转置前后的J_vals内容相同。
theta0_vals = np.linspace(-10, 10, 100) #creates 1D numpy array
theta1_vals = np.linspace(-1, 4, 100) #creates 1D numpy array
J_vals = np.zeros((len(theta0_vals), len(theta1_vals))) #creates 2D numpy matrix
for i in range (0, len(theta0_vals)):
for j in range (0, len(theta1_vals)):
t = ([theta0_vals[i], theta1_vals[j]])
J_vals[i,j] = costCompute(x, y, t) #costCompute is user defined function
np.transpose(J_vals)
以上代码段的输出如下:-
转置前
[[ 328.09290555, 316.6648683, 305.44447299 ..., 186.35412584, 195.06735595, 203.98822799]
[ 323.26504192, 311.92025945, 300.7831189 ..., 189.60197489, 198.39845977, 207.40258658]
[ 318.47799046, 307.21646275, 296.16257698 ..., 192.89063611, 201.77037576, 210.85775734]
...,
[ 49.81156018, 46.45923561, 43.31455298 ..., 691.41691065, 708.20585345, 725.20243817]
[ 48.94247627, 45.67340647, 42.61197861 ..., 698.62353943 715.49573699 732.57557648]
[ 48.11420452, 44.9283895, 41.95021641 ..., 705.87098036, 722.82643269, 739.98952696]]
转置后
[[ 328.09290555, 316.6648683, 305.44447299 ..., 186.35412584, 195.06735595, 203.98822799]
[ 323.26504192, 311.92025945, 300.7831189 ..., 189.60197489, 198.39845977, 207.40258658]
[ 318.47799046, 307.21646275, 296.16257698 ..., 192.89063611, 201.77037576, 210.85775734]
...,
[ 49.81156018, 46.45923561, 43.31455298 ..., 691.41691065, 708.20585345, 725.20243817]
[ 48.94247627, 45.67340647, 42.61197861 ..., 698.62353943, 715.49573699, 732.57557648]
[ 48.11420452, 44.9283895, 41.95021641 ..., 705.87098036, 722.82643269, 739.98952696]]
答案 0 :(得分:2)
有一种更简单的方法来转置numpy
数组。只需A.T
,就可以得到转置矩阵。
参见以下示例
A=numpy.array([[1,1,1],[2,2,2],[3,3,3]])
A_transpose=A.T
您将获得转置矩阵。
答案 1 :(得分:1)