ed.models.VectorExponentialDiag

Class VectorExponentialDiag

Inherits From: RandomVariable

The vectorization of the Exponential distribution on R^k.

The vector exponential distribution is defined over a subset of R^k, and parameterized by a (batch of) length-k loc vector and a (batch of) k x k scale matrix: covariance = scale @ scale.T, where @ denotes matrix-multiplication.

Mathematical Details

The probability density function (pdf) is defined over the image of the scale matrix + loc, applied to the positive half-space: Supp = {loc + scale @ x : x in R^k, x_1 > 0, ..., x_k > 0}. On this set,

pdf(y; loc, scale) = exp(-||x||_1) / Z,  for y in Supp
x = inv(scale) @ (y - loc),
Z = |det(scale)|,

where:

  • loc is a vector in R^k,
  • scale is a linear operator in R^{k x k}, cov = scale @ scale.T,
  • Z denotes the normalization constant, and,
  • ||x||_1 denotes the l1 norm of x, sum_i |x_i|.

The VectorExponential distribution is a member of the location-scale family, i.e., it can be constructed as,

X = (X_1, ..., X_k), each X_i ~ Exponential(rate=1)
Y = (Y_1, ...,Y_k) = scale @ X + loc

About VectorExponential and Vector distributions in TensorFlow.

The VectorExponential is a non-standard distribution that has useful properties.

The marginals Y_1, ..., Y_k are not Exponential random variables, due to the fact that the sum of Exponential random variables is not Exponential.

Instead, Y is a vector whose components are linear combinations of Exponential random variables. Thus, Y lives in the vector space generated by vectors of Exponential distributions. This allows the user to decide the mean and covariance (by setting loc and scale), while preserving some properties of the Exponential distribution. In particular, the tails of Y_i will be (up to polynomial factors) exponentially decaying.

To see this last statement, note that the pdf of Y_i is the convolution of the pdf of k independent Exponential random variables. One can then show by induction that distributions with exponential (up to polynomial factors) tails are closed under convolution.

Examples

ds = tf.contrib.distributions
la = tf.contrib.linalg

# Initialize a single 2-variate VectorExponential, supported on
# {(x, y) in R^2 : x > 0, y > 0}.

# The first component has pdf exp{-x}, the second 0.5 exp{-x / 2}
vex = ds.VectorExponentialDiag(scale_diag=[1., 2.])

# Compute the pdf of an`R^2` observation; return a scalar.
vex.prob([3., 4.]).eval()  # shape: []

# Initialize a 2-batch of 3-variate Vector Exponential's.
loc = [[1., 2, 3],
       [1., 0, 0]]              # shape: [2, 3]
scale_diag = [[1., 2, 3],
              [0.5, 1, 1.5]]     # shape: [2, 3]

vex = ds.VectorExponentialDiag(loc, scale_diag)

# Compute the pdf of two `R^3` observations; return a length-2 vector.
x = [[1.9, 2.2, 3.1],
     [10., 1.0, 9.0]]     # shape: [2, 3]
vex.prob(x).eval()    # shape: [2]

Properties

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.

Returns:

  • allow_nan_stats: Python bool.

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.

Returns:

  • batch_shape: TensorShape, possibly unknown.

bijector

Function transforming x => y.

distribution

Base distribution, p(x).

dtype

The DType of Tensors handled by this Distribution.

event_shape

Shape of a single sample from a single batch as a TensorShape.

May be partially defined or unknown.

Returns:

  • event_shape: TensorShape, possibly unknown.

loc

The loc Tensor in Y = scale @ X + loc.

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.

Returns:

An instance of ReparameterizationType.

sample_shape

Sample shape of random variable.

scale

The scale LinearOperator in Y = scale @ X + loc.

shape

Shape of random variable.

validate_args

Python bool indicating possibly expensive checks are enabled.

Methods

init

__init__(
    *args,
    **kwargs
)

Construct Vector Exponential distribution supported on a subset of R^k.

The batch_shape is the broadcast shape between loc and scale arguments.

