示例#1
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文件： poe_fig.py 项目： crlsmcl/MJHMC 
def ac_plot(n_samples=5000, **kwargs):
    """ Plots the autocorrelation for the best found parameters of the 36
    dimensional product of experts

    :returns: None
    :rtype: None
    """

    from mjhmc.figures.ac_fig import plot_best
    ndims = 36
    nbasis = 36

    np.random.seed(2015)
    poe = ProductOfT(nbatch=25,ndims=ndims,nbasis=nbasis)
    plot_best(poe, num_steps=n_samples, update_params=False, **kwargs)
示例#2
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def ac_plot(n_samples=5000, **kwargs):
    """ Plots the autocorrelation for the best found parameters of the 36
    dimensional product of experts

    :returns: None
    :rtype: None
    """

    from mjhmc.figures.ac_fig import plot_best

    ndims = 36
    nbasis = 36

    np.random.seed(2015)
    poe = ProductOfT(nbatch=25, ndims=ndims, nbasis=nbasis)
    plot_best(poe, num_steps=n_samples, update_params=False, **kwargs)
示例#3
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from mjhmc.figures import ac_fig
from mjhmc.misc.distributions import ProductOfT
import numpy as np

np.random.seed(2015)


#Search for best hyper-parameters

#Parameters for the distribution object
ndims = 36
nbasis = 36
nbatch = 25
POT = ProductOfT(nbasis=nbasis,nbatch=nbatch,ndims=ndims)

#Run a comparison
ac_fig.plot_best(POT,num_steps=100000,update_params=True)