def load_gp(fname_base):
kernel=None
with open(fname_base+".json",'r') as f:
my_json = json.load(f)
my_X = np.loadtxt(fname_base+"_X.dat")
my_y = np.loadtxt(fname_base+"_y.dat")
my_alpha = np.loadtxt(fname_base+"_alpha.dat")
dict_params = my_json['kernel_params']
eval("kernel = "+my_json['kernel'])
gp = GaussianProcessRegressor(kernel=kernel,n_restarts_optimizer=0)
gp.kernel_ = kernel
dict_params_eval = {}
for name in dict_params:
if not('length' in name or 'constant' in name):
continue
if name =="k2__k2__length_scale":
one_space = ' '.join(dict_params[name].split())
dict_params_eval[name] = eval(one_space.replace(' ',','))
else:
dict_params_eval[name] = eval(dict_params[name])
gp.kernel_.set_params(dict_params_eval)
gp.X_train_ = my_X
gp.y_train_ = my_y
gp.alpha_ = my_alpha
gp._y_train_std = float(my_json['y_train_std'])
gp._y_train_mean = float(my_json['y_train_mean'])
return gp
