def run_training(db_file, name_out_path, n_components, data_type):
db = Utility.load_obj(db_file)
Y = []
names = []
for syl in db:
feat = syl['TF'][data_type]['data']
Y.append(feat)
names.append(syl['id'])
# sys.exit()
Y = np.array(Y)
print Y.shape
# print Y[0]
config = {'n_components': n_components, 'data': Y}
print config
m, Y_r = GPy_Interface.pca(config)
# print Y_r.shape
Utility.save_obj(m, '{}/model.pkl'.format(name_out_path))
Utility.save_obj(Y_r, '{}/pca_reduction_output.pkl'.format(name_out_path))
Utility.save_obj(names, '{}/names.pkl'.format(name_out_path))
Utility.save_obj(Y, '{}/training_data.pkl'.format(name_out_path))
pass
def run_training(db_file, name_out_path, data_type, input_dim):
db = Utility.load_obj(db_file)
Y = []
names = []
for syl in db:
feat = syl['TF'][data_type]['data']
Y.append(feat)
names.append(syl['id'])
# sys.exit()
Y = np.array(Y)
print Y.shape
# print Y[0]
num_inducing = int(len(Y) * 0.01)
if num_inducing > 100:
num_inducing = 100
elif num_inducing < 10:
num_inducing = 10
config = {
'input_dim': input_dim,
'data': Y,
'num_inducing': num_inducing,
'max_iters': 500,
'missing_data': True,
'optimize_algo': 'scg'
}
print config
m = GPy_Interface.Bayesian_GPLVM_Training(config)
print m
print '---------------------------'
print m.X
print '---------------------------'
print m.input_sensitivity()
print '---------------------------'
Utility.save_obj(m, '{}/model.pkl'.format(name_out_path))
Utility.save_obj(np.array(m.X.mean), '{}/x.pkl'.format(name_out_path))
Utility.save_obj(m.input_sensitivity(),
'{}/input_sensitivity.pkl'.format(name_out_path))
Utility.save_obj(names, '{}/names.pkl'.format(name_out_path))
Utility.save_obj(Y, '{}/training_data.pkl'.format(name_out_path))
pass
def run_training(db_file, name_out_path):
db = Utility.load_obj(db_file)
Y = []
for syl in db:
feat = syl['TF']['missing151']['data']
Y.append(feat)
# sys.exit()
Y = np.array(Y)
print Y.shape
# print Y[0]
config = {
'input_dim': 10,
'data': Y,
'num_inducing': int(len(Y) * 0.1),
'max_iters': 1000,
'missing_data': True,
'optimize_algo': 'bfgs'
}
print config
m = GPy_Interface.Bayesian_GPLVM_Training(config)
print m
print '---------------------------'
print m.X
print '---------------------------'
print np.array(m.X.mean)
print '---------------------------'
print m.input_sensitivity()
print '---------------------------'
Utility.save_obj(m, '{}/model.pkl'.format(name_out_path))
Utility.save_obj(np.array(m.X.mean), '{}/x.pkl'.format(name_out_path))
Utility.save_obj(m.input_sensitivity(),
'{}/input_sensitivity.pkl'.format(name_out_path))
Utility.save_obj(names, '{}/names.pkl'.format(name_out_path))
Utility.save_obj(Y, '{}/training_data.pkl'.format(name_out_path))
pass
def run_training(db_file, name_out_path):
db = Utility.load_obj(db_file)
Y = []
names = []
for syl in db:
feat = syl['TF']['intepolate151']['data']
Y.append(feat)
names.append(syl['id'])
# sys.exit()
Y = np.array(Y)
print Y.shape
# print Y[0]
config = {
'input_dim': 10,
'data': Y,
'num_inducing': int(len(Y) * 0.1),
'max_iters': 300,
'missing_data': True,
'optimize_algo': 'scg'
}
print config
m = GPy_Interface.Bayesian_GPLVM_Training(config)
Utility.save_obj(m, '{}/model.pkl'.format(name_out_path))
Utility.save_obj(names, '{}/names.pkl'.format(name_out_path))
Utility.save_obj(Y, '{}/training_data.pkl'.format(name_out_path))
pass