import pickle

import numpy as np
from matplotlib import pyplot as plt


class 			UpperCAmelCase__    :



  """simple docstring"""






  def __init__(				self:			List[Any]				,				__lowerCAmelCase:			List[Any]				,				__lowerCAmelCase:			Dict				,				__lowerCAmelCase:			Dict				,				__lowerCAmelCase:			Dict				,				__lowerCAmelCase:			Optional[Any]				,				__lowerCAmelCase:			Optional[int]=0.2				,				__lowerCAmelCase:			Dict=0.2   ) -> Tuple:




    '''simple docstring'''




    __UpperCAmelCase						    =       bp_numa
    __UpperCAmelCase						    =       bp_numa
    __UpperCAmelCase						    =       bp_numa
    __UpperCAmelCase						    =       conva_get[:2]
    __UpperCAmelCase						    =       conva_get[2]
    __UpperCAmelCase						    =       size_pa
    __UpperCAmelCase						    =       rate_w
    __UpperCAmelCase						    =       rate_t
    __UpperCAmelCase						    =       [
        np.mat(-1 * np.random.rand(self.conva[0]				,				self.conva[0]   ) + 0.5   )
        for i in range(self.conva[1]   )
    ]
    __UpperCAmelCase						    =       np.mat(-1 * np.random.rand(self.num_bpa				,				self.num_bpa   ) + 0.5   )
    __UpperCAmelCase						    =       np.mat(-1 * np.random.rand(self.num_bpa				,				self.num_bpa   ) + 0.5   )
    __UpperCAmelCase						    =       -2 * np.random.rand(self.conva[1]   ) + 1
    __UpperCAmelCase						    =       -2 * np.random.rand(self.num_bpa   ) + 1
    __UpperCAmelCase						    =       -2 * np.random.rand(self.num_bpa   ) + 1






  def 	_UpperCAmelCase				(				self:			Dict				,				__lowerCAmelCase:			Union[str, Any]   ) -> Any:




    '''simple docstring'''




    __UpperCAmelCase						    =       {
        "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(__lowerCAmelCase				,				"wb"   ) as f:
      pickle.dump(__lowerCAmelCase				,				__lowerCAmelCase   )

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






  @classmethod
  def 	_UpperCAmelCase				(				cls:			List[str]				,				__lowerCAmelCase:			Optional[int]   ) -> Tuple:




    '''simple docstring'''




    with open(__lowerCAmelCase				,				"rb"   ) as f:
      __UpperCAmelCase						    =       pickle.load(__lowerCAmelCase   )  # noqa: S301

    __UpperCAmelCase						    =       model_dic.get("conv1"   )
    conv_get.append(model_dic.get("step_conv1"   )   )
    __UpperCAmelCase						    =       model_dic.get("size_pooling1"   )
    __UpperCAmelCase						    =       model_dic.get("num_bp1"   )
    __UpperCAmelCase						    =       model_dic.get("num_bp2"   )
    __UpperCAmelCase						    =       model_dic.get("num_bp3"   )
    __UpperCAmelCase						    =       model_dic.get("rate_weight"   )
    __UpperCAmelCase						    =       model_dic.get("rate_thre"   )
    # create model instance
    __UpperCAmelCase						    =       CNN(__lowerCAmelCase				,				__lowerCAmelCase				,				__lowerCAmelCase				,				__lowerCAmelCase				,				__lowerCAmelCase				,				__lowerCAmelCase				,				__lowerCAmelCase   )
    # modify model parameter
    __UpperCAmelCase						    =       model_dic.get("w_conv1"   )
    __UpperCAmelCase						    =       model_dic.get("wkj"   )
    __UpperCAmelCase						    =       model_dic.get("vji"   )
    __UpperCAmelCase						    =       model_dic.get("thre_conv1"   )
    __UpperCAmelCase						    =       model_dic.get("thre_bp2"   )
    __UpperCAmelCase						    =       model_dic.get("thre_bp3"   )
    return conv_ins






  def 	_UpperCAmelCase				(				self:			Union[str, Any]				,				__lowerCAmelCase:			int   ) -> int:




    '''simple docstring'''




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






  def 	_UpperCAmelCase				(				self:			List[Any]				,				__lowerCAmelCase:			List[Any]   ) -> Optional[Any]:




    '''simple docstring'''




    return round(__lowerCAmelCase				,				3   )






  def 	_UpperCAmelCase				(				self:			Optional[Any]				,				__lowerCAmelCase:			List[Any]				,				__lowerCAmelCase:			Optional[int]				,				__lowerCAmelCase:			str				,				__lowerCAmelCase:			Tuple				,				__lowerCAmelCase:			str   ) -> Dict:




    '''simple docstring'''




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

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






  def 	_UpperCAmelCase				(				self:			int				,				__lowerCAmelCase:			Tuple				,				__lowerCAmelCase:			Any				,				__lowerCAmelCase:			List[Any]="average_pool"   ) -> int:




    '''simple docstring'''




