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

statics.py

```
import tensorflow as tf
from ..base import Parameter
from ..utilities import positive
from .base import Kernel
class Static(Kernel):
"""
Kernels who don't depend on the value of the inputs are 'Static'. The only
parameter is a variance, σ².
"""
def __init__(self, variance=1.0, active_dims=None):
super().__init__(active_dims)
self.variance = Parameter(variance, transform=positive())
def K_diag(self, X, presliced=False):
return tf.fill((tf.shape(X)[0],), tf.squeeze(self.variance))
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.
"""
def K(self, X, X2=None, presliced=False):
if X2 is None:
d = tf.fill((tf.shape(X)[0],), tf.squeeze(self.variance))
return tf.linalg.diag(d)
else:
shape = [tf.shape(X)[0], tf.shape(X2)[0]]
return tf.zeros(shape, dtype=X.dtype)
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.
"""
def K(self, X, X2=None, presliced=False):
if X2 is None:
shape = [tf.shape(X)[0], tf.shape(X)[0]]
else:
shape = [tf.shape(X)[0], tf.shape(X2)[0]]
return tf.fill(shape, tf.squeeze(self.variance))
```

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