小编Rop*_*shi的帖子

我只是在 Flutter 中创建一个简单的 ToDo 应用程序。我正在管理列表中的所有待办事项。我想在列表的开头添加任何新的待办事项。我能够使用这种解决方法来实现这一目标。有没有更好的方法来做到这一点？

void _addTodoInList(BuildContext context){
    String val = _textFieldController.text;

    final newTodo = {
      "title": val,
      "id": Uuid().v4(),
      "done": false
    };

    final copiedTodos = List.from(_todos);

    _todos.removeRange(0, _todos.length);
    setState(() {
      _todos.addAll([newTodo, ...copiedTodos]);
    });

    Navigator.pop(context);
  }

{ MongoNetworkError: connection 3 to cluster0-shard-00-02-z0urk.mongodb.net:27017 在 TLSSocket 关闭。(/home/fahad/Personal Work/Nodejs/Node js start/node_modules/mongoose/node_modules/mongodb-core/lib/connection/connection.js:352:9) 在 Object.onceWrapper (events.js:276:13)在 TLSSocket.emit (events.js:188:13) at _handle.close (net.js:610:12) at TCP.done (_tls_wrap.js:386:7) name: 'MongoNetworkError', errorLabels: [ 'TransientTransactionError ' ],
[Symbol(mongoErrorContextSymbol)]: {} }

module.exports = (app, express , mongoose, path, bodyParser) => {

    app.use(express.static(path.resolve(__dirname, "../../dist")));
    app.use(bodyParser.json());


    mongoose.connect('mongodb+srv://fahad:123@cluster0-z0urk.mongodb.net/test?retryWrites=true', {useNewUrlParser: true})
    .then(()=>console.log("DB server connect"))
    .catch(e => console.log("DB error", e));
};

我正在使用 ColumnTransformer 为模型构建管道。这就是我的管道的样子，

from sklearn.preprocessing import StandardScaler
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder,OrdinalEncoder,MinMaxScaler
from sklearn.impute import KNNImputer

imputer_transformer = ColumnTransformer([
    ('knn_imputer',KNNImputer(n_neighbors=5),[0,3,4,6,7])
],remainder='passthrough')

category_transformer = ColumnTransformer([
    ("kms_driven_engine_min_max_scaler",MinMaxScaler(),[0,6]),
    ("owner_ordinal_enc",OrdinalEncoder(categories=[['fourth','third','second','first']],handle_unknown='ignore',dtype=np.int16),[3]),
    ("brand_location_ohe",OneHotEncoder(sparse=False,handle_unknown='ignore'),[2,5]),
],remainder='passthrough')


def build_pipeline_with_estimator(estimator):
    return Pipeline([
    ('imputer',imputer_transformer),
    ('category_transformer',category_transformer),
    ('estimator',estimator),
])

这就是我的数据集的样子，

kms_driven      owner   location    mileage     power    brand              engine  age
34000.0         first       other           NaN         12.0        Yamaha          150.0     9
28000.0         first       other           72.0         7.0         Hero                100.0    16
5947.0           first       other          53.0          19.0       Bajaj                NaN       4
11000.0         first       delhi           40.0          19.8       Royal Enfield   350.0 …