def gpmake(X, Y, name):
print("Making {}".format(name))
kernel = GPy.kern.RBF(M)
noise = 0.1
normalizer = False
gp = reg(X, Y, kernel, noise_var=noise, normalizer=normalizer)
gp.optimize()
Models[name] = gp
def gpmake(X, Y, name):
print("Making {}".format(name))
kernel = GPy.kern.RBF(M)
noise = 0.1
normalizer = False
if name == 'ENKI':
gp = reg(X, Y, kernel, noise_var=noise, normalizer=normalizer)
gp.optimize()
Models[name] = gp
else:
gp = reg(X, Y, kernel, noise_var=noise, normalizer=normalizer)
gp.optimize()
Models[name + 'E500'] = gp
del gp
gp2 = reg(np.append(XENKI, X, axis=0),
np.append(YENKI, Y, axis=0),
kernel,
noise_var=noise,
normalizer=normalizer)
gp2.optimize()
Models[name + 'E2100'] = gp2
del gp2
def gptest(p, i, v):
if v == 0:
V = False
elif v == 1:
V = True
else:
V = None
gp = reg(xtrain, ytrain, kerns[p], noise_var=i, normalizer=V)
gp.optimize()
ymodel = gp.predict(xtest)
meansqdiff = np.mean((ytest - ymodel[0])**2)
print(meansqdiff)
fig = gp.plot()
GPy.plotting.show(fig)
plt.scatter(xtest, ytest)
plt.show()
print("reject")
return z
else:
print("accept")
return zstar
#First stage is to train the Gaussian Emulator
#Train on one parameter initially
print("Train GP")
Tsize = 100 #Size of training se
X = np.linspace(-100, 100, Tsize).reshape((Tsize, 1))
Y = X**3 + 20 * np.random.normal(loc=0.0, scale=1.0, size=Tsize).reshape(
(Tsize, 1))
print("Gaussian Process")
gp = reg(X, Y, noise_var=0.1, normalizer=True)
gp.optimize()
#Obtain the long run statistics (true data)
obs = np.zeros((100))
for i in range(0, 100):
obs[i] = ztrue[:M]**3 + np.random.normal(loc=0.0, scale=1.0)
ztrue[M:] = np.mean(obs)
#Iterate
print("initial positions")
samples = np.zeros((ITER, chains, M))
#Start the initial Markov chains (In future possibly consider starting these in different places)
for i in range(0, chains):
samples[0, i, :] = z_ENKI + np.random.normal(loc=0.0, scale=10.0)
kerns = [
GPy.kern.RBF(M),
GPy.kern.Exponential(M),
GPy.kern.Matern32(M),
GPy.kern.Matern52(M),
GPy.kern.Brownian(M),
GPy.kern.Bias(M),
GPy.kern.Linear(M),
GPy.kern.PeriodicExponential(M),
GPy.kern.White(M)
]
for p in range(0, 9):
k = kerns[p]
for i in [0.0, 0.1, 0.5, 1, 10, 100]:
for V in [True, False, None]:
gp = reg(xtrain, ytrain, k, noise_var=i, normalizer=V)
gp.optimize()
#print(M,k,i,V)
#print(gp)
ymodel = gp.predict(xtest)
#meandiff=np.abs(np.mean(ytest-ymodel[0]))
#meansqdiff=np.mean((ytest-ymodel[0])**2)
#meanstddiff=np.abs(np.mean((ytest-ymodel[0])/ymodel[1]))
#meanstdsqdiff=np.mean(((ytest-ymodel[0])/ymodel[1])**2)
#pdf=np.prod(st.norm.pdf(ytest,loc=ymodel[0],scale=ymodel[1]))
gpcont = ()
B = 0
while True:
try:
gpcont = np.append(gpcont, gp[B])
B += 1
return z, 0
else:
#print("accept")
return zstar, 1
#First stage is to train the Gaussian Emulator
#Train on one parameter initially
print("Train GP")
X = np.load(
"/home/jprosser/Documents/WorkPlacements/Caltech/Data/parameters.npy"
)[:1000, :]
Y = np.load("/home/jprosser/Documents/WorkPlacements/Caltech/Data/Output.npy"
)[:1000, :]
print("Gaussian Process")
gp = reg(X, Y)
gp.optimize()
#fig=gp.plot()
#GPy.plotting.show(fig)
#plt.show()
#Obtain the long run statistics (true data)
ztrue = np.load(
"/home/jprosser/Documents/WorkPlacements/Caltech/Data/ztrue.npy")
print(ztrue)
#Iterate
print("initial positions")
samples = np.zeros((ITER + burnin, chains, M))
#Start the initial Markov chains (In future possibly consider starting these in different places)
for i in range(0, chains):
import GPy import numpy as np from GPy.models.gp_regression import GPRegression as reg import matplotlib.pyplot as plt #Define input data (observations) xmax = 10 xmin = -10 N = 50 X = np.linspace(xmin, xmax, N).reshape((N, 1)) Y = X**2 + np.random.normal(size=N).reshape((N, 1)) #Create gp k = GPy.kern.Brownian(input_dim=1) gp = reg(X, Y, k, noise_var=1, normalizer=True) #plot gp fig = gp.plot() GPy.plotting.show(fig) plt.show()
