以下是僅在縮進上有所不同的代碼。他們給出不同的輸出。第一個輸出
array([[7.68717818e 11, 6.97681947e 11, 7.63307666e 11, 6.47349007e 11,
5.99065727e 11],
[7.66709788e 11, 6.99939453e 11, 7.64013608e 11, 6.45261906e 11,
5.99177884e 11],
[2.94202392e 11, 4.16042886e 12, 3.69055160e 11, 3.08724683e 11,
3.71083543e 11],
...,
[2.32972294e 11, 2.25518151e 11, 2.16985500e 11, 1.52392619e 11,
1.87686750e 11],
[1.31495500e 11, 4.03441481e 11, 1.66796570e 11, 7.37775506e 10,
1.55474795e 11],
[1.26216951e 11, 5.60385882e 11, 1.49446146e 11, 7.23769941e 10,
1.45692856e 11]])
第二個給出
array([[7.66709788e 11, 6.99939453e 11, 7.64013608e 11, 6.45261906e 11,
5.99177884e 11],
[2.94202392e 11, 4.16042886e 12, 3.69055160e 11, 3.08724683e 11,
3.71083543e 11],
[2.92378752e 11, 2.93673677e 12, 3.58775694e 11, 3.06625155e 11,
3.56276475e 11],
...,
[1.31495500e 11, 4.03441481e 11, 1.66796570e 11, 7.37775506e 10,
1.55474795e 11],
[1.26216951e 11, 5.60385882e 11, 1.49446146e 11, 7.23769941e 10,
1.45692856e 11],
[0.00000000e 00, 0.00000000e 00, 0.00000000e 00, 0.00000000e 00,
0.00000000e 00]])
為什么他們給出不同的結果？這是關于python如何作業的一些普遍事實嗎？我認為縮進在這兩種情況下都是正確的，但顯然有幾種型別的“正確縮進”可以產生不同的結果？
mses = np.zeros((len(all_models), 5))
kfold = KFold(5,
random_state = 614,
shuffle = True)
j = 0
for train_index, test_index in kfold.split(cars_train):
cars_tt = cars_train.iloc[train_index]
cars_ho = cars_train.iloc[test_index]
i = 0
for model in all_models:
if model == "baseline":
pred = np.power(10,cars_tt.log_sell.mean()*np.ones(len(cars_ho)))
mses[i, j] = mean_squared_error(cars_ho.selling_price, pred)
else:
if len(model) == 1:
reg = LinearRegression(copy_X = True)
reg.fit(cars_tt[model].values.reshape(-1,1), cars_tt.log_sell.values)
pred = np.power(10,reg.predict(cars_ho[model].values))
mses[i,j] = mean_squared_error(cars_ho.selling_price, pred)
else:
reg = LinearRegression(copy_X = True)
reg.fit(cars_tt[model].values, cars_tt.log_sell.values)
pred = np.power(10,reg.predict(cars_ho[model].values))
mses[i,j] = mean_squared_error(cars_ho.selling_price, pred)
i = i 1
j = j 1
mses
mses = np.zeros((len(all_models), 5))
kfold = KFold(5,
random_state = 614,
shuffle = True)
j = 0
for train_index, test_index in kfold.split(cars_train):
cars_tt = cars_train.iloc[train_index]
cars_ho = cars_train.iloc[test_index]
i = 0
for model in all_models:
if model == "baseline":
pred = np.power(10,cars_tt.log_sell.mean()*np.ones(len(cars_ho)))
mses[i, j] = mean_squared_error(cars_ho.selling_price, pred)
else:
if len(model) == 1:
reg = LinearRegression(copy_X = True)
reg.fit(cars_tt[model].values.reshape(-1,1), cars_tt.log_sell.values)
pred = np.power(10,reg.predict(cars_ho[model].values))
mses[i,j] = mean_squared_error(cars_ho.selling_price, pred)
else:
reg = LinearRegression(copy_X = True)
reg.fit(cars_tt[model].values, cars_tt.log_sell.values)
pred = np.power(10,reg.predict(cars_ho[model].values))
mses[i,j] = mean_squared_error(cars_ho.selling_price, pred)
i = i 1
j = j 1
mses
uj5u.com熱心網友回復：
在第一個片段i = i 1中縮進以與外部else:塊對齊，因此它位于它之后。
if model == "baseline":
...
else:
if len(model) == 1:
...
else:
...
i = i 1
在第二個片段中，它被縮進，使其位于外部else:塊內。
if model == "baseline":
...
else:
if len(model) == 1:
...
else:
...
i = i 1
這種差異會影響執行的時間和次數i = i 1。
如果 Python 有花括號，那將是：
if model == "baseline" {
...
}
else {
if len(model) == 1 {
...
}
else {
...
}
}
i = i 1
和：
if model == "baseline" {
...
}
else {
if len(model) == 1 {
...
}
else {
...
}
i = i 1
}
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