




'''simple docstring'''





import random




class      UpperCamelCase       :



  def __init__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase = 0.01		,     __lowerCAmelCase = 1000		,     __lowerCAmelCase = -1		,     ):







      '''simple docstring'''


      _a				:							int							       =      sample
      if len(self.sample ) == 0:
          raise ValueError("""Sample data can not be empty""" )
      _a				:							Union[str, Any]							       =      target
      if len(self.target ) == 0:
          raise ValueError("""Target data can not be empty""" )
      if len(self.sample ) != len(self.target ):
          raise ValueError("""Sample data and Target data do not have matching lengths""" )
      _a				:							Tuple							       =      learning_rate
      _a				:							Optional[Any]							       =      epoch_number
      _a				:							Optional[int]							       =      bias
      _a				:							List[str]							       =      len(lowerCamelCase_ )
      _a				:							List[Any]							       =      len(sample[0] )  # number of columns in dataset
      _a				:							int							       =      []



  def     a_							(			self ):







      '''simple docstring'''


      for sample in self.sample:
          sample.insert(0		,     self.bias )

      for _ in range(self.col_sample ):
          self.weight.append(random.random() )

      self.weight.insert(0		,     self.bias )

      _a				:							int							       =      0

      while True:
          _a				:							Tuple							       =      False
          for i in range(self.number_sample ):
              _a				:							Dict							       =      0
              for j in range(self.col_sample + 1 ):
                  _a				:							Optional[int]							       =      u + self.weight[j] * self.sample[i][j]
              _a				:							str							       =      self.sign(lowerCamelCase_ )
              if y != self.target[i]:
                  for j in range(self.col_sample + 1 ):
                      _a				:							str							       =      (
                          self.weight[j]
                          + self.learning_rate
                          * (self.target[i] - y)
                          * self.sample[i][j]
                      )
                  _a				:							List[str]							       =      True
            # print('Epoch: \n',epoch_count)
          _a				:							Any							       =      epoch_count + 1
          # if you want control the epoch or just by error
          if not has_misclassified:
              print(("""\nEpoch:\n""", epoch_count) )
              print("""------------------------\n""" )
              # if epoch_count > self.epoch_number or not error:
              break



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      if len(self.sample ) == 0:
          raise ValueError("""Sample data can not be empty""" )
      sample.insert(0		,     self.bias )
      _a				:							List[Any]							       =      0
      for i in range(self.col_sample + 1 ):
          _a				:							Tuple							       =      u + self.weight[i] * sample[i]

      _a				:							List[Any]							       =      self.sign(lowerCamelCase_ )

      if y == -1:
          print(("""Sample: """, sample) )
          print("""classification: P1""" )
      else:
          print(("""Sample: """, sample) )
          print("""classification: P2""" )



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      return 1 if u >= 0 else -1






a		:	Tuple       =      [
    [-0.65_08, 0.10_97, 4.00_09],
    [-1.44_92, 0.88_96, 4.40_05],
    [2.08_50, 0.68_76, 12.07_10],
    [0.26_26, 1.14_76, 7.79_85],
    [0.64_18, 1.02_34, 7.04_27],
    [0.25_69, 0.67_30, 8.32_65],
    [1.11_55, 0.60_43, 7.44_46],
    [0.09_14, 0.33_99, 7.06_77],
    [0.01_21, 0.52_56, 4.63_16],
    [-0.04_29, 0.46_60, 5.43_23],
    [0.43_40, 0.68_70, 8.22_87],
    [0.27_35, 1.02_87, 7.19_34],
    [0.48_39, 0.48_51, 7.48_50],
    [0.40_89, -0.12_67, 5.50_19],
    [1.43_91, 0.16_14, 8.58_43],
    [-0.91_15, -0.19_73, 2.19_62],
    [0.36_54, 1.04_75, 7.48_58],
    [0.21_44, 0.75_15, 7.16_99],
    [0.20_13, 1.00_14, 6.54_89],
    [0.64_83, 0.21_83, 5.89_91],
    [-0.11_47, 0.22_42, 7.24_35],
    [-0.79_70, 0.87_95, 3.87_62],
    [-1.06_25, 0.63_66, 2.47_07],
    [0.53_07, 0.12_85, 5.68_83],
    [-1.22_00, 0.77_77, 1.72_52],
    [0.39_57, 0.10_76, 5.66_23],
    [-0.10_13, 0.59_89, 7.18_12],
    [2.44_82, 0.94_55, 11.20_95],
    [2.01_49, 0.61_92, 10.92_63],
    [0.20_12, 0.26_11, 5.46_31],
]

a		:	Dict       =      [
    -1,
    -1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    -1,
    -1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    -1,
    1,
    -1,
    1,
]


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  a		:	Union[str, Any]       =      Perceptron(
      sample=samples, target=target, learning_rate=0.01, epoch_number=1_0_0_0, bias=-1
  )
  network.training()
  print('''Finished training perceptron''')
  print('''Enter values to predict or q to exit''')
  while True:
    a		:	list       =      []
    for i in range(len(samples[0])):
      a		:	Tuple       =      input('''value: ''').strip()
      if user_input == "q":
        break
      a		:	Optional[Any]       =      float(user_input)
      sample.insert(i, observation)
    network.sort(sample)






from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    bool     ):






