tensorplay.distributions

Classes

`tensorplay.distributions.AbsTransform`	Transform via the mapping $y = \|x\|$ .
`tensorplay.distributions.AffineTransform`	Transform via the pointwise affine mapping $y = \text{loc} + \text{scale} \times x$ .
`tensorplay.distributions.Bernoulli`	Creates a Bernoulli distribution parameterized by `probs` or `logits` (but not both).
`tensorplay.distributions.Beta`	Beta distribution parameterized by `concentration1` and `concentration0`.
`tensorplay.distributions.Binomial`	Creates a Binomial distribution parameterized by `total_count` and either `probs` or `logits` (but not both).
`tensorplay.distributions.CatTransform`	Transform functor that applies a sequence of transforms tseq component-wise to each submatrix at dim, of length lengths[dim], in a way compatible with `tensorplay.cat()`.
`tensorplay.distributions.Categorical`	Creates a categorical distribution parameterized by either `probs` or `logits` (but not both).
`tensorplay.distributions.Cauchy`	Samples from a Cauchy (Lorentz) distribution.
`tensorplay.distributions.Chi2`	Creates a Chi-squared distribution parameterized by shape parameter `df`.
`tensorplay.distributions.ComposeTransform`	Composes multiple transforms in a chain.
`tensorplay.distributions.ContinuousBernoulli`	Creates a continuous Bernoulli distribution parameterized by `probs` or `logits` (but not both).
`tensorplay.distributions.CorrCholeskyTransform`	Transforms an unconstrained real vector $x$ with length $D*(D-1)/2$ into the Cholesky factor of a D-dimension correlation matrix.
`tensorplay.distributions.CumulativeDistributionTransform`	Transform via the cumulative distribution function of a probability distribution.
`tensorplay.distributions.Dirichlet`	Creates a Dirichlet distribution parameterized by concentration `concentration`.
`tensorplay.distributions.Distribution`	Distribution is the abstract base class for probability distributions.
`tensorplay.distributions.ExpTransform`	Transform via the mapping $y = \exp(x)$ .
`tensorplay.distributions.Exponential`	Creates an Exponential distribution parameterized by `rate`.
`tensorplay.distributions.ExponentialFamily`	ExponentialFamily is the abstract base class for probability distributions belonging to an exponential family, whose probability mass/density function is defined below
`tensorplay.distributions.FisherSnedecor`	Creates a Fisher-Snedecor distribution parameterized by `df1` and `df2`.
`tensorplay.distributions.Gamma`	Creates a Gamma distribution parameterized by shape `concentration` and `rate`.
`tensorplay.distributions.GeneralizedPareto`	Creates a Generalized Pareto distribution parameterized by `loc`, `scale`, and `concentration`.
`tensorplay.distributions.Geometric`	Creates a Geometric distribution parameterized by `probs`, where `probs` is the probability of success of Bernoulli trials.
`tensorplay.distributions.Gumbel`	Samples from a Gumbel Distribution.
`tensorplay.distributions.HalfCauchy`	Creates a half-Cauchy distribution parameterized by scale where.
`tensorplay.distributions.HalfNormal`	Creates a half-normal distribution parameterized by scale where.
`tensorplay.distributions.Independent`	Reinterprets some of the batch dims of a distribution as event dims.
`tensorplay.distributions.IndependentTransform`	Wrapper around another transform to treat `reinterpreted_batch_ndims`-many extra of the right most dimensions as dependent.
`tensorplay.distributions.InverseGamma`	Creates an inverse gamma distribution parameterized by `concentration` and `rate` where.
`tensorplay.distributions.Kumaraswamy`	Samples from a Kumaraswamy distribution.
`tensorplay.distributions.LKJCholesky`	LKJ distribution for lower Cholesky factor of correlation matrices. The distribution is controlled by `concentration` parameter $\eta$ to make the probability of the correlation matrix $M$ generated from a Cholesky factor proportional to $\det(M)^{\eta - 1}$ . Because of that, when `concentration == 1`, we have a uniform distribution over Cholesky factors of correlation matrices::.
`tensorplay.distributions.Laplace`	Creates a Laplace distribution parameterized by `loc` and `scale`.
`tensorplay.distributions.LogNormal`	Creates a log-normal distribution parameterized by `loc` and `scale` where.
`tensorplay.distributions.LogisticNormal`	Creates a logistic-normal distribution parameterized by `loc` and `scale` that define the base Normal distribution transformed with the StickBreakingTransform such that.
