision.compareTo(eigenvalues[i].getImaginaryPart(), 0.0, epsilon) < 0) {
                         z = w;
                         s = r;
                     } else {
                         l = i;
                         if (Precision.equals(eigenvalues[i].getImaginaryPart(), 0.0)) {
                             if (w != 0.0) {
                                 matrixT[i][idx] = -r / w;
                             } else {
                                 matrixT[i][idx] = -r / (Precision.EPSILON * norm);
                             }
                         } else {
                             // Solve real equations
                             double x = matrixT[i][i + 1];
                             double y = matrixT[i + 1][i];
                             q = (eigenvalues[i].getRealPart() - p) * (eigenvalues[i].getRealPart() - p) +
                                 eigenvalues[i].getImaginaryPart() * eigenvalues[i].getImaginaryPart();
                             double t = (x * s - z * r) / q;
                             matrixT[i][idx] = t;
                             if (FastMath.abs(x) > FastMath.abs(z)) {
                                 matrixT[i + 1][idx] = (-r - w * t) / x;
                             } else {
                                 matrixT[i + 1][idx] = (-s - y * t) / z;
                             }
                         }
 
                         // Overflow control
                         double t = FastMath.abs(matrixT[i][idx]);
                         if ((Precision.EPSILON * t) * t > 1) {
                             for (int j = i; j <= idx; j++) {
                                 matrixT[j][idx] /= t;
                             }
                         }
                     }
                 }
             } else if (q < 0.0) {
                 // Complex vector
                 int l = idx - 1;
 
                 // Last vector component imaginary so matrix is triangular
                 if (FastMath.abs(matrixT[idx][idx - 1]) > FastMath.abs(matrixT[idx - 1][idx])) {
                     matrixT[idx - 1][idx - 1] = q / matrixT[idx][idx - 1];
                     matrixT[idx - 1][idx]     = -(matrixT[idx][idx] - p) / matrixT[idx][idx - 1];
                 } else {
                     final Complex result = cdiv(0.0, -matrixT[idx - 1][idx],
                                                 matrixT[idx - 1][idx - 1] - p, q);
                     matrixT[idx - 1][idx - 1] = result.getReal();
                     matrixT[idx - 1][idx]     = result.getImaginary();
                 }
 
                 matrixT[idx][idx - 1] = 0.0;
                 matrixT[idx][idx]     = 1.0;
 
                 for (int i = idx - 2; i >= 0; i--) {
                     double ra = 0.0;
                     double sa = 0.0;
                     for (int j = l; j <= idx; j++) {
                         ra += matrixT[i][j] * matrixT[j][idx - 1];
                         sa += matrixT[i][j] * matrixT[j][idx];
                     }
                     double w = matrixT[i][i] - p;
 
                     if (Precision.compareTo(eigenvalues[i].getImaginaryPart(), 0.0, epsilon) < 0) {
                         z = w;
                         r = ra;
                         s = sa;
                     } else {
                         l = i;
                         if (Precision.equals(eigenvalues[i].getImaginaryPart(), 0.0)) {
                             final Complex c = cdiv(-ra, -sa, w, q);
                             matrixT[i][idx - 1] = c.getReal();
                             matrixT[i][idx] = c.getImaginary();
                         } else {
                             // Solve complex equations
                             double x = matrixT[i][i + 1];
                             double y = matrixT[i + 1][i];
                             double vr = (eigenvalues[i].getRealPart() - p) * (eigenvalues[i].getRealPart() - p) +
                                         eigenvalues[i].getImaginaryPart() * eigenvalues[i].getImaginaryPart() - q * q;
                             final double vi = (eigenvalues[i].getRealPart() - p) * 2.0 * q;
                             if (Precision.equals(vr, 0.0) && Precision.equals(vi, 0.0)) {
                                 vr = Precision.EPSILON * norm *
                                      (FastMath.abs(w) + FastMath.abs(q) + FastMath.abs(x) +
                                       FastMath.abs(y) + FastMath.abs(z));
                             }
                             final Complex c     = cdiv(x * r - z * ra + q * sa,
                                                        x * s - z * sa - q * ra, vr, vi);
                             matrixT[i][idx - 1] = c.getReal();
                             matrixT[i][idx]     = c.getImaginary();
 
                             if (FastMath.abs(x) > (FastMath.abs(z) + FastMath.abs(q))) {
                                 matrixT[i + 1][idx - 1] = (-ra - w * matrixT[i][idx - 1] +
                                                            q * matrixT[i][idx]) / x;
                                 matrixT[i + 1][idx]     = (-sa - w * matrixT[i][idx] -
                                                            q * matrixT[i][idx - 1]) / x;
                             } else {
                                 final Complex c2        = cdiv(-r - y * matrixT[i][idx - 1],
                                                                -s - y * matrixT[i][idx], z, q);
                                 matrixT[i + 1][idx - 1] = c2.getReal();
                                 matrixT[i + 1][idx]     = c2.getImaginary();
                             }
                         }
 
                         // Overflow control
                         double t = FastMath.max(FastMath.abs(matrixT[i][idx - 1]),
                                                 FastMath.abs(matrixT[i][idx]));
                         if ((Precision.EPSILON * t) * t > 1) {
                             for (int j = i; j <= idx; j++) {
                                 matrixT[j][idx - 1] /= t;
                                 matrixT[j][idx] /= t;
                             }
                         }
                     }
                 }
             }
         }
 
         // Back transformation to get eigenvectors of original matrix
         for (int j = n - 1; j >= 0; j--) {
             for (int i = 0; i <= n - 1; i++) {
                 z = 0.0;
                 for (int k = 0; k <= FastMath.min(j, n - 1); k++) {
                     z += matrixP[i][k] * matrixT[k][j];
                 }
                 matrixP[i][j] = z;
             }
         }
 
         eigenvectors = new ArrayList<>(n);
         for (int i = 0; i < n; i++) {
             FieldVector<Complex> ei = new ArrayFieldVector<>(ComplexField.getInstance(), n);
             for (int j = 0; j < n; j++) {
                 if (Precision.compareTo(eigenvalues[i].getImaginaryPart(), 0.0, epsilon) > 0) {
                     ei.setEntry(j, new Complex(matrixP[j][i], +matrixP[j][i + 1]));
                 } else if (Precision.compareTo(eigenvalues[i].getImaginaryPart(), 0.0, epsilon) < 0) {
                     ei.setEntry(j, new Complex(matrixP[j][i - 1], -matrixP[j][i]));
                 } else {
                     ei.setEntry(j, new Complex(matrixP[j][i]));
                 }
             }
             eigenvectors.add(ei);
         }
     }
 }