# -*- coding: iso-8859-1 -*-## This file is a part of the Bayes Blocks library## Copyright (C) 2001-2006 Markus Harva, Antti Honkela, Alexander# Ilin, Tapani Raiko, Harri Valpola and Tomas 謘tman.## This program is free software; you can redistribute it and/or modify# it under the terms of the GNU General Public License as published by# the Free Software Foundation; either version 2, or (at your option)# any later version.## This program is distributed in the hope that it will be useful,# but WITHOUT ANY WARRANTY; without even the implied warranty of# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the# GNU General Public License (included in file License.txt in the# program package) for more details.## $Id: Tests.py 5 2006-10-26 09:44:54Z ah $#import unittestimport Numeric, RandomArrayimport MLabimport random, timeimport sysimport PyNet, Helpers, PickleHelpersfrom Label import Labelfrom Helpers import GetMean, GetMeanV, GetDiscrete, GetDiscreteV, GetVar, GetVarVdef path(a, b):    node = a    rootlabel = b.GetLabel()    seq = []    while node.GetLabel() != rootlabel:        seq.append(node.GetLabel())        c = node.GetChild(0)        if c is None:            return None        node = c    return seqclass PyNodeFactoryTestCase(unittest.TestCase):    def setUp(self):        self.net = PyNet.PyNet(1)        self.nf = PyNet.PyNodeFactory(self.net)    def tearDown(self):        pass    def test1(self):        clst = [self.nf.GetConstant(Label("c", i), i) for i in range(10)]        x = self.nf.MakeNodes("Gaussian", "x", 10, clst, clst[0])        for i in range(10):            self.assertEqual(x[i].GetType(), "Gaussian")            self.assertEqual(x[i].GetLabel(), Label("x", i))            self.assertEqual(x[i].GetParent(0).GetLabel(), clst[i].GetLabel())            self.assertEqual(x[i].GetParent(1).GetLabel(), clst[0].GetLabel())    def test2(self):        clst = [self.nf.GetConstant(Label("c", i), i) for i in range(10)]        x = self.nf.MakeNodes("Gaussian", ("x", 1, 2), 10, clst, clst)        for i in range(10):            self.assertEqual(x[i].GetType(), "Gaussian")            self.assertEqual(x[i].GetLabel(), Label("x", 1, 2, i))            self.assertEqual(x[i].GetParent(0).GetLabel(), clst[i].GetLabel())            self.assertEqual(x[i].GetParent(1).GetLabel(), clst[i].GetLabel())    def test3(self):        c0 = self.nf.GetConst0()        summands = self.nf.MakeNodes("Gaussian", "s", 16, c0, c0)        sum = self.nf.BuildSum2Tree(summands, "sumtree")        for term in summands:            self.assertEqual(len(path(term, sum)), 4)    def test4(self):        c0 = self.nf.GetConst0()        s = self.nf.GetGaussian("s", c0, c0)        x = self.nf.GetGaussian("x", s, c0)        x.Clamp(1.0)        self.nf.EvidenceNode(s, mean=0.5)        class VariableGroupTestCase(unittest.TestCase):    def setUp(self):        self.net = PyNet.PyNet(1)        self.nf = PyNet.PyNodeFactory(self.net)    def tearDown(self):        pass    def test1(self):        c0 = self.nf.GetConstant("c0", 0.0)        a1 = self.nf.GetGaussian("ab1", c0, c0)        a2 = self.nf.GetGaussian("ab2", c0, c0)                b1 = self.nf.GetGaussian("ba1", c0, c0)        b2 = self.nf.GetGaussian("ba2", c0, c0)        self.net.DefineVariableGroup("a");        self.net.DefineVariableGroup("b");        groupa = [self.net.GetGroupVariable("a", i).GetLabel()                  for i in range(self.net.NumGroupVariables("a"))]        groupa.sort()        self.assertEqual(groupa, ["ab1", "ab2"])                groupb = [self.net.GetGroupVariable("b", i).GetLabel()                  for i in range(self.net.NumGroupVariables("b"))]        groupb.sort()        self.assertEqual(groupb, ["ba1", "ba2"])    def test2(self):        c0 = self.nf.GetConstant("c0", 0.0)        a1 = self.nf.GetGaussian("ab1", c0, c0)        a2 = self.nf.GetGaussian("ab2", c0, c0)                b1 = self.nf.GetGaussian("ba1", c0, c0)        b2 = self.nf.GetGaussian("ba2", c0, c0)        self.net.DefineVariableGroup("a");        self.net.DefineVariableGroup("b");        b1.Die()        self.net.CleanUp()        a3 = self.nf.GetGaussian("ab3", c0, c0)        groupa = [self.net.GetGroupVariable("a", i).GetLabel()                  for i in range(self.net.NumGroupVariables("a"))]        groupa.sort()        self.assertEqual(groupa, ["ab1", "ab2", "ab3"])                groupb = [self.net.GetGroupVariable("b", i).GetLabel()                  for i in range(self.net.NumGroupVariables("b"))]        groupb.sort()        self.assertEqual(groupb, ["ba2"])    def test3(self):        c0 = self.nf.GetConstant("c0", 0.0)        foo1 = self.nf.GetGaussian("foo1", c0, c0)        foo2 = self.nf.GetGaussian("foo2", c0, c0)                foobar1 = self.nf.GetGaussian("foobar1", c0, c0)        foobar2 = self.nf.GetGaussian("foobar2", c0, c0)        self.net.DefineVariableGroup("foo");        self.net.DefineVariableGroup("foobar");        groupa = [self.net.GetGroupVariable("foo", i).GetLabel()                  for i in range(self.net.NumGroupVariables("foo"))]        groupa.sort()        self.assertEqual(groupa, ["foo1", "foo2", "foobar1", "foobar2"])                groupb = [self.net.GetGroupVariable("foobar", i).GetLabel()                  for i in range(self.net.NumGroupVariables("foobar"))]        groupb.sort()        self.assertEqual(groupb, ["foobar1", "foobar2"])    def setup4(self):        data1 = RandomArray.normal(-1, Numeric.exp(-0.5*2), 100)        data2 = RandomArray.normal(1, Numeric.exp(-0.5*2), 100)        c0 = self.nf.GetConstant("c0", 0.0)        m1 = self.nf.GetGaussian("am", c0, c0)        m1.Clamp(0.0)        m1.Unclamp()        v1 = self.nf.GetGaussian("av", c0, c0)        v1.Clamp(0.0)        v1.Unclamp()                m2 = self.nf.GetGaussian("bm", c0, c0)        m2.Clamp(0.0)        m2.Unclamp()        v2 = self.nf.GetGaussian("bv", c0, c0)        v2.Clamp(0.0)        v2.Unclamp()        x1 = []        for i in range(data1.shape[0]):            x1.append(self.nf.GetGaussian(Label("x1", i), m1, v1))            x1[i].Clamp(data1[i])                x2 = []        for i in range(data1.shape[0]):            x2.append(self.nf.GetGaussian(Label("x2", i), m2, v2))            x2[i].Clamp(data2[i])        self.m1 = m1        self.m2 = m2        self.v1 = v1        self.v2 = v2    def test4a(self):        self.setup4()        self.net.DefineVariableGroup("a")        self.net.DefineVariableGroup("b")        for i in range(5):            self.net.UpdateAll()            #print self.net.Cost()        self.assert_(abs(GetMean(self.m1) + 1.0) < 0.5)        self.assert_(abs(GetMean(self.m2) - 1.0) < 0.5)        self.assert_(abs(GetMean(self.v1) - 2.0) < 0.5)        self.assert_(abs(GetMean(self.v2) - 2.0) < 0.5)    def test4b(self):        self.setup4()        self.net.DefineVariableGroup("a")        for i in range(5):            self.net.UpdateGroup("a")        self.assert_(abs(GetMean(self.m1) + 1.0) < 0.5)        self.assert_(abs(GetMean(self.v1) - 2.0) < 0.5)class PartialUpdateTestCase(unittest.TestCase):    def setUp(self):        pass        