The event_shape is given by last dimension of the matrix implied by scale. The last dimension of loc (if provided) must broadcast with this.

Recall that covariance = scale @ scale.T.

scale = diag(scale_diag + scale_identity_multiplier * ones(k))

where:

  • scale_diag.shape = [k], and,
  • scale_identity_multiplier.shape = [].

Additional leading dimensions (if any) will index batches.

If both scale_diag and scale_identity_multiplier are None, then scale is the Identity matrix.

Args:

  • loc: Floating-point Tensor. If this is set to None, loc is implicitly 0. When specified, may have shape [B1, ..., Bb, k] where b >= 0 and k is the event size.
  • scale_diag: Non-zero, floating-point Tensor representing a diagonal matrix added to scale. May have shape [B1, ..., Bb, k], b >= 0, and characterizes b-batches of k x k diagonal matrices added to scale. When both scale_identity_multiplier and scale_diag are None then scale is the Identity.
  • scale_identity_multiplier: Non-zero, floating-point Tensor representing a scaled-identity-matrix added to scale. May have shape [B1, ..., Bb], b >= 0, and characterizes b-batches of scaled k x k identity matrices added to scale. When both scale_identity_multiplier and scale_diag are None then scale is the Identity.
  • 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 at most scale_identity_multiplier is specified.

abs

__abs__(
    a,
    *args
)

Computes the absolute value of a tensor.

Given a tensor x of complex numbers, this operation returns a tensor of type float32 or float64 that is the absolute value of each element in x. All elements in x must be complex numbers of the form \(a + bj\). The absolute value is computed as \( \). For example:

# tensor 'x' is [[-2.25 + 4.75j], [-3.25 + 5.75j]]
tf.complex_abs(x) ==> [5.25594902, 6.60492229]

Args:

  • x: A Tensor or SparseTensor of type float32, float64, int32, int64, complex64 or complex128.
  • name: A name for the operation (optional).

Returns:

A Tensor or SparseTensor the same size and type as x with absolute values. Note, for complex64 or complex128' input, the returnedTensorwill be of typefloat32orfloat64`, respectively.

add

__add__(
    a,
    *args
)

Returns x + y element-wise.

NOTE: Add supports broadcasting. AddN does not. More about broadcasting here

Args:

  • x: A Tensor. Must be one of the following types: half, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128, string.
  • y: A Tensor. Must have the same type as x.
  • name: A name for the operation (optional).

Returns:

A Tensor. Has the same type as x.

and

__and__(
    a,
    *args
)

Returns the truth value of x AND y element-wise.

NOTE: LogicalAnd supports broadcasting. More about broadcasting here

Args:

  • x: A Tensor of type bool.
  • y: A Tensor of type bool.
  • name: A name for the operation (optional).

Returns:

A Tensor of type bool.

bool

__bool__()

div

__div__(
    a,
    *args
)

Divide two values using Python 2 semantics. Used for Tensor.div.

Args:

  • x: Tensor numerator of real numeric type.
  • y: Tensor denominator of real numeric type.
  • name: A name for the operation (optional).

Returns:

x / y returns the quotient of x and y.

eq

__eq__(other)

floordiv

__floordiv__(
    a,
    *args
)

Divides x / y elementwise, rounding toward the most negative integer.

The same as tf.div(x,y) for integers, but uses tf.floor(tf.div(x,y)) for floating point arguments so that the result is always an integer (though possibly an integer represented as floating point). This op is generated by x // y floor division in Python 3 and in Python 2.7 with from __future__ import division.

Note that for efficiency, floordiv uses C semantics for negative numbers (unlike Python and Numpy).

x and y must have the same type, and the result will have the same type as well.

Args:

  • x: Tensor numerator of real numeric type.
  • y: Tensor denominator of real numeric type.
  • name: A name for the operation (optional).

Returns:

x / y rounded down (except possibly towards zero for negative integers).

Raises:

  • TypeError: If the inputs are complex.

ge

__ge__(
    a,
    *args
)

Returns the truth value of (x >= y) element-wise.

