tf.contrib.distributions.VectorDiffeomixture VectorDiffeomixture distribution. Inherits From: Distribution 
tf.contrib.distributions.VectorDiffeomixture(
    mix_loc, temperature, distribution, loc=None, scale=None, quadrature_size=8, qua
    drature_fn=tf.contrib.distributions.quadrature_scheme_softmaxnormal_quantiles,
    validate_args=False, allow_nan_stats=True, name='VectorDiffeomixture'
)
 A vector diffeomixture (VDM) is a distribution parameterized by a convex combination of K component loc vectors, loc[k], k = 0,...,K-1, and K scale matrices scale[k], k = 0,..., K-1. It approximates the following compound distribution p(x) = int p(x | z) p(z) dz,
where z is in the K-simplex, and
p(x | z) := p(x | loc=sum_k z[k] loc[k], scale=sum_k z[k] scale[k])
 The integral int p(x | z) p(z) dz is approximated with a quadrature scheme adapted to the mixture density p(z). The N quadrature points z_{N, n} and weights w_{N, n} (which are non-negative and sum to 1) are chosen such that as N --> infinity. Since q_N(x) is in fact a mixture (of N points), we may sample from q_N exactly. It is important to note that the VDM is defined as q_N above, and not p(x). Therefore, sampling and pdf may be implemented as exact (up to floating point error) methods. A common choice for the conditional p(x | z) is a multivariate Normal. The implemented marginal p(z) is the SoftmaxNormal, which is a K-1 dimensional Normal transformed by a SoftmaxCentered bijector, making it a density on the K-simplex. That is, Z = SoftmaxCentered(X),
X = Normal(mix_loc / temperature, 1 / temperature)
 The default quadrature scheme chooses z_{N, n} as N midpoints of the quantiles of p(z) (generalized quantiles if K > 2). See [Dillon and Langmore (2018)][1] for more details. About Vector distributions in TensorFlow. The VectorDiffeomixture is a non-standard distribution that has properties particularly useful in variational Bayesian methods. Conditioned on a draw from the SoftmaxNormal, X|z is a vector whose components are linear combinations of affine transformations, thus is itself an affine transformation. 
Note: The marginals X_1|v, ..., X_d|v are not generally identical to some parameterization of distribution. This is due to the fact that the sum of draws from distribution are not generally itself the same distribution.
 About Diffeomixtures and reparameterization. The VectorDiffeomixture is designed to be reparameterized, i.e., its parameters are only used to transform samples from a distribution which has no trainable parameters. This property is important because backprop stops at sources of stochasticity. That is, as long as the parameters are used after the underlying source of stochasticity, the computed gradient is accurate. Reparametrization means that we can use gradient-descent (via backprop) to optimize Monte-Carlo objectives. Such objectives are a finite-sample approximation of an expectation and arise throughout scientific computing. Warning: If you backprop through a VectorDiffeomixture sample and the "base" distribution is both: not FULLY_REPARAMETERIZED and a function of trainable variables, then the gradient is not guaranteed correct! Examples import tensorflow_probability as tfp
tfd = tfp.distributions

# Create two batches of VectorDiffeomixtures, one with mix_loc=[0.],
# another with mix_loc=[1]. In both cases, `K=2` and the affine
# transformations involve:
# k=0: loc=zeros(dims)  scale=LinearOperatorScaledIdentity
# k=1: loc=[2.]*dims    scale=LinOpDiag
dims = 5
vdm = tfd.VectorDiffeomixture(
    mix_loc=[[0.], [1]],
    temperature=[1.],
    distribution=tfd.Normal(loc=0., scale=1.),
    loc=[
        None,  # Equivalent to `np.zeros(dims, dtype=np.float32)`.
        np.float32([2.]*dims),
    ],
    scale=[
        tf.linalg.LinearOperatorScaledIdentity(
          num_rows=dims,
          multiplier=np.float32(1.1),
          is_positive_definite=True),
        tf.linalg.LinearOperatorDiag(
          diag=np.linspace(2.5, 3.5, dims, dtype=np.float32),
          is_positive_definite=True),
    ],
    validate_args=True)
 References [1]: Joshua Dillon and Ian Langmore. Quadrature Compound: An approximating family of distributions. arXiv preprint arXiv:1801.03080, 2018. https://arxiv.org/abs/1801.03080
 


