Ejemplo n.º 1
0
    def test2DCDFandPPF(self):
        # prepare data
        C = np.array([[0.1, 0.08], [0.08, 0.1]]) / 10.
        U = dists.MultivariateNormal([0.5, 0.5], C, 0, 1)
        train_samples = U.rvs(1000)

        fig = plt.figure()
        plotDensity2d(U)
        plt.title('true density')
        fig.show()

        dist = KDEDist(train_samples, bounds=U.getBounds())

        fig = plt.figure()
        plotDensity2d(dist)
        plt.title('estimated KDE density')
        fig.show()

        samples = dists.J([dists.Uniform(0, 1),
                           dists.Uniform(0, 1)]).rvs(1000)

        fig = plt.figure()
        plt.plot(samples[:, 0], samples[:, 1], "o ")
        plt.title('u space')
        plt.xlim(0, 1)
        plt.ylim(0, 1)
        fig.show()

        transformed_samples = dist.ppf(samples)

        fig = plt.figure()
        plt.plot(transformed_samples[:, 0], transformed_samples[:, 1], "o ")
        plt.title('x space (transformed)')
        plt.xlim(0, 1)
        plt.ylim(0, 1)
        fig.show()

        samples = dist.cdf(transformed_samples)

        fig = plt.figure()
        plt.plot(samples[:, 0], samples[:, 1], "o ")
        plt.title('u space (transformed)')
        plt.xlim(0, 1)
        plt.ylim(0, 1)
        fig.show()

        plt.show()
Ejemplo n.º 2
0
    def test1DCDFandPPF(self):
        # prepare data
        U = Normal(0.5, 0.1, 0, 1)
        train_samples = U.rvs(1000).reshape(1000, 1)

        dist = KDEDist(train_samples, kernelType=KernelType_EPANECHNIKOV)

        rc('font', **{'size': 18})

        fig = plt.figure()
        x = np.linspace(0, 1, 1000)
        plt.plot(x, dist.cdf(x), label="estimated")
        plt.plot(x, [U.cdf(xi) for xi in x], label="analytic")
        plt.legend(loc="lower right")
        fig.show()

        fig = plt.figure()
        plt.hist(train_samples, normed=True)
        plotDensity1d(U, label="analytic")
        plotDensity1d(dist, label="estimated")
        plt.title("original space")
        plt.legend()
        fig.show()

        transformed_samples = dist.cdf(train_samples)

        fig = plt.figure()
        plt.hist(transformed_samples, normed=True)
        plt.title("uniform space")
        fig.show()

        transformed_samples = dist.ppf(transformed_samples)

        fig = plt.figure()
        plt.hist(transformed_samples, normed=True)
        plotDensity1d(U, label="analytic")
        plotDensity1d(dist, label="estimated")
        plt.title("original space")
        plt.legend()
        fig.show()

        plt.show()
Ejemplo n.º 3
0
    def test2DPPF(self):
        # prepare data
        C = np.array([[0.1, 0.08], [0.08, 0.1]]) / 10.
        U = dists.MultivariateNormal([0.5, 0.5], C, 0, 1)

        fig = plt.figure()
        plotDensity2d(U)
        plt.title('true density')
        fig.show()

        dist = KDEDist(U.rvs(1000),
                       kernelType=KernelType_EPANECHNIKOV,
                       bounds=U.getBounds())

        fig = plt.figure()
        plotDensity2d(dist)
        plt.title('estimated KDE density')
        fig.show()

        samples = dists.J([dists.Uniform(0, 1),
                           dists.Uniform(0, 1)]).rvs(1000)

        fig = plt.figure()
        plt.plot(samples[:, 0], samples[:, 1], "o ")
        plt.title('uniformly drawn samples')
        plt.xlim(0, 1)
        plt.ylim(0, 1)
        fig.show()

        transformed_samples = dist.ppf(samples)

        fig = plt.figure()
        plt.plot(transformed_samples[:, 0], transformed_samples[:, 1], "o ")
        plt.title('transformed samples')
        plt.xlim(0, 1)
        plt.ylim(0, 1)
        fig.show()

        plt.show()