NOTE: GreaterEqual supports broadcasting. More about broadcasting here

Args:

  • x: A Tensor. Must be one of the following types: float32, float64, int32, int64, uint8, int16, int8, uint16, half.
  • y: A Tensor. Must have the same type as x.
  • name: A name for the operation (optional).

Returns:

A Tensor of type bool.

getitem

__getitem__(
    a,
    *args
)

Overload for Tensor.getitem.

This operation extracts the specified region from the tensor. The notation is similar to NumPy with the restriction that currently only support basic indexing. That means that using a tensor as input is not currently allowed

Some useful examples:

# strip leading and trailing 2 elements
foo = tf.constant([1,2,3,4,5,6])
print(foo[2:-2].eval()) # => [3,4]

# skip every row and reverse every column
foo = tf.constant([[1,2,3], [4,5,6], [7,8,9]])
print(foo[::2,::-1].eval()) # => [[3,2,1], [9,8,7]]

# Insert another dimension
foo = tf.constant([[1,2,3], [4,5,6], [7,8,9]])
print(foo[tf.newaxis, :, :].eval()) # => [[[1,2,3], [4,5,6], [7,8,9]]]
print(foo[:, tf.newaxis, :].eval()) # => [[[1,2,3]], [[4,5,6]], [[7,8,9]]]
print(foo[:, :, tf.newaxis].eval()) # => [[[1],[2],[3]], [[4],[5],[6]], [[7],[8],[9]]]

# Ellipses (3 equivalent operations)
foo = tf.constant([[1,2,3], [4,5,6], [7,8,9]])
print(foo[tf.newaxis, :, :].eval()) # => [[[1,2,3], [4,5,6], [7,8,9]]]
print(foo[tf.newaxis, ...].eval()) # => [[[1,2,3], [4,5,6], [7,8,9]]]
print(foo[tf.newaxis].eval()) # => [[[1,2,3], [4,5,6], [7,8,9]]]

Notes: - tf.newaxis is None as in NumPy. - An implicit ellipsis is placed at the end of the slice_spec - NumPy advanced indexing is currently not supported.

Args:

  • tensor: An ops.Tensor object.
  • slice_spec: The arguments to Tensor.getitem.
  • var: In the case of variable slice assignment, the Variable object to slice (i.e. tensor is the read-only view of this variable).

Returns:

The appropriate slice of "tensor", based on "slice_spec".

Raises:

  • ValueError: If a slice range is negative size.
  • TypeError: If the slice indices aren't int, slice, or Ellipsis.

gt

__gt__(
    a,
    *args
)

Returns the truth value of (x > y) element-wise.

NOTE: Greater supports broadcasting. More about broadcasting here

Args:

  • x: A Tensor. Must be one of the following types: float32, float64, int32, int64, uint8, int16, int8, uint16, half.
  • y: A Tensor. Must have the same type as x.
  • name: A name for the operation (optional).

Returns:

A Tensor of type bool.

invert

__invert__(
    a,
    *args
)

Returns the truth value of NOT x element-wise.

Args:

  • x: A Tensor of type bool.
  • name: A name for the operation (optional).

Returns:

A Tensor of type bool.

iter

__iter__()

le

__le__(
    a,
    *args
)

Returns the truth value of (x <= y) element-wise.

NOTE: LessEqual supports broadcasting. More about broadcasting here

Args:

  • x: A Tensor. Must be one of the following types: float32, float64, int32, int64, uint8, int16, int8, uint16, half.
  • y: A Tensor. Must have the same type as x.
  • name: A name for the operation (optional).

Returns:

A Tensor of type bool.

lt

__lt__(
    a,
    *args
)

Returns the truth value of (x < y) element-wise.

NOTE: Less supports broadcasting. More about broadcasting here

Args:

  • x: A Tensor. Must be one of the following types: float32, float64, int32, int64, uint8, int16, int8, uint16, half.
  • y: A Tensor. Must have the same type as x.
  • name: A name for the operation (optional).

Returns:

A Tensor of type bool.

matmul

__matmul__(
    a,
    *args
)

Multiplies matrix a by matrix b, producing a * b.

