from progapy.factories.json2gp import load_json, build_gp_from_json
from progapy.viewers.view_1d import view as view_this_gp
import pylab as pp
import numpy as np
problem_params = load_default_params()
problem = Problem( problem_params, force_init = True )
nbr_samples = 5000
#epsilon = 0.5
epsilon = 0.1
filename = "./examples/exponential_problem/gp.json"
json_gp = load_json( filename )
gp = build_gp_from_json( json_gp )
pgp = ProductGaussianProcess( [gp] )
surrogate_params = {}
surrogate_params["gp"] = pgp
surrogate = Surrogate( surrogate_params )
response_model_params = {"surrogate":surrogate}
acquistion_params = {}
kernel_params = {}
#kernel_params["lower_epsilon"] = -np.inf
#kernel_params["upper_epsilon"] = epsilon
kernel_params["epsilon"] = epsilon
state_params = {}
print "RANDOM SEED"
np.random.seed(0)
nbr_samples = 2000
reject_epsilon = 3.0
n_reject = 50
nbr_thetas = 6
nbr_stats = 10
filename = "./examples/blowfly/gp.json"
gps = []
for gp_idx in range(nbr_stats):
fn = "./examples/blowfly/p%dgp.json" % ((gp_idx + 1))
json_gp = load_json(fn)
#json_gp["kernel"]["type"]="squared_exponential"
gp = build_gp_from_json(json_gp)
gp.kernel.shrink_length_scales(0.5)
#gp.precomputes()
gps.append(gp)
pgp = ProductGaussianProcess(gps)
#assert False
surrogate_params = {}
surrogate_params["gp"] = pgp
surrogate_params["obs_statistics"] = state_params["obs_statistics"]
surrogate_params["epsilon"] = 0.0
rej_state_params = state_params.copy()
rej_state_params["S"] = 1
rej_state = RejectState(None, rej_state_params)
from progapy.gps.product_gaussian_process import ProductGaussianProcess
from progapy.factories.json2gp import load_json, build_gp_from_json
from progapy.viewers.view_1d import view as view_this_gp
import pylab as pp
import numpy as np
problem_params = load_default_params()
problem = Problem(problem_params, force_init=True)
nbr_samples = 5000
#epsilon = 0.5
epsilon = 0.1
filename = "./examples/exponential_problem/gp.json"
json_gp = load_json(filename)
gp = build_gp_from_json(json_gp)
pgp = ProductGaussianProcess([gp])
surrogate_params = {}
surrogate_params["gp"] = pgp
surrogate = Surrogate(surrogate_params)
response_model_params = {"surrogate": surrogate}
acquistion_params = {}
kernel_params = {}
#kernel_params["lower_epsilon"] = -np.inf
#kernel_params["upper_epsilon"] = epsilon
kernel_params["epsilon"] = epsilon
state_params = {}
print "RANDOM SEED"
np.random.seed(0)
nbr_samples = 15000
reject_epsilon = 3.0
n_reject = 100
nbr_thetas = 6
nbr_stats = 10
filename = "./examples/blowfly/gp.json"
gps = []
for gp_idx in range( nbr_stats ):
fn = "./examples/blowfly/p%dgp.json"%((gp_idx+1))
#json_gp = load_json( filename )
json_gp = load_json( fn )
#json_gp["kernel"]["type"]="squared_exponential"
gp = build_gp_from_json( json_gp )
gps.append( gp )
pgp = ProductGaussianProcess( gps)
#assert False
surrogate_params = {}
surrogate_params["gp"] = pgp
surrogate_params["epsilon"] = 0.5
surrogate_params["obs_statistics"] = state_params["obs_statistics"]
rej_state_params = state_params.copy()
rej_state_params["S"] = 1
rej_state = RejectState(None, rej_state_params )
recorder = Recorder(record_stats=True)