如何修复IndexError:标量变量的索引无效

Question

此代码生成错误:

IndexError: invalid index to scalar variable.

在线: results.append(RMSPE(np.expm1(y_train[testcv]), [y[1] for y in y_test]))

怎么解决？

import pandas as pd
import numpy as np
from sklearn import ensemble
from sklearn import cross_validation

def ToWeight(y):
    w = np.zeros(y.shape, dtype=float)
    ind = y != 0
    w[ind] = 1./(y[ind]**2)
    return w

def RMSPE(y, yhat):
    w = ToWeight(y)
    rmspe = np.sqrt(np.mean( w * (y - yhat)**2 ))
    return rmspe

forest = ensemble.RandomForestRegressor(n_estimators=10, min_samples_split=2, n_jobs=-1)

print ("Cross validations")
cv = cross_validation.KFold(len(train), n_folds=5)

results = []
for traincv, testcv in cv:
    y_test = np.expm1(forest.fit(X_train[traincv], y_train[traincv]).predict(X_train[testcv]))
    results.append(RMSPE(np.expm1(y_train[testcv]), [y[1] for y in y_test]))

testcv 是:

[False False False ...,  True  True  True]

Answer 1

YOLO目标检测

layer_names = net.getLayerNames() output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

不需要在layer_names[i[0] - 1]中对i进行索引 。只需删除它并执行layer_names[i - 1]

layer_names = net.getLayerNames() output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]

它对我有用

Answer 2

您正在尝试索引标量(不可迭代)值:

[y[1] for y in y_test]
#  ^ this is the problem

当你打电话时,[y for y in test]你正在迭代这些值,所以你得到一个值y.

您的代码与尝试执行以下操作相同:

y_test = [1, 2, 3]
y = y_test[0] # y = 1
print(y[0]) # this line will fail

我不确定你想要进入你的结果数组,但是你需要摆脱它[y[1] for y in y_test].

如果要将y_test中的每个y附加到结果中,则需要将列表推导进一步扩展为以下内容:

[results.append(..., y) for y in y_test]

或者只使用for循环:

for y in y_test:
    results.append(..., y)