The inputs must, following any transpositions, be tensors of rank >= 2 where the inner 2 dimensions specify valid matrix multiplication arguments, and any further outer dimensions match.

Both matrices must be of the same type. The supported types are: float16, float32, float64, int32, complex64, complex128.

Either matrix can be transposed or adjointed (conjugated and transposed) on the fly by setting one of the corresponding flag to True. These are False by default.

If one or both of the matrices contain a lot of zeros, a more efficient multiplication algorithm can be used by setting the corresponding a_is_sparse or b_is_sparse flag to True. These are False by default. This optimization is only available for plain matrices (rank-2 tensors) with datatypes bfloat16 or float32.

For example:

# 2-D tensor `a`
a = tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3]) => [[1. 2. 3.]
                                                      [4. 5. 6.]]
# 2-D tensor `b`
b = tf.constant([7, 8, 9, 10, 11, 12], shape=[3, 2]) => [[7. 8.]
                                                         [9. 10.]
                                                         [11. 12.]]
c = tf.matmul(a, b) => [[58 64]
                        [139 154]]


# 3-D tensor `a`
a = tf.constant(np.arange(1, 13, dtype=np.int32),
                shape=[2, 2, 3])                  => [[[ 1.  2.  3.]
                                                       [ 4.  5.  6.]],
                                                      [[ 7.  8.  9.]
                                                       [10. 11. 12.]]]

# 3-D tensor `b`
b = tf.constant(np.arange(13, 25, dtype=np.int32),
                shape=[2, 3, 2])                   => [[[13. 14.]
                                                        [15. 16.]
                                                        [17. 18.]],
                                                       [[19. 20.]
                                                        [21. 22.]
                                                        [23. 24.]]]
c = tf.matmul(a, b) => [[[ 94 100]
                         [229 244]],
                        [[508 532]
                         [697 730]]]

# Since python >= 3.5 the @ operator is supported (see PEP 465).
# In TensorFlow, it simply calls the `tf.matmul()` function, so the
# following lines are equivalent:
d = a @ b @ [[10.], [11.]]
d = tf.matmul(tf.matmul(a, b), [[10.], [11.]])

Args:

  • a: Tensor of type float16, float32, float64, int32, complex64, complex128 and rank > 1.
  • b: Tensor with same type and rank as a.
  • transpose_a: If True, a is transposed before multiplication.
  • transpose_b: If True, b is transposed before multiplication.
  • adjoint_a: If True, a is conjugated and transposed before multiplication.
  • adjoint_b: If True, b is conjugated and transposed before multiplication.
  • a_is_sparse: If True, a is treated as a sparse matrix.
  • b_is_sparse: If True, b is treated as a sparse matrix.
  • name: Name for the operation (optional).

Returns:

A Tensor of the same type as a and b where each inner-most matrix is the product of the corresponding matrices in a and b, e.g. if all transpose or adjoint attributes are False:

output[..., i, j] = sum_k (a[..., i, k] * b[..., k, j]), for all indices i, j.

  • Note: This is matrix product, not element-wise product.

Raises:

  • ValueError: If transpose_a and adjoint_a, or transpose_b and adjoint_b are both set to True.

mod

__mod__(
    a,
    *args
)

Returns element-wise remainder of division. When x < 0 xor y < 0 is

true, this follows Python semantics in that the result here is consistent with a flooring divide. E.g. floor(x / y) * y + mod(x, y) = x.

NOTE: FloorMod supports broadcasting. More about broadcasting here

Args:

  • x: A Tensor. Must be one of the following types: int32, int64, float32, float64.
  • y: A Tensor. Must have the same type as x.
  • name: A name for the operation (optional).

Returns:

A Tensor. Has the same type as x.

mul

__mul__(
    a,
    *args
)

Dispatches cwise mul for "DenseDense" and "DenseSparse".

neg

__neg__(
    a,
    *args
)

Computes numerical negative value element-wise.

I.e., \(y = -x\).

Args:

  • x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128.
  • name: A name for the operation (optional).

