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)