    __UpperCAmelCase						    =       len(featuremaps[0]   )
    __UpperCAmelCase						    =       int(size_map / size_pooling   )
    __UpperCAmelCase						    =       []
    for i_map in range(len(__lowerCAmelCase   )   ):
      __UpperCAmelCase						    =       featuremaps[i_map]
      __UpperCAmelCase						    =       []
      for i_focus in range(0				,				__lowerCAmelCase				,				__lowerCAmelCase   ):
        for j_focus in range(0				,				__lowerCAmelCase				,				__lowerCAmelCase   ):
          __UpperCAmelCase						    =       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(__lowerCAmelCase   )   )
          elif pooling_type == "max_pooling":
            # max pooling
            map_pooled.append(np.max(__lowerCAmelCase   )   )
      __UpperCAmelCase						    =       np.asmatrix(__lowerCAmelCase   ).reshape(__lowerCAmelCase				,				__lowerCAmelCase   )
      featuremap_pooled.append(__lowerCAmelCase   )
    return featuremap_pooled






  def 	_UpperCAmelCase				(				self:			Union[str, Any]				,				__lowerCAmelCase:			Tuple   ) -> Dict:




    '''simple docstring'''




    __UpperCAmelCase						    =       []
    for i in range(len(__lowerCAmelCase   )   ):
      __UpperCAmelCase						    =       np.shape(data[i]   )
      __UpperCAmelCase						    =       data[i].reshape(1				,				shapes[0] * shapes[1]   )
      __UpperCAmelCase						    =       data_listed.getA().tolist()[0]
      data_expanded.extend(__lowerCAmelCase   )
    __UpperCAmelCase						    =       np.asarray(__lowerCAmelCase   )
    return data_expanded






  def 	_UpperCAmelCase				(				self:			List[Any]				,				__lowerCAmelCase:			Dict   ) -> Union[str, Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       np.asarray(__lowerCAmelCase   )
    __UpperCAmelCase						    =       np.shape(__lowerCAmelCase   )
    __UpperCAmelCase						    =       data_mat.reshape(1				,				shapes[0] * shapes[1]   )
    return data_expanded






  def 	_UpperCAmelCase				(				self:			List[str]				,				__lowerCAmelCase:			List[str]				,				__lowerCAmelCase:			List[Any]				,				__lowerCAmelCase:			Any				,				__lowerCAmelCase:			Any				,				__lowerCAmelCase:			Optional[Any]   ) -> Any:




    '''simple docstring'''




    __UpperCAmelCase						    =       []
    __UpperCAmelCase						    =       0
    for i_map in range(__lowerCAmelCase   ):
      __UpperCAmelCase						    =       np.ones((size_map, size_map)   )
      for i in range(0				,				__lowerCAmelCase				,				__lowerCAmelCase   ):
        for j in range(0				,				__lowerCAmelCase				,				__lowerCAmelCase   ):
          __UpperCAmelCase						    =       pd_pool[
              i_pool
          ]
          __UpperCAmelCase						    =       i_pool + 1
      __UpperCAmelCase						    =       np.multiply(
          __lowerCAmelCase				,				np.multiply(out_map[i_map]				,				(1 - out_map[i_map])   )   )
      pd_all.append(__lowerCAmelCase   )
    return pd_all






  def 	_UpperCAmelCase				(				self:			Dict				,				__lowerCAmelCase:			Union[str, Any]				,				__lowerCAmelCase:			Tuple				,				__lowerCAmelCase:			List[Any]				,				__lowerCAmelCase:			Optional[int]				,				__lowerCAmelCase:			Tuple				,				__lowerCAmelCase:			List[str]=bool   ) -> int:




    '''simple docstring'''




    print("----------------------Start Training-------------------------"   )
    print((" - - Shape: Train_Data  ", np.shape(__lowerCAmelCase   ))   )
    print((" - - Shape: Teach_Data  ", np.shape(__lowerCAmelCase   ))   )
    __UpperCAmelCase						    =       0
    __UpperCAmelCase						    =       []
    __UpperCAmelCase						    =       10_000
    while rp < n_repeat and mse >= error_accuracy:
      __UpperCAmelCase						    =       0
      print(F'''-------------Learning Time {rp}--------------'''   )
      for p in range(len(__lowerCAmelCase   )   ):
        # print('------------Learning Image: %d--------------'%p)
        __UpperCAmelCase						    =       np.asmatrix(datas_train[p]   )
        __UpperCAmelCase						    =       np.asarray(datas_teach[p]   )
        __UpperCAmelCase    ,		__UpperCAmelCase						    =       self.convolute(
            __lowerCAmelCase				,				self.conva				,				self.w_conva				,				self.thre_conva				,				conv_step=self.step_conva				,				)
        __UpperCAmelCase						    =       self.pooling(__lowerCAmelCase				,				self.size_poolinga   )
        __UpperCAmelCase						    =       np.shape(__lowerCAmelCase   )
        __UpperCAmelCase						    =       self._expand(__lowerCAmelCase   )
        __UpperCAmelCase						    =       data_bp_input