Returns:

A Tensor. Has the same type as x.

nonzero

__nonzero__()

or

__or__(
    a,
    *args
)

Returns the truth value of x OR y element-wise.

NOTE: LogicalOr supports broadcasting. More about broadcasting here

Args:

  • x: A Tensor of type bool.
  • y: A Tensor of type bool.
  • name: A name for the operation (optional).

Returns:

A Tensor of type bool.

pow

__pow__(
    a,
    *args
)

Computes the power of one value to another.

Given a tensor x and a tensor y, this operation computes \(x^y\) for corresponding elements in x and y. For example:

# tensor 'x' is [[2, 2], [3, 3]]
# tensor 'y' is [[8, 16], [2, 3]]
tf.pow(x, y) ==> [[256, 65536], [9, 27]]

Args:

  • x: A Tensor of type float32, float64, int32, int64, complex64, or complex128.
  • y: A Tensor of type float32, float64, int32, int64, complex64, or complex128.
  • name: A name for the operation (optional).

Returns:

A Tensor.

radd

__radd__(
    a,
    *args
)

Returns x + y element-wise.

NOTE: Add supports broadcasting. AddN does not. More about broadcasting here

Args:

  • x: A Tensor. Must be one of the following types: half, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128, string.
  • y: A Tensor. Must have the same type as x.
  • name: A name for the operation (optional).

Returns:

A Tensor. Has the same type as x.

rand

__rand__(
    a,
    *args
)

Returns the truth value of x AND y element-wise.

NOTE: LogicalAnd supports broadcasting. More about broadcasting here

Args:

  • x: A Tensor of type bool.
  • y: A Tensor of type bool.
  • name: A name for the operation (optional).

Returns:

A Tensor of type bool.

rdiv

__rdiv__(
    a,
    *args
)

Divide two values using Python 2 semantics. Used for Tensor.div.

Args:

  • x: Tensor numerator of real numeric type.
  • y: Tensor denominator of real numeric type.
  • name: A name for the operation (optional).

Returns:

x / y returns the quotient of x and y.

rfloordiv

__rfloordiv__(
    a,
    *args
)

Divides x / y elementwise, rounding toward the most negative integer.

The same as tf.div(x,y) for integers, but uses tf.floor(tf.div(x,y)) for floating point arguments so that the result is always an integer (though possibly an integer represented as floating point). This op is generated by x // y floor division in Python 3 and in Python 2.7 with from __future__ import division.

Note that for efficiency, floordiv uses C semantics for negative numbers (unlike Python and Numpy).

x and y must have the same type, and the result will have the same type as well.

Args:

  • x: Tensor numerator of real numeric type.
  • y: Tensor denominator of real numeric type.
  • name: A name for the operation (optional).

Returns:

x / y rounded down (except possibly towards zero for negative integers).

Raises:

  • TypeError: If the inputs are complex.

rmatmul

__rmatmul__(
    a,
    *args
)

Multiplies matrix a by matrix b, producing a * b.

The inputs must, following any transpositions, be tensors of rank >= 2 where the inner 2 dimensions specify valid matrix multiplication arguments, and any further outer dimensions match.

Both matrices must be of the same type. The supported types are: float16, float32, float64, int32, complex64, complex128.

Either matrix can be transposed or adjointed (conjugated and transposed) on the fly by setting one of the corresponding flag to True. These are False by default.

If one or both of the matrices contain a lot of zeros, a more efficient multiplication algorithm can be used by setting the corresponding a_is_sparse or b_is_sparse flag to True. These are False by default. This optimization is only available for plain matrices (rank-2 tensors) with datatypes bfloat16 or float32.

For example:

# 2-D tensor `a`
a = tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3]) => [[1. 2. 3.]
                                                      [4. 5. 6.]]
# 2-D tensor `b`
b = tf.constant([7, 8, 9, 10, 11, 12], shape=[3, 2]) => [[7. 8.]
                                                         [9. 10.]
                                                         [11. 12.]]
c = tf.matmul(a, b) => [[58 64]
                        [139 154]]


# 3-D tensor `a`
a = tf.constant(np.arange(1, 13, dtype=np.int32),
                shape=[2, 2, 3])                  => [[[ 1.  2.  3.]
                                                       [ 4.  5.  6.]],
                                                      [[ 7.  8.  9.]
                                                       [10. 11. 12.]]]

