
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_ 				=			{"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase_ 				=			[
       """FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """FocalNetForImageClassification""",
       """FocalNetForMaskedImageModeling""",
       """FocalNetBackbone""",
       """FocalNetModel""",
       """FocalNetPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_focalnet import (
          FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          FocalNetBackbone,
          FocalNetForImageClassification,
          FocalNetForMaskedImageModeling,
          FocalNetModel,
          FocalNetPreTrainedModel,
      )

else:
   import sys

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



# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

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





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






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





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






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





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






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





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__=70000 ):







							'''simple docstring'''






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

							for iterations in range(UpperCamelCase__ ):
														snake_case_     							=       np.dot(UpperCamelCase__  ,				UpperCamelCase__ )
														snake_case_     							=       sigmoid_function(UpperCamelCase__ )
														snake_case_     							=       np.dot(x.T  ,				h - y ) / y.size
														snake_case_     							=       theta - alpha * gradient  # updating the weights
														snake_case_     							=       np.dot(UpperCamelCase__  ,				UpperCamelCase__ )
														snake_case_     							=       sigmoid_function(UpperCamelCase__ )
														snake_case_     							=       cost_function(UpperCamelCase__  ,				UpperCamelCase__ )
														if iterations % 100 == 0:
																					print(F'''loss: {j} \t''' )  # printing the loss after every 100 iterations
							return theta


# In[68]:

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

					_UpperCAmelCase						:  Optional[int]									=			0.1
					_UpperCAmelCase						:  Dict									=			logistic_reg(alpha, x, y, max_iterations=7_0000)
					print("""theta: """, theta)  # printing the theta i.e our weights vector





					def    __lowerCamelCase      (					UpperCamelCase__ ):







												'''simple docstring'''






												return sigmoid_function(
												    np.dot(UpperCamelCase__  ,				UpperCamelCase__ ) )  # 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""")
					((_UpperCAmelCase)							,		(_UpperCAmelCase))						:  Any									=			(x[:, 0].min(), x[:, 0].max())
					((_UpperCAmelCase)							,		(_UpperCAmelCase))						:  Union[str, Any]									=			(x[:, 1].min(), x[:, 1].max())
					((_UpperCAmelCase)							,		(_UpperCAmelCase))						:  int									=			np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
					_UpperCAmelCase						:  List[Any]									=			np.c_[xxa.ravel(), xxa.ravel()]
					_UpperCAmelCase						:  Dict									=			predict_prob(grid).reshape(xxa.shape)
					plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

					plt.legend()


					plt.show()




