function [grad] = ComputeGradient(g,y,FreqCycl)%% [grad] = ComputeGradient(g,y)%% This function compute the gradient of the % Shalvi Weinstein cost function at point g.%% Author : Pierre JALLON% Date of creation : 04/23/2005% Date of last modification : 04/23/20005%
r = ComputeSignal(g,y);% Number of observations :L = size(g,1);% Filter length : N = size(g,2);J = ComputeCritere(r,FreqCycl);Den = mean(abs(r).^2).^2;% Computing the gradient :for (iN = 1:N)    for (iL = 1:L)                rbis = r(iL:length(r));        Longueurrbis = length(rbis);        ybis = y(iN,1:Longueurrbis);        derivee_variance = rbis.*conj(ybis);             asymetrique_derivee_variance = conj(rbis).*conj(ybis);                     dDen = 2*(mean(abs(r).^2))*mean(derivee_variance);                % Cyclic correlation coefficient free terms:        dNum =  2*mean(abs(rbis).^2.*derivee_variance) - 2*mean(r.^2)*mean(asymetrique_derivee_variance);                % Derivative of Cyclic correlation coefficient:        if (length(FreqCycl)>0)            for (iC=1:length(FreqCycl));                Exp(iC,:) = exp(-2*i*pi*FreqCycl(iC)*(1:length(r)));            end            ConjExp = conj(Exp);            CCyclicCorrelationCoeff = 1/length(r)*abs(r).^2*(Exp.');            ConjCCyclicCorrelationCoeff = 1/length(r)*abs(r).^2*(ConjExp.');            dCCyclicCorrelationCoeff = 1/length(derivee_variance)*derivee_variance*(Exp(:,iL:length(r)).');            dConjCCyclicCorrelationCoeff = 1/length(derivee_variance)*derivee_variance*(ConjExp(:,iL:length(r)).');                        dC = 4*sum(CCyclicCorrelationCoeff.*dConjCCyclicCorrelationCoeff+ConjCCyclicCorrelationCoeff.*dCCyclicCorrelationCoeff);                        NCCyclicCorrelationCoeff = 1/length(r)*(r.^2)*(Exp.');            ConjNCCyclicCorrelationCoeff = 1/length(r)*(r.^2)*(ConjExp.');            dNCCyclicCorrelationCoeff = 1/length(asymetrique_derivee_variance)*asymetrique_derivee_variance*(Exp(:,iL:length(r)).');            dConjNCCyclicCorrelationCoeff = 1/length(asymetrique_derivee_variance)*asymetrique_derivee_variance*(ConjExp(:,iL:length(r)).');                        dNC = 2*sum(NCCyclicCorrelationCoeff.*dConjNCCyclicCorrelationCoeff+ConjNCCyclicCorrelationCoeff.*dNCCyclicCorrelationCoeff);        else            dC = 0;            dNC = 0;        end        dNum = dNum - dC - dNC;                grad(iL,iN) = 2*J*1/Den*( dNum - (J+2)*dDen );    endend