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Python KNeighborsClassifier.predict_proba方法代碼示例

本文整理匯總了Python中sklearn.neighbors.classification.KNeighborsClassifier.predict_proba方法的典型用法代碼示例。如果您正苦於以下問題:Python KNeighborsClassifier.predict_proba方法的具體用法?Python KNeighborsClassifier.predict_proba怎麽用?Python KNeighborsClassifier.predict_proba使用的例子?那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在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個代碼示例,這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚,您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: InstanceReductionMixin

# 需要導入模塊: from sklearn.neighbors.classification import KNeighborsClassifier [as 別名]
# 或者: from sklearn.neighbors.classification.KNeighborsClassifier import predict_proba [as 別名]

#.........這裏部分代碼省略.........

        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)
開發者ID:dvro,項目名稱:ml,代碼行數:104,代碼來源:baseNew.py


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