base.py
``````# Copyright 2017 Artem Artemev @awav
#
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#
# Unless required by applicable law or agreed to in writing, software
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and

import abc

class IPrior(metaclass=abc.ABCMeta):

@abc.abstractmethod
def logp(self, x):
"""
The log density of the prior as x

All priors (for the moment) are univariate, so if x is a vector or an
array, this is the sum of the log densities.
"""
pass

@abc.abstractmethod
def sample(self, shape=(1,)):
"""
A sample utility function for the prior.
"""
pass

@abc.abstractmethod
def __str__(self):
"""
A short string to describe the prior at print time
"""
pass

class ITransform(metaclass=abc.ABCMeta):
"""
x is the unconstrained, free-space parameter, which can take any value.
y is the constrained, "real units" parameter corresponding to the data.

y = forward(x)
x = backward(y)
"""

@abc.abstractmethod
def forward(self, x):
"""
Map from the free-space to the variable space, using numpy
"""
pass

@abc.abstractmethod
def backward(self, y):
"""
Map from the variable-space to the free space, using numpy
"""
pass

@abc.abstractmethod
def forward_tensor(self, x):
"""
Map from the free-space to the variable space, using tensorflow
"""
pass

@abc.abstractmethod
def backward_tensor(self, x):
"""
Map from the variable-space to the free space, using tensorflow
"""
pass

@abc.abstractmethod
def log_jacobian_tensor(self, x):
"""
Return the log Jacobian of the forward_tensor mapping.

Note that we *could* do this using a tf manipulation of
self.forward_tensor, but tensorflow may have difficulty: it doesn't have a
Jacobian at time of writing. We do this in the tests to make sure the
implementation is correct.
"""
pass

@abc.abstractmethod
def __str__(self):
"""
A short string describing the nature of the constraint
"""
pass
``````