
'''simple docstring'''



import numpy

# List of input, output pairs
__A        =     (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
__A        =     (((515, 22, 13), 555), ((61, 35, 49), 150))
__A        =     [2, 4, 1, 5]
__A        =     len(train_data)
__A        =     0.009





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





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





def 			_A		(				lowercase__		,		lowercase__						):
						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		(				lowercase__		,		lowercase__=m						):
						lowercase__							=							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		(				lowercase__						):
						lowercase__							=							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
						lowercase__							=							0.0_0_0_0_0_2
						lowercase__							=							0
						lowercase__							=							0
						while True:
												j += 1
												lowercase__							=							[0, 0, 0, 0]
												for i in range(0		,		len(UpperCAmelCase_						)						):
																		lowercase__							=							get_cost_derivative(i - 1						)
																		lowercase__							=							(
																		    parameter_vector[i] - LEARNING_RATE * cost_derivative
																		)
												if numpy.allclose(
												    UpperCAmelCase_		,		UpperCAmelCase_		,		atol=UpperCAmelCase_		,		rtol=UpperCAmelCase_		,		):
																		break
												lowercase__							=							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_torch_available, is_vision_available


snake_case_					:      List[Any]    			=    {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      str    			=    [
					    'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'VanForImageClassification',
					    'VanModel',
					    'VanPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_van import (
										    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    VanForImageClassification,
										    VanModel,
										    VanPreTrainedModel,
										)

else:
					import sys

					snake_case_					:      List[Any]    			=    _LazyModule(__name__, globals()['__file__'], _import_structure)


