
"""simple docstring"""







import random




class 					A__			:

		def __init__( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = 0.01			,						_SCREAMING_SNAKE_CASE = 10_00			,						_SCREAMING_SNAKE_CASE = -1			,						):
			__lowerCAmelCase     :		Optional[int]		     = sample
			if len(self.sample					) == 0:
				raise ValueError('Sample data can not be empty'					)
			__lowerCAmelCase     :		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'					)
			__lowerCAmelCase     :		int		     = learning_rate
			__lowerCAmelCase     :		List[Any]		     = epoch_number
			__lowerCAmelCase     :		str		     = bias
			__lowerCAmelCase     :		List[Any]		     = len(_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		Optional[int]		     = len(sample[0]					)  # number of columns in dataset
			__lowerCAmelCase     :		list		     = []

		def 				__lowerCamelCase				( self					):
			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     :		Dict		     = 0

			while True:
				__lowerCAmelCase     :		List[str]		     = False
				for i in range(self.number_sample					):
					__lowerCAmelCase     :		Dict		     = 0
					for j in range(self.col_sample + 1					):
						__lowerCAmelCase     :		List[Any]		     = u + self.weight[j] * self.sample[i][j]
					__lowerCAmelCase     :		Optional[int]		     = self.sign(_SCREAMING_SNAKE_CASE					)
					if y != self.target[i]:
						for j in range(self.col_sample + 1					):
							__lowerCAmelCase     :		Dict		     = (
							    self.weight[j]
							    + self.learning_rate
							    * (self.target[i] - y)
							    * self.sample[i][j]
							)
						__lowerCAmelCase     :		Optional[int]		     = True
            # print('Epoch: \n',epoch_count)
				__lowerCAmelCase     :		Union[str, 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 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE					):
			if len(self.sample					) == 0:
				raise ValueError('Sample data can not be empty'					)
			sample.insert(0			,						self.bias					)
			__lowerCAmelCase     :		Dict		     = 0
			for i in range(self.col_sample + 1					):
				__lowerCAmelCase     :		List[str]		     = u + self.weight[i] * sample[i]

			__lowerCAmelCase     :		Optional[int]		     = self.sign(_SCREAMING_SNAKE_CASE					)

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







		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE					):
			return 1 if u >= 0 else -1



lowerCamelCase__     =						[
    [-0.6_5_0_8, 0.1_0_9_7, 4.0_0_0_9],
    [-1.4_4_9_2, 0.8_8_9_6, 4.4_0_0_5],
    [2.0_8_5_0, 0.6_8_7_6, 1_2.0_7_1_0],
    [0.2_6_2_6, 1.1_4_7_6, 7.7_9_8_5],
    [0.6_4_1_8, 1.0_2_3_4, 7.0_4_2_7],
    [0.2_5_6_9, 0.6_7_3_0, 8.3_2_6_5],
    [1.1_1_5_5, 0.6_0_4_3, 7.4_4_4_6],
    [0.0_9_1_4, 0.3_3_9_9, 7.0_6_7_7],
    [0.0_1_2_1, 0.5_2_5_6, 4.6_3_1_6],
    [-0.0_4_2_9, 0.4_6_6_0, 5.4_3_2_3],
    [0.4_3_4_0, 0.6_8_7_0, 8.2_2_8_7],
    [0.2_7_3_5, 1.0_2_8_7, 7.1_9_3_4],
    [0.4_8_3_9, 0.4_8_5_1, 7.4_8_5_0],
    [0.4_0_8_9, -0.1_2_6_7, 5.5_0_1_9],
    [1.4_3_9_1, 0.1_6_1_4, 8.5_8_4_3],
    [-0.9_1_1_5, -0.1_9_7_3, 2.1_9_6_2],
    [0.3_6_5_4, 1.0_4_7_5, 7.4_8_5_8],
    [0.2_1_4_4, 0.7_5_1_5, 7.1_6_9_9],
    [0.2_0_1_3, 1.0_0_1_4, 6.5_4_8_9],
    [0.6_4_8_3, 0.2_1_8_3, 5.8_9_9_1],
    [-0.1_1_4_7, 0.2_2_4_2, 7.2_4_3_5],
    [-0.7_9_7_0, 0.8_7_9_5, 3.8_7_6_2],
    [-1.0_6_2_5, 0.6_3_6_6, 2.4_7_0_7],
    [0.5_3_0_7, 0.1_2_8_5, 5.6_8_8_3],
    [-1.2_2_0_0, 0.7_7_7_7, 1.7_2_5_2],
    [0.3_9_5_7, 0.1_0_7_6, 5.6_6_2_3],
    [-0.1_0_1_3, 0.5_9_8_9, 7.1_8_1_2],
    [2.4_4_8_2, 0.9_4_5_5, 1_1.2_0_9_5],
    [2.0_1_4_9, 0.6_1_9_2, 1_0.9_2_6_3],
    [0.2_0_1_2, 0.2_6_1_1, 5.4_6_3_1],
]

lowerCamelCase__     =						[
    -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()

							lowerCamelCase__     =						Perceptron(
							    sample=samples, target=target, learning_rate=0.0_1, epoch_number=1_000, bias=-1
							)
							network.training()
							print("""Finished training perceptron""")
							print("""Enter values to predict or q to exit""")
							while True:
														lowerCamelCase__     =						[]
														for i in range(len(samples[0])):
																					lowerCamelCase__     =						input("""value: """).strip()
																					if user_input == "q":
																												break
																					lowerCamelCase__     =						float(user_input)
																					sample.insert(i, observation)
														network.sort(sample)

