import Jama.*;
import Jama.EigenvalueDecomposition.*;
/**
 * Created with IntelliJ IDEA.
 * User: rebeccamancy
 * Date: 10/11/2012
 * Time: 17:26
 * To change this template use File | Settings | File Templates.
 */
public class M {
    Matrix mat, V;
    EigenvalueDecomposition decomp;
    double[] eigVals;

    public M(Habitat hab, Model mod) {
        double[][] mat = new double[hab.getNumPatches()][hab.getNumPatches()];
        for (int focalPatch=0; focalPatch<hab.getNumPatches(); focalPatch++) {
            for (int otherPatch=0; otherPatch<hab.getNumPatches(); otherPatch++) {
                mat[focalPatch][otherPatch] = mod.mijValue(focalPatch,otherPatch,hab);
            }
        }
        this.mat = new Matrix(mat);
        this.decomp = new EigenvalueDecomposition(this.mat);
        this.eigVals = decomp.getRealEigenvalues();
        //System.out.print("Eigenvalues of M: "); Util.print(this.eigVals);
        this.V = decomp.getV();
    }

    /**
     * Returns the metapopulation capacity Lambda_M when called on a matrix M that is assumed to have good form
     * @return  largest absolute eigenvalue
     */
    public double getLambda() {
        return Util.largestAbsVal(this.eigVals);
    }

    private double[] getViVector() {
        int leadingEigenvalue = Util.largestAbsIndex(eigVals);
        int numElts = eigVals.length;
        double[] xi2 = new double[numElts];  // x_i^2 or the square of leading eigenvector

        // Want the square of the values in the leading eigenvector in a double array
        for (int i = 0; i<numElts; i++) {
            xi2[i] = this.V.get(i, leadingEigenvalue) * this.V.get(i, leadingEigenvalue);
        }
        return xi2;
    }

    public int getMaxViPatch() {
        double xi2[] = getViVector();
        //System.out.print("printing xi: ")  ; Util.print(xi2);
        return Util.largestAbsIndexCheck(xi2, " in calculating Vi");
    }

    public double getVi(int patchID) {
        double xi2[] = getViVector();
        return xi2[patchID];
    }

    public double getNorx2Vi(int patchID) {
        return getNorx2Vi(patchID);
    }

    public double[] getNorx2Vi() {
        // normalised Vi is lambda_M * x^2 in this version
        double xi2[] = getViVector();
        double norXi2[] = new double[xi2.length];
        double sumXi2 = Util.sumArray(xi2);
        double lam = this.getLambda();
        for (int i = 0; i < xi2.length; i++) {
            norXi2[i] = lam * (xi2[i] / sumXi2);
            //System.err.print(norXi2[i] + ",");
        }
        return norXi2;
    }

    /// THIS IS A PLACE HOLDER AND DOESNT DO WHAT IT SHOULD
    public double[] getNorxyVi() {
        // normalised Vi is lambda_M * x^2 in this version
        double xi2[] = getViVector();
        double norXi2[] = new double[xi2.length];
        double sumXi2 = Util.sumArray(xi2);
        double lam = this.getLambda();
        for (int i = 0; i < xi2.length; i++) {
            norXi2[i] = lam * (xi2[i] / sumXi2);
            //System.err.print(norXi2[i] + ",");
        }
        return norXi2;
    }

    /*public int getMaxViPatch() {
        int leadingEigenvalue = Util.largestAbsIndex(eigVals);
        int numElts = eigVals.length;
        double[] xi2 = new double[numElts];  // x_i^2 or the square of leading eigenvector

        // Want the square of the values in the leading eigenvector in a double array
        for (int i = 0; i<numElts; i++) {
            xi2[i] = this.V.get(i, leadingEigenvalue) * this.V.get(i, leadingEigenvalue);
        }

        System.out.println("leading Eigenvalue position = " + leadingEigenvalue);
        System.out.print("printing xi: ")  ; Util.print(xi2);
        *//*double[][] test = new double[numElts][numElts];
        for (int i = 0; i<numElts; i++) {
            for (int j = 0; j<numElts; j++) {
                test[i][j] = this.V.get(i,j);
            }
        }
        Util.print(test);
        *//*
        return Util.largestAbsIndexCheck(xi2, " in calculating Vi");
    }*/
}