






"""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 						lowercase__(A						)    ->Optional[Any]:




						"""simple docstring"""



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





def 						lowercase__(A ,		A						)    ->Tuple:




						"""simple docstring"""



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





def 						lowercase__(A ,		A ,		A						)    ->Dict:




						"""simple docstring"""



						lowercase__    :				Any=	np.dot(A ,		A						)
						return np.sum(y * scores - np.log(1 + np.exp(A						)						)						)





def 						lowercase__(A ,		A ,		A ,		A=70_000						)    ->str:




						"""simple docstring"""



						lowercase__    :				str=	np.zeros(x.shape[1]						)

						for iterations in range(A						):
												lowercase__    :				Union[str, Any]=	np.dot(A ,		A						)
												lowercase__    :				Optional[int]=	sigmoid_function(A						)
												lowercase__    :				str=	np.dot(x.T ,		h - y						) / y.size
												lowercase__    :				str=	theta - alpha * gradient  # updating the weights
												lowercase__    :				List[Any]=	np.dot(A ,		A						)
												lowercase__    :				Tuple=	sigmoid_function(A						)
												lowercase__    :				Tuple=	cost_function(A ,		A						)
												if iterations % 100 == 0:
																		print(f'''loss: {j} \t'''						)  # printing the loss after every 100 iterations
						return theta


# In[68]:

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

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





			def 						lowercase__(A						)    ->Dict:




									"""simple docstring"""



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

			plt.legend()







			plt.show()














"""simple docstring"""







from pathlib import Path

import fire
from tqdm import tqdm





def 						lowercase__(A="ro" ,		A="en" ,		A="wmt16" ,		A=None						)    ->None:




						"""simple docstring"""



						try:
												import datasets
						except (ModuleNotFoundError, ImportError):
												raise ImportError("run pip install datasets"						)
						lowercase__    :				int=	f'''{src_lang}-{tgt_lang}'''
						print(f'''Converting {dataset}-{pair}'''						)
						lowercase__    :				List[Any]=	datasets.load_dataset(A ,		A						)
						if save_dir is None:
												lowercase__    :				Union[str, Any]=	f'''{dataset}-{pair}'''
						lowercase__    :				str=	Path(A						)
						save_dir.mkdir(exist_ok=A						)

						for split in ds.keys():
												print(f'''Splitting {split} with {ds[split].num_rows} records'''						)

												# to save to val.source, val.target like summary datasets
												lowercase__    :				Any=	"val" if split == "validation" else split
												lowercase__    :				List[Any]=	save_dir.joinpath(f'''{fn}.source'''						)
												lowercase__    :				Optional[Any]=	save_dir.joinpath(f'''{fn}.target'''						)
												lowercase__    :				Optional[int]=	src_path.open("w+"						)
												lowercase__    :				Any=	tgt_path.open("w+"						)

												# reader is the bottleneck so writing one record at a time doesn't slow things down
												for x in tqdm(ds[split]						):
																		lowercase__    :				int=	x["translation"]
																		src_fp.write(ex[src_lang] + "\n"						)
																		tgt_fp.write(ex[tgt_lang] + "\n"						)

						print(f'''Saved {dataset} dataset to {save_dir}'''						)


if __name__ == "__main__":
			fire.Fire(download_wmt_dataset)






