
"""simple docstring"""






import numpy

# List of input, output pairs
__UpperCAmelCase       =  (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
__UpperCAmelCase       =  (((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
__UpperCAmelCase       =  [2, 4, 1, 5]
__UpperCAmelCase       =  len(train_data)
__UpperCAmelCase       =  0.009






def 						_snake_case       (	lowercase__		:  List[str]      ,     lowercase__		:  str="train"		)  ->				Optional[int]:


			'''simple docstring'''





			return calculate_hypothesis_value(lowercase__      ,     lowercase__		) - output(
			    lowercase__      ,     lowercase__		)






def 						_snake_case       (	lowercase__		:  Optional[Any]		)  ->				List[Any]:


			'''simple docstring'''





			lowerCAmelCase_    :Union[str, Any]			     =  0
			for i in range(len(lowercase__		) - 1		):
						hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
			hyp_val += parameter_vector[0]
			return hyp_val






def 						_snake_case       (	lowercase__		:  str      ,     lowercase__		:  List[str]		)  ->				Optional[Any]:


			'''simple docstring'''





			if data_set == "train":
						return train_data[example_no][1]
			elif data_set == "test":
						return test_data[example_no][1]
			return None






def 						_snake_case       (	lowercase__		:  List[str]      ,     lowercase__		:  Optional[Any]		)  ->				List[str]:


			'''simple docstring'''





			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 						_snake_case       (	lowercase__		:  int      ,     lowercase__		:  int=m		)  ->				str:


			'''simple docstring'''





			lowerCAmelCase_    :str			     =  0
			for i in range(lowercase__		):
						if index == -1:
									summation_value += _error(lowercase__		)
						else:
									summation_value += _error(lowercase__		) * train_data[i][0][index]
			return summation_value






def 						_snake_case       (	lowercase__		:  Optional[int]		)  ->				Any:


			'''simple docstring'''





			lowerCAmelCase_    :List[Any]			     =  summation_of_cost_derivative(lowercase__      ,     lowercase__		) / m
			return cost_derivative_value






def 						_snake_case       (	)  ->				List[Any]:


			'''simple docstring'''





			global parameter_vector
			# Tune these values to set a tolerance value for predicted output
			lowerCAmelCase_    :Union[str, Any]			     =  0.000002
			lowerCAmelCase_    :Optional[Any]			     =  0
			lowerCAmelCase_    :int			     =  0
			while True:
						j += 1
						lowerCAmelCase_    :List[Any]			     =  [0, 0, 0, 0]
						for i in range(0      ,     len(lowercase__		)		):
									lowerCAmelCase_    :Any			     =  get_cost_derivative(i - 1		)
									lowerCAmelCase_    :Optional[int]			     =  (
									    parameter_vector[i] - LEARNING_RATE * cost_derivative
									)
						if numpy.allclose(
						    lowercase__      ,     lowercase__      ,     atol=lowercase__      ,     rtol=lowercase__      ,     ):
									break
						lowerCAmelCase_    :Optional[int]			     =  temp_parameter_vector
			print(("""Number of iterations:""", j)		)






def 						_snake_case       (	)  ->				Dict:


			'''simple docstring'''





			for i in range(len(lowercase__		)		):
						print(("""Actual output value:""", output(lowercase__      ,     """test"""		))		)
						print(("""Hypothesis output:""", calculate_hypothesis_value(lowercase__      ,     """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


UpperCAmelCase :       Union[str, Any]			   =     {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase :       Union[str, Any]			   =     [
      'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
      'SEWForCTC',
      'SEWForSequenceClassification',
      'SEWModel',
      'SEWPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_sew import (
        SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
        SEWForCTC,
        SEWForSequenceClassification,
        SEWModel,
        SEWPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase :       int			   =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






