本文整理汇总了Python中Orange.data.Table.is_sparse方法的典型用法代码示例。如果您正苦于以下问题：Python Table.is_sparse方法的具体用法？Python Table.is_sparse怎么用？Python Table.is_sparse使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Orange.data.Table的用法示例。
在下文中一共展示了Table.is_sparse方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: set_data
# 需要导入模块: from Orange.data import Table [as 别名]
# 或者: from Orange.data.Table import is_sparse [as 别名]
def set_data(self, data: Table):
super().set_data(data)
if data is not None:
# PCA doesn't support normalization on sparse data, as this would
# require centering and normalizing the matrix
self.normalize_cbx.setDisabled(data.is_sparse())
if data.is_sparse():
self.normalize = False
self.normalize_cbx.setToolTip(
"Data normalization is not supported on sparse matrices."
)
else:
self.normalize_cbx.setToolTip("")示例2: test_format_combo
# 需要导入模块: from Orange.data import Table [as 别名]
# 或者: from Orange.data.Table import is_sparse [as 别名]
def test_format_combo(self):
widget = self.widget
filetype = widget.controls.filetype
widget.save_file = Mock()
data = Table("iris")
sparse_data = Table("iris")
sparse_data.is_sparse = Mock(return_value=True)
self.send_signal(widget.Inputs.data, data)
n_nonsparse = filetype.count()
self.send_signal(widget.Inputs.data, sparse_data)
n_sparse = filetype.count()
self.assertGreater(n_nonsparse, n_sparse)
self.send_signal(widget.Inputs.data, sparse_data)
self.assertEqual(filetype.count(), n_sparse)
self.send_signal(widget.Inputs.data, data)
self.assertEqual(filetype.count(), n_nonsparse)
self.send_signal(widget.Inputs.data, None)
self.send_signal(widget.Inputs.data, data)
self.assertEqual(filetype.count(), n_nonsparse)
self.send_signal(widget.Inputs.data, None)
self.send_signal(widget.Inputs.data, sparse_data)
self.assertEqual(filetype.count(), n_sparse)示例3: set_data
# 需要导入模块: from Orange.data import Table [as 别名]
# 或者: from Orange.data.Table import is_sparse [as 别名]
def set_data(self, data):
self.closeContext()
self.clear_messages()
self.clear()
self.start_button.setEnabled(False)
self.information()
self.data = None
if isinstance(data, SqlTable):
if data.approx_len() < AUTO_DL_LIMIT:
data = Table(data)
elif not remotely:
self.information("Data has been sampled")
data_sample = data.sample_time(1, no_cache=True)
data_sample.download_data(2000, partial=True)
data = Table(data_sample)
else: # data was big and remote available
self.sampling_box.setVisible(True)
self.start_button.setText("Start remote computation")
self.start_button.setEnabled(True)
if not isinstance(data, SqlTable):
self.sampling_box.setVisible(False)
if isinstance(data, Table):
if len(data.domain.attributes) == 0:
self.Error.no_features()
self.clear_outputs()
return
if len(data) == 0:
self.Error.no_instances()
self.clear_outputs()
return
self.openContext(data)
sparse_data = data is not None and data.is_sparse()
self.normalize_box.setDisabled(sparse_data)
self.update_buttons(sparse_data=sparse_data)
self.data = data
self.fit()