def postprocess(station_nums, ref_num):
filename = 'P_q'+str(station_nums[0]+1)+'_q'+str(station_nums[1]+1)
if len(station_nums) == 3:
filename += '_q'+str(station_nums[2]+1)
filename += '_ref_'+str(ref_num+1)
data = Q[:, station_nums]
q_ref = Q_ref[ref_num, station_nums]
# Create Simple function approximation
# Save points used to parition D for simple function approximation and the
# approximation itself (this can be used to make close comparisions...)
(rho_D_M, d_distr_samples, d_Tree) = sfun.uniform_hyperrectangle(data,
q_ref, bin_ratio=0.15,
center_pts_per_edge=np.ones((data.shape[1],)))
num_l_emulate = 1e6
lambda_emulate = calcP.emulate_iid_lebesgue(lam_domain, num_l_emulate)
if comm.rank == 0:
print "Finished emulating lambda samples"
mdict = dict()
mdict['rho_D_M'] = rho_D_M
mdict['d_distr_samples'] = d_distr_samples
mdict['num_l_emulate'] = num_l_emulate
# Calculate P on lambda emulate
(P0, lem0, io_ptr0, emulate_ptr0) = calcP.prob_emulated(samples, data,
rho_D_M, d_distr_samples, lambda_emulate, d_Tree)
if comm.rank == 0:
print "Calculating prob_emulated"
mdict['P0'] = P0
mdict['lem0'] = lem0
mdict['io_ptr0'] = io_ptr0
mdict['emulate_ptr0'] = emulate_ptr0
# Calclate P on the actual samples with assumption that voronoi cells have
# equal size
(P1, lam_vol1, io_ptr1) = calcP.prob(samples, data,
rho_D_M, d_distr_samples, d_Tree)
if comm.rank == 0:
print "Calculating prob"
mdict['P1'] = P1
mdict['lam_vol1'] = lam_vol1
mdict['lem1'] = samples
mdict['io_ptr1'] = io_ptr1
# Calculate P on the actual samples estimating voronoi cell volume with MC
# integration
(P3, lam_vol3, lambda_emulate3, io_ptr3, emulate_ptr3) = calcP.prob_mc(samples,
data, rho_D_M, d_distr_samples, lambda_emulate, d_Tree)
if comm.rank == 0:
print "Calculating prob_mc"
mdict['P3'] = P3
mdict['lam_vol3'] = lam_vol3
mdict['io_ptr3'] = io_ptr3
mdict['emulate_ptr3'] = emulate_ptr3
# Export P
sio.savemat(filename, mdict, do_compression=True)
def setUp(self):
"""
Set up problem.
"""
super(Test_prob_mc_1to1, self).setUp()
(self.P, self.lam_vol, _, _, _) = calcP.prob_mc(samples=self.samples,
data=self.data, rho_D_M=self.d_distr_prob,
d_distr_samples=self.d_distr_samples,
lambda_emulate=self.lambda_emulate, d_Tree=self.d_Tree)
def postprocess(station_nums, ref_num):
filename = 'P_q'+str(station_nums[0]+1)+'_q'
if len(station_nums) == 3:
filename += '_q'+str(station_nums[2]+1)
filename += '_ref_'+str(ref_num+1)
data = Q[:, station_nums]
q_ref = Q_ref[ref_num, station_nums]
# Create Simple function approximation
# Save points used to parition D for simple function approximation and the
# approximation itself (this can be used to make close comparisions...)
(rho_D_M, d_distr_samples, d_Tree) = sfun.uniform_hyperrectangle(data,
q_ref, bin_ratio=0.15,
center_pts_per_edge=np.ones((data.shape[1],)))
num_l_emulate = 1e6
lambda_emulate = calcP.emulate_iid_lebesgue(lam_domain, num_l_emulate)
print "Finished emulating lambda samples"
mdict = dict()
mdict['rho_D_M'] = rho_D_M
mdict['d_distr_samples'] = d_distr_samples
mdict['num_l_emulate'] = num_l_emulate
mdict['lambda_emulate'] = lambda_emulate
# Calculate P on lambda emulate
(P0, lem0, io_ptr0, emulate_ptr0) = calcP.prob_emulated(samples, data,
rho_D_M, d_distr_samples, lambda_emulate, d_Tree)
print "Calculating prob_emulated"
mdict['P0'] = P0
mdict['lem0'] = lem0
mdict['io_ptr0'] = io_ptr0
mdict['emulate_ptr0'] = emulate_ptr0
# Calclate P on the actual samples with assumption that voronoi cells have
# equal size
(P1, lam_vol1, io_ptr1) = calcP.prob(samples, data,
rho_D_M, d_distr_samples, d_Tree)
print "Calculating prob"
mdict['P1'] = P1
mdict['lam_vol1'] = lam_vol1
mdict['lem1'] = samples
mdict['io_ptr1'] = io_ptr1
# Calculate P on the actual samples estimating voronoi cell volume with MC
# integration
(P3, lam_vol3, lambda_emulate3, io_ptr3, emulate_ptr3) = calcP.prob_mc(samples,
data, rho_D_M, d_distr_samples, lambda_emulate, d_Tree)
print "Calculating prob_mc"
mdict['P3'] = P3
mdict['lam_vol3'] = lam_vol3
mdict['io_ptr3'] = io_ptr3
mdict['emulate_ptr3'] = emulate_ptr3
# Export P
sio.savemat(filename, mdict, do_compression=True)
def setUp(self):
"""
Set up problem.
