计算N×M(建议维度大于100*100)的0,1矩阵均匀分布程度,值由0到1表示不均匀到均匀
import numpy as np def make_rand_matrix(side=20): # 制作随机矩阵,用于测试 a = np.random.random((side,side)) for i in range(0,side): for j in range(0,side): if a[i,j]>0.3: a[i,j] = 1 else: a[i,j] = 0 return a def get_min_std_matrix(A): # 制作最分布均匀矩阵 sum_ = sum(sum(A)) [x_side, y_side] = A.shape B = np.zeros((x_side,y_side)) long_ = int(x_side*y_side/sum_) cnt = 0 for i in range(0,x_side): for j in range(0,y_side): cnt = cnt + 1 if cnt%long_ == 0: B[i,j] = 1 return B def get_max_std_matrix(A): # 制作分布最不均匀矩阵 sum_ = sum(sum(A)) [x_side, y_side] = A.shape B = np.zeros((x_side,y_side)) cnt = 0 for i in range(0,x_side): for j in range(0,y_side): B[i,j] = 1 cnt = cnt + 1 if cnt >= sum_: break return B def get_rand_std(rand_matrix): # 输入矩阵为0,1矩阵,计算矩阵分布均匀程度 sum_ = sum(sum(rand_matrix)) [x_side, y_side] = rand_matrix.shape fit_x_side = int(x_side/10) fit_y_side = int(y_side/10) location_p = [] for i in range(0, x_side-10, int(fit_x_side/2)): # 让每个元素被扫描两次(规则自己随便定,保证元素都被扫描到就行) for j in range(0,y_side-10, int(fit_y_side/2)): temp_matrix = rand_matrix[i:i+10,j:j+10] cnt_p = sum(sum(temp_matrix)) location_p.append(cnt_p) std = np.std(location_p) return std def get_stand_std(A): # 将均匀程度分布在0,1之间,1表示分布最均匀 if sum(sum(A)) == 0: print('不得传入全0矩阵') exit() A_max = get_max_std_matrix(A) A_min = get_min_std_matrix(A) A_std = get_rand_std(A) A_max_std = get_rand_std(A_max) A_min_std = get_rand_std(A_min) if A_std < A_min_std: return 1 else: return max(0,(A_max_std-A_std)/(A_max_std-A_min_std)) if __name__ == "__main__": # 当矩阵为0或者1元素过少,计算分布没有意义 A = make_rand_matrix(100) # numpy产生随机0,1矩阵均匀程度在0.9~1之间 print(get_stand_std(A))