if repo is None:
raise ValueError(
'environment variable MLPYTHON_DATASET_REPO is not defined')
dataset_dir = os.path.join(
os.environ.get('MLPYTHON_DATASET_REPO') + '/' + dataset_directory,
dataset_name)
gammas = [0.01, 1, 5, 10, 50, 100]
Cs = [1, 10, 50, 100]
hyperparams_grid = []
all_data = data_utils.load_data(dataset_dir)
test = all_data['test']
fulltrain_backup = all_data['finaltrain']
factor = 500
all_data = data_utils.data_reduction(fulltrain_backup, factor)
all_data['test'] = test
print len(fulltrain_backup[0])
print len(all_data['finaltrain'][0])
train = [int(t[1]) for t in all_data['train'][0]]
valid = [int(v[1]) for v in all_data['valid'][0]]
finaltrain = [int(f[1]) for f in all_data['finaltrain'][0]]
train = np.asarray(train)
valid = np.asarray(valid)
finaltrain = np.asarray(finaltrain)
datasets = data_utils.create_datasets(all_data)
testset = datasets['testset']
resultg1, resultg2 = '', ''
for gamma in gammas:
for C in Cs:
for brain in brain_names[0:2]:
dataset_dir = os.path.join(os.environ.get('MLPYTHON_DATASET_REPO') + '/' + dataset_directory, brain)
all_data = data_utils.load_data(dataset_dir)
test = all_data['test']
fulltrain_backup = all_data['finaltrain']
resultc1, resultc2 = '' ,''
brain_str = brain + ' \n'
for factor in Factors:
resultc3 = ''
dice_t = np.zeros((10))
processed_timet = np.zeros((10))
for nb in range(10):
resultc3 = ''
all_data = data_utils.data_reduction(fulltrain_backup , factor)
all_data['test'] = test
print len(fulltrain_backup[0])
print len(all_data['finaltrain'][0])
train = [int(t[1]) for t in all_data['train'][0]]
valid = [int(v[1]) for v in all_data['valid'][0]]
finaltrain = [int(f[1]) for f in all_data['finaltrain'][0]]
train = np.asarray(train)
valid = np.asarray(valid)
finaltrain = np.asarray(finaltrain)
datasets = create_datasets(all_data)
dice_t[nb] , processed_timet[nb] = svm_model(datasets)
resultc3 += 'factor = ' + str(factor) + '\n'
resultc3 += 'finaltrain = ' + str(len(all_data['finaltrain'][0])) + '\n'