Revision

**291ae6c7dbfcbded27c604f136982a5067d14b8e**authored by thevincentadam on**20 January 2020, 12:17:20 UTC**, committed by thevincentadam on**20 January 2020, 12:17:20 UTC****1 parent**5dc31b8

changepoints.py

```
from collections.abc 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):
r"""
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".
@incollection{lloyd2014,
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 = {http://dl.acm.org/citation.cfm?id=2893873.2894066},
}
"""
def __init__(self,
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 = list(kernels)
def K(self, X: tf.Tensor, X2: Optional[tf.Tensor] = None, presliced: bool = False) -> tf.Tensor:
sig_X = self._sigmoids(X) # N x 1 x Ncp
sig_X2 = self._sigmoids(X2) if X2 is not None else sig_X
# `starters` are the sigmoids going from 0 -> 1, whilst `stoppers` go
# from 1 -> 0, dimensions are N x N 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
N = tf.shape(X)[0]
ones = tf.ones((N, N, 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, presliced: bool = False) -> 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=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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