





from sklearn.metrics import recall_score

import datasets


lowercase				:	Tuple      						=  """
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
"""


lowercase				:	List[str]      						=  """
Args:
- **predictions** (`list` of `int`): The predicted labels.
- **references** (`list` of `int`): The ground truth labels.
- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.
- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.
- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.
    - `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.
    - `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.
    - `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
    - `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.
    - `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.
- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .
    - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.
    - `0`: If there is a zero division, the return value is `0`.
    - `1`: If there is a zero division, the return value is `1`.

Returns:
- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.

Examples:

    Example 1-A simple example with some errors
        >>> recall_metric = datasets.load_metric('recall')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])
        >>> print(results)
        {'recall': 0.6666666666666666}

    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.
        >>> recall_metric = datasets.load_metric('recall')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)
        >>> print(results)
        {'recall': 0.5}

    Example 3-The same example as Example 1, but with `sample_weight` included.
        >>> recall_metric = datasets.load_metric('recall')
        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)
        >>> print(results)
        {'recall': 0.55}

    Example 4-A multiclass example, using different averages.
        >>> recall_metric = datasets.load_metric('recall')
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {'recall': array([1., 0., 0.])}
"""


lowercase				:	str      						=  """
@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}
"""





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,  _KWARGS_DESCRIPTION     )
class 			A__   (							datasets.Metric     ):




   """simple docstring"""



   def 							__lowercase		( self)      ->						Any:







          '''simple docstring'''

          return datasets.MetricInfo(
              description=_DESCRIPTION ,  citation=_CITATION ,  inputs_description=_KWARGS_DESCRIPTION ,  features=datasets.Features(
                  {
                      'predictions': datasets.Sequence(datasets.Value('int32')),
                      'references': datasets.Sequence(datasets.Value('int32')),
                  }
                  if self.config_name == 'multilabel'
                  else {
                      'predictions': datasets.Value('int32'),
                      'references': datasets.Value('int32'),
                  }) ,  reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html'] ,  )

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase=None ,  lowercase=1 ,  lowercase="binary" ,  lowercase=None ,  lowercase="warn" ,  )      ->						List[str]:







          '''simple docstring'''

          a__						:     int			=    recall_score(
              lowercase ,  lowercase ,  labels=lowercase ,  pos_label=lowercase ,  average=lowercase ,  sample_weight=lowercase ,  zero_division=lowercase ,  )
          return {"recall": float(lowercase) if score.size == 1 else score}









import pickle

import numpy as np
from matplotlib import pyplot as plt





class 			A__   :




   """simple docstring"""



   def __init__( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase=0.2 ,  lowercase=0.2)      ->						Any:







          '''simple docstring'''

          a__						:     Tuple			=    bp_numa
          a__						:     Union[str, Any]			=    bp_numa
          a__						:     Optional[int]			=    bp_numa
          a__						:     Optional[int]			=    conva_get[:2]
          a__						:     Optional[Any]			=    conva_get[2]
          a__						:     Optional[int]			=    size_pa
          a__						:     Union[str, Any]			=    rate_w
          a__						:     Dict			=    rate_t
          a__						:     int			=    [
              np.mat(-1 * np.random.rand(self.conva[0] ,  self.conva[0]) + 0.5)
              for i in range(self.conva[1])
          ]
          a__						:     Optional[int]			=    np.mat(-1 * np.random.rand(self.num_bpa ,  self.num_bpa) + 0.5)
          a__						:     Optional[int]			=    np.mat(-1 * np.random.rand(self.num_bpa ,  self.num_bpa) + 0.5)
          a__						:     Any			=    -2 * np.random.rand(self.conva[1]) + 1
          a__						:     Optional[Any]			=    -2 * np.random.rand(self.num_bpa) + 1
          a__						:     Optional[int]			=    -2 * np.random.rand(self.num_bpa) + 1

   def 							__lowercase		( self ,  lowercase)      ->						List[Any]:







