void Sinusoid::generate(RNG& rng)
{
A = exp(0.1*rng.randn());
log10_period = -rng.rand();
phi = 2.0 * M_PI * rng.rand();
calculate_mu();
for(size_t i=0; i<N; ++i)
y[i] = mu[i] + sigma*rng.randn();
calculate_logl();
}
void Sinusoid::generate(RNG& rng)
{
A = -log(1.0 - rng.rand());
log10_period = log10(1E-3 * t_range) + 3.0 * rng.rand();
phi = 2.0 * M_PI * rng.rand();
for(double& nn : n)
nn = rng.randn();
assemble();
}
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 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;
}
