def posterior_covariance(make = 1, plot = 1):
if(make == 1):
imgNr = 16
dim = 8
imgDB = loadtxt('patches_vanhateren_64.txt')
imgDB = imgDB.reshape(16, 8, 8)
# imgDB = random.random((imgNr, dim, dim))
#print amax(imgDB)
for i in range(0, imgNr):
imgDB[i] *= 1 + i * 9.0/imgNr
#plot_images(imgDB)
start = 0
end = 9
ni=3
pointNr = 20
A = identity(dim**2) #numpy.random.random((16,16))
C = identity(dim**2)
invC = linalg.inv(C)
sum_cov = zeros(imgNr)
z_arr = zeros(imgNr)
[sat, icorr, s, s2] = init_pux(A, C, start, end, pointNr)
for ni in range(0,imgNr):
[c, m, z] = pux_integration(imgDB[ni], sat, icorr, s, s2, start, end, pointNr)
z_arr[ni] = z
#sampling based on these values
#samps = mixt_samp(c, m, s, 10)
#mean calcualtion and comparison with original images
sum_m = m[0]*0
for i in range(0,size(c)):
sum_m += c[i]*m[i]
# sum_m = sum_m.reshape(8,8)
# plot_images([sum_m, imgDB[ni]], "original vs. inferred", "orig_vs_inf_%1.1f.png" %z, contr=False, rng=[0,40])
#The covariance of more random variables is the mean of the conditional covariances plus the covariance of the conditional means.
cov = s[0]*0
for k in range(0,size(s,0)):
cov += c[k]*s[k]
for i in range(0, size(s,1)):
for j in range (0, size(s,2)):
cov[i][j] += c[k]*m[k][i]*m[k][j]
for i in range (0, size(s,1)):
for j in range (0, size(s,2)):
cov[i][j] -= sum_m[i]*sum_m[j]
if (ni == 9):
corr = correlate_cov(cov)
sum_cov[ni] = mean(abs(corr-diag(diag(corr)))) # for correlations
# sum_cov[ni] = mean(cov)
savetxt('posterior_correlations_sums.txt', sum_cov)
savetxt('posterior_correlations_cont.txt', z_arr)
else:
sum_cov = loadtxt('posterior_correlations_sums.txt')
z_arr = loadtxt('posterior_correlations_cont.txt')
if (plot == 1):
plt.plot(z_arr, sum_cov, 'bo')
myplot("contrast", "correlations", "posterior correlations as a function of contrast", "posterior_correlations.png")
data[gwf] = []
with open(gwf + '_avg_reward.data', 'r') as f:
i = 0
j = 0
for e in episodelen:
print(e)
j = e
lst = f.readlines()[i:j]
lst = [float(i) for i in lst]
print(lst[0], type(lst[0]))
i = e
#exit()
#print ([float(f.next()) for x in range(e)])
#exit()
#data[gwf].append(sum())
print(data['9x9_illustrative_3'])
exit()
data = {}
data['GSAT'] = [61.03, 73.25, 76.05, 79.69, 83.19]
data['SUMBT'] = [59.23, 71.37, 74.41, 74.47, 77.14]
data['GCE'] = [51.67, 64.49, 71.73, 71.98, 76.83]
data['GLAD'] = [49.24, 61.76, 71.98, 72.57, 75.25]
data['Simple BERT-DST'] = [49, 49.60, 63.89, 64.44, 68.75]
data['NBT-CNN'] = [40.50, 53.60, 62.90, 67.20, 69.00]
data['NBT-DNN'] = [35.30, 46.60, 51.90, 56.20, 61.50]
index = ['20', '40', '60', '80', '100']
myplot(data, index, 'line_plot')
#bar_plot(data,index,'bar_plot')
