#舉個栗子如下: x_vals = np.linspace(0, 10, 5) #print(x_vals) [ 0. 2.5 5. 7.5 10. ] 1 2 3 4 #轉化數組為矩陣 x_vals_column = np.transpose(np.matrix(x_vals)) #print(x_vals_column) ([[ 0 ], [ 2.5], [ 5. ], [ 7.5], [10. ]]) 1 2 3 4 5 6 7 #生成一個列矩陣如下: ones_column = np.transpose(np.matrix(np.repeat(1, 5))) #print(ones_column) [[1] [1] [1] [1] [1]] 1 2 3 4 5 6 7 操作一下,函數功能很明確,將2個矩陣按列合並 A = np.column_stack((x_vals_column, ones_column)) #print(A) [[ 0. 1. ] [ 2.5 1. ] [ 5. 1. ] [ 7.5 1. ] [10. 1. ]] 1 2 3 4 5 6 7 8 將2個矩陣按行合並 b = np.row_stack((x_vals_column, ones_column)) print(B) [[ 0. ] [ 2.5] [ 5. ] [ 7.5] [10. ] [ 1. ] [ 1. ] [ 1. ] [ 1. ] [ 1. ]] 1 2 3 4 5 6 7 8 9 10 11 12 ———————————————— 版權聲明:本文為CSDN博主「Rock_Huang~」的原創文章,遵循CC 4.0 BY-SA版權協議,轉載請附上原文出處鏈接及本聲明。 原文鏈接:https://blog.csdn.net/weixin_38632246/article/details/86713078