        __UpperCAmelCase						    =       np.dot(__lowerCAmelCase				,				self.vji.T   ) - self.thre_bpa
        __UpperCAmelCase						    =       self.sig(__lowerCAmelCase   )
        __UpperCAmelCase						    =       np.dot(__lowerCAmelCase				,				self.wkj.T   ) - self.thre_bpa
        __UpperCAmelCase						    =       self.sig(__lowerCAmelCase   )

        # --------------Model Leaning ------------------------
        # calculate error and gradient---------------
        __UpperCAmelCase						    =       np.multiply(
            (data_teach - bp_outa)				,				np.multiply(__lowerCAmelCase				,				(1 - bp_outa)   )   )
        __UpperCAmelCase						    =       np.multiply(
            np.dot(__lowerCAmelCase				,				self.wkj   )				,				np.multiply(__lowerCAmelCase				,				(1 - bp_outa)   )   )
        __UpperCAmelCase						    =       np.dot(__lowerCAmelCase				,				self.vji   )

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

          __UpperCAmelCase						    =       self.w_conva[k_conv] + delta_w.reshape(
              (self.conva[0], self.conva[0])   )

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

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

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






  def 	_UpperCAmelCase				(				self:			int				,				__lowerCAmelCase:			int   ) -> Optional[Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       []
    print("-------------------Start Testing-------------------------"   )
    print((" - - Shape: Test_Data  ", np.shape(__lowerCAmelCase   ))   )
    for p in range(len(__lowerCAmelCase   )   ):
      __UpperCAmelCase						    =       np.asmatrix(datas_test[p]   )
      __UpperCAmelCase    ,		__UpperCAmelCase						    =       self.convolute(
          __lowerCAmelCase				,				self.conva				,				self.w_conva				,				self.thre_conva				,				conv_step=self.step_conva				,				)
      __UpperCAmelCase						    =       self.pooling(__lowerCAmelCase				,				self.size_poolinga   )
      __UpperCAmelCase						    =       self._expand(__lowerCAmelCase   )

      __UpperCAmelCase						    =       data_bp_input
      __UpperCAmelCase						    =       bp_outa * self.vji.T - self.thre_bpa
      __UpperCAmelCase						    =       self.sig(__lowerCAmelCase   )
      __UpperCAmelCase						    =       bp_outa * self.wkj.T - self.thre_bpa
      __UpperCAmelCase						    =       self.sig(__lowerCAmelCase   )
      produce_out.extend(bp_outa.getA().tolist()   )
    __UpperCAmelCase						    =       [list(map(self.do_round				,				__lowerCAmelCase   )   ) for each in produce_out]
    return np.asarray(__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			Union[str, Any]				,				__lowerCAmelCase:			Any   ) -> str:




    '''simple docstring'''




    __UpperCAmelCase						    =       np.asmatrix(__lowerCAmelCase   )
    __UpperCAmelCase    ,		__UpperCAmelCase						    =       self.convolute(
        __lowerCAmelCase				,				self.conva				,				self.w_conva				,				self.thre_conva				,				conv_step=self.step_conva				,				)
    __UpperCAmelCase						    =       self.pooling(__lowerCAmelCase				,				self.size_poolinga   )

    return data_conveda, data_pooleda


if __name__ == "__main__":
       pass




from sklearn.metrics import fa_score

import datasets


a_							=      """
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""


a_							=      """
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. 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 `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `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 classes found in `predictions` and `references` 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. This option 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.

Returns:
    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.

Examples:

    Example 1-A simple binary example
        >>> f1_metric = datasets.load_metric(\"f1\")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {'f1': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> f1_metric = datasets.load_metric(\"f1\")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results['f1'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric(\"f1\")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(round(results['f1'], 2))
        0.35

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"macro\")
        >>> print(round(results['f1'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"micro\")
        >>> print(round(results['f1'], 2))
        0.33
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"weighted\")
        >>> print(round(results['f1'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {'f1': array([0.8, 0. , 0. ])}
"""


a_							=      """
@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 			UpperCAmelCase__    (		datasets.Metric					):



  """simple docstring"""






  def 	_UpperCAmelCase				(				self:			int   ) -> Dict:




    '''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.f1_score.html"]				,				)






  def 	_UpperCAmelCase				(				self:			Any				,				__lowerCAmelCase:			Tuple				,				__lowerCAmelCase:			Any				,				__lowerCAmelCase:			List[Any]=None				,				__lowerCAmelCase:			Optional[Any]=1				,				__lowerCAmelCase:			List[Any]="binary"				,				__lowerCAmelCase:			List[Any]=None   ) -> Optional[int]:




    '''simple docstring'''




    __UpperCAmelCase						    =       fa_score(
        __lowerCAmelCase				,				__lowerCAmelCase				,				labels=__lowerCAmelCase				,				pos_label=__lowerCAmelCase				,				average=__lowerCAmelCase				,				sample_weight=__lowerCAmelCase   )
    return {"f1": float(__lowerCAmelCase   ) if score.size == 1 else score}