`tensorplay.distributions.LowRankMultivariateNormal`	Creates a multivariate normal distribution with covariance matrix having a low-rank form parameterized by `cov_factor` and `cov_diag`.
`tensorplay.distributions.LowerCholeskyTransform`	Transform from unconstrained matrices to lower-triangular matrices with nonnegative diagonal entries.
`tensorplay.distributions.MixtureSameFamily`	The MixtureSameFamily distribution implements a (batch of) mixture distribution where all components are from different parameterizations of the same distribution type.
`tensorplay.distributions.Multinomial`	Creates a Multinomial distribution parameterized by `total_count` and either `probs` or `logits` (but not both).
`tensorplay.distributions.MultivariateNormal`	Creates a multivariate normal (also called Gaussian) distribution parameterized by a mean vector and a covariance matrix.
`tensorplay.distributions.NegativeBinomial`	Creates a Negative Binomial distribution, i.e. distribution of the number of successful independent and identical Bernoulli trials before `total_count` failures are achieved.
`tensorplay.distributions.Normal`	Creates a normal (also called Gaussian) distribution parameterized by `loc` and `scale`.
`tensorplay.distributions.OneHotCategorical`	Creates a one-hot categorical distribution parameterized by `probs` or `logits`.
`tensorplay.distributions.OneHotCategoricalStraightThrough`	Creates a reparameterizable `OneHotCategorical` distribution based on the straight- through gradient estimator from [1].
`tensorplay.distributions.Pareto`	Samples from a Pareto Type 1 distribution.
`tensorplay.distributions.Poisson`	Creates a Poisson distribution parameterized by `rate`, the rate parameter.
`tensorplay.distributions.PositiveDefiniteTransform`	Transform from unconstrained matrices to positive-definite matrices.
`tensorplay.distributions.PowerTransform`	Transform via the mapping $y = x^{\text{exponent}}$ .
`tensorplay.distributions.RelaxedBernoulli`	Creates a RelaxedBernoulli distribution, parameterized by `temperature`, and either `probs` or `logits` (but not both).
`tensorplay.distributions.RelaxedOneHotCategorical`	Creates a RelaxedOneHotCategorical distribution parameterized by `temperature`, and either `probs` or `logits`.
`tensorplay.distributions.ReshapeTransform`	Unit Jacobian transform to reshape the rightmost part of a tensor.
`tensorplay.distributions.SigmoidTransform`	Transform via the mapping $y = \frac{1}{1 + \exp(-x)}$ and $x = \text{logit}(y)$ .
`tensorplay.distributions.SoftmaxTransform`	Transform from unconstrained space to the simplex via $y = \exp(x)$ then normalizing.
`tensorplay.distributions.SoftplusTransform`	Transform via the mapping $\text{Softplus}(x) = \log(1 + \exp(x))$ .
`tensorplay.distributions.StackTransform`	Transform functor that applies a sequence of transforms tseq component-wise to each submatrix at dim in a way compatible with `tensorplay.stack()`.
`tensorplay.distributions.StickBreakingTransform`	Transform from unconstrained space to the simplex of one additional dimension via a stick-breaking process.
`tensorplay.distributions.StudentT`	Creates a Student's t-distribution parameterized by degree of freedom `df`, mean `loc` and scale `scale`.
`tensorplay.distributions.TanhTransform`	Transform via the mapping $y = \tanh(x)$ .
`tensorplay.distributions.Transform`	Abstract class for invertible transformations with computable log det jacobians.
`tensorplay.distributions.TransformedDistribution`	Extension of the Distribution class, which applies a sequence of Transforms to a base distribution. Let f be the composition of transforms applied::.
`tensorplay.distributions.Uniform`	Generates uniformly distributed random samples from the half-open interval `[low, high)`.
`tensorplay.distributions.VonMises`	A circular von Mises distribution.
`tensorplay.distributions.Weibull`	Samples from a two-parameter Weibull distribution.
`tensorplay.distributions.Wishart`	Creates a Wishart distribution parameterized by a symmetric positive definite matrix $\Sigma$ , or its Cholesky decomposition $\mathbf{\Sigma} = \mathbf{L}\mathbf{L}^\top$

Functions

`tensorplay.distributions.kl_divergence`	Compute Kullback-Leibler divergence $KL(p \\| q)$ between two distributions.
`tensorplay.distributions.register_kl`	Decorator to register a pairwise function with `kl_divergence()`. Usage::.

tensorplay.distributions

Classes

Functions

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