



"""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    (		__UpperCamelCase   :      int     ):


						'''simple docstring'''






						return 1 / (1 + np.exp(-z     ))
def 						__lowerCAmelCase    (		__UpperCamelCase   :      Optional[Any]       ,			__UpperCamelCase   :      int     ):


						'''simple docstring'''






						return (-y * np.log(__UpperCamelCase     ) - (1 - y) * np.log(1 - h     )).mean()
def 						__lowerCAmelCase    (		__UpperCamelCase   :      Optional[Any]       ,			__UpperCamelCase   :      List[Any]       ,			__UpperCamelCase   :      Dict     ):


						'''simple docstring'''






						snake_case_ :			Optional[int]            =      np.dot(__UpperCamelCase       ,			__UpperCamelCase     )
						return np.sum(y * scores - np.log(1 + np.exp(__UpperCamelCase     )     )     )
def 						__lowerCAmelCase    (		__UpperCamelCase   :      Optional[Any]       ,			__UpperCamelCase   :      int       ,			__UpperCamelCase   :      int       ,			__UpperCamelCase   :      int=7_0_0_0_0     ):


						'''simple docstring'''






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

						for iterations in range(__UpperCamelCase     ):
												snake_case_ :			Any            =      np.dot(__UpperCamelCase       ,			__UpperCamelCase     )
												snake_case_ :			List[str]            =      sigmoid_function(__UpperCamelCase     )
												snake_case_ :			Optional[Any]            =      np.dot(x.T       ,			h - y     ) / y.size
												snake_case_ :			str            =      theta - alpha * gradient  # updating the weights
												snake_case_ :			int            =      np.dot(__UpperCamelCase       ,			__UpperCamelCase     )
												snake_case_ :			List[str]            =      sigmoid_function(__UpperCamelCase     )
												snake_case_ :			Dict            =      cost_function(__UpperCamelCase       ,			__UpperCamelCase     )
												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__":
					__lowerCAmelCase							:       Any          =       datasets.load_iris()
					__lowerCAmelCase							:       List[Any]          =       iris.data[:, :2]
					__lowerCAmelCase							:       Tuple          =       (iris.target != 0) * 1

					__lowerCAmelCase							:       Any          =       0.1
					__lowerCAmelCase							:       List[Any]          =       logistic_reg(alpha, x, y, max_iterations=7_0000)
					print('''theta: ''', theta)  # printing the theta i.e our weights vector
					def 						__lowerCAmelCase    (		__UpperCamelCase   :      List[str]     ):


											'''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''')
					((__lowerCAmelCase)       ,			(__lowerCAmelCase))							:       Union[str, Any]          =       (x[:, 0].min(), x[:, 0].max())
					((__lowerCAmelCase)       ,			(__lowerCAmelCase))							:       Tuple          =       (x[:, 1].min(), x[:, 1].max())
					((__lowerCAmelCase)       ,			(__lowerCAmelCase))							:       Optional[Any]          =       np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
					__lowerCAmelCase							:       Any          =       np.c_[xxa.ravel(), xxa.ravel()]
					__lowerCAmelCase							:       Optional[int]          =       predict_prob(grid).reshape(xxa.shape)
					plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')

					plt.legend()






					plt.show()





'''simple docstring'''






import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType




class 						a__							:





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_=2   ,       lowerCamelCase_=3   ,       lowerCamelCase_=64   ,       lowerCamelCase_=None  )			->				Dict:
														lowerCAmelCase__			  =      np.random.default_rng(lowerCamelCase_  )
														lowerCAmelCase__			  =      length
														lowerCAmelCase__			  =      rng.normal(size=(length,)  ).astype(np.floataa  )
														lowerCAmelCase__			  =      a * self.x + b + rng.normal(scale=0.1   ,       size=(length,)  ).astype(np.floataa  )







							def __len__(						self  )			->				Any:
														return self.length







							def __getitem__(						self   ,       lowerCamelCase_  )			->				List[str]:
														return {"x": self.x[i], "y": self.y[i]}




class 						a__							(				torch.nn.Module    ):





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_=0   ,       lowerCamelCase_=0   ,       lowerCamelCase_=False  )			->				List[Any]:
														super().__init__()
														lowerCAmelCase__			  =      torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
														lowerCAmelCase__			  =      torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
														lowerCAmelCase__			  =      True







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_=None  )			->				Optional[Any]:
														if self.first_batch:
																					print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""  )
																					lowerCAmelCase__			  =      False
														return x * self.a[0] + self.b[0]




class 						a__							(				torch.nn.Module    ):





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_=0   ,       lowerCamelCase_=0   ,       lowerCamelCase_=False  )			->				Any:
														super().__init__()
														lowerCAmelCase__			  =      torch.nn.Parameter(torch.tensor(lowerCamelCase_  ).float()  )
														lowerCAmelCase__			  =      torch.nn.Parameter(torch.tensor(lowerCamelCase_  ).float()  )
														lowerCAmelCase__			  =      True







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_=None  )			->				Any:
														if self.first_batch:
																					print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""  )
																					lowerCAmelCase__			  =      False
														return x * self.a + self.b


def    _snake_case  (	A					,       A = 16			)		->       Any:
							from datasets import load_dataset
							from transformers import AutoTokenizer

							lowerCAmelCase__			  =      AutoTokenizer.from_pretrained('''bert-base-cased'''			)
							lowerCAmelCase__			  =      {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
							lowerCAmelCase__			  =      load_dataset('''csv'''					,       data_files=A			)
							lowerCAmelCase__			  =      datasets['''train'''].unique('''label'''			)

							lowerCAmelCase__			  =      {v: i for i, v in enumerate(A			)}

							def tokenize_function(A			):
														# max_length=None => use the model max length (it's actually the default)
														lowerCAmelCase__			  =      tokenizer(
														    examples['''sentence1''']					,       examples['''sentence2''']					,       truncation=A					,       max_length=A					,       padding='''max_length'''			)
														if "label" in examples:
																					lowerCAmelCase__			  =      [label_to_id[l] for l in examples['''label''']]
														return outputs

							# Apply the method we just defined to all the examples in all the splits of the dataset
							lowerCAmelCase__			  =      datasets.map(
							    A					,       batched=A					,       remove_columns=['''sentence1''', '''sentence2''', '''label''']					,       )

							def collate_fn(A			):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														if accelerator.distributed_type == DistributedType.TPU:
																					return tokenizer.pad(A					,       padding='''max_length'''					,       max_length=128					,       return_tensors='''pt'''			)
														return tokenizer.pad(A					,       padding='''longest'''					,       return_tensors='''pt'''			)

							# Instantiate dataloaders.
							lowerCAmelCase__			  =      DataLoader(tokenized_datasets['''train''']					,       shuffle=A					,       collate_fn=A					,       batch_size=2			)
							lowerCAmelCase__			  =      DataLoader(tokenized_datasets['''validation''']					,       shuffle=A					,       collate_fn=A					,       batch_size=1			)

							return train_dataloader, eval_dataloader