urls.py
#...
from myapp.views import MyView
from django.contrib.auth.decorators import login_required
urlpatterns = [
#....
url(r'^terminator/', login_required(MyView.as_view()), name='sexy')
]
视图.py
class MyView(View):
def get(self, request):
return render(request, 'itworks.html')
我正在MyView通过Somelogin重定向到“terminator url”的类访问类。但问题是：django 将我重定向到以下 url http://127.0.0.1:8000/accounts/login/?next=/terminator。
当然，这个地址没有定义，给了我 404。
我LOGIN_REDIRECT_URL在设置中进行了操作，但这只会给代码带来更多混乱。那么有没有办法避免 django 中的这种“默认/下一个”并直接转到http://127.0.0.1:8000/terminator.
from sklearn.preprocessing import LabelEncoder as LE, OneHotEncoder as OHE
import numpy as np
a = np.array([[0,1,100],[1,2,200],[2,3,400]])
oh = OHE(categorical_features=[0,1])
a = oh.fit_transform(a).toarray()
Let's assume first and second column are categorical data. This code does one hot encoding, but for the regression problem, I would like to remove first column after encoding categorical data. In this example, there are two and I could do it manually. But what if you have many categorical features, how would you solve this problem?
我不是来自统计数据,但是通过机器学习和NN的一项工作,我看到缩放数据会产生很多伤害.根据我的经验,在列车测试之前缩放数据并不是一个好的选择,但请在列车测试分离后进行缩放时查看此示例.
import numpy as np
from sklearn.preprocessing import StandardScaler
train_matrix = np.array([[1,2,3,4,5]]).T
test_matrix = np.array([[1]]).T
e =StandardScaler()
train_matrix = e.fit_transform(train_matrix)
test_matrix = e.fit_transform(test_matrix)
print(train_matrix)
print(test_matrix)
[out]:
[[-1.41421356] #train data
[-0.70710678]
[ 0. ]
[ 0.70710678]
[ 1.41421356]]
[[ 0.]] #test data
StandardScaler类将为每个数据集执行两个不同的缩放过程,并且可能损害您的NN结果的错误是:
在列车矩阵1中是-1.41421356,而在测试矩阵1中是0.现在想象你做一个带有训练权重测试数据的预测模型.对于1,您将收到完全不同的结果.怎么克服这个？