				'''simple docstring'''






				if not inplace:
								_lowerCAmelCase  						=ma + ma
				else:
								ma += ma

				return ma

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    List[int]     ):






				'''simple docstring'''






				_lowerCAmelCase  						=-1 * len(lowercase__     )
				_lowerCAmelCase  						=list(range(len(tensor.shape[:zero_index]     )     )     )
				return tensor.permute(first_inds + [zero_index + i for i in inds]     )

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    int     ):






				'''simple docstring'''






				return t.reshape(t.shape[:-no_dims] + (-1,)     )

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    int       ,     lowercase__     :    float = 1e-4     ):






				'''simple docstring'''






				_lowerCAmelCase  						=mask.expand(*value.shape     )
				return torch.sum(mask * value       ,     dim=lowercase__     ) / (eps + torch.sum(lowercase__       ,     dim=lowercase__     ))

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.types.Number = 2.3_1_2_5       ,     lowercase__     :    torch.types.Number = 2_1.6_8_7_5       ,     lowercase__     :    int = 64     ):






				'''simple docstring'''






				_lowerCAmelCase  						=torch.linspace(lowercase__       ,     lowercase__       ,     no_bins - 1       ,     device=pts.device     )
				_lowerCAmelCase  						=torch.sqrt(torch.sum((pts.unsqueeze(-2     ) - pts.unsqueeze(-3     )) ** 2       ,     dim=-1     )     )
				return torch.bucketize(lowercase__       ,     lowercase__     )

def 		snake_case_   (				lowercase__     :    Callable[[list], Any]       ,     lowercase__     :    List[dict]     ):






				'''simple docstring'''






				_lowerCAmelCase  						=dicts[0]
				_lowerCAmelCase  						={}
				for k, v in first.items():
								_lowerCAmelCase  						=[d[k] for d in dicts]
								if isinstance(lowercase__       ,     lowercase__     ):
												_lowerCAmelCase  						=dict_multimap(lowercase__       ,     lowercase__     )
								else:
												_lowerCAmelCase  						=fn(lowercase__     )

				return new_dict

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor     ):






				'''simple docstring'''






				_lowerCAmelCase  						=v_bins.view(((1,) * len(x.shape     )) + (len(lowercase__     ),)     )
				_lowerCAmelCase  						=x[..., None] - reshaped_bins
				_lowerCAmelCase  						=torch.argmin(torch.abs(lowercase__     )       ,     dim=-1     )
				return nn.functional.one_hot(lowercase__       ,     num_classes=len(lowercase__     )     ).float()

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    int = 0       ,     lowercase__     :    int = 0     ):






				'''simple docstring'''






				_lowerCAmelCase  						=[]
				for i, s in enumerate(data.shape[:no_batch_dims]     ):
								_lowerCAmelCase  						=torch.arange(lowercase__     )
								_lowerCAmelCase  						=r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape     ) - i - 1)))     )
								ranges.append(lowercase__     )

				_lowerCAmelCase  						=[slice(lowercase__     ) for _ in range(len(data.shape     ) - no_batch_dims     )]
				_lowerCAmelCase  						=inds
				ranges.extend(lowercase__     )
				# Matt note: Editing this to get around the behaviour of using a list as an array index changing
				# in recent Numpy versions
				return data[tuple(lowercase__     )]


__SCREAMING_SNAKE_CASE	:							str           =  TypeVar('''T''')

def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    Dict[Any, Union[dict, list, tuple, T]]       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				_lowerCAmelCase  						={}
				for k, v in dic.items():
								if isinstance(lowercase__       ,     lowercase__     ):
												_lowerCAmelCase  						=dict_map(lowercase__       ,     lowercase__       ,     lowercase__     )
								else:
												_lowerCAmelCase  						=tree_map(lowercase__       ,     lowercase__       ,     lowercase__     )

				return new_dict

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    T       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    dict       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    list       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    tuple       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

def 		snake_case_   (				lowercase__     :    Optional[Any]       ,     lowercase__     :    str       ,     lowercase__     :    Optional[int]     ):






				'''simple docstring'''






				if isinstance(lowercase__       ,     lowercase__     ):
								return dict_map(lowercase__       ,     lowercase__       ,     lowercase__     )
				elif isinstance(lowercase__       ,     lowercase__     ):
								return [tree_map(lowercase__       ,     lowercase__       ,     lowercase__     ) for x in tree]
				elif isinstance(lowercase__       ,     lowercase__     ):
								return tuple(tree_map(lowercase__       ,     lowercase__       ,     lowercase__     ) for x in tree     )
				elif isinstance(lowercase__       ,     lowercase__     ):
								return fn(lowercase__     )
				else:
								print(type(lowercase__     )     )
								raise ValueError("""Not supported"""     )





__SCREAMING_SNAKE_CASE	:							Any           =  partial(tree_map, leaf_type=torch.Tensor)

