我在计算LDA的特征值和特征向量。在获得类内散布矩阵值（SW）后，我对矩阵进行求逆，以便将它乘以类间散布矩阵的值Sb。然而，当我尝试通过乘以Sb来计算Sw的逆时，我得到了标题中描述的错误。
这是我的实际InvSw值：
[[ 1.04681227e-02, -8.88438953e-03, -1.49760770e-03, -1.40836916e-04, 5.62586740e-04], [-8.88438953e-03, 2.51997617e-02, -1.29503509e-02, -1.58583123e-03, -1.93338715e-03], [-1.49760770e-03, -1.29503509e-02, 1.96652733e-01, -1.26808048e-01, -5.57741506e-02], [-1.40836916e-04, -1.58583123e-03, -1.26808048e-01, 2.72992280e-01, -1.45652927e-01], [ 5.62586740e-04, -1.93338715e-03, -5.57741506e-02, -1.45652927e-01, 2.04121963e-01]]我的Sb值：
[1.29960e+02, 4.09600e+01, 4.00000e-02, 9.24160e+02, 1.00000e+00, 5.10760e+02, 7.95240e+02, 8.50084e+03, 7.84000e+00, 5.21284e+03, 1.96000e+02, 1.63840e+02, 3.38560e+02, 1.96000e+00, 3.68640e+02, 3.60000e-01, 4.00000e-02, 2.50000e+01, 3.38560e+02, 3.53440e+02]我尝试的乘法方式：
invSw_by_Sb = np.dot(invSw, Sb)整个代码：
c_A_array = [[ 31, 25, 17, 62, 26, 23, 193, 143, 37, 29, 220, 216, 175, 195, 207, 198, 190, 222, 178, 214], [ 31, 26, 19, 59, 25, 23, 193, 140, 37, 29, 220, 216, 174, 195, 207, 198, 190, 220, 178, 214], [ 31, 23, 17, 67, 23, 22, 195, 147, 38, 31, 222, 215, 182, 195, 213, 198, 185, 221, 178, 207], [ 31, 23, 19, 67, 23, 23, 194, 144, 37, 31, 222, 218, 179, 198, 216, 198, 186, 221, 179, 207], [ 31, 28, 17, 65, 23, 22, 193, 142, 36, 31, 222, 217, 177, 195, 216, 196, 182, 220, 174, 207]]c_B_array = [[ 16, 24, 33, 43, 43, 58, 163, 76, 57, 105, 205, 200, 193, 188, 186, 193, 182, 227, 193, 227], [ 9, 13, 22, 36, 13, 49, 163, 39, 33, 105, 204, 200, 193, 191, 188, 193, 183, 224, 194, 227], [ 23, 17, 10, 28, 21, 40, 166, 46, 28, 102, 208, 206, 196, 198, 195, 202, 190, 225, 196, 229], [ 25, 19, 11, 30, 23, 39, 166, 46, 26, 99, 208, 206, 199, 196, 198, 201, 189, 227, 198, 231], [ 25, 20, 12, 31, 25, 40, 169, 48, 27, 101, 211, 206, 198, 198, 196, 202, 190, 226, 198, 229]]c_A_array = np.asarray(c_A_array)c_B_array = np.asarray(c_B_array)c_1_mean = c_A_array.mean(axis=0)c_2_mean = c_B_array.mean(axis=0)S1_c1 = np.cov(c_A_array)S2_c2 = np.cov(c_B_array)Sw = S1_c1 + S2_c2 Sb = (c_1_mean - c_2_mean) * (c_1_mean - c_2_mean)invSw = np.linalg.inv(Sw)invSw_by_Sb = np.dot(invSw, Sb)[V, D] = np.linalg.eig(invSw_by_Sb)回答：
你在使用np.cov和np.mean时使用了不同的维度。如果你想使用np.mean(…, axis=0)，那么你也应该相应地更改cov的维度，如下所示：
c_1_mean = c_A_array.mean(axis=0)c_2_mean = c_B_array.mean(axis=0)S1_c1 = np.cov(c_A_array.T)S2_c2 = np.cov(c_B_array.T)Sw = S1_c1 + S2_c2 此外，你的Sb应该是一个协方差矩阵：
Sb = (c_1_mean - c_2_mean) * (c_1_mean - c_2_mean).reshape([-1, 1])