Python Banana.sample示例

示例#1

def main():
    dist=Ring(dimension=50)
    X=dist.sample(10000).samples
    #print X[:,2:dist.dimension]
    print dist.emp_quantiles(X)
    
    dist2=Banana(dimension=50)
    X2=dist2.sample(10000).samples
    
    print dist2.emp_quantiles(X2)

示例#2

文件： KameleonTest.py 项目： karlnapf/kameleon-mcmc

def main():
    distribution = Banana(dimension=8)
    
    sigma=5
    print "using sigma", sigma
    kernel = GaussianKernel(sigma=sigma)
    
    mcmc_sampler = Kameleon(distribution, kernel, distribution.sample(100).samples)
    
    start = zeros(distribution.dimension)
    mcmc_params = MCMCParams(start=start, num_iterations=20000)
    chain = MCMCChain(mcmc_sampler, mcmc_params)
    
    chain.append_mcmc_output(StatisticsOutput(plot_times=True))
    chain.run()

示例#3

文件： KameleonTest.py 项目： mgong2/kameleon-mcmc

def main():
    distribution = Banana(dimension=8)

    sigma = 5
    print "using sigma", sigma
    kernel = GaussianKernel(sigma=sigma)

    mcmc_sampler = Kameleon(distribution, kernel,
                            distribution.sample(100).samples)

    start = zeros(distribution.dimension)
    mcmc_params = MCMCParams(start=start, num_iterations=20000)
    chain = MCMCChain(mcmc_sampler, mcmc_params)

    chain.append_mcmc_output(StatisticsOutput(plot_times=True))
    chain.run()

示例#4

from kameleon_mcmc.mcmc.MCMCParams import MCMCParams
from kameleon_mcmc.mcmc.output.StatisticsOutput import StatisticsOutput
from kameleon_mcmc.mcmc.samplers.AdaptiveMetropolis import AdaptiveMetropolis
from kameleon_mcmc.mcmc.samplers.AdaptiveMetropolisLearnScale import \
    AdaptiveMetropolisLearnScale
from kameleon_mcmc.mcmc.samplers.KameleonWindowLearnScale import \
    KameleonWindowLearnScale
from kameleon_mcmc.mcmc.samplers.StandardMetropolis import StandardMetropolis

if __name__ == '__main__':
    experiment_dir = str(os.path.abspath(sys.argv[0])).split(
        os.sep)[-1].split(".")[0] + os.sep

    distribution = Banana(dimension=8, bananicity=0.03, V=100)
    sigma = GaussianKernel.get_sigma_median_heuristic(
        distribution.sample(1000).samples)
    sigma = 10
    print "using sigma", sigma
    kernel = GaussianKernel(sigma=sigma)

    burnin = 20000
    num_iterations = 40000

    mcmc_sampler = KameleonWindowLearnScale(distribution,
                                            kernel,
                                            stop_adapt=burnin)
    mean_est = zeros(distribution.dimension, dtype="float64")
    cov_est = 1.0 * eye(distribution.dimension)
    cov_est[0, 0] = distribution.V
    #mcmc_sampler = AdaptiveMetropolisLearnScale(distribution, mean_est=mean_est, cov_est=cov_est)
    #mcmc_sampler = AdaptiveMetropolis(distribution, mean_est=mean_est, cov_est=cov_est)

示例#5

文件： banana_8_big_local_StandardMetropolis.py 项目： karlnapf/kameleon-mcmc

from kameleon_mcmc.mcmc.MCMCChain import MCMCChain
from kameleon_mcmc.mcmc.MCMCParams import MCMCParams
from kameleon_mcmc.mcmc.output.StatisticsOutput import StatisticsOutput
from kameleon_mcmc.mcmc.samplers.AdaptiveMetropolis import AdaptiveMetropolis
from kameleon_mcmc.mcmc.samplers.AdaptiveMetropolisLearnScale import \
    AdaptiveMetropolisLearnScale
from kameleon_mcmc.mcmc.samplers.KameleonWindowLearnScale import \
    KameleonWindowLearnScale
from kameleon_mcmc.mcmc.samplers.StandardMetropolis import StandardMetropolis


if __name__ == '__main__':
    experiment_dir = str(os.path.abspath(sys.argv[0])).split(os.sep)[-1].split(".")[0] + os.sep
    
    distribution = Banana(dimension=8, bananicity=0.1, V=100)
    sigma = GaussianKernel.get_sigma_median_heuristic(distribution.sample(1000).samples)
    sigma = 10
    print "using sigma", sigma
    kernel = GaussianKernel(sigma=sigma)
    
    
    burnin = 40000
    num_iterations = 80000
    
    #mcmc_sampler = KameleonWindowLearnScale(distribution, kernel, stop_adapt=burnin)
    mean_est = zeros(distribution.dimension, dtype="float64")
    cov_est = 1.0 * eye(distribution.dimension)
    cov_est[0, 0] = distribution.V
    #mcmc_sampler = AdaptiveMetropolisLearnScale(distribution, mean_est=mean_est, cov_est=cov_est)
    #mcmc_sampler = AdaptiveMetropolis(distribution, mean_est=mean_est, cov_est=cov_est)
    mcmc_sampler = StandardMetropolis(distribution)

