



"""simple docstring"""






import matplotlib.pyplot as plt
import numpy as np


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


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

  return weights


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			weight_matrix(lowercase__       ,							lowercase__       ,							lowercase__    )
  _SCREAMING_SNAKE_CASE        =			np.linalg.inv(x_train.T @ weight_mat @ x_train    ) @ (
      x_train.T @ weight_mat @ y_train.T
  )

  return weight


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


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

  return y_pred


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  import seaborn as sns

  _SCREAMING_SNAKE_CASE        =			sns.load_dataset(lowercase__    )
  _SCREAMING_SNAKE_CASE        =			np.array(data[x_name]    )
  _SCREAMING_SNAKE_CASE        =			np.array(data[y_name]    )

  _SCREAMING_SNAKE_CASE        =			np.ones(len(lowercase__    )    )

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

  return x_train, x_data, y_data


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			np.sort(lowercase__       ,							axis=0    )
  plt.scatter(lowercase__       ,							lowercase__       ,							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(lowercase__    )
  plt.ylabel(lowercase__    )
  plt.show()


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # Demo with a dataset from the seaborn module
    lowerCamelCase				:     Tuple								=  load_data("""tips""", """total_bill""", """tip""")
    lowerCamelCase				:     Optional[int]								=  local_weight_regression(training_data_x, tip, 5)
    plot_preds(training_data_x, predictions, total_bill, tip, """total_bill""", """tip""")





def    UpperCAmelCase					(  lowercase__						:					List[Any]				,					lowercase__						:					Optional[int]	):



			'''simple docstring'''





			print("""\nVertex Distance"""	)
			for i in range(lowercase__	):
						if dist[i] != float("""inf"""	):
									print(lowercase__				,					"""\t"""				,					int(dist[i]	)				,					end="""\t"""	)
						else:
									print(lowercase__				,					"""\t"""				,					"""INF"""				,					end="""\t"""	)
						print()




def    UpperCAmelCase					(  lowercase__						:					Optional[int]				,					lowercase__						:					Any				,					lowercase__						:					Tuple	):



			'''simple docstring'''





			a__					=  float("""inf"""	)
			a__					=  -1
			for i in range(lowercase__	):
						if (not vset[i]) and mdist[i] < min_val:
									a__					=  i
									a__					=  mdist[i]
			return min_ind




def    UpperCAmelCase					(  lowercase__						:					Optional[Any]				,					lowercase__						:					Tuple				,					lowercase__						:					List[Any]	):



			'''simple docstring'''





			a__					=  [float("""inf"""	) for _ in range(lowercase__	)]
			a__					=  [False for _ in range(lowercase__	)]
			a__					=  0.0

			for _ in range(v - 1	):
						a__					=  min_dist(lowercase__				,					lowercase__				,					lowercase__	)
						a__					=  True

						for i in range(lowercase__	):
									if (
									    (not vset[i])
									    and graph[u][i] != float("""inf"""	)
									    and mdist[u] + graph[u][i] < mdist[i]
									):
												a__					=  mdist[u] + graph[u][i]

			print_dist(lowercase__				,					lowercase__	)


if __name__ == "__main__":
	_lowercase				:					Any     					=int(input("""Enter number of vertices: """).strip())
	_lowercase				:					List[str]     					=int(input("""Enter number of edges: """).strip())

	_lowercase				:					List[str]     					=[[float("""inf""") for i in range(V)] for j in range(V)]

	for i in range(V):
		_lowercase				:					Dict     					=0.0

	for i in range(E):
		print("""\nEdge """, i + 1)
		_lowercase				:					Dict     					=int(input("""Enter source:""").strip())
		_lowercase				:					Optional[Any]     					=int(input("""Enter destination:""").strip())
		_lowercase				:					List[Any]     					=float(input("""Enter weight:""").strip())
		_lowercase				:					List[str]     					=weight

	_lowercase				:					List[Any]     					=int(input("""\nEnter shortest path source:""").strip())
	dijkstra(graph, V, gsrc)