"""
super(Test_prob_mc_3to1, self).setUp()
(self.P, self.lam_vol, _, _, _) = calcP.prob_mc(samples=self.samples,
data=self.data, rho_D_M=self.d_distr_prob,
d_distr_samples=self.d_distr_samples,
lambda_emulate=self.lambda_emulate, d_Tree=self.d_Tree)
self.P_ref = np.loadtxt(data_path + "/3to1_prob_mc.txt.gz")
def setUp(self):
"""
Set up problem.
"""
super(Test_prob_mc_1to1, self).setUp()
(self.P, self.lam_vol, _, _,
_) = calcP.prob_mc(samples=self.samples,
data=self.data,
rho_D_M=self.d_distr_prob,
d_distr_samples=self.d_distr_samples,
lambda_emulate=self.lambda_emulate,
d_Tree=self.d_Tree)
def setUp(self):
"""
Set up problem.
"""
super(Test_prob_mc_3to1, self).setUp()
(self.P, self.lam_vol, _, _,
_) = calcP.prob_mc(samples=self.samples,
data=self.data,
rho_D_M=self.d_distr_prob,
d_distr_samples=self.d_distr_samples,
lambda_emulate=self.lambda_emulate,
d_Tree=self.d_Tree)
self.P_ref = np.loadtxt(data_path + "/3to1_prob_mc.txt.gz")
def postprocess(station_nums, ref_num):
filename = 'P_q'+str(station_nums[0]+1)+'_q'+str(station_nums[1]+1)
if len(station_nums) == 3:
filename += '_q'+str(station_nums[2]+1)
filename += '_truth_'+str(ref_num+1)
data = Q[:, station_nums]
q_ref = Q_ref[ref_num, station_nums]
# Create Simple function approximation
# Save points used to parition D for simple function approximation and the
# approximation itself (this can be used to make close comparisions...)
(rho_D_M, d_distr_samples, d_Tree) = sfun.uniform_hyperrectangle(data,
q_ref, bin_ratio=0.15,
center_pts_per_edge=np.ones((data.shape[1],)))
num_l_emulate = 1e6
lambda_emulate = calcP.emulate_iid_lebesgue(lam_domain, num_l_emulate)
print "Finished emulating lambda samples"
# Calculate P on the actual samples estimating voronoi cell volume with MC
# integration
(P3, lam_vol3, lambda_emulate3, io_ptr3, emulate_ptr3) = calcP.prob_mc(samples,
data, rho_D_M, d_distr_samples, lam_domain, lambda_emulate, d_Tree)
print "Calculating prob_mc"
mdict = dict()
mdict['rho_D_M'] = rho_D_M
mdict['d_distr_samples'] = d_distr_samples
mdict['lambda_emulate'] = util.get_global_values(lambda_emulate)
mdict['num_l_emulate'] = mdict['lambda_emulate'].shape[1]
mdict['P3'] = util.get_global_values(P3)
mdict['lam_vol3'] = util.get_global_values(lam_vol3)
mdict['io_ptr3'] = util.get_global_values(io_ptr3)
mdict['emulate_ptr3'] = emulate_ptr3
if rank == 0:
# Export P and compare to MATLAB solution visually
sio.savemat(filename, mdict, do_compression=True)
# result_wtree, result_wsamples, result_emulated_rg
self.result_emulated_rg = calcP.prob_emulated(self.r_samples, self.r_data,
self.rho_D_M, self.d_distr_samples, self.lam_domain,
self.r_samples, self.d_Tree)
self.result_wtree = result_emulated_rg
self.result_wsamples = result_emulated_rg
self.result_wotree = calcP.prob_emulated(self.r_samples, self.r_data,
self.rho_D_M, self.d_distr_samples, self.lam_domain,
self.r_samples)
self.result_wosamples = calcP.prob_emulated(self.r_samples, self.r_data,
self.rho_D_M, self.d_distr_samples, self.lam_domain)
self.result_prob_rg = calcP.prob(self.r_samples, self.r_data,
self.rho_D_M, self.d_distr_samples, self.lam_domain,
self.d_Tree)
self.result_mc_rg = calcP.prob_mc(self.r_samples, self.r_data,
self.rho_D_M, self.d_distr_samples, self.lam_domain,
self.r_samples, self.d_Tree)
# samples are iid
self.result_emulated_iid = calcP.prob_emulated(self.u_samples, self.u_data,
self.rho_D_M, self.d_distr_samples, self.lam_domain,
self.u_samples, self.d_Tree)
self.result_prob_iid = calcP.prob(self.u_samples, self.u_data,
self.rho_D_M, self.d_distr_samples, self.lam_domain,
self.d_Tree)
self.result_mc_iid = calcP.prob_mc(self.u_samples, self.u_data,
self.rho_D_M, self.d_distr_samples, self.lam_domain,
self.u_samples, self.d_Tree)
# RESULTS WHERE SAMPLES != LAMBDA_EMULATE
