IMPISD_BEGIN_NAMESPACE
GaussianProcessInterpolation::GaussianProcessInterpolation(
FloatsList x, Floats sample_mean, Floats sample_std, unsigned n_obs,
UnivariateFunction *mean_function, BivariateFunction *covariance_function,
Particle *sigma, double sparse_cutoff)
: Object("GaussianProcessInterpolation%1%"),
x_(x),
n_obs_(n_obs),
mean_function_(mean_function),
covariance_function_(covariance_function),
sigma_(sigma),
cutoff_(sparse_cutoff) {
// O(M)
// store dimensions
M_ = x.size();
N_ = x[0].size();
sigma_val_ = Scale(sigma_).get_nuisance();
// basic checks
IMP_USAGE_CHECK(sample_mean.size() == M_,
"sample_mean should have the same size as x");
IMP_USAGE_CHECK(sample_std.size() == M_,
"sample_std should have the same size as x");
IMP_USAGE_CHECK(mean_function->get_ndims_x() == N_,
"mean_function should have " << N_ << " input dimensions");
IMP_USAGE_CHECK(mean_function->get_ndims_y() == 1,
"mean_function should have 1 output dimension");
IMP_USAGE_CHECK(covariance_function->get_ndims_x1() == N_,
"covariance_function should have "
<< N_ << " input dimensions for first vector");
IMP_USAGE_CHECK(covariance_function->get_ndims_x2() == N_,
"covariance_function should have "
<< N_ << " input dimensions for second vector");
IMP_USAGE_CHECK(covariance_function->get_ndims_y() == 1,
"covariance_function should have 1 output dimension");
// set all flags to false = need update.
force_mean_update();
force_covariance_update();
// compute needed matrices
compute_I(sample_mean);
compute_S(sample_std);
}
void _calculate_parameters(double h,my_point p[],double w[],int num) {
double H,I,J,K,L,A0, A1;
double x,y,d;
if (num > MAX_POINTS_NUM) {
fprintf(stderr,"Point number is larger than previous set!\n");
return;
}
is_set_ret = false;
compute_Aj(h,w,num);
H = compute_H(p,num);
I = compute_I(p,num);
J = compute_J(p,num);
K = compute_K(p,num);
L = compute_L(p,num);
A0 = H -h*h*J*J-K+h*h*L*L;
A1 = 2*(I-h*h*J*L);
// printf("H=%.3lf I=%.3lf J=%.3lf K=%.3lf L=%.3lf A0=%.3lf A1=%.3lf\n",
// H,I,J,K,L,A0,A1);
// printf("Calculated as follows:\n");
if (0 == A0) {
if (0 == A1) { // A0 A1 are0
// x,y could be any value
#if 1
printf("The distribution of the given points is a circle.\n");
x = y = sqrt(2.0) / 2;
d = -(h*h*(J*x+L*y));
compute_error(d,x,y,p,num);
#else
#endif
}
else { // A0 is 0 A1 is not 0，x2=1/2,x2+y2=1
double ar[2] = {sqrt(2.0)/2,-sqrt(2.0)/2};// possible values of x,y
int i,j;
for (i=0;i<2;i++) {
x = ar[i];
for (j=0;j<2;j++) {
y = ar[j];
d = -(h*h*(J*x+L*y));
compute_error(d,x,y,p,num);
}
}
}
}
else if (0 == A1) {
double x_ar[4] = {0,0,1,-1};
double y_ar[4] = {1,-1,0,0};//possible values of x,y
int i;
for (i=0;i<4;i++) {
x = x_ar[i];
y = y_ar[i];
d = -(h*h*(J*x+L*y));
compute_error(d,x,y,p,num);
}
}
else { // A0!=0 A1!=0
double t = A0 / sqrt (A1*A1+A0*A0); // 0 < t < 1
double x_ar[4] = {sqrt (0.5*(1+t)),sqrt (0.5*(1-t)),
-sqrt (0.5*(1+t)),-sqrt (0.5*(1-t))}; // possible values of x , x2 ≠ 0 or 1
int i;
for (i=0;i<4;i++) {
x = x_ar[i];
y = (A1/A0)* (x - 0.5/x);
d = -(h*h*(J*x+L*y));
compute_error(d,x,y,p,num);
}
}
}
