使用 Sympy 创建模块化符号（和索引）矩阵

Question

我正在尝试使用 sympy 创建一个符号和模块化的 Python 函数，该函数采用维度 j（行）和 k（列）并返回一个索引矩阵。这是函数：

import sympy
from sympy import *
from sympy import init_printing,Matrix
from sympy.physics.vector import dynamicsymbols
import numpy as np
init_printing()    
def weights_func_L(j,k):
    #
    # jth nuerons in the k ( or L - 1 ) layer   
    # j rows
    # k columns

    #in other words, this is the weight (in a specific layer j) for the jth nueron and the kth neuron in the 
    #previous (L-1 layer) i.e wjk

    #this will return a weight matrix for j rows (i.e corresponds to the # of layers) 
    #and k (corresponds to the # of nuerons)

    #
    matrix_bin = ones(j,k)
    matrix_bin = flatten(matrix_bin)
    basic_w_string  = 'w'

    for a in np.arange(len(matrix_bin)):
        #symbols(basic_w_string+str(countj)+str(countk))
        if j == k:
            matrix_bin[a] = symbols(basic_w_string+str(a // k)+str( a % j))
        else:
            matrix_bin[a] = symbols(basic_w_string+str(a // k)+str( a % j))
        #print matrix_bin[a]

#         for aa in np.arange(j):
#             matrix_bin[countj,countk] = symbols(basic_w_string+str(countj)+str(countk))
#         countj += 1
#         countk += 1


    matrix_bin2 = np.reshape(matrix_bin,(j,k))
    return Matrix(matrix_bin2)

这适用于方阵：

weights_func_L(3,3)

产生：

但是对于非方阵，j!=k：

weights_func_L(5,3) 产量

类似地调用weights_func_L(3,5) 会产生：

任何指导将不胜感激！

Answer 1

a % j 应该是a % k，像这样：

def weights_func_L(j,k):
    matrix_bin = ones(j,k)
    matrix_bin = flatten(matrix_bin)
    basic_w_string  = 'w'

    for a in np.arange(len(matrix_bin)):
        matrix_bin[a] = symbols(basic_w_string+str(a // k)+str(a % k))

    matrix_bin2 = np.reshape(matrix_bin,(j,k))
    return Matrix(matrix_bin2)

>>> weights_func_L(5, 3)
⎡w₀₀  w₀₁  w₀₂⎤
⎢             ⎥
⎢w₁₀  w₁₁  w₁₂⎥
⎢             ⎥
⎢w₂₀  w₂₁  w₂₂⎥
⎢             ⎥
⎢w₃₀  w₃₁  w₃₂⎥
⎢             ⎥
⎣w₄₀  w₄₁  w₄₂⎦