Question

我正在尝试匹配泛型类型的特征范围：

macro_rules! test {
    (
        where $(
            $bounded_type:ident: $( $bound:tt )++,
        )+
    ) => {
        // Dummy expansion for test:
        struct Foo<T, U>
        where $(
            $bounded_type : $( $bound )++,
        )+
        {
            t: T,
            u: U
        }
    }
}

test! {
    where
        T: PartialEq + Clone,
        U: PartialEq,
}

fn main() {}

不幸的是，如果我理解得很好，匹配特征的唯一方法是tt片段，但是该片段几乎可以匹配任何东西，所以无论我做什么，都会遇到错误：

error: local ambiguity: multiple parsing options: built-in NTs tt ('bound') or 1 other option.

如何匹配这段代码？

请注意，我不需要非常优雅的东西（对于普通用户不需要），但当然，它越优雅越好。

Answer 1

我能够通过将第一个界限与其他界限分开来使其匹配。

 I am a getting following ouput 
(17, 7)
[[2 0 0 'Offline' 'Low' 'Cold' 'No']
 [0 0 0 'Offline' 'High' 'Cold' 'No']
 [3 0 1 'Online' 'High' 'Cold' 'Yes']
 [2 0 1 'Offline' 'Low' 'Hot' 'Yes']
 [2 0 1 'Offline' 'High' 'Hot' 'Yes']
 [2 0 0 'Online' 'High' 'Cold' 'Yes']
 [2 1 1 'Offline' 'Low' 'Hot' 'No']
 [2 1 0 'Offline' 'Low' 'Cold' 'No']
 [0 1 0 'Online' 'Low' 'Cold' 'Yes']
 [3 1 1 'Online' 'Low' 'Hot' 'Yes']
 [1 1 0 'Offline' 'Low' 'Hot' 'No']
 [2 1 1 'Offline' 'Low' 'Hot' 'Yes']
 [3 1 1 'Online' 'High' 'Hot' 'Yes']
 [2 1 0 'Online' 'High' 'Hot' 'No']
 [2 2 2 'Offline' 'Low' 'Hot' 'Yes']
 [2 2 1 'Offline' 'Low' 'Cold' 'No']
 [1 2 0 'Offline' 'High' 'Cold' 'Yes']]
(17, 7)
['Low' 'Low' 'High' 'High' 'High' 'Low' 'Low' 'Low' 'Low' 'High' 'Low'
 'High' 'High' 'High' 'High' 'Low' 'Low']
(17, 7)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-42-84bec98371d4> in <module>()
     28 print(X.shape)
     29 onehotencoder = OneHotEncoder(categorical_features = [0])
---> 30 X = onehotencoder.fit_transform(X).toarray()
     31 print(X.shape)
     32 print(X)

C:\Users\patilsi\AppData\Local\Enthought\Canopy\edm\envs\User\lib\site-packages\sklearn\preprocessing\data.py in fit_transform(self, X, y)
   2017         """
   2018         return _transform_selected(X, self._fit_transform,
-> 2019                                    self.categorical_features, copy=True)
   2020 
   2021     def _transform(self, X):

C:\Users\patilsi\AppData\Local\Enthought\Canopy\edm\envs\User\lib\site-packages\sklearn\preprocessing\data.py in _transform_selected(X, transform, selected, copy)
   1807     X : array or sparse matrix, shape=(n_samples, n_features_new)
   1808     """
-> 1809     X = check_array(X, accept_sparse='csc', copy=copy, dtype=FLOAT_DTYPES)
   1810 
   1811     if isinstance(selected, six.string_types) and selected == "all":

C:\Users\patilsi\AppData\Local\Enthought\Canopy\edm\envs\User\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
    431                                       force_all_finite)
    432     else:
--> 433         array = np.array(array, dtype=dtype, order=order, copy=copy)
    434 
    435         if ensure_2d:

(17, 7)
[[2 0 0 'Offline' 'Low' 'Cold' 'No']
 [0 0 0 'Offline' 'High' 'Cold' 'No']
 [3 0 1 'Online' 'High' 'Cold' 'Yes']
 [2 0 1 'Offline' 'Low' 'Hot' 'Yes']
 [2 0 1 'Offline' 'High' 'Hot' 'Yes']
 [2 0 0 'Online' 'High' 'Cold' 'Yes']
 [2 1 1 'Offline' 'Low' 'Hot' 'No']
 [2 1 0 'Offline' 'Low' 'Cold' 'No']
 [0 1 0 'Online' 'Low' 'Cold' 'Yes']
 [3 1 1 'Online' 'Low' 'Hot' 'Yes']
 [1 1 0 'Offline' 'Low' 'Hot' 'No']
 [2 1 1 'Offline' 'Low' 'Hot' 'Yes']
 [3 1 1 'Online' 'High' 'Hot' 'Yes']
 [2 1 0 'Online' 'High' 'Hot' 'No']
 [2 2 2 'Offline' 'Low' 'Hot' 'Yes']
 [2 2 1 'Offline' 'Low' 'Cold' 'No']
 [1 2 0 'Offline' 'High' 'Cold' 'Yes']]
(17, 7)
['Low' 'Low' 'High' 'High' 'High' 'Low' 'Low' 'Low' 'Low' 'High' 'Low'
 'High' 'High' 'High' 'High' 'Low' 'Low']
(17, 7)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-42-84bec98371d4> in <module>()
     28 print(X.shape)
     29 onehotencoder = OneHotEncoder(categorical_features = [0])
---> 30 X = onehotencoder.fit_transform(X).toarray()
     31 print(X.shape)
     32 print(X)

C:\Users\patilsi\AppData\Local\Enthought\Canopy\edm\envs\User\lib\site-packages\sklearn\preprocessing\data.py in fit_transform(self, X, y)
   2017         """
   2018         return _transform_selected(X, self._fit_transform,
-> 2019                                    self.categorical_features, copy=True)
   2020 
   2021     def _transform(self, X):

C:\Users\patilsi\AppData\Local\Enthought\Canopy\edm\envs\User\lib\site-packages\sklearn\preprocessing\data.py in _transform_selected(X, transform, selected, copy)
   1807     X : array or sparse matrix, shape=(n_samples, n_features_new)
   1808     """
-> 1809     X = check_array(X, accept_sparse='csc', copy=copy, dtype=FLOAT_DTYPES)
   1810 
   1811     if isinstance(selected, six.string_types) and selected == "all":

C:\Users\patilsi\AppData\Local\Enthought\Canopy\edm\envs\User\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
    431                                       force_all_finite)
    432     else:
--> 433         array = np.array(array, dtype=dtype, order=order, copy=copy)
    434 
    435         if ensure_2d:

ValueError: could not convert string to float: 'Yes'

但是，如果特征具有参数，这将不起作用。

Answer 2

您最好的选择是阅读source code for the parse-generics-shim crate；它有点旧了，但还是应该可以使用。方法太复杂了，无法在Stack Overflow问题中进行解释，因为它基本上涉及将板条箱的源复制并粘贴到答案中。

更简单的方法是仅不解析实际的Rust语法，并使用宏解析器可以处理的内容，例如将约束包装在一组中（例如{{1 }}。

Answer 3

自2016年左右this issue结束以来，您可以使用path宏类型来匹配TypePath。

例如，根据我自己的代码：

($name:ident<$($l:lifetime, )*$($x:ident : $xt:path),+>($s:ident$(, $a:ident: $t:ty)*) -> $ret:ty  => $body:block) => {
}

如何匹配宏中的特征范围？

3 个答案: