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Tip revision: 6fd1a26809a4c754b73c1a645b48d7cda35b2cd6 authored by John Bradshaw on 24 October 2017, 10:29:09 UTC
Merge remote-tracking branch 'origin/GPflow-1.0-RC' into john-bradshaw/linear-features-for-kernels-gpflow1.0
Tip revision: 6fd1a26
# Copyright 2016 James Hensman, alexggmatthews, PabloLeon, Valentine Svensson
# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

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
    def __call__(self, X):
        raise NotImplementedError("Implement the __call__ method for this mean function")

    def __add__(self, other):
        return Additive(self, other)

    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

        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
        self.A = Parameter(np.atleast_2d(A))
        self.b = Parameter(b)

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

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

    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):
        for m in meanfunction_list:
            assert isinstance(m, MeanFunction)
        self.meanfunction_list = ParamList(meanfunction_list)
        self.num_meanfunctions = len(self.meanfunction_list)

    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)

class Additive(MeanFunction):
    def __init__(self, first_part, second_part):
        self.add_1 = first_part
        self.add_2 = second_part

    def __call__(self, X):
        return tf.add(self.add_1(X), self.add_2(X))

class Product(MeanFunction):
    def __init__(self, first_part, second_part):

        self.prod_1 = first_part
        self.prod_2 = second_part

    def __call__(self, X):
        return tf.multiply(self.prod_1(X), self.prod_2(X))
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