          '''simple docstring'''

          a__						:     Optional[Any]			=    {
              'num_bp1': self.num_bpa,
              'num_bp2': self.num_bpa,
              'num_bp3': self.num_bpa,
              'conv1': self.conva,
              'step_conv1': self.step_conva,
              'size_pooling1': self.size_poolinga,
              'rate_weight': self.rate_weight,
              'rate_thre': self.rate_thre,
              'w_conv1': self.w_conva,
              'wkj': self.wkj,
              'vji': self.vji,
              'thre_conv1': self.thre_conva,
              'thre_bp2': self.thre_bpa,
              'thre_bp3': self.thre_bpa,
          }
          with open(lowercase ,  'wb') as f:
                 pickle.dump(lowercase ,  lowercase)

          print(F'Model saved： {save_path}')

   @classmethod
   def 							__lowercase		( cls ,  lowercase)      ->						Any:







          '''simple docstring'''

          with open(lowercase ,  'rb') as f:
                 a__						:     Any			=    pickle.load(lowercase)  # noqa: S301

          a__						:     Dict			=    model_dic.get('conv1')
          conv_get.append(model_dic.get('step_conv1'))
          a__						:     Tuple			=    model_dic.get('size_pooling1')
          a__						:     Optional[int]			=    model_dic.get('num_bp1')
          a__						:     Tuple			=    model_dic.get('num_bp2')
          a__						:     Optional[Any]			=    model_dic.get('num_bp3')
          a__						:     Optional[Any]			=    model_dic.get('rate_weight')
          a__						:     int			=    model_dic.get('rate_thre')
          # create model instance
          a__						:     Union[str, Any]			=    CNN(lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)
          # modify model parameter
          a__						:     str			=    model_dic.get('w_conv1')
          a__						:     Optional[int]			=    model_dic.get('wkj')
          a__						:     Tuple			=    model_dic.get('vji')
          a__						:     str			=    model_dic.get('thre_conv1')
          a__						:     List[str]			=    model_dic.get('thre_bp2')
          a__						:     Tuple			=    model_dic.get('thre_bp3')
          return conv_ins

   def 							__lowercase		( self ,  lowercase)      ->						Any:







          '''simple docstring'''

          return 1 / (1 + np.exp(-1 * x))

   def 							__lowercase		( self ,  lowercase)      ->						Optional[int]:







          '''simple docstring'''

          return round(lowercase ,  3)

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						Any:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    convs[0]
          a__						:     Tuple			=    convs[1]
          a__						:     Any			=    np.shape(lowercase)[0]
          # get the data slice of original image data, data_focus
          a__						:     Tuple			=    []
          for i_focus in range(0 ,  size_data - size_conv + 1 ,  lowercase):
                 for j_focus in range(0 ,  size_data - size_conv + 1 ,  lowercase):
                        a__						:     str			=    data[
                            i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
                        ]
                        data_focus.append(lowercase)
        # calculate the feature map of every single kernel, and saved as list of matrix
          a__						:     str			=    []
          a__						:     Union[str, Any]			=    int((size_data - size_conv) / conv_step + 1)
          for i_map in range(lowercase):
                 a__						:     Tuple			=    []
                 for i_focus in range(len(lowercase)):
                        a__						:     Optional[Any]			=    (
                            np.sum(np.multiply(data_focus[i_focus] ,  w_convs[i_map]))
                            - thre_convs[i_map]
                        )
                        featuremap.append(self.sig(lowercase))
                 a__						:     Dict			=    np.asmatrix(lowercase).reshape(
                     lowercase ,  lowercase)
                 data_featuremap.append(lowercase)

          # expanding the data slice to One dimenssion
          a__						:     int			=    []
          for each_focus in data_focus:
                 focusa_list.extend(self.Expand_Mat(lowercase))
          a__						:     Optional[int]			=    np.asarray(lowercase)
          return focus_list, data_featuremap

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase="average_pool")      ->						str:







