model.py 文件源码

def compute_distance_of_cluster_heads(self):
        # list all possible combinations of two cluster heads
        num_combination = self.nCr(self.ndim_y, 2)

        # a_labels
        # [0, 1, 0, 0]
        # [0, 0, 1, 0]
        # [0, 0, 1, 0]
        # [0, 0, 0, 1]
        # [0, 0, 0, 1]
        # [0, 0, 0, 1]
        a_labels = np.zeros((num_combination, self.ndim_y), dtype=np.float32)
        for i in range(1, self.ndim_y):
            for n in range(i):
                j = int(0.5 * i * (i - 1) + n)
                a_labels[j, i] = 1

        # b_labels
        # [1, 0, 0, 0]
        # [1, 0, 0, 0]
        # [0, 1, 0, 0]
        # [1, 0, 0, 0]
        # [0, 1, 0, 0]
        # [0, 0, 1, 0]
        b_labels = np.zeros((num_combination, self.ndim_y), dtype=np.float32)
        for i in range(1, self.ndim_y):
            for n in range(i):
                j = int(0.5 * i * (i - 1) + n)
                b_labels[j, n] = 1


        xp = self.xp
        if xp is not np:
            a_labels = cuda.to_gpu(a_labels)
            b_labels = cuda.to_gpu(b_labels)

        a_vector = self.cluster_head(a_labels)
        b_vector = self.cluster_head(b_labels)
        distance = functions.sqrt(functions.sum((a_vector - b_vector) ** 2, axis=1))

        # clip
        distance = functions.clip(distance, 0.0, float(self.cluster_head_distance_threshold))

        return distance