

"""simple docstring"""





import matplotlib.pyplot as plt
import numpy as np




def 						_A						(						UpperCamelCase_  : np.ndarray,    UpperCamelCase_  : np.ndarray,    UpperCamelCase_  : float)			->		np.ndarray:
   '''simple docstring'''


   __lowercase			=		len(UpperCamelCase_)  # Number of training samples
   __lowercase			=		np.eye(UpperCamelCase_)  # Initialize weights as identity matrix
   for j in range(UpperCamelCase_):
      __lowercase			=		point - x_train[j]
      __lowercase			=		np.exp(diff @ diff.T / (-2.0 * tau**2))

   return weights




def 						_A						(						UpperCamelCase_  : np.ndarray,    UpperCamelCase_  : np.ndarray,    UpperCamelCase_  : np.ndarray,    UpperCamelCase_  : float)			->		np.ndarray:
   '''simple docstring'''


   __lowercase			=		weight_matrix(UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_)
   __lowercase			=		np.linalg.inv(x_train.T @ weight_mat @ x_train) @ (
       x_train.T @ weight_mat @ y_train.T
   )

   return weight




def 						_A						(						UpperCamelCase_  : np.ndarray,    UpperCamelCase_  : np.ndarray,    UpperCamelCase_  : float)			->		np.ndarray:
   '''simple docstring'''


   __lowercase			=		np.zeros(len(UpperCamelCase_))  # Initialize array of predictions
   for i, item in enumerate(UpperCamelCase_):
      __lowercase			=		item @ local_weight(UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_)

   return y_pred




def 						_A						(						UpperCamelCase_  : str,    UpperCamelCase_  : str,    UpperCamelCase_  : str)			->		tuple[np.ndarray, np.ndarray, np.ndarray]:
   '''simple docstring'''


   import seaborn as sns

   __lowercase			=		sns.load_dataset(UpperCamelCase_)
   __lowercase			=		np.array(data[x_name])
   __lowercase			=		np.array(data[y_name])

   __lowercase			=		np.ones(len(UpperCamelCase_))

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

   return x_train, x_data, y_data




def 						_A						(						UpperCamelCase_  : np.ndarray,    UpperCamelCase_  : np.ndarray,    UpperCamelCase_  : np.ndarray,    UpperCamelCase_  : np.ndarray,    UpperCamelCase_  : str,    UpperCamelCase_  : str,    )			->		None:
   '''simple docstring'''


   __lowercase			=		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
   _a   ,		_a   ,		_a				   =							load_data('tips', 'total_bill', 'tip')
   _a				   =							local_weight_regression(training_data_x, tip, 5)
   plot_preds(training_data_x, predictions, total_bill, tip, 'total_bill', 'tip')







"""simple docstring"""





import shutil

import requests




def 						_A						(						UpperCamelCase_  : str,    UpperCamelCase_  : bool = False,    UpperCamelCase_  : str = ".")			->		dict:
   '''simple docstring'''


   __lowercase			=		"https://api.nasa.gov/planetary/apod"
   return requests.get(UpperCamelCase_,    params={"api_key": api_key}).json()




def 						_A						(						UpperCamelCase_  : str,    UpperCamelCase_  : str = ".")			->		dict:
   '''simple docstring'''


   __lowercase			=		get_apod_data(UpperCamelCase_)
   __lowercase			=		apod_data["url"]
   __lowercase			=		img_url.split("/")[-1]
   __lowercase			=		requests.get(UpperCamelCase_,    stream=UpperCamelCase_)

   with open(F"""{path}/{img_name}""",    "wb+") as img_file:
      shutil.copyfileobj(response.raw,    UpperCamelCase_)
   del response
   return apod_data




def 						_A						(						UpperCamelCase_  : str)			->		dict:
   '''simple docstring'''


   __lowercase			=		"https://images-api.nasa.gov/search"
   return requests.get(UpperCamelCase_,    params={"q": query}).json()


if __name__ == "__main__":
   print(save_apod('YOUR API KEY'))
   _a				   =							get_archive_data('apollo 2011')['collection']['items']
   print(apollo_aaaa_items[0]['data'][0]['description'])




