
# 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			(			a__  )       ->  Dict:
				return 1 / (1 + np.exp(-z  ))


def     __lowerCAmelCase			(			a__						,    a__  )       ->  Tuple:
				return (-y * np.log(a__  ) - (1 - y) * np.log(1 - h  )).mean()


def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  List[Any]:
				__a										=    np.dot(a__						,    a__  )
				return np.sum(y * scores - np.log(1 + np.exp(a__  )  )  )


def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__=7_0000  )       ->  Tuple:
				__a										=    np.zeros(x.shape[1]  )

				for iterations in range(a__  ):
								__a										=    np.dot(a__						,    a__  )
								__a										=    sigmoid_function(a__  )
								__a										=    np.dot(x.T						,    h - y  ) / y.size
								__a										=    theta - alpha * gradient  # updating the weights
								__a										=    np.dot(a__						,    a__  )
								__a										=    sigmoid_function(a__  )
								__a										=    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     :	List[Any]    = datasets.load_iris()
					A     :	Any    = iris.data[:, :2]
					A     :	int    = (iris.target != 0) * 1

					A     :	Dict    = 0.1
					A     :	str    = logistic_reg(alpha, x, y, max_iterations=7_0_0_0_0)
					print('theta: ', theta)  # printing the theta i.e our weights vector


					def     __lowerCAmelCase			(			a__  )       ->  Union[str, Any]:
									return sigmoid_function(
									    np.dot(a__						,    a__  )  )  # predicting the value of probability from the logistic regression algorithm

					plt.figure(figsize=(1_0, 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))     :	Tuple    = (x[:, 0].min(), x[:, 0].max())
					((A) ,   (A))     :	int    = (x[:, 1].min(), x[:, 1].max())
					((A) ,   (A))     :	List[str]    = np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
					A     :	int    = np.c_[xxa.ravel(), xxa.ravel()]
					A     :	Any    = 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_speech_available, is_torch_available


__A        =    {
    "configuration_audio_spectrogram_transformer": [
        "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ASTConfig",
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__A        =    [
					    "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "ASTForAudioClassification",
					    "ASTModel",
					    "ASTPreTrainedModel",
					]

try:
					if not is_speech_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__A        =    ["ASTFeatureExtractor"]

if TYPE_CHECKING:
					from .configuration_audio_spectrogram_transformer import (
					    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    ASTConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_audio_spectrogram_transformer import (
										    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ASTForAudioClassification,
										    ASTModel,
										    ASTPreTrainedModel,
										)

					try:
										if not is_speech_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
					import sys

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