 Args
  mix_loc   float-like Tensor with shape [b1, ..., bB, K-1]. In terms of samples, larger mix_loc[..., k] ==> Z is more likely to put more weight on its kth component.  
  temperature   float-like Tensor. Broadcastable with mix_loc. In terms of samples, smaller temperature means one component is more likely to dominate. I.e., smaller temperature makes the VDM look more like a standard mixture of K components.  
  distribution   tf.Distribution-like instance. Distribution from which d iid samples are used as input to the selected affine transformation. Must be a scalar-batch, scalar-event distribution. Typically distribution.reparameterization_type = FULLY_REPARAMETERIZED or it is a function of non-trainable parameters. WARNING: If you backprop through a VectorDiffeomixture sample and the distribution is not FULLY_REPARAMETERIZED yet is a function of trainable variables, then the gradient will be incorrect!  
  loc   Length-K list of float-type Tensors. The k-th element represents the shift used for the k-th affine transformation. If the k-th item is None, loc is implicitly 0. When specified, must have shape [B1, ..., Bb, d] where b >= 0 and d is the event size.  
  scale   Length-K list of LinearOperators. Each should be positive-definite and operate on a d-dimensional vector space. The k-th element represents the scale used for the k-th affine transformation. LinearOperators must have shape [B1, ..., Bb, d, d], b >= 0, i.e., characterizes b-batches of d x d matrices  
  quadrature_size   Python int scalar representing number of quadrature points. Larger quadrature_size means q_N(x) better approximates p(x).  
  quadrature_fn   Python callable taking normal_loc, normal_scale, quadrature_size, validate_args and returning tuple(grid, probs) representing the SoftmaxNormal grid and corresponding normalized weight. normalized) weight. Default value: quadrature_scheme_softmaxnormal_quantiles.  
  validate_args   Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.  
  allow_nan_stats   Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value "NaN" to indicate the result is undefined. When False, an exception is raised if one or more of the statistic's batch members are undefined.  
  name   Python str name prefixed to Ops created by this class.   
 


 Raises
  ValueError   if not scale or len(scale) < 2.  
  ValueError   if len(loc) != len(scale)  
  ValueError   if quadrature_grid_and_probs is not None and len(quadrature_grid_and_probs[0]) != len(quadrature_grid_and_probs[1])  
  ValueError   if validate_args and any not scale.is_positive_definite.  
  TypeError   if any scale.dtype != scale[0].dtype.  
  TypeError   if any loc.dtype != scale[0].dtype.  
  NotImplementedError   if len(scale) != 2.  
  ValueError   if not distribution.is_scalar_batch.  
  ValueError   if not distribution.is_scalar_event.   
 


 Attributes
  allow_nan_stats   Python bool describing behavior when a stat is undefined. Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1 is undefined (no clear way to say it is either + or - infinity), so the variance = E[(X - mean)**2] is also undefined. 
 
  batch_shape   Shape of a single sample from a single event index as a TensorShape. May be partially defined or unknown. The batch dimensions are indexes into independent, non-identical parameterizations of this distribution. 
 
  distribution   Base scalar-event, scalar-batch distribution.  
  dtype   The DType of Tensors handled by this Distribution.  
  endpoint_affine   Affine transformation for each of K components.  
  event_shape   Shape of a single sample from a single batch as a TensorShape. May be partially defined or unknown. 
 
  grid   Grid of mixing probabilities, one for each grid point.  
  interpolated_affine   Affine transformation for each convex combination of K components.  
  mixture_distribution   Distribution used to select a convex combination of affine transforms.  
  name   Name prepended to all ops created by this Distribution.  
  parameters   Dictionary of parameters used to instantiate this Distribution.  
  reparameterization_type   Describes how samples from the distribution are reparameterized. Currently this is one of the static instances distributions.FULLY_REPARAMETERIZED or distributions.NOT_REPARAMETERIZED. 
 
  validate_args   Python bool indicating possibly expensive checks are enabled.    Methods batch_shape_tensor View source 
batch_shape_tensor(
    name='batch_shape_tensor'
)
 Shape of a single sample from a single event index as a 1-D Tensor. The batch dimensions are indexes into independent, non-identical parameterizations of this distribution.
 


 Args
  name   name to give to the op   
 


 Returns
  batch_shape   Tensor.    cdf View source 
cdf(
    value, name='cdf'
)
 Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x]

 


 Args
  value   float or double Tensor.  
  name   Python str prepended to names of ops created by this function.   
 


