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mean.py
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mean.py
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# Copyright 2023 The PyMC Developers
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pytensor.tensor as pt
__all__ = ["Zero", "Constant", "Linear"]
class Mean:
R"""
Base class for mean functions
"""
def __call__(self, X):
R"""
Evaluate the mean function.
Parameters
----------
X: The training inputs to the mean function.
"""
raise NotImplementedError
def __add__(self, other):
return Add(self, other)
def __mul__(self, other):
return Prod(self, other)
class Zero(Mean):
R"""
Zero mean function for Gaussian process.
"""
def __call__(self, X):
return pt.alloc(0.0, X.shape[0])
class Constant(Mean):
R"""
Constant mean function for Gaussian process.
Parameters
----------
c: variable, array or integer
Constant mean value
"""
def __init__(self, c=0):
super().__init__()
self.c = c
def __call__(self, X):
return pt.alloc(1.0, X.shape[0]) * self.c
class Linear(Mean):
R"""
Linear mean function for Gaussian process.
Parameters
----------
coeffs: variables
Linear coefficients
intercept: variable, array or integer
Intercept for linear function (Defaults to zero)
"""
def __init__(self, coeffs, intercept=0):
super().__init__()
self.b = intercept
self.A = coeffs
def __call__(self, X):
return pt.squeeze(pt.dot(X, self.A) + self.b)
class Add(Mean):
def __init__(self, first_mean, second_mean):
super().__init__()
self.m1 = first_mean
self.m2 = second_mean
def __call__(self, X):
return pt.add(self.m1(X), self.m2(X))
class Prod(Mean):
def __init__(self, first_mean, second_mean):
super().__init__()
self.m1 = first_mean
self.m2 = second_mean
def __call__(self, X):
return pt.mul(self.m1(X), self.m2(X))