Source code for gpflow.kernels.statics

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from typing import Optional

import tensorflow as tf
from check_shapes import check_shapes, inherit_check_shapes

from ..base import Parameter, TensorType
from ..utilities import positive
from .base import ActiveDims, Kernel


[docs]class Static(Kernel): """ Kernels who don't depend on the value of the inputs are 'Static'. The only parameter is a variance, σ². """ @check_shapes( "variance: []", ) def __init__( self, variance: TensorType = 1.0, active_dims: Optional[ActiveDims] = None ) -> None: super().__init__(active_dims) self.variance = Parameter(variance, transform=positive()) @inherit_check_shapes def K_diag(self, X: TensorType) -> tf.Tensor: return tf.fill(tf.shape(X)[:-1], tf.squeeze(self.variance))
[docs]class White(Static): """ The White kernel: this kernel produces 'white noise'. The kernel equation is k(x_n, x_m) = δ(n, m) σ² where: δ(.,.) is the Kronecker delta, σ² is the variance parameter. """ @inherit_check_shapes def K(self, X: TensorType, X2: Optional[TensorType] = None) -> tf.Tensor: if X2 is None: d = tf.fill(tf.shape(X)[:-1], tf.squeeze(self.variance)) return tf.linalg.diag(d) else: shape = tf.concat([tf.shape(X)[:-1], tf.shape(X2)[:-1]], axis=0) return tf.zeros(shape, dtype=X.dtype)
[docs]class Constant(Static): """ The Constant (aka Bias) kernel. Functions drawn from a GP with this kernel are constant, i.e. f(x) = c, with c ~ N(0, σ^2). The kernel equation is k(x, y) = σ² where: σ² is the variance parameter. """ @inherit_check_shapes def K(self, X: TensorType, X2: Optional[TensorType] = None) -> tf.Tensor: if X2 is None: shape = tf.concat( [ tf.shape(X)[:-2], tf.reshape(tf.shape(X)[-2], [1]), tf.reshape(tf.shape(X)[-2], [1]), ], axis=0, ) else: shape = tf.concat([tf.shape(X)[:-1], tf.shape(X2)[:-1]], axis=0) return tf.fill(shape, tf.squeeze(self.variance))