distributions utils

class scconfluence.distributions.NegativeBinomial(total_count: Tensor | None = None, probs: Tensor | None = None, logits: Tensor | None = None, mu: Tensor | None = None, theta: Tensor | None = None, validate_args: bool = False)

Bases: Distribution

Negative binomial distribution. One of the following parameterizations must be provided: (1), (total_count, probs) where total_count is the number of failures until the experiment is stopped and probs the success probability. (2), (mu, theta) parameterization, which is the one used by scvi-tools. These parameters respectively control the mean and inverse dispersion of the distribution.

arg_constraints = {'mu': GreaterThanEq(lower_bound=0), 'theta': GreaterThanEq(lower_bound=0)}

log_prob(value: Tensor) → Tensor

Returns the log of the probability density/mass function evaluated at value.

Parameters:

value (Tensor) –

property mean

Returns the mean of the distribution.

sample(sample_shape: Size | Tuple = torch.Size([])) → Tensor

Generates a sample_shape shaped sample or sample_shape shaped batch of samples if the distribution parameters are batched.

support = IntegerGreaterThan(lower_bound=0)

property variance

Returns the variance of the distribution.

class scconfluence.distributions.ZeroInflatedNegativeBinomial(total_count: Tensor | None = None, probs: Tensor | None = None, logits: Tensor | None = None, mu: Tensor | None = None, theta: Tensor | None = None, zi_logits: Tensor | None = None, validate_args: bool = False)

Bases: NegativeBinomial

Zero-inflated negative binomial distribution. One of the following parameterizations must be provided: (1), (total_count, probs) where total_count is the number of failures until the experiment is stopped and probs the success probability. (2), (mu, theta) parameterization, which is the one used by scvi-tools. These parameters respectively control the mean and inverse dispersion of the distribution.

arg_constraints = {'mu': GreaterThanEq(lower_bound=0), 'theta': GreaterThanEq(lower_bound=0), 'zi_logits': Real(), 'zi_probs': HalfOpenInterval(lower_bound=0.0, upper_bound=1.0)}

log_prob(value: Tensor) → Tensor

Returns the log of the probability density/mass function evaluated at value.

Parameters:

value (Tensor) –

property mean

Returns the mean of the distribution.

sample(sample_shape: Size | Tuple = torch.Size([])) → Tensor

Generates a sample_shape shaped sample or sample_shape shaped batch of samples if the distribution parameters are batched.

support = IntegerGreaterThan(lower_bound=0)

property variance

Returns the variance of the distribution.

property zi_logits: Tensor

property zi_probs: Tensor

scconfluence.distributions.log_nb_positive(x: Tensor, mu: Tensor, theta: Tensor, eps=1e-08)

Log likelihood (scalar) of a minibatch according to a nb model.

scconfluence.distributions.log_zinb_positive(x: Tensor, mu: Tensor, theta: Tensor, pi: Tensor, eps=1e-08)

Log likelihood (scalar) of a minibatch according to a zinb model.