Revision 291ae6c7dbfcbded27c604f136982a5067d14b8e authored by thevincentadam on 20 January 2020, 12:17:20 UTC, committed by thevincentadam on 20 January 2020, 12:17:20 UTC
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from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from ..base import Parameter
from ..utilities import positive
from .base import Kernel
from .stationaries import Stationary

class Periodic(Kernel):
    The periodic family of kernels. Can be used to wrap any Stationary kernel
    to transform it into a periodic version. The canonical form (based on the
    SquaredExponential kernel) can be found in Equation (47) of

    D.J.C.MacKay. Introduction to Gaussian processes. In C.M.Bishop, editor,
    Neural Networks and Machine Learning, pages 133--165. Springer, 1998.

    The derivation can be achieved by mapping the original inputs through the
    transformation u = (cos(x), sin(x)).

    For the SquaredExponential base kernel, the result can be expressed as:

        k(r) =  σ² exp{ -0.5 sin²(π r / γ) / ℓ²}

    r is the Euclidean distance between the input points
    ℓ is the lengthscale parameter,
    σ² is the variance parameter,
    γ is the period parameter.

    (note that usually we have a factor of 4 instead of 0.5 in front but this
    is absorbed into lengthscale hyperparameter).

    def __init__(self, base: Stationary, period: Union[float, List[float]] = 1.0):
        :param base: the base kernel to make periodic; must inherit from Stationary
        :param period: the period; to induce a different period per active dimention
            this must be initialized with an array the same length as the the number
            of active dimensions in the base e.g. [1., 1., 1.]
        if not isinstance(base, Stationary):
            raise TypeError("Periodic requires a Stationary kernel as the `base`")

        self.base = base
        self.period = Parameter(period, transform=positive())

    def K_diag(self, X: tf.Tensor, presliced: bool = False) -> tf.Tensor:
        return self.base.K_diag(X)

    def K(self, X: tf.Tensor, X2: Optional[tf.Tensor] = None, presliced: bool = False) -> tf.Tensor:
        if not presliced:
            # active_dims is specified in the base, so use base.slice
            X, X2 = self.base.slice(X, X2)
        if X2 is None:
            X2 = X

        # Introduce dummy dimension so we can use broadcasting
        f = tf.expand_dims(X, 1)  # now [N, 1, D]
        f2 = tf.expand_dims(X2, 0)  # now [1, M, D]

        r = np.pi * (f - f2) / self.period
        scaled_sine = tf.sin(r) / self.base.lengthscale
        if hasattr(self.base, "K_r"):
            sine_r = tf.reduce_sum(tf.abs(scaled_sine), -1)
            K = self.base.K_r(sine_r)
            sine_r2 = tf.reduce_sum(tf.square(scaled_sine), -1)
            K = self.base.K_r2(sine_r2)
        return K
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