





# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


UpperCAmelCase	:	List[Any]		     =       logging.get_logger(__name__)

UpperCAmelCase	:	Dict[Optional[str], Type[Formatter]]		     =       {}
UpperCAmelCase	:	Dict[Optional[str], str]		     =       {}
UpperCAmelCase	:	Dict[Optional[str], Exception]		     =       {}
def      _A       (		SCREAMING_SNAKE_CASE     :   type       ,       SCREAMING_SNAKE_CASE     :   Optional[str]       ,       SCREAMING_SNAKE_CASE     :   Optional[List[str]] = None       ,       ):





  """simple docstring"""



  a__	:					Optional[int]										=aliases if aliases is not None else []
  if format_type in _FORMAT_TYPES:
    logger.warning(
        f'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''						)
  a__	:					Tuple										=formatter_cls
  for alias in set(aliases + [format_type]						):
    if alias in _FORMAT_TYPES_ALIASES:
      logger.warning(
          f'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''						)
    a__	:					int										=format_type
def      _A       (		SCREAMING_SNAKE_CASE     :   Exception       ,       SCREAMING_SNAKE_CASE     :   Optional[str]       ,       SCREAMING_SNAKE_CASE     :   Optional[List[str]] = None						):





  """simple docstring"""



  a__	:					Tuple										=aliases if aliases is not None else []
  for alias in set(aliases + [format_type]						):
    a__	:					str										=unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=["""python"""])
_register_formatter(ArrowFormatter, """arrow""", aliases=["""pa""", """pyarrow"""])
_register_formatter(NumpyFormatter, """numpy""", aliases=["""np"""])
_register_formatter(PandasFormatter, """pandas""", aliases=["""pd"""])
_register_formatter(CustomFormatter, """custom""")

if config.TORCH_AVAILABLE:
     from .torch_formatter import TorchFormatter

     _register_formatter(TorchFormatter, """torch""", aliases=["""pt""", """pytorch"""])
else:
     UpperCAmelCase	:	int		     =       ValueError("""PyTorch needs to be installed to be able to return PyTorch tensors.""")
     _register_unavailable_formatter(_torch_error, """torch""", aliases=["""pt""", """pytorch"""])

if config.TF_AVAILABLE:
     from .tf_formatter import TFFormatter

     _register_formatter(TFFormatter, """tensorflow""", aliases=["""tf"""])
else:
     UpperCAmelCase	:	Dict		     =       ValueError("""Tensorflow needs to be installed to be able to return Tensorflow tensors.""")
     _register_unavailable_formatter(_tf_error, """tensorflow""", aliases=["""tf"""])

if config.JAX_AVAILABLE:
     from .jax_formatter import JaxFormatter

     _register_formatter(JaxFormatter, """jax""", aliases=[])
else:
     UpperCAmelCase	:	List[Any]		     =       ValueError("""JAX needs to be installed to be able to return JAX arrays.""")
     _register_unavailable_formatter(_jax_error, """jax""", aliases=[])
def      _A       (		SCREAMING_SNAKE_CASE     :   Optional[str]						):





  """simple docstring"""



  if format_type in _FORMAT_TYPES_ALIASES:
    return _FORMAT_TYPES_ALIASES[format_type]
  else:
    return format_type





def      _A       (		SCREAMING_SNAKE_CASE     :   Optional[str]       ,       **SCREAMING_SNAKE_CASE     :   Tuple						):





  """simple docstring"""



  a__	:					Optional[Any]										=get_format_type_from_alias(SCREAMING_SNAKE_CASE						)
  if format_type in _FORMAT_TYPES:
    return _FORMAT_TYPES[format_type](**SCREAMING_SNAKE_CASE						)
  if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
    raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
  else:
    raise ValueError(
        f'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None						)}, but got \'{format_type}\''''						)













import numpy

# List of input, output pairs
UpperCAmelCase	:	str		     =       (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
UpperCAmelCase	:	Optional[int]		     =       (((515, 22, 13), 555), ((61, 35, 49), 150))
UpperCAmelCase	:	str		     =       [2, 4, 1, 5]
UpperCAmelCase	:	List[str]		     =       len(train_data)
UpperCAmelCase	:	Dict		     =       0.0_0_9
def      _A       (		SCREAMING_SNAKE_CASE     :   Tuple       ,       SCREAMING_SNAKE_CASE     :   Tuple="train"						):





  """simple docstring"""



  return calculate_hypothesis_value(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						) - output(
      SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
def      _A       (		SCREAMING_SNAKE_CASE     :   Tuple						):





  """simple docstring"""



  a__	:					Tuple										=0
  for i in range(len(SCREAMING_SNAKE_CASE						) - 1						):
    hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
  hyp_val += parameter_vector[0]
  return hyp_val
def      _A       (		SCREAMING_SNAKE_CASE     :   Dict       ,       SCREAMING_SNAKE_CASE     :   Dict						):





  """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      _A       (		SCREAMING_SNAKE_CASE     :   Optional[Any]       ,       SCREAMING_SNAKE_CASE     :   Tuple						):





  """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      _A       (		SCREAMING_SNAKE_CASE     :   Union[str, Any]       ,       SCREAMING_SNAKE_CASE     :   int=m						):





  """simple docstring"""



  a__	:					Any										=0
  for i in range(SCREAMING_SNAKE_CASE						):
    if index == -1:
      summation_value += _error(SCREAMING_SNAKE_CASE						)
    else:
      summation_value += _error(SCREAMING_SNAKE_CASE						) * train_data[i][0][index]
  return summation_value
def      _A       (		SCREAMING_SNAKE_CASE     :   int						):





  """simple docstring"""



  a__	:					Any										=summation_of_cost_derivative(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						) / m
  return cost_derivative_value
def      _A       (		):





  """simple docstring"""



  global parameter_vector
  # Tune these values to set a tolerance value for predicted output
  a__	:					Dict										=0.0_0_0_0_0_2
  a__	:					Union[str, Any]										=0
  a__	:					Any										=0
  while True:
    j += 1
    a__	:					Any										=[0, 0, 0, 0]
    for i in range(0       ,       len(SCREAMING_SNAKE_CASE						)						):
      a__	:					Tuple										=get_cost_derivative(i - 1						)
      a__	:					List[Any]										=(
          parameter_vector[i] - LEARNING_RATE * cost_derivative
      )
    if numpy.allclose(
        SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       atol=SCREAMING_SNAKE_CASE       ,       rtol=SCREAMING_SNAKE_CASE       ,       ):
      break
    a__	:					Optional[Any]										=temp_parameter_vector
  print(("Number of iterations:", j)						)
def      _A       (		):





  """simple docstring"""



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


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






