Question

当我尝试访问import sklearn.svm import re from sklearn import metrics import numpy import scipy.sparse import datetime from sklearn.feature_extraction.text import CountVectorizer from sklearn.svm import SVC from sklearn.svm import LinearSVC from nltk.tokenize import word_tokenize, sent_tokenize from sklearn.preprocessing import StandardScaler # custom feature example def words_capitalized(sentence): tokens = [] # tokenize the sentence tokens = word_tokenize(sentence) counter = 0 for word in tokens: if word[0].isupper(): counter += 1 return counter # custom feature example def words_length(sentence): tokens = [] # tokenize the sentence tokens = word_tokenize(sentence) list_of_length = list() for word in tokens: list_of_length.append(length(word)) return list_of_length def get_features(untagged_text, value, scaler): # this function extracts the custom features # transforms the vectorizer # scales the features # and finally stacks all of them list_of_length = list() list_of_capitals = list() # transform vectorizer X_bow = countVecWord.transform(untagged_text) # I also see some people use X_bow = countVecWord.transform(untagged_text).todense(), what does the .todense() option do here? for sentence in untagged_text: list_of_urls.append([words_length(sentence)]) list_of_capitals.append([words_capitalized(sentence)]) # turn the feature output into a numpy vector X_length = numpy.array(list_of_urls) X_capitals = numpy.array(list_of_capitals) if value == 1: # fit transform for training set X_length = = scaler.fit_transform(X_length) X_capitals = scaler.fit_transform(X_capitals) # if test set else: # transform only for test set X_length = = scaler.transform(X_length) X_capitals = scaler.transform(X_capitals) # stack all features as a sparse matrix X_two_bows = scipy.sparse.hstack((X_bow, X_length)) X_two_bows = scipy.sparse.hstack((X_two_bows , X_length)) X_two_bows = scipy.sparse.hstack((X_two_bows , X_capitals)) return X_two_bows def fit_and_predict(train_labels, train_features, test_features, classifier): # fit the training set classifier.fit(train_features, train_labels) # return the classification result return classifier.predict(test_features) if __name__ == '__main__': input_sets = read_data() X = input_sets[0] Y = input_sets[1] X_dev = input_sets[2] Y_dev = input_sets[3] # initialize the count vectorizer countVecWord = sklearn.feature_extraction.text.CountVectorizer(ngram_range=(1, 3)) scaler= StandardScaler() # extract features # for training X_total = get_features(X, 1, scaler) # for dev set X_total_dev = get_features(X_dev, 2, scaler) # store labels as numpy array y_train = numpy.asarray(Y) y_dev = numpy.asarray(Y_dev) # train the classifier SVC1 = LinearSVC(C = 1.0) y_predicted = list() y_predicted = fit_and_predict(y_train, X_total, X_total_dev, SVC1) print "Result for dev set" precision, recall, f1, _ = metrics.precision_recall_fscore_support(y_dev, y_predicted) print "Precision: ", precision, " Recall: ", recall, " F1-Score: ", f1中的任何内容时，我会被重定向到admin/someSecureThing。如何更改它以便重定向回/？

路线：

admin/login

Answer 1

在AuthController.php中，您可以设置：

protected $loginPath = 'admin/login';

请注意$loginPath如果用户尝试访问受保护的路由，则不会更改用户退回的位置。这是由App \ Http \ Middleware \ Authenticate中间件的句柄方法控制的。

public function handle($request, Closure $next, $guard = null)
    {
        if (Auth::guard($guard)->guest()) {
            if ($request->ajax()) {
                return response('Unauthorized.', 401);
            } else {
                return redirect()->guest('admin/login');
            }
        }

        return $next($request);
    }

如果您使用自定义AuthController，那么创建自定义中间件可能会更好。

php artisan make:middleware AdminAuthController

这将在app \ Http \ Middleware文件夹下创建一个AdminAuthController.php。

接下来我们将如上所述编辑句柄功能，但也要确保：

use Illuminate\Support\Facades\Auth;

一旦完成，我们需要在app \ Http \ Kernel.php下注册我们的新中间件

protected $routeMiddleware = [
        'auth' => \App\Http\Middleware\Authenticate::class,
        'auth.basic' => \Illuminate\Auth\Middleware\AuthenticateWithBasicAuth::class,
        'guest' => \App\Http\Middleware\RedirectIfAuthenticated::class,
        'throttle' => \Illuminate\Routing\Middleware\ThrottleRequests::class,
        'auth.admin' => \App\Http\Middleware\AdminAuthController::class
    ];

在此之后，您所要做的就是使用auth.admin中间件而不是对于您想要的任何路线，auth。

您的路线将如下所示：

Route::group(['namespace' => 'Admin', 'prefix' => 'admin', 'middleware' => 'web'], function() {

 Route::group(['middleware' => 'auth.admin'], function(){
    /* Admin Auth */
    Route::get('login', 'Auth\AuthController@getLogin');
    Route::post('login', 'Auth\AuthController@postLogin');
    Route::get('register', 'Auth\AuthController@getRegister');
    Route::post('register', 'Auth\AuthController@postRegister');
    Route::get('logout', 'Auth\AuthController@getLogout');
  }

  Route::group(['middleware' => 'auth'], function(){
    /*Admin Dashboard Routes */

        Route::get('dashboard', 'AdminController@getDashboard');    
    });
});

路由重定向到自定义位置

1 个答案: