gpc.py 文件源码

def fit(self, X, y):
        """Fit Gaussian process classification model

        Parameters
        ----------
        X : array-like, shape = (n_samples, n_features)
            Training data

        y : array-like, shape = (n_samples,)
            Target values, must be binary

        Returns
        -------
        self : returns an instance of self.
        """
        X, y = check_X_y(X, y, multi_output=False)

        self.base_estimator_ = _BinaryGaussianProcessClassifierLaplace(
            self.kernel, self.optimizer, self.n_restarts_optimizer,
            self.max_iter_predict, self.warm_start, self.copy_X_train,
            self.random_state)

        self.classes_ = np.unique(y)
        self.n_classes_ = self.classes_.size
        if self.n_classes_ == 1:
            raise ValueError("GaussianProcessClassifier requires 2 or more "
                             "distinct classes. Only class %s present."
                             % self.classes_[0])
        if self.n_classes_ > 2:
            if self.multi_class == "one_vs_rest":
                self.base_estimator_ = \
                    OneVsRestClassifier(self.base_estimator_,
                                        n_jobs=self.n_jobs)
            elif self.multi_class == "one_vs_one":
                self.base_estimator_ = \
                    OneVsOneClassifier(self.base_estimator_,
                                       n_jobs=self.n_jobs)
            else:
                raise ValueError("Unknown multi-class mode %s"
                                 % self.multi_class)

        self.base_estimator_.fit(X, y)

        if self.n_classes_ > 2:
            self.log_marginal_likelihood_value_ = np.mean(
                [estimator.log_marginal_likelihood()
                 for estimator in self.base_estimator_.estimators_])
        else:
            self.log_marginal_likelihood_value_ = \
                self.base_estimator_.log_marginal_likelihood()

        return self