本文整理汇总了Python中AnyQt.QtWidgets.QSplitter.sizes方法的典型用法代码示例。如果您正苦于以下问题：Python QSplitter.sizes方法的具体用法？Python QSplitter.sizes怎么用？Python QSplitter.sizes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AnyQt.QtWidgets.QSplitter的用法示例。
在下文中一共展示了QSplitter.sizes方法的2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: OWPredictions
# 需要导入模块: from AnyQt.QtWidgets import QSplitter [as 别名]
# 或者: from AnyQt.QtWidgets.QSplitter import sizes [as 别名]
#.........这里部分代码省略.........
if self.show_predictions:
pred_fmt = "{value!s}"
if pred_fmt and dist_fmt:
fmt = dist_fmt + " \N{RIGHTWARDS ARROW} " + pred_fmt
else:
fmt = dist_fmt or pred_fmt
else:
assert isinstance(self.class_var, ContinuousVariable)
fmt = "{{value:.{}f}}".format(
self.class_var.number_of_decimals)
delegate.setFormat(fmt)
if self.draw_dist and colors is not None:
delegate.setColors(colors)
self.predictionsview.setItemDelegate(delegate)
self.predictionsview.resizeColumnsToContents()
if self.class_var is not None and self.class_var.is_discrete:
proxy = self.predictionsview.model()
if proxy is not None:
proxy.setProbInd(numpy.array(self.selected_classes, dtype=int))
self._update_spliter()
def _update_spliter(self):
if self.data is None:
return
def width(view):
h_header = view.horizontalHeader()
v_header = view.verticalHeader()
return h_header.length() + v_header.width()
w = width(self.predictionsview) + 4
w1, w2 = self.splitter.sizes()
self.splitter.setSizes([w, w1 + w2 - w])
def commit(self):
if self.data is None or not self.predictors:
self.send("Predictions", None)
self.send("Evaluation Results", None)
return
predictor = next(iter(self.predictors.values())).predictor
class_var = predictor.domain.class_var
classification = class_var and class_var.is_discrete
newmetas = []
newcolumns = []
slots = list(self.predictors.values())
if classification:
if self.output_predictions:
mc = [DiscreteVariable(name=p.name, values=class_var.values)
for p in slots]
newmetas.extend(mc)
newcolumns.extend(p.results[0].reshape((-1, 1))
for p in slots)
if self.output_probabilities:
for p in slots:
m = [ContinuousVariable(name="%s(%s)" % (p.name, value))
for value in class_var.values]
newmetas.extend(m)
newcolumns.extend(p.results[1] for p in slots)
else:示例2: OWPredictions
# 需要导入模块: from AnyQt.QtWidgets import QSplitter [as 别名]
# 或者: from AnyQt.QtWidgets.QSplitter import sizes [as 别名]
#.........这里部分代码省略.........
if proxy is not None:
proxy.setProbInd(
numpy.array(self.selected_classes, dtype=int))
self.predictionsview.setItemDelegate(delegate)
self.predictionsview.resizeColumnsToContents()
self._update_spliter()
def _setup_delegate_discrete(self, delegate):
colors = [QtGui.QColor(*rgb) for rgb in self.class_var.colors]
fmt = []
if self.show_probabilities:
fmt.append(" : ".join("{{dist[{}]:.2f}}".format(i)
for i in sorted(self.selected_classes)))
if self.show_predictions:
fmt.append("{value!s}")
delegate.setFormat(" \N{RIGHTWARDS ARROW} ".join(fmt))
if self.draw_dist and colors is not None:
delegate.setColors(colors)
return delegate
def _setup_delegate_continuous(self, delegate):
delegate.setFormat("{{value:{}}}".format(self.class_var.format_str[1:]))
def _update_spliter(self):
if self.data is None:
return
def width(view):
h_header = view.horizontalHeader()
v_header = view.verticalHeader()
return h_header.length() + v_header.width()
w = width(self.predictionsview) + 4
w1, w2 = self.splitter.sizes()
self.splitter.setSizes([w, w1 + w2 - w])
def commit(self):
self._commit_predictions()
self._commit_evaluation_results()
def _commit_evaluation_results(self):
slots = self._valid_predictors()
if not slots or self.data.domain.class_var is None:
self.Outputs.evaluation_results.send(None)
return
class_var = self.class_var
nanmask = numpy.isnan(self.data.get_column_view(class_var)[0])
data = self.data[~nanmask]
N = len(data)
results = Orange.evaluation.Results(data, store_data=True)
results.folds = None
results.row_indices = numpy.arange(N)
results.actual = data.Y.ravel()
results.predicted = numpy.vstack(
tuple(p.results[0][~nanmask] for p in slots))
if class_var and class_var.is_discrete:
results.probabilities = numpy.array(
[p.results[1][~nanmask] for p in slots])
results.learner_names = [p.name for p in slots]
self.Outputs.evaluation_results.send(results)
def _commit_predictions(self):
slots = self._valid_predictors()
if not slots:
self.Outputs.predictions.send(None)