# 3-D tensor `b`
b = tf.constant(np.arange(13, 25, dtype=np.int32),
                shape=[2, 3, 2])                   => [[[13. 14.]
                                                        [15. 16.]
                                                        [17. 18.]],
                                                       [[19. 20.]
                                                        [21. 22.]
                                                        [23. 24.]]]
c = tf.matmul(a, b) => [[[ 94 100]
                         [229 244]],
                        [[508 532]
                         [697 730]]]

# Since python >= 3.5 the @ operator is supported (see PEP 465).
# In TensorFlow, it simply calls the `tf.matmul()` function, so the
# following lines are equivalent:
d = a @ b @ [[10.], [11.]]
d = tf.matmul(tf.matmul(a, b), [[10.], [11.]])

Args:

  • a: Tensor of type float16, float32, float64, int32, complex64, complex128 and rank > 1.
  • b: Tensor with same type and rank as a.
  • transpose_a: If True, a is transposed before multiplication.
  • transpose_b: If True, b is transposed before multiplication.
  • adjoint_a: If True, a is conjugated and transposed before multiplication.
  • adjoint_b: If True, b is conjugated and transposed before multiplication.
  • a_is_sparse: If True, a is treated as a sparse matrix.
  • b_is_sparse: If True, b is treated as a sparse matrix.
  • name: Name for the operation (optional).

Returns:

A Tensor of the same type as a and b where each inner-most matrix is the product of the corresponding matrices in a and b, e.g. if all transpose or adjoint attributes are False:

output[..., i, j] = sum_k (a[..., i, k] * b[..., k, j]), for all indices i, j.

  • Note: This is matrix product, not element-wise product.

Raises:

  • ValueError: If transpose_a and adjoint_a, or transpose_b and adjoint_b are both set to True.

rmod

__rmod__(
    a,
    *args
)

Returns element-wise remainder of division. When x < 0 xor y < 0 is

true, this follows Python semantics in that the result here is consistent with a flooring divide. E.g. floor(x / y) * y + mod(x, y) = x.

NOTE: FloorMod supports broadcasting. More about broadcasting here

Args:

  • x: A Tensor. Must be one of the following types: int32, int64, float32, float64.
  • y: A Tensor. Must have the same type as x.
  • name: A name for the operation (optional).

Returns:

A Tensor. Has the same type as x.

rmul

__rmul__(
    a,
    *args
)

Dispatches cwise mul for "DenseDense" and "DenseSparse".

ror

__ror__(
    a,
    *args
)

Returns the truth value of x OR y element-wise.

NOTE: LogicalOr supports broadcasting. More about broadcasting here

Args:

  • x: A Tensor of type bool.
  • y: A Tensor of type bool.
  • name: A name for the operation (optional).

Returns:

A Tensor of type bool.

rpow

__rpow__(
    a,
    *args
)

Computes the power of one value to another.

Given a tensor x and a tensor y, this operation computes \(x^y\) for corresponding elements in x and y. For example:

# tensor 'x' is [[2, 2], [3, 3]]
# tensor 'y' is [[8, 16], [2, 3]]
tf.pow(x, y) ==> [[256, 65536], [9, 27]]

Args:

  • x: A Tensor of type float32, float64, int32, int64, complex64, or complex128.
  • y: A Tensor of type float32, float64, int32, int64, complex64, or complex128.
  • name: A name for the operation (optional).

Returns:

A Tensor.

rsub

__rsub__(
    a,
    *args
)

Returns x - y element-wise.

NOTE: Sub supports broadcasting. More about broadcasting here

Args:

  • x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128.
  • y: A Tensor. Must have the same type as x.
  • name: A name for the operation (optional).

