learn.py

                x_data = tf.placeholder(shape=[None, 4], dtype=tf.float32)
                y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32)
                A2 = tf.Variable(tf.random_normal(shape=[first_layer_nodes, 1]))  # first_layer nodes -> sum node
                c = tf.Variable(tf.random_uniform([first_layer_nodes, 4], dtype=tf.float32)) # centroids
                exp_list = []
                for i in range(first_layer_nodes):
                    euclid_distance = tf.reduce_sum(tf.square(tf.subtract(x_data, c[i, :])), 1)
                    exp_list.append(tf.exp(-SC * euclid_distance))
                phi = tf.transpose(tf.stack(exp_list))
                final_output = tf.matmul(phi, A2)