double BasicCircular::perturb_hyperparameters(RNG& rng)
{
double logH = 0.;
int which = rng.rand_int(3);
if(which == 0)
{
xc += size*rng.randh();
yc += size*rng.randh();
xc = DNest4::mod(xc - x_min, x_max - x_min) + x_min;
yc = DNest4::mod(yc - y_min, y_max - y_min) + y_min;
}
else if(which == 1)
{
width = log(width);
width += log(1E3)*rng.randh();
width = DNest4::mod(width - log(1E-2*size), log(1E3)) + log(1E-2*size);
width = exp(width);
}
else if(which == 2)
{
mu = log(mu);
mu += log(mu_max/mu_min)*rng.randh();
mu = DNest4::mod(mu - log(mu_min), log(mu_max/mu_min)) + log(mu_min);
mu = exp(mu);
}
return logH;
}
double Sinusoid::perturb_parameters(RNG& rng)
{
double logH = 0.0;
// Perturb one of the three parameters
int which = rng.rand_int(3);
if(which == 0)
{
A = log(A);
logH -= -0.5*pow(A/0.1, 2);
A += 0.1*rng.randh();
logH += -0.5*pow(A/0.1, 2);
A = exp(A);
}
else if(which == 1)
{
log10_period += rng.randh();
wrap(log10_period, -1.0, 0.0);
}
else
{
phi += 2 * M_PI * rng.randh();
wrap(phi, 0.0, 2 * M_PI);
}
return logH;
}
double MyConditionalPrior::perturb_hyperparameters(RNG& rng)
{
double logH = 0.0;
int which = rng.rand_int(4);
if(which == 0)
{
logH += cauchy.perturb(location_log_period, rng);
}
else if(which == 1)
{
scale_log_period += 5.0*rng.randh();
wrap(scale_log_period, 0.0, 5.0);
}
else if(which == 2)
{
logH += cauchy.perturb(location_log_amplitude, rng);
}
else
{
scale_log_amplitude += 5.0*rng.randh();
wrap(scale_log_amplitude, 0.0, 5.0);
}
return logH;
}
double Sinusoid::perturb(RNG& rng)
{
double logH = 0.0;
if(rng.rand() < 0.5)
{
// Perturb one of the three parameters
int which = rng.rand_int(3);
if(which == 0)
{
A = 1.0 - exp(-A);
A += rng.randh();
wrap(A, 0.0, 1.0);
A = -log(1.0 - A);
}
else if(which == 1)
{
log10_period += 3.0 * rng.randh();
wrap(log10_period, log10(1E-3 * t_range), log10(t_range));
}
else
{
phi += 2 * M_PI * rng.randh();
wrap(phi, 0.0, 2 * M_PI);
}
}
else
{
// Perturb some of the ns.
if(rng.rand() <= 0.5)
{
// Just change one
int which = rng.rand_int(N);
logH -= -0.5*pow(n[which], 2);
n[which] += rng.randh();
logH += -0.5*pow(n[which], 2);
}
else
{
// Potentially regenerate many
int reps = pow(N, rng.rand());
for(int i=0; i<reps; ++i)
n[rng.rand_int(N)] = rng.randn();
}
}
assemble();
return logH;
}
double Pareto::perturb_hyperparameters(RNG& rng)
{
double logH = 0.;
int which = rng.rand_int(2);
if(which == 0)
{
f0 = log(f0);
f0 += log(f0_max/f0_min)*rng.randh();
f0 = mod(f0 - log(f0_min), log(f0_max/f0_min)) + log(f0_min);
f0 = exp(f0);
}
else
{
alpha += 4.*rng.randh();
alpha = mod(alpha - 1., 4.) + 1.;
}
return logH;
}
double Gravity::perturb(RNG& rng)
{
double logH = 0.;
int which = rng.rand_int(x.size());
int what = rng.rand_int(6);
// Remove effect of this particle
increment(which, -1);
if(what == 0)
{
x[which] += 20.*rng.randh();
x[which] = mod(x[which] + 10., 20.) - 10.;
}
else if(what == 1)
{
y[which] += 20.*rng.randh();
y[which] = mod(y[which] + 10., 20.) - 10.;
}
else if(what == 2)
{
z[which] += 20.*rng.randh();
z[which] = mod(z[which] + 10., 20.) - 10.;
}
else if(what == 3)
{
vx[which] += 20.*rng.randh();
vx[which] = mod(vx[which] + 10., 20.) - 10.;
}
else if(what == 4)
{
vy[which] += 20.*rng.randh();
vy[which] = mod(vy[which] + 10., 20.) - 10.;
}
else
{
vz[which] += 20.*rng.randh();
vz[which] = mod(vz[which] + 10., 20.) - 10.;
}
if(x[which]*x[which] + y[which]*y[which] + z[which]*z[which] > 100.)
logH = -1E250;
if(vx[which]*vx[which] + vy[which]*vy[which] + vz[which]*vz[which] > 100.)
logH = -1E250;
increment(which, +1);
staleness++;
if(staleness >= 100)
refresh();
compute_scalars();
return logH;
}
double MyModel::perturb(RNG& rng)
{
double logH = 0.;
if(rng.rand() <= 0.75)
{
logH += objects.perturb(rng);
calculate_image();
}
else
{
sigma = log(sigma);
sigma += log(1E6)*rng.randh();
sigma = mod(sigma - log(1.), log(1E6)) + log(1.);
sigma = exp(sigma);
}
return logH;
}
double MyConditionalPrior::perturb_hyperparameters(RNG& rng)
{
double logH = 0.0;
int which = rng.rand_int(2);
if(which == 0)
{
Cauchy c;
A_min = log(A_min);
logH += c.perturb(A_min, rng);
A_min = exp(A_min);
}
else
{
mu += 3.0*rng.randh();
wrap(mu, 0.0, 3.0);
}
return logH;
}
double MyModel::perturb(RNG& rng)
{
double logH = 0.;
if(rng.rand() <= 0.75)
{
logH += objects.perturb(rng);
objects.consolidate_diff();
calculate_mu();
}
else
{
sigma = log(sigma);
sigma += log(1E6)*rng.randh();
sigma = mod(sigma - log(1E-3), log(1E6)) + log(1E-3);
sigma = exp(sigma);
}
return logH;
}
double Sinusoid::perturb(RNG& rng)
{
double logH = 0.0;
int proposal_type = 1;//rng.rand_int(4);
if(proposal_type == 0)
{
// Perturb parameters, changing data along with it
std::vector<double> mu_old = mu;
logH += perturb_parameters(rng);
calculate_mu();
double n;
for(size_t i=0; i<N; ++i)
{
n = (y[i] - mu_old[i]) / sigma;
y[i] = mu[i] + sigma * n;
}
calculate_logl();
}
else if(proposal_type == 1)
{
// Perturb parameters, keeping data constant
// (aka Metropolis step of the posterior!)
logH -= logl;
logH += perturb_parameters(rng);
calculate_mu();
calculate_logl();
logH += logl;
}
else if(proposal_type == 2)
{
// Just change one datum
int which = rng.rand_int(N);
logH -= -0.5*pow((y[which] - mu[which])/sigma, 2);
y[which] += sigma * rng.randh();
logH += -0.5*pow((y[which] - mu[which])/sigma, 2);
calculate_logl();
}
else
{
// Potentially regenerate many of the data points
int reps = pow(N, rng.rand());
int which;
for(int i=0; i<reps; ++i)
{
which = rng.rand_int(N);
y[which] = mu[which] + sigma * rng.randn();
}
calculate_logl();
}
return logH;
}