          '''simple docstring'''

          a__						:     Any			=    len(featuremaps[0])
          a__						:     int			=    int(size_map / size_pooling)
          a__						:     Optional[Any]			=    []
          for i_map in range(len(lowercase)):
                 a__						:     Any			=    featuremaps[i_map]
                 a__						:     Optional[int]			=    []
                 for i_focus in range(0 ,  lowercase ,  lowercase):
                        for j_focus in range(0 ,  lowercase ,  lowercase):
                               a__						:     Any			=    feature_map[
                                   i_focus : i_focus + size_pooling,
                                   j_focus : j_focus + size_pooling,
                               ]
                               if pooling_type == "average_pool":
                                      # average pooling
                                      map_pooled.append(np.average(lowercase))
                               elif pooling_type == "max_pooling":
                                      # max pooling
                                      map_pooled.append(np.max(lowercase))
                 a__						:     List[str]			=    np.asmatrix(lowercase).reshape(lowercase ,  lowercase)
                 featuremap_pooled.append(lowercase)
          return featuremap_pooled

   def 							__lowercase		( self ,  lowercase)      ->						Optional[Any]:







          '''simple docstring'''

          a__						:     Any			=    []
          for i in range(len(lowercase)):
                 a__						:     Tuple			=    np.shape(data[i])
                 a__						:     List[str]			=    data[i].reshape(1 ,  shapes[0] * shapes[1])
                 a__						:     Optional[Any]			=    data_listed.getA().tolist()[0]
                 data_expanded.extend(lowercase)
          a__						:     Union[str, Any]			=    np.asarray(lowercase)
          return data_expanded

   def 							__lowercase		( self ,  lowercase)      ->						Dict:







          '''simple docstring'''

          a__						:     Dict			=    np.asarray(lowercase)
          a__						:     Optional[int]			=    np.shape(lowercase)
          a__						:     Any			=    data_mat.reshape(1 ,  shapes[0] * shapes[1])
          return data_expanded

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						str:







          '''simple docstring'''

          a__						:     int			=    []
          a__						:     Optional[int]			=    0
          for i_map in range(lowercase):
                 a__						:     Optional[Any]			=    np.ones((size_map, size_map))
                 for i in range(0 ,  lowercase ,  lowercase):
                        for j in range(0 ,  lowercase ,  lowercase):
                               a__						:     Union[str, Any]			=    pd_pool[
                                   i_pool
                               ]
                               a__						:     Tuple			=    i_pool + 1
                 a__						:     Optional[int]			=    np.multiply(
                     lowercase ,  np.multiply(out_map[i_map] ,  (1 - out_map[i_map])))
                 pd_all.append(lowercase)
          return pd_all

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase=bool)      ->						str:







          '''simple docstring'''

          print('----------------------Start Training-------------------------')
          print((' - - Shape: Train_Data  ', np.shape(lowercase)))
          print((' - - Shape: Teach_Data  ', np.shape(lowercase)))
          a__						:     Dict			=    0
          a__						:     List[Any]			=    []
          a__						:     Optional[int]			=    1_0000
          while rp < n_repeat and mse >= error_accuracy:
                 a__						:     Dict			=    0
                 print(F'-------------Learning Time {rp}--------------')
                 for p in range(len(lowercase)):
                        # print('------------Learning Image: %d--------------'%p)
                        a__						:     Dict			=    np.asmatrix(datas_train[p])
                        a__						:     Any			=    np.asarray(datas_teach[p])
                        a__			,							a__						:     Optional[int]			=    self.convolute(
                            lowercase ,  self.conva ,  self.w_conva ,  self.thre_conva ,  conv_step=self.step_conva ,  )
                        a__						:     Dict			=    self.pooling(lowercase ,  self.size_poolinga)
                        a__						:     Optional[Any]			=    np.shape(lowercase)
                        a__						:     Union[str, Any]			=    self._expand(lowercase)
                        a__						:     List[Any]			=    data_bp_input

                        a__						:     Tuple			=    np.dot(lowercase ,  self.vji.T) - self.thre_bpa
                        a__						:     Any			=    self.sig(lowercase)
                        a__						:     Any			=    np.dot(lowercase ,  self.wkj.T) - self.thre_bpa
                        a__						:     Any			=    self.sig(lowercase)

                        # --------------Model Leaning ------------------------
                        # calculate error and gradient---------------
                        a__						:     Any			=    np.multiply(
                            (data_teach - bp_outa) ,  np.multiply(lowercase ,  (1 - bp_outa)))
                        a__						:     Optional[Any]			=    np.multiply(
                            np.dot(lowercase ,  self.wkj) ,  np.multiply(lowercase ,  (1 - bp_outa)))
                        a__						:     Tuple			=    np.dot(lowercase ,  self.vji)

