本文整理汇总了Python中sklearn.neighbors.classification.KNeighborsClassifier.predict_proba方法的典型用法代码示例。如果您正苦于以下问题：Python KNeighborsClassifier.predict_proba方法的具体用法？Python KNeighborsClassifier.predict_proba怎么用？Python KNeighborsClassifier.predict_proba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sklearn.neighbors.classification.KNeighborsClassifier的用法示例。
在下文中一共展示了KNeighborsClassifier.predict_proba方法的1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: InstanceReductionMixin
# 需要导入模块: from sklearn.neighbors.classification import KNeighborsClassifier [as 别名]
# 或者: from sklearn.neighbors.classification.KNeighborsClassifier import predict_proba [as 别名]
#.........这里部分代码省略.........
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training set.0
y : array-like, shape = [n_samples]
Labels for X.
Returns
-------
X_ : array-like, shape = [indeterminated, n_features]
Resulting training set.
y_ : array-like, shape = [indertaminated]
Labels for X_
"""
pass
def get_prototypes(self):
return self.X_, self.y_
def fit(self, X, y, reduce_data=True):
"""
Fit the InstanceReduction model according to the given training data.
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training vector, where n_samples in the number of samples and
n_features is the number of features.
Note that centroid shrinking cannot be used with sparse matrices.
y : array, shape = [n_samples]
Target values (integers)
reduce_data : bool, flag indicating if the reduction would be performed
"""
self.X = X
self.y = y
self.labels = set(y)
self.prototypes = None
self.prototypes_labels = None
self.reduction_ratio = 0.0
if reduce_data:
self.reduce_data(X, y)
return self
def predict(self, X, n_neighbors=1):
"""Perform classification on an array of test vectors X.
The predicted class C for each sample in X is returned.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Returns
-------
C : array, shape = [n_samples]
Notes
-----
The default prediction is using KNeighborsClassifier, if the
instance reducition algorithm is to be performed with another
classifier, it should be explicited overwritten and explained
in the documentation.
"""
X = atleast2d_or_csr(X)
if not hasattr(self, "X_") or self.X_ is None:
raise AttributeError("Model has not been trained yet.")
if not hasattr(self, "y_") or self.y_ is None:
raise AttributeError("Model has not been trained yet.")
if self.classifier == None:
self.classifier = KNeighborsClassifier(n_neighbors=n_neighbors)
self.classifier.fit(self.X_, self.y_)
return self.classifier.predict(X)
def predict_proba(self, X):
"""Return probability estimates for the test data X.
after a given prototype selection algorithm.
Parameters
----------
X : array, shape = (n_samples, n_features)
A 2-D array representing the test points.
Returns
-------
p : array of shape = [n_samples, n_classes], or a list of n_outputs
of such arrays if n_outputs > 1.
The class probabilities of the input samples. Classes are ordered
by lexicographic order.
"""
self.classifier.fit(self.X_, self.y_)
return self.classifier.predict_proba(X)