void potential_Neuro(double *y, double *x, double *xh, int dy, int dx, int time, int N, double *theta, double *result)
{
int i,j,k;
int npx=pow(dx,2);
double lambda, fy;
for(j=0;j<dy;j++)
{
fy=logfactorial((int)y[dy*time+j]);
for(i=0;i<N;i++)
{
if(j==0)
{
result[i]=0;
}
lambda=theta[dx+j];
for(k=0;k<dx;k++)
{
lambda+=theta[dx+dy+npx+j*dx+k]*x[dx*i+k];
}
if(lambda>700)
{
result[i]+=-exp(700)+y[dy*time+j]*lambda-fy;
}
else
{
result[i]+=-exp(lambda)+y[dy*time+j]*lambda-fy;
}
}
}
}
double LogPoissonProb(double mean, unsigned n)
{
return -1*mean + n*log(mean) - logfactorial(n);
}
double logchoose(int n, int k)
{
return logfactorial(n)-logfactorial(k)-logfactorial(n-k);
}
