def predict_proba(self, X):
        """Predict class probabilities of the input samples X.

        Parameters
        ----------
        X : array-like of shape = [n_samples, n_features]
            The input samples.

        Returns
        -------
        prob : array of shape = [n_samples, 2]
            The class probabilities of the input samples.
        """
        #TODO: Check consistency of X
        n_samples, n_features = X.shape
        prob = np.zeros((n_samples, 2))

        if self.pruned:
            tree_ = self.tree_.tree_pruned
        else:
            tree_ = self.tree_.tree

        prob[:, 1] = self._classify(X, tree_, proba=True)
        prob[:, 0] = 1 - prob[:, 1]

        return prob
def predict_proba ( self , X ) : n_samples , n_features = X . shape prob = np . zeros ( ( n_samples , 2 ) ) if self . pruned : tree_ = self . tree_ . tree_pruned else : tree_ = self . tree_ . tree prob [ : , 1 ] = self . _classify ( X , tree_ , proba = True ) prob [ : , 0 ] = 1 - prob [ : , 1 ] return prob
Predict class probabilities of the input samples X.

        Parameters
        ----------
        X : array-like of shape = [n_samples, n_features]
            The input samples.

        Returns
        -------
        prob : array of shape = [n_samples, 2]
            The class probabilities of the input samples.