






"""simple docstring"""





import random


class   _lowercase			:


      def __init__(     self					:						Optional[Any]				, a					:						list[list[float]]				, a					:						list[int]				, a					:						float = 0.0_1				, a					:						int = 1_0_0_0				, a					:						float = -1				, ):





            """simple docstring"""







            __snake_case   :     Union[str, Any]	      =sample
            if len(self.sample    ) == 0:
                  raise ValueError('''Sample data can not be empty'''    )
            __snake_case   :     Dict	      =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'''    )
            __snake_case   :     Optional[Any]	      =learning_rate
            __snake_case   :     str	      =epoch_number
            __snake_case   :     List[str]	      =bias
            __snake_case   :     Tuple	      =len(a    )
            __snake_case   :     List[str]	      =len(sample[0]    )  # number of columns in dataset
            __snake_case   :     list	      =[]


      def _UpperCamelCase    (     self					:						Optional[Any]    ):





            """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    )

            __snake_case   :     Dict	      =0

            while True:
                  __snake_case   :     Optional[Any]	      =False
                  for i in range(self.number_sample    ):
                        __snake_case   :     Dict	      =0
                        for j in range(self.col_sample + 1    ):
                              __snake_case   :     int	      =u + self.weight[j] * self.sample[i][j]
                        __snake_case   :     Tuple	      =self.sign(a    )
                        if y != self.target[i]:
                              for j in range(self.col_sample + 1    ):
                                    __snake_case   :     Tuple	      =(
                                        self.weight[j]
                                        + self.learning_rate
                                        * (self.target[i] - y)
                                        * self.sample[i][j]
                                    )
                              __snake_case   :     Any	      =True
            # print('Epoch: \n',epoch_count)
                  __snake_case   :     List[str]	      =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 _UpperCamelCase    (     self					:						Dict				, a					:						list[float]    ):





            """simple docstring"""







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

            __snake_case   :     Dict	      =self.sign(a    )

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







      def _UpperCamelCase    (     self					:						List[str]				, a					:						float    ):





            """simple docstring"""







            return 1 if u >= 0 else -1



UpperCamelCase_						:					int      				=			[
    [-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],
]

UpperCamelCase_						:					Optional[Any]      				=			[
    -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()

    UpperCamelCase_						:					str      				=			Perceptron(
        sample=samples, target=target, learning_rate=0.01, epoch_number=1000, bias=-1
    )
    network.training()
    print("""Finished training perceptron""")
    print("""Enter values to predict or q to exit""")
    while True:
        UpperCamelCase_						:					list      				=			[]
        for i in range(len(samples[0])):
            UpperCamelCase_						:					Tuple      				=			input("""value: """).strip()
            if user_input == "q":
                break
            UpperCamelCase_						:					List[str]      				=			float(user_input)
            sample.insert(i, observation)
        network.sort(sample)










"""simple docstring"""





from typing import TYPE_CHECKING

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


UpperCamelCase_						:					Dict      				=			{
    """configuration_layoutlm""": ["""LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMConfig""", """LayoutLMOnnxConfig"""],
    """tokenization_layoutlm""": ["""LayoutLMTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					List[Any]      				=			["""LayoutLMTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Optional[Any]      				=			[
        """LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """LayoutLMForMaskedLM""",
        """LayoutLMForSequenceClassification""",
        """LayoutLMForTokenClassification""",
        """LayoutLMForQuestionAnswering""",
        """LayoutLMModel""",
        """LayoutLMPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Optional[int]      				=			[
        """TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFLayoutLMForMaskedLM""",
        """TFLayoutLMForSequenceClassification""",
        """TFLayoutLMForTokenClassification""",
        """TFLayoutLMForQuestionAnswering""",
        """TFLayoutLMMainLayer""",
        """TFLayoutLMModel""",
        """TFLayoutLMPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
    from .tokenization_layoutlm import LayoutLMTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_layoutlm import (
            LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            LayoutLMForMaskedLM,
            LayoutLMForQuestionAnswering,
            LayoutLMForSequenceClassification,
            LayoutLMForTokenClassification,
            LayoutLMModel,
            LayoutLMPreTrainedModel,
        )
    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_layoutlm import (
            TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFLayoutLMForMaskedLM,
            TFLayoutLMForQuestionAnswering,
            TFLayoutLMForSequenceClassification,
            TFLayoutLMForTokenClassification,
            TFLayoutLMMainLayer,
            TFLayoutLMModel,
            TFLayoutLMPreTrainedModel,
        )

else:
    import sys

    UpperCamelCase_						:					Optional[Any]      				=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


