from pymri.dataset.datasets import DatasetManager mvpa_directory = '/tmp/Maestro_Project1/GK011RZJA/Right_Hand/mvpa' roi_path = '/tmp/Maestro_Project1/GK011RZJA/Right_Hand/mvpa/ROIs/pSMG.nii.gz' runs = 5 volumes = 145 n_time = 0 # Load the dataset 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.feature_reduction( roi_path=roi_path, k_features=784, reduction_method='SKB' ) training_data, test_data, validation_data = dataset.leave_one_run_out( runs=runs, volumes=volumes, n_time=n_time )
# 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,
############################################################################### # # LOAD DATA # ############################################################################### from pymri.dataset.datasets import DatasetManager # mvpa_directory = '/tmp/Maestro_Project1/GK011RZJA/Right_Hand/mvpa/' mvpa_directory = \ '/amu/master/Maestro_Project1.preprocessed/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=k_features, reduction_method='SelectKBest (SKB)', normalize=True, nnadl=True ) from pymri.model import FNN # create Classifier cls = FNN(
############################################################################### # # LOAD DATA # ############################################################################### from pymri.dataset.datasets import DatasetManager # mvpa_directory = '/tmp/Maestro_Project1/GK011RZJA/Right_Hand/mvpa/' mvpa_directory = \ '/amu/master/Maestro_Project1.preprocessed/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=k_features, reduction_method='SelectKBest (SKB)', normalize=True, nnadl=True) from pymri.model import FNN # create Classifier cls = FNN( type='FNN simple', input_layer_size=k_features,
############################################################################### # # 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,