def load_data(mvpa_directory):
print('Loading database from %s' % mvpa_directory)
dataset = DatasetManager(
mvpa_directory=mvpa_directory,
# conditions has to be tuples
contrast=(tuple(var_class_00.get().split(' ')),
tuple(var_class_01.get().split(' '))))
return dataset
###############################################################################
#
# LOAD DATA
#
###############################################################################
from pymri.dataset.datasets import DatasetManager
mvpa_directory = '/tmp/Maestro_Project1/GK011RZJA/Right_Hand/mvpa/'
print('Loading database from %s' % mvpa_directory)
dataset = DatasetManager(
mvpa_directory=mvpa_directory,
# conditions has to be tuples
contrast=(('PlanTool_0', 'PlanTool_5'), ('PlanCtrl_0', 'PlanCtrl_5')),
)
dataset_reduced = dataset.feature_reduction(
k_features=784,
reduction_method='SelectKBest (SKB)',
normalize=True,
nnadl=True)
from pymri.model import FNN
# create Classifier
cls = FNN(type='FNN simple',
input_layer_size=784,
hidden_layer_size=46,
output_layer_size=2,
epochs=100,
# perform LeavePOut n times
n_times_LeavePOut = 6
###############################################################################
#
# LOAD DATA
#
###############################################################################
from pymri.dataset.datasets import DatasetManager
# dataset settings
path_base = '/home/jesmasta/amu/master/nifti/bold/'
ds = DatasetManager(path_bold=path_base + 'bold.nii.gz',
path_attr=path_base + 'attributes.txt',
path_attr_lit=path_base + 'attributes_literal.txt',
path_mask_brain=path_base + 'mask.nii.gz',
contrast=(('ExeTool_0', 'ExeTool_5'), ('ExeCtrl_0',
'ExeCtrl_5')),
nnadl=True)
# load data
ds.load_data()
###############################################################################
#
# CHOOSE ROIs
#
###############################################################################
# select feature reduction method
ds.feature_reduction(roi_selection='SelectKBest',
k_features=k_features,
