在 Python CVXPY 中，不同元素的变量矩阵中是否有计数函数？

如何解决在 Python CVXPY 中，不同元素的变量矩阵中是否有计数函数？

对于CVXPY变量矩阵及其变形差分矩阵h，如何计算每列不同非零元素的个数。

当前错误是 CVXPY 与 Numpy Reshape 和 Delete 函数不兼容

我想解决一个工作分配问题，目标是尽可能多地将相同类型的任务分配给一台机器，我使用矩阵“C”代表工作和机器能力矩阵，矩阵“D”代表工作类型，为了便于使用数组乘法函数，我重复了 3 次，我的想法是最小化作业分配矩阵在每列中不同非零元素的数量。

# The output #
(CVXPY) Apr 21 06:43:55 PM: Problem status: optimal
(CVXPY) Apr 21 06:43:55 PM: Optimal value: 2.000e+00
(CVXPY) Apr 21 06:43:55 PM: Compilation took 7.812e-02 seconds
(CVXPY) Apr 21 06:43:55 PM: Solver (including time spent in interface) took 0.000e+00 seconds
Best sloution is：
 [[1. 0. 0.]
 [1. 0. 0.]
 [1. 0. 0.]
 [0. 0. 1.]
 [0. 0. 1.]
 [0. 0. 1.]
 [1. 0. 0.]
 [1. 0. 0.]
 [1. 0. 0.]]
Best sloution is：
 [[1. 0. 0.]
 [1. 0. 0.]
 [3. 0. 0.]
 [0. 0. 2.]
 [0. 0. 2.]
 [0. 0. 2.]
 [3. 0. 0.]
 [3. 0. 0.]
 [3. 0. 0.]]
Traceback (most recent call last):
  File "C:/Users/N000377/Documents/python/help need.py",line 47,in <module>
    print("Best sloution is：\n",h.value)
AttributeError: 'numpy.ndarray' object has no attribute 'value'

## The input python code ##
#Basic 9 lots 3 tool assignment 

import cvxpy as cp
import numpy as np
# Task assignable schema matrix--c #
c=np.array([[1,1,0],[1,1],])
# Task type matrix#
d = np.array ([[1,[3,3,3],[2,2,2],])
# variable matrix #
x = cp.Variable((9,3),integer=True)

x1 = cp.multiply(d,x)
# Task type matrix #
h = cp.diff(x1,axis=0)
h=np.reshape(h,(1,-1))
#print(h)
g= np.delete(h,np.where (h==0))
obj = cp.Minimize(np.size(g)+1)
con= [0 <= x,x <= 1,cp.sum(x,axis=1,keepdims=True)==1,cp.sum(cp.multiply(c,x))==9]
prob = cp.Problem(obj,con)

print("Is DCP? ",obj.is_dcp())

prob.solve(solver='CBC',verbose =True)

print("Best sloution is：\n",x.value)
print("Best sloution is：\n",x1.value)
print("Best sloution is：\n",h.value)

当前错误是 CVXPY 与 Numpy Reshape 和 Delete 函数不兼容