Revision ff4bb90d1135f1b57db3e4f6e4a2173894aa1b73 authored by st-- on 01 December 2020, 12:56:56 UTC, committed by GitHub on 01 December 2020, 12:56:56 UTC
* Replace len(inducing_variable) with inducing_variable.num inducing property (#1594).

  Adds support for inducing variables with dynamically changing shape. Change usage from `len(inducing_variable)` to `inducing_variable.num_inducing` instead. Resolves #1578.

* HeteroskedasticTFPConditional should construct tensors at class-construction, not at module-import time (#1598)
2 parent s 6f7f0d8 + 60e19f8
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# Copyright 2017-2020 The GPflow Contributors. 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

from import Iterable
from typing import List, Optional, Union

import tensorflow as tf

from ..base import Parameter
from ..utilities import positive
from .base import Combination, Kernel

class ChangePoints(Combination):
    The ChangePoints kernel defines a fixed number of change-points along a 1d
    input space where different kernels govern different parts of the space.

    The kernel is by multiplication and addition of the base kernels with
    sigmoid functions (σ). A single change-point kernel is defined as:

        K₁(x, x') * (1 - σ(x)) * (1 - σ(x')) + K₂(x, x') * σ(x) * σ(x')

    where K₁ is deactivated around the change-point and K₂ is activated. The
    single change-point version can be found in \citet{lloyd2014}. Each sigmoid
    is a logistic function defined as:

        σ(x) = 1 / (1 + exp{-s(x - x₀)})

    parameterized by location "x₀" and steepness "s".

      author = {Lloyd, James Robert et al},
      title = {Automatic Construction and Natural-language Description of Nonparametric Regression Models},
      booktitle = {Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence},
      year = {2014},
      url = {},

    def __init__(
        kernels: List[Kernel],
        locations: List[float],
        steepness: Union[float, List[float]] = 1.0,
        name: Optional[str] = None,
        :param kernels: list of kernels defining the different regimes
        :param locations: list of change-point locations in the 1d input space
        :param steepness: the steepness parameter(s) of the sigmoids, this can be
            common between them or decoupled
        if len(kernels) != len(locations) + 1:
            raise ValueError(
                "Number of kernels ({nk}) must be one more than the number of "
                "changepoint locations ({nl})".format(nk=len(kernels), nl=len(locations))

        if isinstance(steepness, Iterable) and len(steepness) != len(locations):
            raise ValueError(
                "Dimension of steepness ({ns}) does not match number of changepoint "
                "locations ({nl})".format(ns=len(steepness), nl=len(locations))

        super().__init__(kernels, name=name)

        self.locations = Parameter(locations)
        self.steepness = Parameter(steepness, transform=positive())

    def _set_kernels(self, kernels: List[Kernel]):
        # it is not clear how to flatten out nested change-points
        self.kernels = kernels

    def K(self, X: tf.Tensor, X2: Optional[tf.Tensor] = None) -> tf.Tensor:
        sig_X = self._sigmoids(X)  # N1 x 1 x Ncp
        sig_X2 = self._sigmoids(X2) if X2 is not None else sig_X  # N2 x 1 x Ncp

        # `starters` are the sigmoids going from 0 -> 1, whilst `stoppers` go
        # from 1 -> 0, dimensions are N1 x N2 x Ncp
        starters = sig_X * tf.transpose(sig_X2, perm=(1, 0, 2))
        stoppers = (1 - sig_X) * tf.transpose((1 - sig_X2), perm=(1, 0, 2))

        # prepend `starters` with ones and append ones to `stoppers` since the
        # first kernel has no start and the last kernel has no end
        N1 = tf.shape(X)[0]
        N2 = tf.shape(X2)[0] if X2 is not None else N1
        ones = tf.ones((N1, N2, 1), dtype=X.dtype)
        starters = tf.concat([ones, starters], axis=2)
        stoppers = tf.concat([stoppers, ones], axis=2)

        # now combine with the underlying kernels
        kernel_stack = tf.stack([k(X, X2) for k in self.kernels], axis=2)
        return tf.reduce_sum(kernel_stack * starters * stoppers, axis=2)

    def K_diag(self, X: tf.Tensor) -> tf.Tensor:
        N = tf.shape(X)[0]
        sig_X = tf.reshape(self._sigmoids(X), (N, -1))  # N x Ncp

        ones = tf.ones((N, 1), dtype=X.dtype)
        starters = tf.concat([ones, sig_X * sig_X], axis=1)  # N x Ncp
        stoppers = tf.concat([(1 - sig_X) * (1 - sig_X), ones], axis=1)

        kernel_stack = tf.stack([k(X, full_cov=False) for k in self.kernels], axis=1)
        return tf.reduce_sum(kernel_stack * starters * stoppers, axis=1)

    def _sigmoids(self, X: tf.Tensor) -> tf.Tensor:
        locations = tf.sort(self.locations)  # ensure locations are ordered
        locations = tf.reshape(locations, (1, 1, -1))
        steepness = tf.reshape(self.steepness, (1, 1, -1))
        return tf.sigmoid(steepness * (X[:, :, None] - locations))
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