




import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			Optional[Any]:



       """simple docstring"""

       return 1 / (1 + np.exp(-z		))







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			List[Any]:



       """simple docstring"""

       return (-y * np.log(lowerCamelCase_		) - (1 - y) * np.log(1 - h		)).mean()







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_		)  ->			List[str]:



       """simple docstring"""

       A__ 					=       np.dot(lowerCamelCase_					,							lowerCamelCase_		)
       return np.sum(y * scores - np.log(1 + np.exp(lowerCamelCase_		)		)		)







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_					,							lowercase_=70_000		)  ->			int:



       """simple docstring"""

       A__ 					=       np.zeros(x.shape[1]		)

       for iterations in range(lowerCamelCase_		):
              A__ 					=       np.dot(lowerCamelCase_					,							lowerCamelCase_		)
              A__ 					=       sigmoid_function(lowerCamelCase_		)
              A__ 					=       np.dot(x.T					,							h - y		) / y.size
              A__ 					=       theta - alpha * gradient  # updating the weights
              A__ 					=       np.dot(lowerCamelCase_					,							lowerCamelCase_		)
              A__ 					=       sigmoid_function(lowerCamelCase_		)
              A__ 					=       cost_function(lowerCamelCase_					,							lowerCamelCase_		)
              if iterations % 100 == 0:
                     print(f"""loss: {j} \t"""		)  # printing the loss after every 100 iterations
       return theta


# In[68]:

if __name__ == "__main__":
  _lowerCamelCase      :						List[str]							=  datasets.load_iris()
  _lowerCamelCase      :						Optional[Any]							=  iris.data[:, :2]
  _lowerCamelCase      :						Any							=  (iris.target != 0) * 1

  _lowerCamelCase      :						int							=  0.1
  _lowerCamelCase      :						Dict							=  logistic_reg(alpha, x, y, max_iterations=70000)
  print("""theta: """, theta)  # printing the theta i.e our weights vector







  def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			str:



         """simple docstring"""

         return sigmoid_function(
             np.dot(lowerCamelCase_					,							lowerCamelCase_		)		)  # predicting the value of probability from the logistic regression algorithm

  plt.figure(figsize=(10, 6))
  plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color="""b""", label="""0""")
  plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color="""r""", label="""1""")
  ((_lowerCamelCase)			,     (_lowerCamelCase))      :						int							=  (x[:, 0].min(), x[:, 0].max())
  ((_lowerCamelCase)			,     (_lowerCamelCase))      :						Optional[Any]							=  (x[:, 1].min(), x[:, 1].max())
  ((_lowerCamelCase)			,     (_lowerCamelCase))      :						Union[str, Any]							=  np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
  _lowerCamelCase      :						Dict							=  np.c_[xxa.ravel(), xxa.ravel()]
  _lowerCamelCase      :						List[str]							=  predict_prob(grid).reshape(xxa.shape)
  plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

  plt.legend()
  plt.show()









'''simple docstring'''




from typing import TYPE_CHECKING

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


__UpperCAmelCase            =       {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCAmelCase            =       ["""GPTNeoXTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCAmelCase            =       [
							    """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """GPTNeoXForCausalLM""",
							    """GPTNeoXForQuestionAnswering""",
							    """GPTNeoXForSequenceClassification""",
							    """GPTNeoXForTokenClassification""",
							    """GPTNeoXLayer""",
							    """GPTNeoXModel""",
							    """GPTNeoXPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gpt_neox import (
														    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTNeoXForCausalLM,
														    GPTNeoXForQuestionAnswering,
														    GPTNeoXForSequenceClassification,
														    GPTNeoXForTokenClassification,
														    GPTNeoXLayer,
														    GPTNeoXModel,
														    GPTNeoXPreTrainedModel,
														)


else:
							import sys

							__UpperCAmelCase            =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



