




'''simple docstring'''
# 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(							SCREAMING_SNAKE_CASE_     )		->		Optional[Any]:
			return 1 / (1 + np.exp(-z     ))





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		int:
			return (-y * np.log(SCREAMING_SNAKE_CASE_     ) - (1 - y) * np.log(1 - h     )).mean()





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		str:
			A_									= np.dot(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )
			return np.sum(y * scores - np.log(1 + np.exp(SCREAMING_SNAKE_CASE_     )     )     )





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_=7_0000     )		->		Any:
			A_									= np.zeros(x.shape[1]     )

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


# In[68]:

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

							_SCREAMING_SNAKE_CASE			=						0.1
							_SCREAMING_SNAKE_CASE			=						logistic_reg(alpha, x, y, max_iterations=7_00_00)
							print("theta: ", theta)  # printing the theta i.e our weights vector





							def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		Tuple:
										return sigmoid_function(
										    np.dot(SCREAMING_SNAKE_CASE_       ,SCREAMING_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")
							((_SCREAMING_SNAKE_CASE)			,    (_SCREAMING_SNAKE_CASE))			=						(x[:, 0].min(), x[:, 0].max())
							((_SCREAMING_SNAKE_CASE)			,    (_SCREAMING_SNAKE_CASE))			=						(x[:, 1].min(), x[:, 1].max())
							((_SCREAMING_SNAKE_CASE)			,    (_SCREAMING_SNAKE_CASE))			=						np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
							_SCREAMING_SNAKE_CASE			=						np.c_[xxa.ravel(), xxa.ravel()]
							_SCREAMING_SNAKE_CASE			=						predict_prob(grid).reshape(xxa.shape)
							plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="black")

							plt.legend()






							plt.show()










'''simple docstring'''
import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict





@pytest.mark.parametrize(
    'split_dict'       ,[
        SplitDict(),
        SplitDict({'train': SplitInfo(name='train'       ,num_bytes=1337       ,num_examples=42       ,dataset_name='my_dataset'     )}     ),
        SplitDict({'train': SplitInfo(name='train'       ,num_bytes=1337       ,num_examples=42     )}     ),
        SplitDict({'train': SplitInfo()}     ),
    ]       ,)
def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		List[str]:
			A_									= split_dict._to_yaml_list()
			assert len(SCREAMING_SNAKE_CASE_     ) == len(SCREAMING_SNAKE_CASE_     )
			A_									= SplitDict._from_yaml_list(SCREAMING_SNAKE_CASE_     )
			for split_name, split_info in split_dict.items():
						# dataset_name field is deprecated, and is therefore not part of the YAML dump
						A_									= None
						# the split name of split_dict takes over the name of the split info object
						A_									= split_name
			assert split_dict == reloaded






@pytest.mark.parametrize(
    'split_info'       ,[SplitInfo(), SplitInfo(dataset_name=SCREAMING_SNAKE_CASE_     ), SplitInfo(dataset_name='my_dataset'     )]     )
def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		Optional[Any]:
			# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
			# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
			A_									= asdict(SplitDict({'train': split_info}     )     )
			assert "dataset_name" in split_dict_asdict["train"]
			assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name




