

# 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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					List[Any]:
	return 1 / (1 + np.exp(-z      ))






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					Optional[int]:
	return (-y * np.log(snake_case__      ) - (1 - y) * np.log(1 - h      )).mean()






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					List[str]:
	__lowerCamelCase    :    Optional[Any] 	=			np.dot(snake_case__     ,			snake_case__      )
	return np.sum(y * scores - np.log(1 + np.exp(snake_case__      )      )      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__=7_0_0_0_0      )  ->					Tuple:
	__lowerCamelCase    :    Dict 	=			np.zeros(x.shape[1]      )

	for iterations in range(snake_case__      ):
		__lowerCamelCase    :    Optional[Any] 	=			np.dot(snake_case__     ,			snake_case__      )
		__lowerCamelCase    :    int 	=			sigmoid_function(snake_case__      )
		__lowerCamelCase    :    str 	=			np.dot(x.T     ,			h - y      ) / y.size
		__lowerCamelCase    :    Optional[Any] 	=			theta - alpha * gradient  # updating the weights
		__lowerCamelCase    :    List[Any] 	=			np.dot(snake_case__     ,			snake_case__      )
		__lowerCamelCase    :    Tuple 	=			sigmoid_function(snake_case__      )
		__lowerCamelCase    :    int 	=			cost_function(snake_case__     ,			snake_case__      )
		if iterations % 1_0_0 == 0:
			print(F"loss: {j} \t"      )  # printing the loss after every 100 iterations
	return theta


# In[68]:

if __name__ == "__main__":
		a		=datasets.load_iris()
		a		=iris.data[:, :2]
		a		=(iris.target != 0) * 1

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






		def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Union[str, Any]:
			return sigmoid_function(
			    np.dot(snake_case__     ,			snake_case__      )      )  # 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""")
		((a)		,	(a))		=(x[:, 0].min(), x[:, 0].max())
		((a)		,	(a))		=(x[:, 1].min(), x[:, 1].max())
		((a)		,	(a))		=np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
		a		=np.c_[xxa.ravel(), xxa.ravel()]
		a		=predict_prob(grid).reshape(xxa.shape)
		plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

		plt.legend()

		plt.show()







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a		={
    """configuration_pix2struct""": [
        """PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Pix2StructConfig""",
        """Pix2StructTextConfig""",
        """Pix2StructVisionConfig""",
    ],
    """processing_pix2struct""": ["""Pix2StructProcessor"""],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=["""Pix2StructImageProcessor"""]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """Pix2StructPreTrainedModel""",
					    """Pix2StructForConditionalGeneration""",
					    """Pix2StructVisionModel""",
					    """Pix2StructTextModel""",
					]

if TYPE_CHECKING:
					from .configuration_pixastruct import (
					    PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    PixaStructConfig,
					    PixaStructTextConfig,
					    PixaStructVisionConfig,
					)
					from .processing_pixastruct import PixaStructProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_pixastruct import PixaStructImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_pixastruct import (
										    PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    PixaStructForConditionalGeneration,
										    PixaStructPreTrainedModel,
										    PixaStructTextModel,
										    PixaStructVisionModel,
										)

else:
					import sys

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




