import numpy as np
import theano.tensor as T
import matplotlib.pyplot as plt
np.random.seed(2016)
nbasis=36
ndims=12
n_steps=1000
half_window=False
rand_val = rand(ndims,nbasis/2,density=0.1)
W = np.concatenate([rand_val.toarray(), -rand_val.toarray()],axis=1)
logalpha = np.random.randn(nbasis,1)
PoT_instance = ProductOfT(nbatch=100,ndims=ndims,nbasis=nbasis,W=W,logalpha=logalpha)
simulate = hmc.wrapper_hmc(s_rng=s_rng,energy_fn=rw.E_val,dim=dim)
a = np.zeros([1,10,n_samples])
b = np.zeros([2,10,n_samples])
for ii in np.arange(n_steps):
a[:,:,ii],b[:,:,ii] = simulate()
print "Acceptance Rate"
print a[:,:,ii]
print "Samples"
print b[:,:,ii]
ac_df = autocorrelation(df,half_window)
#Now, we can run the same autocorrelation from the data we will extract from the data frame
#but with the theano function
ac= hmc.normed_autocorrelation(df)
#Temp script to test the fast HMC
from mjhmc.fast import hmc
from mjhmc.fast.distributions_T import RoughWell
import theano.tensor as T
import numpy as np
import time
tm1 = time.time()
seed = 1234
s_rng = T.shared_randomstreams.RandomStreams(seed)
ndims = 64
nbatch = 250
rw = RoughWell(nbatch=nbatch)
simulate = hmc.wrapper_hmc(s_rng=s_rng,energy_fn=rw.E_val,dim=[ndims,nbatch])
n_samples = 1000
a = np.zeros([1,nbatch,n_samples])
b = np.zeros([ndims,nbatch,n_samples])
for ii in np.arange(n_samples):
a[:,:,ii],b[:,:,ii] = simulate()
print "Acceptance Rate"
print a[:,:,ii]
print "Samples"
print b[:,:,ii]
tm2 = time.time()
print tm2-tm1
