def __init__(self):
self.eeg_data = BlockInput(name='EEGData',
min_cardinality=1,
max_cardinality=1,
attribute_type=ParameterType.EEGDATA)
self.pre_stimulus_onset = BlockParameter(
name='PreStimulus onset',
attribute_type=ParameterType.NUMBER,
defaultvalue='-0.2',
description='')
self.post_stimulus_onset = BlockParameter(
name='PostStimulus onset',
attribute_type=ParameterType.NUMBER,
defaultvalue='0.5',
description='')
self.baseline_start_time = BlockParameter(
name='Baseline Correction Start Time',
attribute_type=ParameterType.STRING,
defaultvalue='None',
description='None means beginning of the data')
self.baseline_end_time = BlockParameter(
name='Baseline Correction End Time',
attribute_type=ParameterType.STRING,
defaultvalue='0',
description='None means end of the data')
self.event_id = BlockParameter(name='Event Id',
attribute_type=ParameterType.STRING,
defaultvalue='None',
description='None means all the events')
self.eeg_data_output = BlockOutput(name='Epochs',
min_cardinality=1,
max_cardinality=100,
attribute_type=ParameterType.EPOCHS)
def __init__(self):
self.model = BlockInput(name='Model',
min_cardinality=0,
max_cardinality=1,
attribute_type=ParameterType.MODEL)
self.feature_vector = BlockInput(
name='FeatureVector',
min_cardinality=0,
max_cardinality=1,
attribute_type=ParameterType.FEATUREVECTOR)
self.optimizer = BlockParameter(
name='Optimizer',
attribute_type=ParameterType.STRING,
defaultvalue='adam',
description="Either, 'adam', 'adagrad', 'rmsprop'")
self.test_size = BlockParameter(
name='Test Size',
attribute_type=ParameterType.NUMBER,
defaultvalue='0.33',
description="Test Train Split. Ratio of test size")
self.epochs = BlockParameter(name='Number of Epochs',
attribute_type=ParameterType.NUMBER,
defaultvalue='100',
description="")
self.classes = BlockParameter(
name='Number of Classes',
attribute_type=ParameterType.NUMBER,
defaultvalue='',
description="Number of Classification classes")
self.model_out = BlockOutput(name='Model',
min_cardinality=1,
max_cardinality=100,
attribute_type=ParameterType.MODEL)
def __init__(self):
self.model = BlockInput(name='Model',
min_cardinality=0,
max_cardinality=1,
attribute_type=ParameterType.MODEL)
self.units = BlockParameter(name='Number of Units',
attribute_type=ParameterType.NUMBER,
defaultvalue='',
description='Must be an Integer')
self.activation = BlockParameter(
name='Activation Function',
attribute_type=ParameterType.STRING,
defaultvalue='relu',
description=
"Either, 'sigmoid', 'relu', 'softmax', 'elu', 'leaky-relu', 'selu' or 'gelu'"
)
self.dropout = BlockParameter(
name='Dropout',
attribute_type=ParameterType.STRING,
defaultvalue='0',
description="Fraction of the input units to drop")
self.model_out = BlockOutput(name='Model',
min_cardinality=1,
max_cardinality=100,
attribute_type=ParameterType.MODEL)
def __init__(self):
self.model = BlockInput(name='Model',
min_cardinality=0,
max_cardinality=1,
attribute_type=ParameterType.MODEL)
self.model_out = BlockOutput(name='Model',
min_cardinality=1,
max_cardinality=100,
attribute_type=ParameterType.MODEL)
def __init__(self):
self.epochs = BlockInput(name='Epochs',
min_cardinality=1,
max_cardinality=1,
attribute_type=ParameterType.EPOCHS)
self.feature_vector = BlockOutput(
name='FeatureVector',
min_cardinality=1,
max_cardinality=100,
attribute_type=ParameterType.FEATUREVECTOR)
def __init__(self):
self.num1 = BlockInput(name='num1',
min_cardinality=1,
max_cardinality=1,
attribute_type=ParameterType.NUMBER)
self.num2 = BlockInput(name='num2',
min_cardinality=1,
max_cardinality=1,
attribute_type=ParameterType.NUMBER)
self.output = BlockOutput(name='output',
min_cardinality=1,
max_cardinality=1,
attribute_type=ParameterType.NUMBER)
def __init__(self):
self.epochs = BlockInput(
name='Epochs',
min_cardinality=1,
max_cardinality=1,
attribute_type=ParameterType.EPOCHS
)
self.epochs_output = BlockOutput(
name='Epochs',
min_cardinality=1,
max_cardinality=100,
attribute_type=ParameterType.EPOCHS
)
def __init__(self):
self.num = BlockParameter(
name='constant value',
attribute_type=ParameterType.NUMBER,
defaultvalue=10
# defaultvalue=''
)
self.output = BlockOutput(
name='output',
min_cardinality=1,
max_cardinality=1,
attribute_type=ParameterType.NUMBER
)
def __init__(self):
self.eeg_data = BlockParameter(
name='EEG File',
attribute_type=ParameterType.FILE,
defaultvalue='',
description='Select only the ".eeg" file, but folder must contain ".vhdr" and ".vmrk" files'
)
self.eeg_data_output = BlockOutput(
name='EEGData',
min_cardinality=1,
max_cardinality=100,
attribute_type=ParameterType.EEGDATA
)
def __init__(self):
self.feature_vector = BlockInput(name='FeatureVector',
min_cardinality=1,
max_cardinality=1,
attribute_type=ParameterType.EPOCHS)
self.labels = BlockParameter(
name='Labels',
attribute_type=ParameterType.STRING_ARRAY,
defaultvalue='',
description=
'All comma separated event ids are considered as one class.<br>Event id will not be considered if not added'
)
self.feature_vector_out = BlockOutput(
name='FeatureVector',
min_cardinality=1,
max_cardinality=100,
attribute_type=ParameterType.FEATUREVECTOR)
def __init__(self):
self.eeg_data = BlockInput(
name='EEGData',
min_cardinality=1,
max_cardinality=1,
attribute_type=ParameterType.EEGDATA
)
self.channels = BlockParameter(
name='Channels',
attribute_type=ParameterType.STRING_ARRAY,
defaultvalue='',
description='Select channel'
)
self.eeg_data_output = BlockOutput(
name='EEGData',
min_cardinality=1,
max_cardinality=100,
attribute_type=ParameterType.EEGDATA
)
def __init__(self):
self.eeg_data = BlockInput(name='EEGData',
min_cardinality=1,
max_cardinality=1,
attribute_type=ParameterType.EEGDATA)
self.low_cutoff_freq = BlockParameter(
name='Low Cutoff Frequency',
attribute_type=ParameterType.NUMBER,
defaultvalue='None',
description='If None the data are only low-passed')
self.high_cutoff_freq = BlockParameter(
name='High Cutoff Frequency',
attribute_type=ParameterType.NUMBER,
defaultvalue='None',
description='If None the data are only high-passed')
self.eeg_data_output = BlockOutput(
name='EEGData',
min_cardinality=1,
max_cardinality=100,
attribute_type=ParameterType.EEGDATA)