def plot_ppca_model_2D(N=1000, D=2, M=1):
# z: [N, M], M = 1
p_z = Normal(loc=0.0, scale=1.0)
z = p_z.sample(sample_shape=(N, ))
# w: [M, D], M = 1, D = 2
p_w = Normal(loc=torch.zeros([M, D]), scale=torch.ones([M, D])).to_event(D)
w = p_w.sample()
print(w)
plot_dist(z, label="p(z)", name="z_hist1D")
def generate(self, num_to_sample: int = 1):
"""Generate samples from prior."""
cuda_device = self._get_prediction_device()
prior_mean = nn_util.move_to_device(
torch.zeros((num_to_sample, self._latent_dim)),
cuda_device,
)
prior_stddev = torch.ones_like(prior_mean)
prior = Normal(prior_mean, prior_stddev)
latent = prior.sample()
generated = self._decoder.generate(latent)
return self.make_output_human_readable(generated)
