# Copyright 2016 the gpflow authors.
#
# 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.from __future__ import print_function

import unittest
import numpy as np

import gpflow
from gpflow import settings
from gpflow.test_util import GPflowTestCase


class PriorModeTests(GPflowTestCase):
    """
    these tests optimize the prior to find the mode numerically. Make sure the
    mode is the same as the known mode.
    """
    def setUp(self):
        class FlatModel(gpflow.models.Model):
            def _build_likelihood(self):
                return np.array(0., dtype=settings.np_float)
        self.m = FlatModel()

    def testGaussianMode(self):
        with self.test_context():
            self.m.x = gpflow.Param(1.)
            self.m.x.prior = gpflow.priors.Gaussian(3., 1.)

            self.m.compile()
            opt = gpflow.train.ScipyOptimizer(options={'disp': 0})
            opt.minimize(self.m)
            xmax = self.m.read_trainables()
            self.assertTrue(np.allclose(xmax, 3))

    def testGaussianModeMatrix(self):
        with self.test_context():
            self.m.x = gpflow.Param(np.random.randn(4, 4))
            self.m.x.prior = gpflow.priors.Gaussian(-1., 10.)

            self.m.compile()
            opt = gpflow.train.ScipyOptimizer(options={'disp': 0})
            opt.minimize(self.m)
            xmax = self.m.read_trainables()
            self.assertTrue(np.allclose(xmax, -1.))

    def testGammaMode(self):
        with self.test_context():
            self.m.x = gpflow.Param(1.0)
            shape, scale = 4., 5.
            self.m.x.prior = gpflow.priors.Gamma(shape, scale)

            self.m.compile()
            opt = gpflow.train.ScipyOptimizer(options={'disp': 0})
            opt.minimize(self.m)

            true_mode = (shape - 1.) * scale
            self.assertTrue(np.allclose(self.m.x.read_value(), true_mode, 1e-3))

    def testLaplaceMode(self):
        with self.test_context():
            self.m.x = gpflow.Param(1.0)
            self.m.x.prior = gpflow.priors.Laplace(3., 10.)

            self.m.compile()
            opt = gpflow.train.ScipyOptimizer(options={'disp': 0})
            opt.minimize(self.m)
            xmax = self.m.read_trainables()
            self.assertTrue(np.allclose(xmax, 3))

    def testLogNormalMode(self):
        with self.test_context():
            self.m.x = gpflow.Param(1.0)
            self.m.x.prior = gpflow.priors.LogNormal(3., 10.)
            transform = gpflow.transforms.Exp()
            self.m.x.transform = transform

            self.m.compile()
            opt = gpflow.train.ScipyOptimizer()
            opt.minimize(self.m)
            xmax = [transform.backward(x) for x in self.m.read_trainables()]
            self.assertTrue(np.allclose(xmax, 3))

    def testBetaMode(self):
        with self.test_context():
            self.m.x = gpflow.Param(0.1)
            self.m.x.prior = gpflow.priors.Beta(3., 3.)
            transform = gpflow.transforms.Logistic()
            self.m.x.transform = transform

            self.m.compile()
            opt = gpflow.train.ScipyOptimizer(options={'disp': 0})
            opt.minimize(self.m)
            xmax = [transform.backward(x) for x in self.m.read_trainables()]
            self.assertTrue(np.allclose(0.0, xmax, atol=1.e-6))

    def testUniform(self):
        with self.test_context():
            self.m.x = gpflow.Param(1.0)
            self.m.x.prior = gpflow.priors.Uniform(-2., 3.)
            self.m.x.transform = gpflow.transforms.Logistic(-2., 3.)
            self.m.compile()

            self.m.x = np.random.randn(1)[0]
            p1 = self.m.compute_log_prior()

            self.m.x = np.random.randn(1)[0]
            p2 = self.m.compute_log_prior()

            # prior should no be the same because a transformation has been applied.
            self.assertFalse(p1 == p2)


if __name__ == "__main__":
    unittest.main()