def fit(self, X, variational_dist=None, elbo_kwargs={}, **kwargs):
if variational_dist is None:
variational_dist = PPCA_Variational_V2(self)
data = Data(X)
stats = train(data, self, ELBO(variational_dist, **elbo_kwargs),
**kwargs)
return stats
def fit(self, x, **kwargs):
data = Data(x)
stats = train(data,
self.model,
self.criterion,
optimizer='RMSprop',
track_parameters=False,
**kwargs)
return stats
def test_data_dist(n_dims):
data = torch.randn(1000, n_dims)
dist = Data(data)
assert dist.sample(1).shape == (1, n_dims)
assert dist.sample(64).shape == (64, n_dims)
try:
dist.log_prob(dist.sample(64))
except NotImplementedError:
pass
assert dist.get_parameters()['n_dims'] == n_dims
assert dist.get_parameters()['n_samples'] == 1000
data = np.random.randn(100, n_dims)
dist = Data(data)
assert dist.sample(1).shape == (1, n_dims)
assert dist.sample(64).shape == (64, n_dims)
assert dist.get_parameters()['n_dims'] == n_dims
assert dist.get_parameters()['n_samples'] == 100
def fit(self, x, **kwargs):
data = Data(x)
stats = train(data, self.model, cross_entropy, **kwargs)
return stats
def fit(self, x, **kwargs):
data = Data(x)
return train(data, self, self.criterion, **kwargs)
def fit(self, R, **kwargs):
data = Data(R.view(-1, self.N * self.M))
stats = train(data, self, cross_entropy, **kwargs)
return stats
def fit(self, x, use_elbo=True, **kwargs):
data = Data(x)
if use_elbo:
return train(data, self, self.criterion, **kwargs)
return train(data, self, cross_entropy, **kwargs)