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)
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)
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)