




import random






class       A      :
     """simple docstring"""

     def __init__(     self	: Optional[Any]						,	A__	: Tuple						,	A__	: str						,	A__	: Any = 0.01						,	A__	: Tuple = 1_0_0_0						,	A__	: Optional[int] = -1						,	)					->							Optional[Any]:
           lowerCamelCase__   	=					sample
           if len(self.sample      ) == 0:
                 raise ValueError('Sample data can not be empty'      )
           lowerCamelCase__   	=					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'      )
           lowerCamelCase__   	=					learning_rate
           lowerCamelCase__   	=					epoch_number
           lowerCamelCase__   	=					bias
           lowerCamelCase__   	=					len(A__      )
           lowerCamelCase__   	=					len(sample[0]      )  # number of columns in dataset
           lowerCamelCase__   	=					[]




     def 			__snake_case (     self	: Any      )					->							int:

           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      )

           lowerCamelCase__   	=					0

           while True:
                 lowerCamelCase__   	=					False
                 for i in range(self.number_sample      ):
                       lowerCamelCase__   	=					0
                       for j in range(self.col_sample + 1      ):
                             lowerCamelCase__   	=					u + self.weight[j] * self.sample[i][j]
                       lowerCamelCase__   	=					self.sign(A__      )
                       if y != self.target[i]:
                             for j in range(self.col_sample + 1      ):
                                   lowerCamelCase__   	=					(
                                       self.weight[j]
                                       + self.learning_rate
                                       * (self.target[i] - y)
                                       * self.sample[i][j]
                                   )
                             lowerCamelCase__   	=					True
            # print('Epoch: \n',epoch_count)
                 lowerCamelCase__   	=					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 			__snake_case (     self	: Optional[Any]						,	A__	: Any      )					->							Optional[Any]:

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

           lowerCamelCase__   	=					self.sign(A__      )

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




     def 			__snake_case (     self	: List[str]						,	A__	: Dict      )					->							Union[str, Any]:
           return 1 if u >= 0 else -1
__SCREAMING_SNAKE_CASE     : Optional[Any]      =[
    [-0.6508, 0.1097, 4.0009],
    [-1.4492, 0.8896, 4.4005],
    [2.0850, 0.6876, 12.0710],
    [0.2626, 1.1476, 7.7985],
    [0.6418, 1.0234, 7.0427],
    [0.2569, 0.6730, 8.3265],
    [1.1155, 0.6043, 7.4446],
    [0.0914, 0.3399, 7.0677],
    [0.0121, 0.5256, 4.6316],
    [-0.0429, 0.4660, 5.4323],
    [0.4340, 0.6870, 8.2287],
    [0.2735, 1.0287, 7.1934],
    [0.4839, 0.4851, 7.4850],
    [0.4089, -0.1267, 5.5019],
    [1.4391, 0.1614, 8.5843],
    [-0.9115, -0.1973, 2.1962],
    [0.3654, 1.0475, 7.4858],
    [0.2144, 0.7515, 7.1699],
    [0.2013, 1.0014, 6.5489],
    [0.6483, 0.2183, 5.8991],
    [-0.1147, 0.2242, 7.2435],
    [-0.7970, 0.8795, 3.8762],
    [-1.0625, 0.6366, 2.4707],
    [0.5307, 0.1285, 5.6883],
    [-1.2200, 0.7777, 1.7252],
    [0.3957, 0.1076, 5.6623],
    [-0.1013, 0.5989, 7.1812],
    [2.4482, 0.9455, 11.2095],
    [2.0149, 0.6192, 10.9263],
    [0.2012, 0.2611, 5.4631],
]

__SCREAMING_SNAKE_CASE     : Optional[int]      =[
    -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()

      __SCREAMING_SNAKE_CASE     : str      =Perceptron(
          sample=samples, target=target, learning_rate=0.01, epoch_number=1_000, bias=-1
      )
      network.training()
      print("""Finished training perceptron""")
      print("""Enter values to predict or q to exit""")
      while True:
            __SCREAMING_SNAKE_CASE     : int      =[]
            for i in range(len(samples[0])):
                  __SCREAMING_SNAKE_CASE     : List[str]      =input("""value: """).strip()
                  if user_input == "q":
                        break
                  __SCREAMING_SNAKE_CASE     : int      =float(user_input)
                  sample.insert(i, observation)
            network.sort(sample)
"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__       =	{
    "configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	["MarianTokenizer"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	[
					    "MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "MarianForCausalLM",
					    "MarianModel",
					    "MarianMTModel",
					    "MarianPreTrainedModel",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]

if TYPE_CHECKING:
					from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_marian import MarianTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_marian import (
										    MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MarianForCausalLM,
										    MarianModel,
										    MarianMTModel,
										    MarianPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
					import sys

					SCREAMING_SNAKE_CASE__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


