| # Copyright 2017 The TensorFlow Authors. All Rights Reserved. |
| # |
| # Licensed under the Apache License, Version 2.0 (the "License"); |
| # you may not use this file except in compliance with the License. |
| # You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| # ============================================================================== |
| """Distribution of a vectorized Laplace, with uncorrelated components.""" |
| |
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| |
| from tensorflow.contrib.distributions.python.ops import distribution_util |
| from tensorflow.contrib.distributions.python.ops import vector_laplace_linear_operator as vector_laplace_linop |
| from tensorflow.python.framework import ops |
| from tensorflow.python.util import deprecation |
| |
| |
| __all__ = [ |
| "VectorLaplaceDiag", |
| ] |
| |
| |
| class VectorLaplaceDiag( |
| vector_laplace_linop.VectorLaplaceLinearOperator): |
| """The vectorization of the Laplace distribution on `R^k`. |
| |
| The vector laplace distribution is defined over `R^k`, and parameterized by |
| a (batch of) length-`k` `loc` vector (the means) and a (batch of) `k x k` |
| `scale` matrix: `covariance = 2 * scale @ scale.T`, where `@` denotes |
| matrix-multiplication. |
| |
| #### Mathematical Details |
| |
| The probability density function (pdf) is, |
| |
| ```none |
| pdf(x; loc, scale) = exp(-||y||_1) / Z, |
| y = inv(scale) @ (x - loc), |
| Z = 2**k |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, |
| * `||y||_1` denotes the `l1` norm of `y`, `sum_i |y_i|. |
| |
| A (non-batch) `scale` matrix is: |
| |
| ```none |
| 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. |
| |
| The VectorLaplace distribution is a member of the [location-scale |
| family](https://en.wikipedia.org/wiki/Location-scale_family), i.e., it can be |
| constructed as, |
| |
| ```none |
| X = (X_1, ..., X_k), each X_i ~ Laplace(loc=0, scale=1) |
| Y = (Y_1, ...,Y_k) = scale @ X + loc |
| ``` |
| |
| #### About `VectorLaplace` and `Vector` distributions in TensorFlow. |
| |
| The `VectorLaplace` is a non-standard distribution that has useful properties. |
| |
| The marginals `Y_1, ..., Y_k` are *not* Laplace random variables, due to |
| the fact that the sum of Laplace random variables is not Laplace. |
| |
| Instead, `Y` is a vector whose components are linear combinations of Laplace |
| random variables. Thus, `Y` lives in the vector space generated by `vectors` |
| of Laplace distributions. This allows the user to decide the mean and |
| covariance (by setting `loc` and `scale`), while preserving some properties of |
| the Laplace 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 Laplace random variables. One can then show by |
| induction that distributions with exponential (up to polynomial factors) tails |
| are closed under convolution. |
| |
| #### Examples |
| |
| ```python |
| tfd = tf.contrib.distributions |
| |
| # Initialize a single 2-variate VectorLaplace. |
| vla = tfd.VectorLaplaceDiag( |
| loc=[1., -1], |
| scale_diag=[1, 2.]) |
| |
| vla.mean().eval() |
| # ==> [1., -1] |
| |
| vla.stddev().eval() |
| # ==> [1., 2] * sqrt(2) |
| |
| # Evaluate this on an observation in `R^2`, returning a scalar. |
| vla.prob([-1., 0]).eval() # shape: [] |
| |
| # Initialize a 3-batch, 2-variate scaled-identity VectorLaplace. |
| vla = tfd.VectorLaplaceDiag( |
| loc=[1., -1], |
| scale_identity_multiplier=[1, 2., 3]) |
| |
| vla.mean().eval() # shape: [3, 2] |
| # ==> [[1., -1] |
| # [1, -1], |
| # [1, -1]] |
| |
| vla.stddev().eval() # shape: [3, 2] |
| # ==> sqrt(2) * [[1., 1], |
| # [2, 2], |
| # [3, 3]] |
| |
| # Evaluate this on an observation in `R^2`, returning a length-3 vector. |
| vla.prob([-1., 0]).eval() # shape: [3] |
| |
| # Initialize a 2-batch of 3-variate VectorLaplace's. |
| vla = tfd.VectorLaplaceDiag( |
| loc=[[1., 2, 3], |
| [11, 22, 33]] # shape: [2, 3] |
| scale_diag=[[1., 2, 3], |
| [0.5, 1, 1.5]]) # shape: [2, 3] |
| |
| # Evaluate this on a two observations, each in `R^3`, returning a length-2 |
| # vector. |
| x = [[-1., 0, 1], |
| [-11, 0, 11.]] # shape: [2, 3]. |
| vla.prob(x).eval() # shape: [2] |
| ``` |
| |
| """ |
| |
| @deprecation.deprecated( |
| "2018-10-01", |
| "The TensorFlow Distributions library has moved to " |
| "TensorFlow Probability " |
| "(https://github.com/tensorflow/probability). You " |
| "should update all references to use `tfp.distributions` " |
| "instead of `tf.contrib.distributions`.", |
| warn_once=True) |
| def __init__(self, |
| loc=None, |
| scale_diag=None, |
| scale_identity_multiplier=None, |
| validate_args=False, |
| allow_nan_stats=True, |
| name="VectorLaplaceDiag"): |
| """Construct Vector Laplace distribution on `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 = 2 * scale @ scale.T`. |
| |
| ```none |
| 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. |
| """ |
| parameters = dict(locals()) |
| with ops.name_scope(name): |
| with ops.name_scope("init", values=[ |
| loc, scale_diag, scale_identity_multiplier]): |
| # No need to validate_args while making diag_scale. The returned |
| # LinearOperatorDiag has an assert_non_singular method that is called by |
| # the Bijector. |
| scale = distribution_util.make_diag_scale( |
| loc=loc, |
| scale_diag=scale_diag, |
| scale_identity_multiplier=scale_identity_multiplier, |
| validate_args=False, |
| assert_positive=False) |
| super(VectorLaplaceDiag, self).__init__( |
| loc=loc, |
| scale=scale, |
| validate_args=validate_args, |
| allow_nan_stats=allow_nan_stats, |
| name=name) |
| self._parameters = parameters |