def partial_fit(self, X, y, classes=None, sample_weight=None):
        X = check_array(X, accept_sparse='csr', dtype=np.float64)
        _, n_features = X.shape

        if _check_partial_fit_first_call(self, classes):
            # This is the first call to partial_fit:
            # initialize various cumulative counters
            n_effective_classes = len(classes) if len(classes) > 1 else 2
            self.class_count_ = np.zeros(n_effective_classes, dtype=np.float64)
            self.feature_count_ = np.zeros((n_effective_classes, n_features),
                                           dtype=np.float64)
        elif n_features != self.coef_.shape[1]:
            msg = "Number of features %d does not match previous data %d."
            raise ValueError(msg % (n_features, self.coef_.shape[-1]))

        Y = label_binarize(y, classes=self.classes_)
        if Y.shape[1] == 1:
            Y = np.concatenate((1 - Y, Y), axis=1)

        n_samples, n_classes = Y.shape

        if X.shape[0] != Y.shape[0]:
            msg = "X.shape[0]=%d and y.shape[0]=%d are incompatible."
            raise ValueError(msg % (X.shape[0], y.shape[0]))

        # label_binarize() returns arrays with dtype=np.int64.
        # We convert it to np.float64 to support sample_weight consistently
        Y = Y.astype(np.float64)
        if sample_weight is not None:
            sample_weight = np.atleast_2d(sample_weight)
            Y *= check_array(sample_weight).T

        class_prior = self.class_prior

        # Count raw events from data before updating the class log prior
        # and feature log probas
        self._count(X, Y)

        # XXX: OPTIM: we could introduce a public finalization method to
        # be called by the user explicitly just once after several consecutive
        # calls to partial_fit and prior any call to predict[_[log_]proba]
        # to avoid computing the smooth log probas at each call to partial fit
        self._update_feature_log_prob()
        self._update_class_log_prior(class_prior=class_prior)
        return self
Add the specified member to the Set value at key
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/naive_bayes.py/BaseDiscreteNB.partial_fit--crossover with--dataset/ETHPy150Open jookies/jasmin/jasmin/vendor/txredisapi.py/RedisProtocol.sadd