 Returns
  cdf   a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.    copy View source 
copy(
    **override_parameters_kwargs
)
 Creates a deep copy of the distribution. 
Note: the copy distribution may continue to depend on the original initialization arguments.

 


 Args
  **override_parameters_kwargs   String/value dictionary of initialization arguments to override with new values.   
 


 Returns
  distribution   A new instance of type(self) initialized from the union of self.parameters and override_parameters_kwargs, i.e., dict(self.parameters, **override_parameters_kwargs).    covariance View source 
covariance(
    name='covariance'
)
 Covariance. Covariance is (possibly) defined only for non-scalar-event distributions. For example, for a length-k, vector-valued distribution, it is calculated as, Cov[i, j] = Covariance(X_i, X_j) = E[(X_i - E[X_i]) (X_j - E[X_j])]
 where Cov is a (batch of) k x k matrix, 0 <= (i, j) < k, and E denotes expectation. Alternatively, for non-vector, multivariate distributions (e.g., matrix-valued, Wishart), Covariance shall return a (batch of) matrices under some vectorization of the events, i.e., Cov[i, j] = Covariance(Vec(X)_i, Vec(X)_j) = [as above]
 where Cov is a (batch of) k' x k' matrices, 0 <= (i, j) < k' = reduce_prod(event_shape), and Vec is some function mapping indices of this distribution's event dimensions to indices of a length-k' vector.
 


 Args
  name   Python str prepended to names of ops created by this function.   
 


 Returns
  covariance   Floating-point Tensor with shape [B1, ..., Bn, k', k'] where the first n dimensions are batch coordinates and k' = reduce_prod(self.event_shape).    cross_entropy View source 
cross_entropy(
    other, name='cross_entropy'
)
 Computes the (Shannon) cross entropy. Denote this distribution (self) by P and the other distribution by Q. Assuming P, Q are absolutely continuous with respect to one another and permit densities p(x) dr(x) and q(x) dr(x), (Shanon) cross entropy is defined as: H[P, Q] = E_p[-log q(X)] = -int_F p(x) log q(x) dr(x)
 where F denotes the support of the random variable X ~ P.
 


 Args
  other   tfp.distributions.Distribution instance.  
  name   Python str prepended to names of ops created by this function.   
 


 Returns
  cross_entropy   self.dtype Tensor with shape [B1, ..., Bn] representing n different calculations of (Shanon) cross entropy.    entropy View source 
entropy(
    name='entropy'
)
 Shannon entropy in nats. event_shape_tensor View source 
event_shape_tensor(
    name='event_shape_tensor'
)
 Shape of a single sample from a single batch as a 1-D int32 Tensor.
 


 Args
  name   name to give to the op   
 


 Returns
  event_shape   Tensor.    is_scalar_batch View source 
is_scalar_batch(
    name='is_scalar_batch'
)
 Indicates that batch_shape == [].
 


 Args
  name   Python str prepended to names of ops created by this function.   
 


 Returns
  is_scalar_batch   bool scalar Tensor.    is_scalar_event View source 
is_scalar_event(
    name='is_scalar_event'
)
 Indicates that event_shape == [].
 


 Args
  name   Python str prepended to names of ops created by this function.   
 


 Returns
  is_scalar_event   bool scalar Tensor.    kl_divergence View source 
kl_divergence(
    other, name='kl_divergence'
)
 Computes the Kullback--Leibler divergence. Denote this distribution (self) by p and the other distribution by q. Assuming p, q are absolutely continuous with respect to reference measure r, the KL divergence is defined as: KL[p, q] = E_p[log(p(X)/q(X))]
         = -int_F p(x) log q(x) dr(x) + int_F p(x) log p(x) dr(x)
         = H[p, q] - H[p]
 where F denotes the support of the random variable X ~ p, H[., .] denotes (Shanon) cross entropy, and H[.] denotes (Shanon) entropy.
 


 Args
  other   tfp.distributions.Distribution instance.  
  name   Python str prepended to names of ops created by this function.   
 


 Returns
  kl_divergence   self.dtype Tensor with shape [B1, ..., Bn] representing n different calculations of the Kullback-Leibler divergence.    log_cdf View source 
log_cdf(
    value, name='log_cdf'
)
 Log cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: log_cdf(x) := Log[ P[X <= x] ]
 Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1.
 