def test1(self):        tdim = 100        net = PyNet.PyNet(tdim)        nf = PyNet.PyNodeFactory(net)        indices = RandomArray.permutation(tdim)[:10].tolist()        net.DefineTimeIndexGroup("mygroup", Helpers.Array2IntV(indices))        c0 = nf.GetConstant("c0", 0.0)        c_5 = nf.GetConstant("c_5", -5.0)        c5 = nf.GetConstant("c5", 5.0)        s = nf.GetGaussianV("s", c0, c0)        x = nf.GetGaussianV("x", s, c5)        data = RandomArray.standard_normal(tdim)        var = Numeric.ones(tdim) * 1e-8        x.Clamp(Helpers.Array2DV(data), Helpers.Array2DV(var))        net.UpdateAll()        ms = GetMeanV(s)        for t in range(tdim):            self.assert_((data[t] - ms[t])**2 < 1e-2,                         "%f vs. %f" % (data[t], ms[t]))        newdata = RandomArray.standard_normal(tdim)        x.Clamp(Helpers.Array2DV(newdata), Helpers.Array2DV(var))        net.EnableTimeIndexGroup("mygroup")        net.UpdateAll()        ms = GetMeanV(s)                for t in range(tdim):            if t in indices:                xt = newdata[t]            else:                xt = data[t]            self.assert_((xt - ms[t])**2 < 1e-2,                         "%f vs. %f" % (xt, ms[t]))        net.DisableTimeIndexGroup("mygroup")        net.UpdateAll()        ms = GetMeanV(s)                for t in range(tdim):            self.assert_((newdata[t] - ms[t])**2 < 1e-2,                         "%f vs. %f" % (newdata[t], ms[t]))class MoGTestCase(unittest.TestCase):    def setUp(self):        # N(-1, 0.5^2)        data1 = Numeric.array(            [-1.48372132, -0.74105301, -1.46452126, -0.74465525,             -0.74137861, -0.81603923, -1.22201809, -1.40728462,             -0.25976342, -1.07168696, -0.80576369, -1.28271085,             -0.97642539, -1.36678675, -1.10264056, -0.53583777,             -1.32091546, -0.74083391, -1.34777945, -0.87874509],            Numeric.Float)        # N(1, 0.1^2)        data2 = Numeric.array(            [ 0.8612323 ,  1.05193536,  1.10445087,  0.89876609,              0.96364794,  1.14201061,  0.96772116,  0.8137229 ,              0.9958501 ,  0.99648816], Numeric.Float)        self.data = Numeric.concatenate((data1, data2))        def buildnet1(self):        net = PyNet.PyNet(self.data.shape[0])        nf = PyNet.PyNodeFactory(net)        numComp = 2                prior = Helpers.Array2DV(Numeric.ones(numComp))        const = nf.GetConstantV("c_prior", prior)        c = nf.GetDirichlet(Label("c"), const)        d = nf.GetDiscreteDirichletV("d", c)        s = nf.GetMoGV("s", d)        c0 = nf.GetConstant("c0", 0.0)        c_5 = nf.GetConstant("c_5", -5)        m1 = nf.GetGaussian("m1", c0, c_5)        v1 = nf.GetGaussian("v1", c0, c_5)        m2 = nf.GetGaussian("m2", c0, c_5)        v2 = nf.GetGaussian("v2", c0, c_5)        s.AddComponent(m1, v1)        s.AddComponent(m2, v2)        vx = nf.GetConstant("vx", 9.21)        x = nf.GetGaussianV("x", s, vx)        x.Clamp(Helpers.Array2DV(self.data))        nf.EvidenceNode(m1, mean=-1.0)        nf.EvidenceNode(m2, mean=1.0)        return net            def buildnet2(self):        net = PyNet.PyNet(1)        nf = PyNet.PyNodeFactory(net)        numComp = 2        dim = self.data.shape[0]                prior = Helpers.Array2DV(Numeric.ones(numComp))        const = nf.GetConstantV("c_prior", prior)        c = nf.GetDirichlet(Label("c"), const)        c0 = nf.GetConstant("c0", 0.0)        c_5 = nf.GetConstant("c_5", -5)        m1 = nf.GetGaussian("m1", c0, c_5)        v1 = nf.GetGaussian("v1", c0, c_5)