                        a__						:     Union[str, Any]			=    pd_i_all / (self.size_poolinga * self.size_poolinga)
                        a__						:     List[str]			=    pd_conva_pooled.T.getA().tolist()
                        a__						:     str			=    self._calculate_gradient_from_pool(
                            lowercase ,  lowercase ,  shape_featuremapa[0] ,  shape_featuremapa[1] ,  self.size_poolinga ,  )
                        # weight and threshold learning process---------
                        # convolution layer
                        for k_conv in range(self.conva[1]):
                               a__						:     Optional[int]			=    self._expand_mat(pd_conva_all[k_conv])
                               a__						:     int			=    self.rate_weight * np.dot(lowercase ,  lowercase)

                               a__						:     List[str]			=    self.w_conva[k_conv] + delta_w.reshape(
                                   (self.conva[0], self.conva[0]))

                               a__						:     str			=    (
                                   self.thre_conva[k_conv]
                                   - np.sum(pd_conva_all[k_conv]) * self.rate_thre
                               )
                        # all connected layer
                        a__						:     List[str]			=    self.wkj + pd_k_all.T * bp_outa * self.rate_weight
                        a__						:     List[str]			=    self.vji + pd_j_all.T * bp_outa * self.rate_weight
                        a__						:     Tuple			=    self.thre_bpa - pd_k_all * self.rate_thre
                        a__						:     Tuple			=    self.thre_bpa - pd_j_all * self.rate_thre
                        # calculate the sum error of all single image
                        a__						:     List[str]			=    np.sum(abs(data_teach - bp_outa))
                        error_count += errors
                        # print('   ----Teach      ',data_teach)
                        # print('   ----BP_output  ',bp_out3)
                 a__						:     Any			=    rp + 1
                 a__						:     Optional[Any]			=    error_count / patterns
                 all_mse.append(lowercase)

          def draw_error():
                 a__						:     int			=    [error_accuracy for i in range(int(n_repeat * 1.2))]
                 plt.plot(lowercase ,  '+-')
                 plt.plot(lowercase ,  'r--')
                 plt.xlabel('Learning Times')
                 plt.ylabel('All_mse')
                 plt.grid(lowercase ,  alpha=0.5)
                 plt.show()

          print('------------------Training Complished---------------------')
          print((' - - Training epoch: ', rp, F'     - - Mse: {mse:.6f}'))
          if draw_e:
                 draw_error()
          return mse

   def 							__lowercase		( self ,  lowercase)      ->						Optional[int]:







          '''simple docstring'''

          a__						:     str			=    []
          print('-------------------Start Testing-------------------------')
          print((' - - Shape: Test_Data  ', np.shape(lowercase)))
          for p in range(len(lowercase)):
                 a__						:     int			=    np.asmatrix(datas_test[p])
                 a__			,							a__						:     Optional[int]			=    self.convolute(
                     lowercase ,  self.conva ,  self.w_conva ,  self.thre_conva ,  conv_step=self.step_conva ,  )
                 a__						:     str			=    self.pooling(lowercase ,  self.size_poolinga)
                 a__						:     Optional[int]			=    self._expand(lowercase)

                 a__						:     str			=    data_bp_input
                 a__						:     Union[str, Any]			=    bp_outa * self.vji.T - self.thre_bpa
                 a__						:     Optional[Any]			=    self.sig(lowercase)
                 a__						:     int			=    bp_outa * self.wkj.T - self.thre_bpa
                 a__						:     Dict			=    self.sig(lowercase)
                 produce_out.extend(bp_outa.getA().tolist())
          a__						:     List[Any]			=    [list(map(self.do_round ,  lowercase)) for each in produce_out]
          return np.asarray(lowercase)

   def 							__lowercase		( self ,  lowercase)      ->						str:







          '''simple docstring'''

          a__						:     str			=    np.asmatrix(lowercase)
          a__			,							a__						:     str			=    self.convolute(
              lowercase ,  self.conva ,  self.w_conva ,  self.thre_conva ,  conv_step=self.step_conva ,  )
          a__						:     List[str]			=    self.pooling(lowercase ,  self.size_poolinga)

          return data_conveda, data_pooleda


if __name__ == "__main__":
    pass