示例#6

from kameleon_mcmc.mcmc.MCMCChain import MCMCChain
from kameleon_mcmc.mcmc.MCMCParams import MCMCParams
from kameleon_mcmc.mcmc.output.StatisticsOutput import StatisticsOutput
from kameleon_mcmc.mcmc.samplers.AdaptiveMetropolis import AdaptiveMetropolis
from kameleon_mcmc.mcmc.samplers.AdaptiveMetropolisLearnScale import \
    AdaptiveMetropolisLearnScale
from kameleon_mcmc.mcmc.samplers.KameleonWindowLearnScale import \
    KameleonWindowLearnScale
from kameleon_mcmc.mcmc.samplers.StandardMetropolis import StandardMetropolis


if __name__ == '__main__':
    experiment_dir = str(os.path.abspath(sys.argv[0])).split(os.sep)[-1].split(".")[0] + os.sep
    
    distribution = Banana(dimension=8, bananicity=0.03, V=100)
    sigma = GaussianKernel.get_sigma_median_heuristic(distribution.sample(1000).samples)
    sigma = 10
    print "using sigma", sigma
    kernel = GaussianKernel(sigma=sigma)
    
    
    burnin = 20000
    num_iterations = 40000
    
    #mcmc_sampler = KameleonWindowLearnScale(distribution, kernel, stop_adapt=burnin)
    mean_est = zeros(distribution.dimension, dtype="float64")
    cov_est = 1.0 * eye(distribution.dimension)
    cov_est[0, 0] = distribution.V
    #mcmc_sampler = AdaptiveMetropolisLearnScale(distribution, mean_est=mean_est, cov_est=cov_est)
    #mcmc_sampler = AdaptiveMetropolis(distribution, mean_est=mean_est, cov_est=cov_est)
    mcmc_sampler = StandardMetropolis(distribution)

示例#7

    def gradient(self, x, Y):
        assert (len(shape(x)) == 1)
        assert (len(shape(Y)) == 2)
        assert (len(x) == shape(Y)[1])

        if self.nu == 1.5 or self.nu == 2.5:
            x_2d = reshape(x, (1, len(x)))
            lower_order_rho = self.rho * sqrt(2 * (self.nu - 1)) / sqrt(
                2 * self.nu)
            lower_order_kernel = MaternKernel(lower_order_rho, self.nu - 1,
                                              self.sigma)
            k = lower_order_kernel.kernel(x_2d, Y)
            differences = Y - x
            G = (1.0 / lower_order_rho**2) * (k.T * differences)
            return G
        else:
            raise NotImplementedError()


if __name__ == '__main__':
    distribution = Banana()
    Z = distribution.sample(50).samples
    Z2 = distribution.sample(50).samples
    kernel = MaternKernel(5.0, nu=1.5, sigma=2.0)
    K = kernel.kernel(Z, Z2)
    imshow(K, interpolation="nearest")
    #G = kernel.gradient(Z[0],Z2)
    #print G
    show()

示例#8

文件： MaternKernel.py 项目： karlnapf/kameleon-mcmc

        else:
            raise NotImplementedError()
        return K
    
    def gradient(self, x, Y):
        assert(len(shape(x))==1)
        assert(len(shape(Y))==2)
        assert(len(x)==shape(Y)[1])
        
        if self.nu==1.5 or self.nu==2.5:
            x_2d=reshape(x, (1, len(x)))
            lower_order_rho = self.rho * sqrt(2*(self.nu-1)) / sqrt(2*self.nu)
            lower_order_kernel = MaternKernel(lower_order_rho,self.nu-1,self.sigma)
            k = lower_order_kernel.kernel(x_2d, Y)
            differences = Y - x
            G = ( 1.0 / lower_order_rho ** 2 ) * (k.T * differences)
            return G
        else:
            raise NotImplementedError()
    
if __name__ == '__main__':
    distribution = Banana()
    Z = distribution.sample(50).samples
    Z2 = distribution.sample(50).samples
    kernel = MaternKernel(5.0, nu=1.5, sigma=2.0)
    K = kernel.kernel(Z, Z2)
    imshow(K, interpolation="nearest")
    #G = kernel.gradient(Z[0],Z2)
    #print G
    show()