plt.show()
### zero mean (cond)
nlocObs = 100
nloc = res**2
locObs = gridLoc[0:nlocObs,:]
locPred = gridLoc[nlocObs:nloc,:]
valObs = newGP.rGP(gridLoc[0:nlocObs,:])
resCondGP = newGP.rCondGP(locPred, locObs, valObs)
resGP = np.concatenate((valObs, resCondGP))
#### to make plot ####
imGP = resGP.reshape(res,res)
x = np.linspace(0,1, res+1)
y = np.linspace(0,1, res+1)
X, Y = np.meshgrid(x,y)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_aspect('equal')
return (np.exp(x) / (1 + np.exp(x)))
import matplotlib.pyplot as plt
resGP = np.empty(shape=(niter - 1, res**2, 1))
# meanGP = np.zeros(shape=(res**2,1))
i = 0
t0 = time.time()
while (i < niter - 1):
locations = np.loadtxt("locations" + str(i) + ".csv", delimiter=",")
values = np.loadtxt("values" + str(i) + ".csv", delimiter=",")
# np.savetxt("resGP"+str(i)+".csv",lams[i+1]*expit(newGP.rCondGP(gridLoc,locations,np.transpose([values]))) ,delimiter=",")
newGP = GP(zeroMean, expCov(taus[i + 1], rhos[i + 1]))
resGP[i] = lams[i + 1] * expit(
newGP.rCondGP(gridLoc, locations, np.transpose([values])))
# meanGP = ((i+1)*meanGP + lams[i+1]*expit(newGP.rCondGP(gridLoc,locations,np.transpose([values]))))/(i+2)
# imGP = np.transpose(resGP[i].reshape(res,res))
# x = np.linspace(0,1, res+1)
# y = np.linspace(0,1, res+1)
# X, Y = np.meshgrid(x,y)
# fig = plt.figure()
# ax = fig.add_subplot(111)
# ax.set_aspect('equal')
# plt.pcolormesh(X,Y,imGP, cmap='cool')
# plt.xlim(0,1)