 Args
  value   float or double Tensor.  
  name   Python str prepended to names of ops created by this function.   
 


 Returns
  logcdf   a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.    log_prob View source 
log_prob(
    value, name='log_prob'
)
 Log probability density/mass function.
 


 Args
  value   float or double Tensor.  
  name   Python str prepended to names of ops created by this function.   
 


 Returns
  log_prob   a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.    log_survival_function View source 
log_survival_function(
    value, name='log_survival_function'
)
 Log survival function. Given random variable X, the survival function is defined: log_survival_function(x) = Log[ P[X > x] ]
                         = Log[ 1 - P[X <= x] ]
                         = Log[ 1 - cdf(x) ]
 Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1.
 


 Args
  value   float or double Tensor.  
  name   Python str prepended to names of ops created by this function.   
 


 Returns   Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.  
 mean View source 
mean(
    name='mean'
)
 Mean. mode View source 
mode(
    name='mode'
)
 Mode. param_shapes View source 
@classmethod
param_shapes(
    sample_shape, name='DistributionParamShapes'
)
 Shapes of parameters given the desired shape of a call to sample(). This is a class method that describes what key/value arguments are required to instantiate the given Distribution so that a particular shape is returned for that instance's call to sample(). Subclasses should override class method _param_shapes.
 


 Args
  sample_shape   Tensor or python list/tuple. Desired shape of a call to sample().  
  name   name to prepend ops with.   
 


 Returns   dict of parameter name to Tensor shapes.  
 param_static_shapes View source 
@classmethod
param_static_shapes(
    sample_shape
)
 param_shapes with static (i.e. TensorShape) shapes. This is a class method that describes what key/value arguments are required to instantiate the given Distribution so that a particular shape is returned for that instance's call to sample(). Assumes that the sample's shape is known statically. Subclasses should override class method _param_shapes to return constant-valued tensors when constant values are fed.
 


 Args
  sample_shape   TensorShape or python list/tuple. Desired shape of a call to sample().   
 


 Returns   dict of parameter name to TensorShape.  

 


 Raises
  ValueError   if sample_shape is a TensorShape and is not fully defined.    prob View source 
prob(
    value, name='prob'
)
 Probability density/mass function.
 


 Args
  value   float or double Tensor.  
  name   Python str prepended to names of ops created by this function.   
 


 Returns
  prob   a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.    quantile View source 
quantile(
    value, name='quantile'
)
 Quantile function. Aka "inverse cdf" or "percent point function". Given random variable X and p in [0, 1], the quantile is: quantile(p) := x such that P[X <= x] == p

 


 Args
  value   float or double Tensor.  
  name   Python str prepended to names of ops created by this function.   
 


 Returns
  quantile   a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.    sample View source 
sample(
    sample_shape=(), seed=None, name='sample'
)
 Generate samples of the specified shape. Note that a call to sample() without arguments will generate a single sample.
 


 Args
  sample_shape   0D or 1D int32 Tensor. Shape of the generated samples.  
  seed   Python integer seed for RNG  
  name   name to give to the op.   
 


 Returns
  samples   a Tensor with prepended dimensions sample_shape.    stddev View source 
stddev(
    name='stddev'
)
 Standard deviation. Standard deviation is defined as, stddev = E[(X - E[X])**2]**0.5
 where X is the random variable associated with this distribution, E denotes expectation, and stddev.shape = batch_shape + event_shape.
 


 Args
  name   Python str prepended to names of ops created by this function.   
 


 Returns
  stddev   Floating-point Tensor with shape identical to batch_shape + event_shape, i.e., the same shape as self.mean().    survival_function View source 
survival_function(
    value, name='survival_function'
)
 Survival function. Given random variable X, the survival function is defined: survival_function(x) = P[X > x]
                     = 1 - P[X <= x]
                     = 1 - cdf(x).

 


 Args
  value   float or double Tensor.  
  name   Python str prepended to names of ops created by this function.   
 


 Returns   Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.  
 variance View source 
variance(
    name='variance'
)
 Variance. Variance is defined as, Var = E[(X - E[X])**2]
 where X is the random variable associated with this distribution, E denotes expectation, and Var.shape = batch_shape + event_shape.
 


 Args
  name   Python str prepended to names of ops created by this function.   
 


 Returns
  variance   Floating-point Tensor with shape identical to batch_shape + event_shape, i.e., the same shape as self.mean().     

    
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