Exemple #1
0
    def test1DNormalDist(self):
        # prepare data
        U = dists.Normal(1.85, .3, 0, 3)
        trainSamples = np.array([U.rvs(500)]).T
        testSamples = np.array([U.rvs(1000)]).T

        # build parameter set
        dist = KDEDist(trainSamples,
                       kernelType=KernelType_GAUSSIAN,
                       bandwidthOptimizationType=
                       BandwidthOptimizationType_MAXIMUMLIKELIHOOD,
                       bounds=U.getBounds())

        #         fig = plt.figure()
        #         plotDensity1d(U)
        #         plotDensity1d(dist)

        print("quad = %s" % (quad(lambda x: dist.pdf([x]), 0, 3), ))
        print("mean = %g ~ %g" % (U.mean(), dist.mean()))
        print("var = %g ~ %g" % (U.var(), dist.var()))
        print("KL = %g" % U.klDivergence(dist, testSamples, testSamples))
        print("CE = %g" % dist.crossEntropy(testSamples))
        print("MSE = %g" % dist.l2error(U, testSamples, testSamples))

        plt.show()
Exemple #2
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    def test2DNormalMoments(self):
        mean = 0
        var = 0.5

        U = dists.J([dists.Normal(mean, var, -2, 2),
                     dists.Normal(mean, var, -2, 2)])

        trainSamples = U.rvs(10000)
        dist = KDEDist(trainSamples)

        # -----------------------------------------------
        self.assertTrue(np.abs(U.mean() - dist.mean()) < 1e-2, "KDE mean wrong")
        self.assertTrue(np.abs(U.var() - dist.var()) < 1e-2, "KDE variance wrong")