##### https://github.com/GPflow/GPflow
Tip revision: 6fd1a26
mean_functions.py
``````# Copyright 2016 James Hensman, alexggmatthews, PabloLeon, Valentine Svensson
#
# 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 tensorflow as tf
import numpy as np

from . import settings
from .params import Parameter
from .params import Parameterized
from .params import ParamList
from .decors import params_as_tensors

class MeanFunction(Parameterized):
"""
The base mean function class.
To implement a mean function, write the __call__ method. This takes a
tensor X and returns a tensor m(X). In accordance with the GPflow
standard, each row of X represents one datum, and each row of Y is computed
independently for each row of X.

MeanFunction classes can have parameters, see the Linear class for an
example.
"""
def __call__(self, X):
raise NotImplementedError("Implement the __call__ method for this mean function")

def __mul__(self, other):
return Product(self, other)

class Zero(MeanFunction):
def __call__(self, X):
return tf.zeros(tf.stack([tf.shape(X)[0], 1]), dtype=settings.tf_float)

class Linear(MeanFunction):
"""
y_i = A x_i + b
"""
def __init__(self, A=None, b=None):
"""
A is a matrix which maps each element of X to Y, b is an additive
constant.

If X has N rows and D columns, and Y is intended to have Q columns,
then A must be D x Q, b must be a vector of length Q.
"""
A = np.ones((1, 1)) if A is None else A
b = np.zeros(1) if b is None else b
MeanFunction.__init__(self)
self.A = Parameter(np.atleast_2d(A))
self.b = Parameter(b)

@params_as_tensors
def __call__(self, X):
return tf.matmul(X, self.A) + self.b

class Constant(MeanFunction):
"""
y_i = c,,
"""
def __init__(self, c=None):
MeanFunction.__init__(self)
c = np.zeros(1) if c is None else c
self.c = Parameter(c)

@params_as_tensors
def __call__(self, X):
shape = tf.stack([tf.shape(X)[0], 1])
return tf.tile(tf.reshape(self.c, (1, -1)), shape)

class SwitchedMeanFunction(MeanFunction):
"""
This class enables to use different (independent) mean_functions respective
to the data 'label'.
We assume the 'label' is stored in the extra column of X.
"""
def __init__(self, meanfunction_list):
MeanFunction.__init__(self)
for m in meanfunction_list:
assert isinstance(m, MeanFunction)
self.meanfunction_list = ParamList(meanfunction_list)
self.num_meanfunctions = len(self.meanfunction_list)

@params_as_tensors
def __call__(self, X):
ind = tf.gather(tf.transpose(X), tf.shape(X)[1]-1)  # ind = X[:,-1]
ind = tf.cast(ind, tf.int32)
X = tf.transpose(tf.gather(tf.transpose(X), tf.range(0, tf.shape(X)[1]-1)))  # X = X[:,:-1]

# split up X into chunks corresponding to the relevant likelihoods
x_list = tf.dynamic_partition(X, ind, self.num_meanfunctions)
# apply the likelihood-function to each section of the data
results = [m(x) for x, m in zip(x_list, self.meanfunction_list)]
# stitch the results back together
partitions = tf.dynamic_partition(tf.range(0, tf.size(ind)), ind, self.num_meanfunctions)
return tf.dynamic_stitch(partitions, results)

def __init__(self, first_part, second_part):
MeanFunction.__init__(self)

def __call__(self, X):