text: from __future__ import annotations
  from typing import Callable
  from typing import NoReturn
  
  import IMLearn.metrics
  from ...base import BaseEstimator
  from ...metrics import misclassification_error
  
  import numpy as np
  
  
  def default_callback(fit: Perceptron, x: np.ndarray, y: int):
      pass
  
  
  class Perceptron(BaseEstimator):
      """
      Perceptron half-space classifier
  
      Finds a separating hyperplane for given linearly separable data.
  
      Attributes
      ----------
      include_intercept: bool, default = True
          Should fitted model include an intercept or not
  
      max_iter_: int, default = 1000
          Maximum number of passes over training data
  
      coefs_: ndarray of shape (n_features,) or (n_features+1,)
          Coefficients vector fitted by Perceptron algorithm. To be set in
          `Perceptron.fit` function.
  
      callback_: Callable[[Perceptron, np.ndarray, int], None]
              A callable to be called after each update of the model while fitting to given data
              Callable function should receive as input a Perceptron instance, current sample and current response
      """
      def __init__(self,
                   include_intercept: bool = True,
                   max_iter: int = 1000,
                   callback: Callable[[Perceptron, np.ndarray, int], None] = default_callback):
          """
          Instantiate a Perceptron classifier
  
          Parameters
          ----------
          include_intercept: bool, default=True
              Should fitted model include an intercept or not
  
          max_iter: int, default = 1000
              Maximum number of passes over training data
  
          callback: Callable[[Perceptron, np.ndarray, int], None]
              A callable to be called after each update of the model while fitting to given data
              Callable function should receive as input a Perceptron instance, current sample and current response
          """
          super().__init__()
          self.include_intercept_ = False
          self.max_iter_ = max_iter
          self.callback_ = callback
          self.coefs_ = None
  
      def _fit(self, X: np.ndarray, y: np.ndarray) -> NoReturn:
          """
          Fit a halfspace to to given samples. Iterate over given data as long as there exists a sample misclassified
          or that did not reach `self.max_iter_`
  
          Parameters
          ----------
          X : ndarray of shape (n_samples, n_features)
              Input data to fit an estimator for
  
          y : ndarray of shape (n_samples, )
              Responses of input data to fit to
  
          Notes
          -----
          Fits model with or without an intercept depending on value of `self.fit_intercept_`
          """
          if self.include_intercept_:
              X = np.hstack([np.ones((len(X), 1)), X])
          self.coefs_ = np.zeros(X.shape[1])
          for i in range(1, self.max_iter_ + 1):
              flag = False
              for row in range(len(y)):
                  if y[row] * (X[row] @ self.coefs_) <= 0:
                      flag = True
                      print(row)
                      self.coefs_ = self.coefs_ + (y[row] * X[row])
                      self.fitted_ = True
                      self.callback_(self, X[row], y[row])
                      break
              if not flag:
                  return
      def _predict(self, X: np.ndarray) -> np.ndarray:
          """
          Predict responses for given samples using fitted estimator
  
          Parameters
          ----------
          X : ndarray of shape (n_samples, n_features)
              Input data to predict responses for
  
          Returns
          -------
          responses : ndarray of shape (n_samples, )
              Predicted responses of given samples
          """
          if self.include_intercept_:
              X = np.hstack([np.ones((len(X), 1)), X])
          pred =  X @ self.coefs_
          pred = np.where(pred > 0, 1, -1)
          return pred
  
      def _loss(self, X: np.ndarray, y: np.ndarray) -> float:
          """
          Evaluate performance under misclassification loss function
  
          Parameters
          ----------
          X : ndarray of shape (n_samples, n_features)
              Test samples
  
          y : ndarray of shape (n_samples, )
              True labels of test samples
  
          Returns
          -------
          loss : float
              Performance under missclassification loss function
          """
  
          error = misclassification_error(y, self._predict(X))
          return error
  