

"""simple docstring"""







import matplotlib.pyplot as plt
import numpy as np


def   a__      (		UpperCAmelCase__       :			Dict		,			UpperCAmelCase__       :			str		,			UpperCAmelCase__       :			Any			)->	np.ndarray:
						UpperCAmelCase__						=		len(_UpperCAmelCase			)  # Number of training samples
						UpperCAmelCase__						=		np.eye(_UpperCAmelCase			)  # Initialize weights as identity matrix
						for j in range(_UpperCAmelCase			):
												UpperCAmelCase__						=		point - x_train[j]
												UpperCAmelCase__						=		np.exp(diff @ diff.T / (-2.0 * tau**2)			)

						return weights


def   a__      (		UpperCAmelCase__       :			Union[str, Any]		,			UpperCAmelCase__       :			int		,			UpperCAmelCase__       :			List[Any]		,			UpperCAmelCase__       :			Any			)->	np.ndarray:
						UpperCAmelCase__						=		weight_matrix(_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase			)
						UpperCAmelCase__						=		np.linalg.inv(x_train.T @ weight_mat @ x_train			) @ (
						    x_train.T @ weight_mat @ y_train.T
						)

						return weight


def   a__      (		UpperCAmelCase__       :			Dict		,			UpperCAmelCase__       :			List[Any]		,			UpperCAmelCase__       :			Optional[int]			)->	np.ndarray:
						UpperCAmelCase__						=		np.zeros(len(_UpperCAmelCase			)			)  # Initialize array of predictions
						for i, item in enumerate(_UpperCAmelCase			):
												UpperCAmelCase__						=		item @ local_weight(_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase			)

						return y_pred


def   a__      (		UpperCAmelCase__       :			int		,			UpperCAmelCase__       :			Optional[Any]		,			UpperCAmelCase__       :			List[str]			)->	tuple[np.ndarray, np.ndarray, np.ndarray]:
						import seaborn as sns

						UpperCAmelCase__						=		sns.load_dataset(_UpperCAmelCase			)
						UpperCAmelCase__						=		np.array(data[x_name]			)
						UpperCAmelCase__						=		np.array(data[y_name]			)

						UpperCAmelCase__						=		np.ones(len(_UpperCAmelCase			)			)

						# pairing elements of one and x_data
						UpperCAmelCase__						=		np.column_stack((one, x_data)			)

						return x_train, x_data, y_data


def   a__      (		UpperCAmelCase__       :			Optional[int]		,			UpperCAmelCase__       :			str		,			UpperCAmelCase__       :			Dict		,			UpperCAmelCase__       :			str		,			UpperCAmelCase__       :			List[Any]		,			UpperCAmelCase__       :			int		,			)->	None:
						UpperCAmelCase__						=		np.sort(_UpperCAmelCase		,			axis=0			)
						plt.scatter(_UpperCAmelCase		,			_UpperCAmelCase		,			color="""blue"""			)
						plt.plot(
						    x_train_sorted[:, 1]		,			preds[x_train[:, 1].argsort(0			)]		,			color="""yellow"""		,			linewidth=5		,			)
						plt.title("""Local Weighted Regression"""			)
						plt.xlabel(_UpperCAmelCase			)
						plt.ylabel(_UpperCAmelCase			)
						plt.show()


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						# Demo with a dataset from the seaborn module
						UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_       :       Dict							   =		load_data("""tips""", """total_bill""", """tip""")
						UpperCamelCase_       :       Tuple							   =		local_weight_regression(training_data_x, tip, 5)
						plot_preds(training_data_x, predictions, total_bill, tip, """total_bill""", """tip""")


"""simple docstring"""







from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
UpperCamelCase_       :       Optional[int]							   =		load_iris()
iris.keys()


print(F"""Target names: \n {iris.target_names} """)
print(F"""\n Features: \n {iris.feature_names}""")

# Train set e Test set
UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_       :       int							   =		train_test_split(
    iris["""data"""], iris["""target"""], random_state=4
)

# KNN

UpperCamelCase_       :       str							   =		KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
UpperCamelCase_       :       Union[str, Any]							   =		[[1, 2, 1, 4], [2, 3, 4, 5]]

UpperCamelCase_       :       Union[str, Any]							   =		knn.predict(X_new)

print(
    F"""\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"""
    F""" {iris["target_names"][prediction]}"""
)

