def _joint_log_likelihood(self, X):
        check_is_fitted(self, "classes_")

        X = check_array(X, accept_sparse='csr')

        if self.binarize is not None:
            X = binarize(X, threshold=self.binarize)

        n_classes, n_features = self.feature_log_prob_.shape
        n_samples, n_features_X = X.shape

        if n_features_X != n_features:
            raise ValueError("Expected input with %d features, got %d instead"
                             % (n_features, n_features_X))

        neg_prob = np.log(1 - np.exp(self.feature_log_prob_))
        # Compute  neg_prob · (1 - X).T  as  ∑neg_prob - X · neg_prob
        jll = safe_sparse_dot(X, (self.feature_log_prob_ - neg_prob).T)
        jll += self.class_log_prior_ + neg_prob.sum(axis=1)

        return jll
Send `data' to the server.
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/naive_bayes.py/BernoulliNB._joint_log_likelihood--crossover with--dataset/ETHPy150Open GoogleCloudPlatform/python-compat-runtime/appengine-compat/exported_appengine_sdk/google/appengine/dist27/gae_override/httplib.py/HTTPConnection.send