'sgt1-150', 'training' + str(training_number), 'E_curvelhs'
]
params_save_path = '/net/data1/ml2017/gpyparams/E_curve_gpy_log/' + str(
kernel) + '_E_curve_training_' + str(
training_number) + '_lhs_sgt1_150_multidim_log.pickle'
#Set empty arrays with zeros for training and validation data
train_obs = np.array([0])
train_energy = np.array([0])
LEC_LENGTH = 16
train_lecs = np.array(np.zeros(LEC_LENGTH))
#Read database data with the specified tags
for row in dm.read(training_tags):
train_obs = np.vstack((train_obs, row.observable))
train_energy = np.vstack((train_energy, row.energy))
train_lecs = np.vstack((train_lecs, row.LECs))
# Clean up initialized zeros
train_obs = np.delete(train_obs, 0, 0)
train_energy = np.delete(train_energy, 0, 0)
train_lecs = np.delete(train_lecs, 0, 0)
train_lecs = np.hstack((train_lecs, train_energy))
train_obs = np.log(train_obs)
train_parameter_dim = 17
lengthscale = 1.0
nsopt = NsoptCaller()
gauss = Gaussfit()
dm = Datamanager(echo=False)
gauss.save_fig = save_fig
gauss.save_path = save_path
if dm.num_matches(training_tags) <= 0 or dm.num_matches(validation_tags) <= 0:
sys.exit('Check your tags. No matched found in database.')
else:
#Set empty arrays with zeros for training and validation data
train_obs = np.array([0])
train_energy = np.array([0])
train_lecs = np.array(np.zeros(LEC_LENGTH))
#Read database data with the specified tags
for row in dm.read(training_tags):
train_obs = np.vstack((train_obs, row.observable))
train_energy = np.vstack((train_energy, row.energy))
train_lecs = np.vstack((train_lecs, row.LECs))
# Clean up initialized zeros
train_obs = np.delete(train_obs, 0, 0)
train_energy = np.delete(train_energy, 0, 0)
train_lecs = np.delete(train_lecs, 0, 0)
# Set up Gaussfit stuff and plot our model error
gauss.set_gp_kernel(kernel=kernel,
in_dim=LEC_LENGTH,
lengthscale=lengthscale,
multi_dim=multi_dim)
def main():
"""Controls the program flow determined by user input"""
# Parses arguments supplied by the user.
parser = argparse.ArgumentParser()
parser.add_argument("-v",
"--verbosity",
action="store_true",
help="Show data output")
# Add a group of arguments that can't coexist
group = parser.add_mutually_exclusive_group()
group.add_argument("-f",
"--function",
action="store",
help="Use specified function for GP")
group.add_argument("-n",
"--nsopt",
action="store_true",
help="Use nsopt calculation for GP")
#Add tag handling. Supports multiple arg seperated by space.
group.add_argument("-l",
"--load",
nargs='+',
action='append',
help="Use stored data for GP")
args = parser.parse_args()
# Initialize project class objects
nsopt = NsoptCaller()
gauss = Gaussfit()
dm = Datamanager()
param = Parameters(
0.1,
100,
)
LECS = param.create_lhs_lecs()
# Suggestion: X should have a more intuitive name (energy) //Martin
# Read input arguments to get input vector
X = nsopt.read_ini(args)
# Load all data with the right tags and convert to array
if args.load:
print args.load
data_list = dm.read(args.load[0])
#TODO(Martin) Format data from data objects
observables = []
energies = []
LECs = []
for row in data_list:
observables.append(row.observable)
energies.append(row.energy)
LECs.append(row.LECs)
#data = np.asarray(data)
return None #Returning None for the moment, when LECs are in database this will work
# Do we want to genereate new nsopt values or use specified function.
if args.nsopt:
Y = nsopt.get_nsopt_observable()
Y = np.trim_zeros(Y)
Y = Y.reshape(1, len(Y))
else:
Y = np.sin(X) + np.random.randn(20, 1) * 0.03
# Set up GP-processes and plot output after optimization.
gauss.set_gp_kernel()
if args.load:
#gauss.populate_gp_model(data) #TODO(rikard, Martin) Check data format.
pass
else:
gauss.populate_gp_model(X, Y)
gauss.optimize()
gauss.plot()