def predict_ratio(self, theta0, theta1, x, log=False):
# If just one theta given, broadcast to number of samples
theta0 = expand_array_2d(theta0, x.shape[0])
theta1 = expand_array_2d(theta1, x.shape[0])
self.global_model = self.global_model.to(self.device, self.dtype)
theta0_tensor = tensor(theta0).to(self.device, self.dtype)
theta1_tensor = tensor(theta1).to(self.device, self.dtype)
x_tensor = tensor(x).to(self.device, self.dtype)
_, log_likelihood_theta0 = self.global_model.log_likelihood(
theta0_tensor, x_tensor)
_, log_likelihood_theta1 = self.global_model.log_likelihood(
theta1_tensor, x_tensor)
log_likelihood_theta0 = log_likelihood_theta0.detach().numpy()
log_likelihood_theta1 = log_likelihood_theta1.detach().numpy()
if log:
return log_likelihood_theta0 - log_likelihood_theta1
return np.exp(log_likelihood_theta0 - log_likelihood_theta1)
def predict_score(self, theta, x):
# If just one theta given, broadcast to number of samples
theta = expand_array_2d(theta, x.shape[0])
self.maf = self.maf.to(self.device, self.dtype)
theta_tensor = tensor(theta).to(self.device, self.dtype)
x_tensor = tensor(x).to(self.device, self.dtype)
_, _, score = self.maf.log_likelihood_and_score(theta_tensor, x_tensor)
score = score.detach().numpy()
return score
def predict_checkpoint_scores(self, theta, z_checkpoints):
# If just one theta given, broadcast to number of samples
theta = expand_array_2d(theta, z_checkpoints.shape[0])
self.step_model = self.step_model.to(self.device, self.dtype)
theta_tensor = tensor(theta).to(self.device, self.dtype)
z_checkpoints = tensor(z_checkpoints).to(self.device, self.dtype)
that_xv_checkpoints = self.step_model.forward_trajectory(
theta_tensor, z_checkpoints)
that_xv_checkpoints = that_xv_checkpoints.detach().numpy()
return that_xv_checkpoints
def predict_density(self, theta, x, log=False):
# If just one theta given, broadcast to number of samples
theta = expand_array_2d(theta, x.shape[0])
self.maf = self.maf.to(self.device, self.dtype)
theta_tensor = tensor(theta).to(self.device, self.dtype)
x_tensor = tensor(x).to(self.device, self.dtype)
_, log_likelihood = self.maf.log_likelihood(theta_tensor, x_tensor)
log_likelihood = log_likelihood.detach().numpy()
if log:
return log_likelihood
return np.exp(log_likelihood)