torchelie.distributions¶

class
torchelie.distributions.
GaussianMixture
(weights: torch.Tensor, locs: torch.Tensor, scales: torch.Tensor)¶ Mixture of gaussian distributions. Each tensor contains an additional dimension with number of distributions elements.
 Parameters
weights (tensor) – unnormalized weights of distributions
loc (tensor) – mean of the distributions
scale (tensor) – scale of the distributions
dim (int) – dimension reprenseting the various distributions, that will weighted and averaged on.

log_prob
(x: torch.Tensor) → torch.Tensor¶

property
mean
¶

class
torchelie.distributions.
Logistic
(loc: torch.Tensor, scale: torch.Tensor)¶ Logistic distribution
 Parameters
loc (tensor) – mean of the distribution
scale (tensor) – scale of the distribution

class
torchelie.distributions.
LogisticMixture
(weights, locs, scales, dim)¶ Mixture of Logistic distributions. Each tensor contains an additional dimension with number of distributions elements.
 Parameters
weights (tensor) – unnormalized weights of distributions
loc (tensor) – mean of the distributions
scale (tensor) – scale of the distributions
dim (int) – dimension reprenseting the various distributions, that will weighted and averaged on.

log_prob
(x: torch.Tensor) → torch.Tensor¶

property
mean
¶

torchelie.distributions.
parameterized_truncated_normal
(uniform: torch.Tensor, mu: float, sigma: float, a: float, b: float) → torch.Tensor¶ Experimental
Warning
parameterized_truncated_normal() is experimental, and may change or be deleted soon if not already broken
.

torchelie.distributions.
sample_truncated_normal
(*shape, cutoff: float = 2)¶ Experimental
Warning
sample_truncated_normal() is experimental, and may change or be deleted soon if not already broken
.

torchelie.distributions.
truncated_normal
(uniform: torch.Tensor, a: float, b: float) → torch.Tensor¶ Experimental
Warning
truncated_normal() is experimental, and may change or be deleted soon if not already broken
.