



'''simple docstring'''






import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets


def 				snake_case_  (      _lowerCAmelCase						:		List[Any]						)   ->     Tuple:
							return 1 / (1 + np.exp(-z						))


def 				snake_case_  (      _lowerCAmelCase						:		List[str]     ,   _lowerCAmelCase						:		Union[str, Any]						)   ->     int:
							return (-y * np.log(_A						) - (1 - y) * np.log(1 - h						)).mean()


def 				snake_case_  (      _lowerCAmelCase						:		Any     ,   _lowerCAmelCase						:		str     ,   _lowerCAmelCase						:		Tuple						)   ->     Dict:
							UpperCAmelCase				: List[Any]											=	np.dot(_A     ,   _A						)
							return np.sum(y * scores - np.log(1 + np.exp(_A						)						)						)


def 				snake_case_  (      _lowerCAmelCase						:		Tuple     ,   _lowerCAmelCase						:		Optional[Any]     ,   _lowerCAmelCase						:		Any     ,   _lowerCAmelCase						:		Tuple=70000						)   ->     int:
							UpperCAmelCase				: List[Any]											=	np.zeros(x.shape[1]						)

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

			UpperCamelCase__:     List[Any]										=    0.1
			UpperCamelCase__:     Optional[int]										=    logistic_reg(alpha, x, y, max_iterations=70000)
			print("theta: ", theta)  # printing the theta i.e our weights vector


			def 				snake_case_  (      _lowerCAmelCase						:		Dict						)   ->     str:
										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")
			(UpperCamelCase__):     str										=    (x[:, 0].min(), x[:, 0].max())
			(UpperCamelCase__):     Tuple										=    (x[:, 1].min(), x[:, 1].max())
			(UpperCamelCase__):     List[str]										=    np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
			UpperCamelCase__:     Any										=    np.c_[xxa.ravel(), xxa.ravel()]
			UpperCamelCase__:     str										=    predict_prob(grid).reshape(xxa.shape)
			plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="black")

			plt.legend()




			plt.show()










'''simple docstring'''






import math

import tensorflow as tf
from packaging import version


def 				snake_case_  (      _lowerCAmelCase						:		List[str]						)   ->     Dict:
							UpperCAmelCase				: Tuple											=	tf.convert_to_tensor(_lowerCAmelCase						)
							UpperCAmelCase				: Dict											=	0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0						)     ,   x.dtype						)						))

							return x * cdf


def 				snake_case_  (      _lowerCAmelCase						:		int						)   ->     List[Any]:
							UpperCAmelCase				: str											=	tf.convert_to_tensor(_lowerCAmelCase						)
							UpperCAmelCase				: Union[str, Any]											=	tf.cast(math.pi     ,   x.dtype						)
							UpperCAmelCase				: Tuple											=	tf.cast(0.0_4_4_7_1_5     ,   x.dtype						)
							UpperCAmelCase				: List[Any]											=	0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi						) * (x + coeff * tf.pow(_lowerCAmelCase     ,   3						))						))

							return x * cdf


def 				snake_case_  (      _lowerCAmelCase						:		Optional[Any]						)   ->     Any:
							UpperCAmelCase				: List[Any]											=	tf.convert_to_tensor(_lowerCAmelCase						)

							return x * tf.tanh(tf.math.softplus(_lowerCAmelCase						)						)


def 				snake_case_  (      _lowerCAmelCase						:		str						)   ->     Tuple:
							UpperCAmelCase				: Union[str, Any]											=	tf.convert_to_tensor(_lowerCAmelCase						)
							UpperCAmelCase				: Union[str, Any]											=	tf.cast(0.0_4_4_7_1_5     ,   x.dtype						)
							UpperCAmelCase				: Tuple											=	tf.cast(0.7_9_7_8_8_4_5_6_0_8     ,   x.dtype						)

							return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)						))


def 				snake_case_  (      _lowerCAmelCase						:		Optional[Any]						)   ->     Optional[int]:
							UpperCAmelCase				: str											=	tf.convert_to_tensor(_lowerCAmelCase						)
							UpperCAmelCase				: Optional[int]											=	tf.cast(1.7_0_2     ,   x.dtype						)
							return x * tf.math.sigmoid(coeff * x						)


def 				snake_case_  (      _lowerCAmelCase						:		Any						)   ->     Any:
							return tf.clip_by_value(_gelu(_lowerCAmelCase						)     ,   -10     ,   10						)


def 				snake_case_  (      _lowerCAmelCase						:		Dict     ,   _lowerCAmelCase						:		Optional[Any]=-1						)   ->     Union[str, Any]:
							UpperCAmelCase							,	UpperCAmelCase				: Optional[Any]											=	tf.split(_lowerCAmelCase     ,   2     ,   axis=_lowerCAmelCase						)
							return a * tf.math.sigmoid(_lowerCAmelCase						)


if version.parse(tf.version.VERSION) >= version.parse("2.4"):


						def 				snake_case_  (      _lowerCAmelCase						:		Tuple						)   ->     Any:
													return tf.keras.activations.gelu(_lowerCAmelCase     ,   approximate=_lowerCAmelCase						)

						UpperCamelCase__:     List[Any]										=    tf.keras.activations.gelu




						UpperCamelCase__:     List[Any]										=    approximate_gelu_wrap
else:
						UpperCamelCase__:     List[str]										=    _gelu
						UpperCamelCase__:     Tuple										=    _gelu_new


UpperCamelCase__:     List[Any]										=    {
    "gelu": gelu,
    "gelu_10": gelu_aa,
    "gelu_fast": gelu_fast,
    "gelu_new": gelu_new,
    "glu": glu,
    "mish": mish,
    "quick_gelu": quick_gelu,
    "relu": tf.keras.activations.relu,
    "sigmoid": tf.keras.activations.sigmoid,
    "silu": tf.keras.activations.swish,
    "swish": tf.keras.activations.swish,
    "tanh": tf.keras.activations.tanh,
}




def 				snake_case_  (      _lowerCAmelCase						:		Dict						)   ->     List[Any]:
							if activation_string in ACTaFN:
														return ACTaFN[activation_string]
							else:
														raise KeyError(f"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()						)}"""						)





