class Outputs:
annotated_data = Output(ANNOTATED_DATA_SIGNAL_Chinese_NAME,
Table,
default=True,
replaces=['Data'])
if Network is not None:
graph = Output("网络(Network)", Network, replaces=['Network'])
class Outputs:
selected_data = Output("Selected Data",
Table,
default=True,
id="selected-data")
annotated_data = Output(ANNOTATED_DATA_SIGNAL_NAME,
Table,
id="annotated-data")
class Outputs:
selected_data = Output("选定的数据(Selected Data)",
Table,
default=True,
replaces=['Selected Data'])
annotated_data = Output(ANNOTATED_DATA_SIGNAL_Chinese_NAME,
Table,
replaces=['Data'])
class Outputs:
learner = Output("学习器(Learner)",
Learner,
dynamic=False,
replaces=["Learner"])
model = Output("模型(Model)",
Model,
dynamic=False,
replaces=["Classifier", "Predictor", 'Model'])
class Outputs:
train_data = Output('训练数据(Train Data)',
DataLoader,
default=True,
replaces=['Data'])
test_data = Output('测试数据(Test Data)',
DataLoader,
default=True,
replaces=['Data'])
image = Output('图片(Image)', Path, replaces=['Image', 'Path'])
class Outputs(OWBaseLearner.Outputs):
support_vectors = Output(
"支持向量(Support vectors)",
Table,
explicit=True,
replaces=["Support vectors", "Support Vectors"],
)
def test_bind_and_send(self):
widget = MagicMock()
output = Output("a name", int, "an id", "a doc", ["x"])
bound = output.bound_signal(widget)
value = object()
id = 42
bound.send(value, id)
widget.signalManager.send.assert_called_with(widget, "a name", value,
id)
def test_init(self):
with patch("Orange.widgets.utils.signals._Signal.get_flags",
return_value=42) as getflags:
signal = Output("a name", int, "an id", "a doc", ["x"])
self.assertEqual(signal.name, "a name")
self.assertEqual(signal.type, int)
self.assertEqual(signal.id, "an id")
self.assertEqual(signal.doc, "a doc")
self.assertEqual(signal.replaces, ["x"])
self.assertEqual(signal.flags, 42)
getflags.assert_called_with(False, False, False, True)
Output("a name", int, "an id", "a doc", ["x"], default=True)
getflags.assert_called_with(False, True, False, True)
Output("a name", int, "an id", "a doc", ["x"], explicit=True)
getflags.assert_called_with(False, False, True, True)
Output("a name", int, "an id", "a doc", ["x"], dynamic=False)
getflags.assert_called_with(False, False, False, False)
class Outputs:
annotated_data = Output(ANNOTATED_DATA_SIGNAL_NAME,
Table,
default=True)
graph = Output('Network', Graph)
class Outputs:
learner = Output("Learner", Learner, dynamic=False)
model = Output("Model",
Model,
dynamic=False,
replaces=["Classifier", "Predictor"])
class Outputs:
"""
Outputs
"""
data = Output("Data", Table)
class Outputs:
data_table = Output("Data", Table)
gene_matcher_results = Output("Genes", Table)
class Outputs:
reduced_data = Output('Reduced Data', Table, default=True)
statistics = Output('Statistics', Table)
class Outputs:
data = Output("数据(Data)", Orange.data.Table, replaces=['Data'])
class Outputs:
selected_data = Output("Selected Data", Table, default=True)
annotated_data = Output(ANNOTATED_DATA_SIGNAL_NAME, Table)
contingency = Output("Contingency Table", Table)
class Outputs:
data = Output("Neighbors", Table)
class Outputs:
fit_params = Output("Fit Parameters", Table, default=True)
fits = Output("Fits", Table)
residuals = Output("Residuals", Table)
annotated_data = Output(ANNOTATED_DATA_SIGNAL_NAME, Table)
class Outputs:
tree = Output("Tree", TreeModel)
class Outputs:
data = Output("邻近(Neighbors)", Table, replaces=['Neighbors'])
class Outputs:
reduced_data = Output('选中的数据(Reduced Data)',
Table,
default=True,
replaces=['Reduced Data'])
statistics = Output('统计(Statistics)', Table, replaces=['Statistics'])
class Outputs:
matched_genes = Output("Matched Genes", Table)
class Outputs(WidgetA.Outputs):
test = Output("test", str)
class Outputs:
environment_id = Output("Environment", str)
class Outputs:
data = Output("Data", Orange.data.Table)
class Outputs:
tree = Output("树(Tree)", TreeModel, replaces=['Tree'])
class Outputs:
data = Output("数据", Table)
features = Output("特征", AttributeList)
correlations = Output("相关性", Table)
class Outputs:
model = Output('CNN 模型 (CNN model)',
nn.Module,
default=True,
replaces=['CNN model'])
class Outputs:
selected_data = Output('Selected Data', Table)
gene_scores = Output('Gene Scores', Table)
gene_set_scores = Output('Gene Set Scores', Table)
class Outputs:
annotated_data = Output(ANNOTATED_DATA_SIGNAL_NAME,
Table,
default=True)
class Outputs(OWBaseLearner.Outputs):
coefficients = Output("Coefficients", Table, explicit=True)
