解释这行代码 H = np.delete(H,SlackNode_1level,axis=0)
时间: 2024-04-12 21:33:58 浏览: 14
这行代码使用了 NumPy 中的 `delete()` 函数,将 `H` 数组的指定行删除,并将结果赋值给 `H`。
具体来说,`np.delete()` 函数用于从数组中删除指定的行或列。在这里,通过调用 `np.delete(H, SlackNode_1level, axis=0)`,我们删除了 `H` 数组中索引为 `SlackNode_1level` 的行。
参数 `H` 是要操作的数组,`SlackNode_1level` 是要删除的行的索引,`axis=0` 表示按行操作。
所以,这行代码将从数组 `H` 中删除了索引为 `SlackNode_1level` 的行,并将结果赋值给 `H`。最终得到的 `H` 是删除了特定行后的新数组。
相关问题
优化这段代码dst = np.array(dst) if len(dst) == 4: pass else: dis_arr = np.sqrt(dist.cdist(dst, dst)) uptri_idx = np.triu_indices_from(dis_arr, k=1) delete_pos = np.where(dis_arr[uptri_idx] < 5) dst = np.delete(dst, uptri_idx[1][delete_pos[0]], axis=0)
# 将原来的代码拆分成两个函数,提高可读性和复用性
def optimize_dst(dst):
if len(dst) == 4:
return dst
else:
dis_arr = np.sqrt(dist.cdist(dst, dst))
uptri_idx = np.triu_indices_from(dis_arr, k=1)
delete_pos = np.where(dis_arr[uptri_idx] < 5)
dst = np.delete(dst, uptri_idx[1][delete_pos[0]], axis=0)
return dst
def test_optimize_dst():
dst1 = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
dst2 = np.array([[0, 0], [0, 1], [1, 0], [2, 0], [2, 1]])
dst3 = np.array([[0, 0], [0, 1], [1, 0], [2, 0], [2, 1], [3, 1]])
assert np.array_equal(optimize_dst(dst1), dst1)
assert np.array_equal(optimize_dst(dst2), np.array([[0, 0], [0, 1], [1, 0], [2, 0], [2, 1]]))
assert np.array_equal(optimize_dst(dst3), np.array([[0, 0], [0, 1], [1, 0], [2, 0], [2, 1], [3, 1]]))
print("All test cases pass")
test_optimize_dst()
data00=data m,n = np.shape(data00) a = np.array(data00) Data00 = a[1:m,2:n] Data00 = Data00.astype(np.float64) Power = Data00[:,13] Power_train = Power[0:96] P_min = np.min(Power_train) P_gap = np.max(Power_train)-np.min(Power_train) Power_uni = (Power-P_min)/P_gap # 提取imfs和剩余信号res emd = EMD() emd.emd(Power_uni) imfs, res = emd.get_imfs_and_residue() N = len(imfs) P_H = np.sum(imfs[0:6,:],axis=0) P_M = np.sum(imfs[6:12,:],axis=0) P_L = res P_H =np.expand_dims(P_H,axis=1) P_M =np.expand_dims(P_M,axis=1) P_L =np.expand_dims(P_L,axis=1) Nwp = Data00[:,0:7] Nwp_train = Nwp[0:96] N_min = np.min(Nwp_train,axis=0) N_gap = np.max(Nwp_train,axis=0)-np.min(Nwp_train,axis=0) Nwp_uni = (Nwp-N_min)/N_gap#(N,7) Weather = Data00[:,7:13] Weather_train = Weather[0:96] W_min = np.min(Weather_train,axis=0) W_gap = np.max(Weather_train,axis=0)-np.min(Weather_train,axis=0) Weather_uni = (Weather-W_min)/W_gap#(N,6) 优化代码
以下是部分代码的优化建议:
1. 对于以下代码段:
```
a = np.array(data00)
Data00 = a[1:m,2:n]
```
可以合并为一行:
```
Data00 = np.array(data00)[1:m,2:n]
```
2. 对于以下代码段:
```
P_H = np.sum(imfs[0:6,:],axis=0)
P_M = np.sum(imfs[6:12,:],axis=0)
P_L = res
P_H =np.expand_dims(P_H,axis=1)
P_M =np.expand_dims(P_M,axis=1)
P_L =np.expand_dims(P_L,axis=1)
```
可以使用 `np.newaxis` 替代 `np.expand_dims` 来实现:
```
P_H = np.sum(imfs[0:6,:],axis=0)[:, np.newaxis]
P_M = np.sum(imfs[6:12,:],axis=0)[:, np.newaxis]
P_L = res[:, np.newaxis]
```
3. 对于以下代码段:
```
N_min = np.min(Nwp_train,axis=0)
N_gap = np.max(Nwp_train,axis=0)-np.min(Nwp_train,axis=0)
Nwp_uni = (Nwp-N_min)/N_gap#(N,7)
```
可以使用 `np.ptp` 函数(peak-to-peak)来计算最大值和最小值的差:
```
N_min = np.min(Nwp_train,axis=0)
N_gap = np.ptp(Nwp_train,axis=0)
Nwp_uni = (Nwp-N_min)/N_gap#(N,7)
```
4. 对于以下代码段:
```
Weather = Data00[:,7:13]
Weather_train = Weather[0:96]
W_min = np.min(Weather_train,axis=0)
W_gap = np.max(Weather_train,axis=0)-np.min(Weather_train,axis=0)
Weather_uni = (Weather-W_min)/W_gap#(N,6)
```
可以使用与第三个优化建议类似的方法:
```
Weather = Data00[:,7:13]
Weather_train = Weather[0:96]
W_min = np.min(Weather_train,axis=0)
W_gap = np.ptp(Weather_train,axis=0)
Weather_uni = (Weather-W_min)/W_gap#(N,6)
```
当然,以上只是一些简单的优化建议,具体的优化效果还需要根据实际情况进行评估。