gp_tf.py 文件源码

def fit(self, X_train, y_train, ridge=1.0):
        self._reset()
        X_train, y_train = self.check_X_y(X_train, y_train)
        self.X_train = np.float32(X_train)
        self.y_train = np.float32(y_train)
        sample_size = self.X_train.shape[0]

        if np.isscalar(ridge):
            ridge = np.ones(sample_size) * ridge
        assert ridge.ndim == 1

        X_dists = np.zeros((sample_size, sample_size), dtype=np.float32)
        with tf.Session(graph=self.graph, config=tf.ConfigProto(
                intra_op_parallelism_threads=self.NUM_THREADS)) as sess:
            dist_op = self.ops['dist_op']
            v1, v2 = self.vars['v1_h'], self.vars['v2_h']
            for i in range(sample_size):
                X_dists[i] = sess.run(dist_op, feed_dict={v1:self.X_train[i], v2:self.X_train})

            K_ridge_op = self.ops['K_ridge_op']
            X_dists_ph = self.vars['X_dists_h']
            ridge_ph = self.vars['ridge_h']

            self.K = sess.run(K_ridge_op, feed_dict={X_dists_ph:X_dists, ridge_ph:ridge})

            K_ph = self.vars['K_h']

            K_inv_op = self.ops['K_inv_op']
            self.K_inv = sess.run(K_inv_op, feed_dict={K_ph:self.K})

            xy_op = self.ops['xy_op']
            K_inv_ph = self.vars['K_inv_h']
            yt_ph = self.vars['yt_h']
            self.xy_ = sess.run(xy_op, feed_dict={K_inv_ph:self.K_inv,
                                                  yt_ph:self.y_train})

        return self