"""simple docstring"""







from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
)


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_pt_objects import *  # noqa F403
else:
							from .consistency_models import ConsistencyModelPipeline
							from .dance_diffusion import DanceDiffusionPipeline
							from .ddim import DDIMPipeline
							from .ddpm import DDPMPipeline
							from .dit import DiTPipeline
							from .latent_diffusion import LDMSuperResolutionPipeline
							from .latent_diffusion_uncond import LDMPipeline
							from .pipeline_utils import AudioPipelineOutput, DiffusionPipeline, ImagePipelineOutput
							from .pndm import PNDMPipeline
							from .repaint import RePaintPipeline
							from .score_sde_ve import ScoreSdeVePipeline
							from .stochastic_karras_ve import KarrasVePipeline

try:
							if not (is_torch_available() and is_librosa_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
							from .audio_diffusion import AudioDiffusionPipeline, Mel

try:
							if not (is_torch_available() and is_transformers_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
							from .alt_diffusion import AltDiffusionImgaImgPipeline, AltDiffusionPipeline
							from .audioldm import AudioLDMPipeline
							from .controlnet import (
							    StableDiffusionControlNetImgaImgPipeline,
							    StableDiffusionControlNetInpaintPipeline,
							    StableDiffusionControlNetPipeline,
							)
							from .deepfloyd_if import (
							    IFImgaImgPipeline,
							    IFImgaImgSuperResolutionPipeline,
							    IFInpaintingPipeline,
							    IFInpaintingSuperResolutionPipeline,
							    IFPipeline,
							    IFSuperResolutionPipeline,
							)
							from .kandinsky import (
							    KandinskyImgaImgPipeline,
							    KandinskyInpaintPipeline,
							    KandinskyPipeline,
							    KandinskyPriorPipeline,
							)
							from .kandinskya_a import (
							    KandinskyVaaControlnetImgaImgPipeline,
							    KandinskyVaaControlnetPipeline,
							    KandinskyVaaImgaImgPipeline,
							    KandinskyVaaInpaintPipeline,
							    KandinskyVaaPipeline,
							    KandinskyVaaPriorEmbaEmbPipeline,
							    KandinskyVaaPriorPipeline,
							)
							from .latent_diffusion import LDMTextToImagePipeline
							from .paint_by_example import PaintByExamplePipeline
							from .semantic_stable_diffusion import SemanticStableDiffusionPipeline
							from .shap_e import ShapEImgaImgPipeline, ShapEPipeline
							from .stable_diffusion import (
							    CycleDiffusionPipeline,
							    StableDiffusionAttendAndExcitePipeline,
							    StableDiffusionDepthaImgPipeline,
							    StableDiffusionDiffEditPipeline,
							    StableDiffusionImageVariationPipeline,
							    StableDiffusionImgaImgPipeline,
							    StableDiffusionInpaintPipeline,
							    StableDiffusionInpaintPipelineLegacy,
							    StableDiffusionInstructPixaPixPipeline,
							    StableDiffusionLatentUpscalePipeline,
							    StableDiffusionLDMaDPipeline,
							    StableDiffusionModelEditingPipeline,
							    StableDiffusionPanoramaPipeline,
							    StableDiffusionParadigmsPipeline,
							    StableDiffusionPipeline,
							    StableDiffusionPixaPixZeroPipeline,
							    StableDiffusionSAGPipeline,
							    StableDiffusionUpscalePipeline,
							    StableUnCLIPImgaImgPipeline,
							    StableUnCLIPPipeline,
							)
							from .stable_diffusion_safe import StableDiffusionPipelineSafe
							from .text_to_video_synthesis import TextToVideoSDPipeline, TextToVideoZeroPipeline, VideoToVideoSDPipeline
							from .unclip import UnCLIPImageVariationPipeline, UnCLIPPipeline
							from .unidiffuser import ImageTextPipelineOutput, UniDiffuserModel, UniDiffuserPipeline, UniDiffuserTextDecoder
							from .versatile_diffusion import (
							    VersatileDiffusionDualGuidedPipeline,
							    VersatileDiffusionImageVariationPipeline,
							    VersatileDiffusionPipeline,
							    VersatileDiffusionTextToImagePipeline,
							)
							from .vq_diffusion import VQDiffusionPipeline


try:
							if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
							from .stable_diffusion_xl import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
							if not is_onnx_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_onnx_objects import *  # noqa F403
else:
							from .onnx_utils import OnnxRuntimeModel

try:
							if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
							from .stable_diffusion import (
							    OnnxStableDiffusionImgaImgPipeline,
							    OnnxStableDiffusionInpaintPipeline,
							    OnnxStableDiffusionInpaintPipelineLegacy,
							    OnnxStableDiffusionPipeline,
							    OnnxStableDiffusionUpscalePipeline,
							    StableDiffusionOnnxPipeline,
							)

try:
							if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
							from .stable_diffusion import StableDiffusionKDiffusionPipeline

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_flax_objects import *  # noqa F403
else:
							from .pipeline_flax_utils import FlaxDiffusionPipeline


try:
							if not (is_flax_available() and is_transformers_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
							from .controlnet import FlaxStableDiffusionControlNetPipeline
							from .stable_diffusion import (
							    FlaxStableDiffusionImgaImgPipeline,
							    FlaxStableDiffusionInpaintPipeline,
							    FlaxStableDiffusionPipeline,
							)
try:
							if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
							from .spectrogram_diffusion import MidiProcessor, SpectrogramDiffusionPipeline