我通過創(chuàng)建 for 循環(huán)隨機(jī)刪除數(shù)據(jù)數(shù)組的行并重新計算指標(biāo)（總和）。在 for 循環(huán)的每次迭代中，我都會打亂數(shù)據(jù)并刪除第一行值。然后我對剩余行中的值求和。對于每次迭代或運行，我想跟蹤運行編號、剩余點的總和以及被刪除的行。我通過為每次運行創(chuàng)建結(jié)果字典，然后將這些結(jié)果附加到列表中來實現(xiàn)此目的。但是，當(dāng)我打印出結(jié)果字典列表時，運行編號和總和是正確的，但每個結(jié)果字典中已刪除行的值似乎始終是上次運行中刪除的行，而不是其特定的值跑步。import randomimport numpy as npPoints = np.array([[1,1,1,1],[2,2,2,2],[3,3,3,3],[4,4,4,4],[5,5,5,5],[6,6,6,6]])index = 1all_results = []N = 5for i in range(N): np.random.shuffle(Points) removed_row = Points[:index] print(f'Removed row from run {i}: {removed_row}') remaining_rows = Points[index:] sum = np.sum(remaining_rows) run_results = {'Run':i,"Sum of remaining points": sum ,"Removed row": removed_row} all_results.append(run_results)print(all_results)#outputRemoved row from run 0: [[2 2 2 2]]Removed row from run 1: [[2 2 2 2]]Removed row from run 2: [[4 4 4 4]]Removed row from run 3: [[5 5 5 5]]Removed row from run 4: [[4 4 4 4]][{'Run': 0, 'Sum of remaining points': 76, 'Removed row': array([[4, 4, 4, 4]])}, {'Run': 1, 'Sum of remaining points': 76, 'Removed row': array([[4, 4, 4, 4]])}, {'Run': 2, 'Sum of remaining points': 68, 'Removed row': array([[4, 4, 4, 4]])}, {'Run': 3, 'Sum of remaining points': 64, 'Removed row': array([[4, 4, 4, 4]])}, {'Run': 4, 'Sum of remaining points': 68, 'Removed row': array([[4, 4, 4, 4]])}]正如您所看到的，它似乎總是使用最后一次運行的“removed_row”變量，而不是特定于運行的“removed_row”
3 回答
侃侃無極
TA貢獻(xiàn)2051條經(jīng)驗 獲得超10個贊
嗯，確實很棘手！
問題出在任務(wù)上：
run_results = {'Run':i,"Sum of remaining points": sum ,"Removed row": removed_row}
它存儲對 的引用removed_row，就像在 Python 中變量只是對對象的引用一樣。
創(chuàng)建新數(shù)組np.array(removed_row)：
import random
import numpy as np
points = np.array([[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4], [5, 5, 5, 5], [6, 6, 6, 6]])
index = 1
all_results = []
N = 5
for i in range(N):
np.random.shuffle(points)
removed_row = points[:index]
print(f'Removed row from run {i}: {removed_row}')
remaining_rows = points[index:]
sum = np.sum(remaining_rows)
run_results = {'Run':i,"Sum of remaining points": sum ,"Removed row": np.array(removed_row)}
all_results.append(run_results)
print(all_results)
輸出：
Removed row from run 0: [[4 4 4 4]]
Removed row from run 1: [[6 6 6 6]]
Removed row from run 2: [[6 6 6 6]]
Removed row from run 3: [[3 3 3 3]]
Removed row from run 4: [[4 4 4 4]]
[{'Run': 0, 'Sum of remaining points': 68, 'Removed row': array([[4, 4, 4, 4]])}, {'Run': 1, 'Sum of remaining points': 60, 'Removed row': array([[6, 6, 6, 6]])}, {'Run': 2, 'Sum of remaining points': 60, 'Removed row': array([[6, 6, 6, 6]])}, {'Run': 3, 'Sum of remaining points': 72, 'Removed row': array([[3, 3, 3, 3]])}, {'Run': 4, 'Sum of remaining points': 68, 'Removed row': array([[4, 4, 4, 4]])}]
炎炎設(shè)計
TA貢獻(xiàn)1808條經(jīng)驗 獲得超4個贊
您需要實際刪除該行：
import random
import numpy as np
Points = np.array([[1,1,1,1],[2,2,2,2],[3,3,3,3],[4,4,4,4],[5,5,5,5],[6,6,6,6]])
index = 1
all_results = []
N = 5
for i in range(N):
np.random.shuffle(Points)
removed_row = Points[:index]
Points = Points[index:] # <<<<<<< Remove row
print(f'Removed row from run {i}: {removed_row}')
remaining_rows = Points[index:]
sum = np.sum(remaining_rows)
run_results = {'Run':i,"Sum of remaining points": sum ,"Removed row": removed_row}
all_results.append(run_results)
print(all_results)
輸出
Removed row from run 0: [[3 3 3 3]]
Removed row from run 1: [[6 6 6 6]]
Removed row from run 2: [[1 1 1 1]]
Removed row from run 3: [[2 2 2 2]]
Removed row from run 4: [[4 4 4 4]]
[{'Run': 0, 'Sum of remaining points': 56, 'Removed row': array([[3, 3, 3, 3]])},
{'Run': 1, 'Sum of remaining points': 40, 'Removed row': array([[6, 6, 6, 6]])},
{'Run': 2, 'Sum of remaining points': 24, 'Removed row': array([[1, 1, 1, 1]])},
{'Run': 3, 'Sum of remaining points': 16, 'Removed row': array([[2, 2, 2, 2]])},
{'Run': 4, 'Sum of remaining points': 0, 'Removed row': array([[4, 4, 4, 4]])}]
白衣染霜花
TA貢獻(xiàn)1796條經(jīng)驗 獲得超10個贊
請注意，隨機(jī)播放需要很長時間。這是一種大量矢量化的方法：
# choose the index to drop first:
to_drop = np.random.choice(np.arange(len(Points)), N, replace=False)
# remain sum:
remains = Points.sum(0) - Points[to_drop[::-1]].cumsum(0)
out = [{'run':i, 'sum_renmaining': remains[i], 'remove row': Points[to_drop[i]]} for i in range(N)]
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