



'''simple docstring'''
import numpy

# List of input, output pairs
snake_case_			:     Tuple  	=  (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
snake_case_			:     Optional[Any]  	=  (((515, 22, 13), 555), ((61, 35, 49), 150))
snake_case_			:     List[Any]  	=  [2, 4, 1, 5]
snake_case_			:     int  	=  len(train_data)
snake_case_			:     Union[str, Any]  	=  0.0_09






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_="train"							):
						return calculate_hypothesis_value(UpperCAmelCase_  ,  UpperCAmelCase_							) - output(
						    UpperCAmelCase_  ,  UpperCAmelCase_							)






def A__   (					UpperCAmelCase_							):
						_UpperCamelCase :		Dict			    =       0
						for i in range(len(UpperCAmelCase_							) - 1							):
												hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
						hyp_val += parameter_vector[0]
						return hyp_val






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_							):
						if data_set == "train":
												return train_data[example_no][1]
						elif data_set == "test":
												return test_data[example_no][1]
						return None






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_							):
						if data_set == "train":
												return _hypothesis_value(train_data[example_no][0]							)
						elif data_set == "test":
												return _hypothesis_value(test_data[example_no][0]							)
						return None






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_=m							):
						_UpperCamelCase :		Union[str, Any]			    =       0
						for i in range(UpperCAmelCase_							):
												if index == -1:
																		summation_value += _error(UpperCAmelCase_							)
												else:
																		summation_value += _error(UpperCAmelCase_							) * train_data[i][0][index]
						return summation_value






def A__   (					UpperCAmelCase_							):
						_UpperCamelCase :		Union[str, Any]			    =       summation_of_cost_derivative(UpperCAmelCase_  ,  UpperCAmelCase_							) / m
						return cost_derivative_value






def A__   (					):
						global parameter_vector
						# Tune these values to set a tolerance value for predicted output
						_UpperCamelCase :		str			    =       0.000_002
						_UpperCamelCase :		Any			    =       0
						_UpperCamelCase :		List[Any]			    =       0
						while True:
												j += 1
												_UpperCamelCase :		List[str]			    =       [0, 0, 0, 0]
												for i in range(0  ,  len(UpperCAmelCase_							)							):
																		_UpperCamelCase :		List[str]			    =       get_cost_derivative(i - 1							)
																		_UpperCamelCase :		Optional[Any]			    =       (
																		    parameter_vector[i] - LEARNING_RATE * cost_derivative
																		)
												if numpy.allclose(
												    UpperCAmelCase_  ,  UpperCAmelCase_  ,  atol=UpperCAmelCase_  ,  rtol=UpperCAmelCase_  ,  ):
																		break
												_UpperCamelCase :		Tuple			    =       temp_parameter_vector
						print(('Number of iterations:', j)							)






def A__   (					):
						for i in range(len(UpperCAmelCase_							)							):
												print(('Actual output value:', output(UpperCAmelCase_  ,  'test'							))							)
												print(('Hypothesis output:', calculate_hypothesis_value(UpperCAmelCase_  ,  'test'							))							)


if __name__ == "__main__":
					run_gradient_descent()
					print('\nTesting gradient descent for a linear hypothesis function.\n')
					test_gradient_descent()







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case_			:     Any  	=  {
    'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
    'tokenization_mvp': ['MvpTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     Dict  	=  ['MvpTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     List[Any]  	=  [
					    'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'MvpForCausalLM',
					    'MvpForConditionalGeneration',
					    'MvpForQuestionAnswering',
					    'MvpForSequenceClassification',
					    'MvpModel',
					    'MvpPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
					from .tokenization_mvp import MvpTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mvp_fast import MvpTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mvp import (
										    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MvpForCausalLM,
										    MvpForConditionalGeneration,
										    MvpForQuestionAnswering,
										    MvpForSequenceClassification,
										    MvpModel,
										    MvpPreTrainedModel,
										)

else:
					import sys

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