Returns:

A Tensor. Has the same type as x.

rtruediv

__rtruediv__(
    a,
    *args
)

rxor

__rxor__(
    a,
    *args
)

x ^ y = (x | y) & ~(x & y).

sub

__sub__(
    a,
    *args
)

Returns x - y element-wise.

NOTE: Sub supports broadcasting. More about broadcasting here

Args:

  • x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128.
  • y: A Tensor. Must have the same type as x.
  • name: A name for the operation (optional).

Returns:

A Tensor. Has the same type as x.

truediv

__truediv__(
    a,
    *args
)

xor

__xor__(
    a,
    *args
)

x ^ y = (x | y) & ~(x & y).

batch_shape_tensor

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

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: The name to give this op.

Returns:

  • cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

copy

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

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: The name to give this op.

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).

entropy

entropy(name='entropy')

Shannon entropy in nats.

eval

eval(
    session=None,
    feed_dict=None
)

In a session, computes and returns the value of this random variable.

This is not a graph construction method, it does not add ops to the graph.

This convenience method requires a session where the graph containing this variable has been launched. If no session is passed, the default session is used.

Args:

  • session: tf.BaseSession, optional. The tf.Session to use to evaluate this random variable. If none, the default session is used.
  • feed_dict: dict, optional. A dictionary that maps tf.Tensor objects to feed values. See tf.Session.run() for a description of the valid feed values.

Examples

x = Normal(0.0, 1.0)
with tf.Session() as sess:
  # Usage passing the session explicitly.
  print(x.eval(sess))
  # Usage with the default session.  The 'with' block
  # above makes 'sess' the default session.
  print(x.eval())

event_shape_tensor

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.

get_ancestors

get_ancestors(collection=None)

Get ancestor random variables.

get_blanket

get_blanket(collection=None)

Get the random variable's Markov blanket.

get_children

get_children(collection=None)

Get child random variables.

get_descendants

get_descendants(collection=None)

Get descendant random variables.

get_parents

get_parents(collection=None)

Get parent random variables.

get_shape

get_shape()

Get shape of random variable.

get_siblings

get_siblings(collection=None)

Get sibling random variables.

get_variables

get_variables(collection=None)

Get TensorFlow variables that the random variable depends on.

is_scalar_batch

is_scalar_batch(name='is_scalar_batch')

Indicates that batch_shape == [].

Args:

  • name: The name to give this op.

Returns:

  • is_scalar_batch: bool scalar Tensor.

is_scalar_event

is_scalar_event(name='is_scalar_event')

Indicates that event_shape == [].

Args:

  • name: The name to give this op.

Returns:

  • is_scalar_event: bool scalar Tensor.

log_cdf

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: The name to give this op.

Returns:

  • logcdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

log_prob

log_prob(
    value,
    name='log_prob'
)

Log probability density/mass function.

Additional documentation from VectorExponentialLinearOperator:

value is a batch vector with compatible shape if value is a Tensor whose shape can be broadcast up to either:

self.batch_shape + self.event_shape

or

[M1, ..., Mm] + self.batch_shape + self.event_shape

Args:

  • value: float or double Tensor.
  • name: The name to give this op.

Returns:

  • log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

log_survival_function

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: The name to give this op.

Returns:

Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

mean

mean(name='mean')

Mean.

mode

mode(name='mode')

Mode.

param_shapes

param_shapes(
    cls,
    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

param_static_shapes(
    cls,
    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

prob(
    value,
    name='prob'
)

Probability density/mass function.

Additional documentation from VectorExponentialLinearOperator:

value is a batch vector with compatible shape if value is a Tensor whose shape can be broadcast up to either:

self.batch_shape + self.event_shape

or

[M1, ..., Mm] + self.batch_shape + self.event_shape

Args:

  • value: float or double Tensor.
  • name: The name to give this op.

Returns:

  • prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

quantile

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: The name to give this op.

Returns:

  • quantile: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

sample

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

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: The name to give this op.

Returns:

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

survival_function

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: The name to give this op.

Returns:

Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

value

value()

Get tensor that the random variable corresponds to.

variance

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: The name to give this op.

Returns:

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

Class Members

array_priority