code: 
  """simple docstring"""
  import numpy as np
  
  
  class    _lowerCAmelCase						:
  
  
  
  							def __init__(      self ,						a_=5 ,						a_=5   )							->							str:
  								_UpperCAmelCase =						depth
  								_UpperCAmelCase =						0
  								_UpperCAmelCase =						None
  								_UpperCAmelCase =						None
  								_UpperCAmelCase =						min_leaf_size
  								_UpperCAmelCase =						None
  
  
  
  							def       _a   (      self ,						a_ ,						a_   )							->							Tuple:
  								if labels.ndim != 1:
  									print("Error: Input labels must be one dimensional"   )
  
  								return np.mean((labels - prediction) ** 2   )
  
  
  
  							def       _a   (      self ,						a_ ,						a_   )							->							Tuple:
  								if x.ndim != 1:
  									print("Error: Input data set must be one dimensional"   )
  									return
  								if len(a_   ) != len(a_   ):
  									print("Error: X and y have different lengths"   )
  									return
  								if y.ndim != 1:
  									print("Error: Data set labels must be one dimensional"   )
  									return
  
  								if len(a_   ) < 2 * self.min_leaf_size:
  									_UpperCAmelCase =						np.mean(a_   )
  									return
  
  								if self.depth == 1:
  									_UpperCAmelCase =						np.mean(a_   )
  									return
  
  								_UpperCAmelCase =						0
  								_UpperCAmelCase =						self.mean_squared_error(a_ ,						np.mean(a_   )   ) * 2
  								for i in range(len(a_   )   ):
  									if len(x[:i]   ) < self.min_leaf_size:
  										continue
  									elif len(x[i:]   ) < self.min_leaf_size:
  										continue
  									else:
  										_UpperCAmelCase =						self.mean_squared_error(x[:i] ,						np.mean(y[:i]   )   )
  										_UpperCAmelCase =						self.mean_squared_error(x[i:] ,						np.mean(y[i:]   )   )
  										_UpperCAmelCase =						error_left + error_right
  										if error < min_error:
  											_UpperCAmelCase =						i
  											_UpperCAmelCase =						error
  
  								if best_split != 0:
  									_UpperCAmelCase =						x[:best_split]
  									_UpperCAmelCase =						y[:best_split]
  									_UpperCAmelCase =						x[best_split:]
  									_UpperCAmelCase =						y[best_split:]
  
  									_UpperCAmelCase =						x[best_split]
  									_UpperCAmelCase =						DecisionTree(
  									    depth=self.depth - 1 ,						min_leaf_size=self.min_leaf_size   )
  									_UpperCAmelCase =						DecisionTree(
  									    depth=self.depth - 1 ,						min_leaf_size=self.min_leaf_size   )
  									self.left.train(a_ ,						a_   )
  									self.right.train(a_ ,						a_   )
  								else:
  									_UpperCAmelCase =						np.mean(a_   )
  
  								return
  
  
  
  							def       _a   (      self ,						a_   )							->							Tuple:
  								if self.prediction is not None:
  									return self.prediction
  								elif self.left or self.right is not None:
  									if x >= self.decision_boundary:
  										return self.right.predict(a_   )
  									else:
  										return self.left.predict(a_   )
  								else:
  									print("Error: Decision tree not yet trained"   )
  									return None
  
  
  
  
  
  class    _lowerCAmelCase						:
  
  
  
  							@staticmethod
  							def       _a   (      a_ ,						a_   )							->							str:
  								_UpperCAmelCase =						float(0   )
  								for label in labels:
  									squared_error_sum += (label - prediction) ** 2
  
  								return float(squared_error_sum / labels.size   )
  
  
  
  
  
  def     __lowerCamelCase  (		):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						np.arange(-1.0				,     1.0				,     0.005      )
  	_UpperCAmelCase =						np.sin(UpperCamelCase__      )
  
  	_UpperCAmelCase =						DecisionTree(depth=10				,     min_leaf_size=10      )
  	tree.train(UpperCamelCase__				,     UpperCamelCase__      )
  
  	_UpperCAmelCase =						(np.random.rand(10      ) * 2) - 1
  	_UpperCAmelCase =						np.array([tree.predict(UpperCamelCase__      ) for x in test_cases]      )
  	_UpperCAmelCase =						np.mean((predictions - test_cases) ** 2      )
  
  	print("Test values: " + str(UpperCamelCase__      )      )
  	print("Predictions: " + str(UpperCamelCase__      )      )
  	print("Average error: " + str(UpperCamelCase__      )      )
  
  
  if __name__ == "__main__":
  			main()
  			import doctest
  
  			doctest.testmod(name='''mean_squarred_error''', verbose=True)
  
  
  
  
style_context: 
  """simple docstring"""
  import heapq
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						[(0, start)]  # cost from start node,end node
  	_UpperCAmelCase =						set()
  	while heap:
  		((_UpperCAmelCase) ,      (_UpperCAmelCase)) =						heapq.heappop(UpperCamelCase__      )
  		if u in visited:
  			continue
  		visited.add(UpperCamelCase__      )
  		if u == end:
  			return cost
  		for v, c in graph[u]:
  			if v in visited:
  				continue
  			_UpperCAmelCase =						cost + c
  			heapq.heappush(UpperCamelCase__				,     (next_item, v)      )
  	return -1
  
  
  __magic_name__		       =				{
      '''A''': [['''B''', 2], ['''C''', 5]],
      '''B''': [['''A''', 2], ['''D''', 3], ['''E''', 1], ['''F''', 1]],
      '''C''': [['''A''', 5], ['''F''', 3]],
      '''D''': [['''B''', 3]],
      '''E''': [['''B''', 4], ['''F''', 3]],
      '''F''': [['''C''', 3], ['''E''', 3]],
  }
  __magic_name__		       =				{
      '''B''': [['''C''', 1]],
      '''C''': [['''D''', 1]],
      '''D''': [['''F''', 1]],
      '''E''': [['''B''', 1], ['''F''', 3]],
      '''F''': [],
  }
  __magic_name__		       =				{
      '''B''': [['''C''', 1]],
      '''C''': [['''D''', 1]],
      '''D''': [['''F''', 1]],
      '''E''': [['''B''', 1], ['''G''', 2]],
      '''F''': [],
      '''G''': [['''F''', 1]],
  }
  
  __magic_name__		       =				dijkstra(G, '''E''', '''C''')
  print(short_distance)  # E -- 3 --> F -- 3 --> C == 6
  
  __magic_name__		       =				dijkstra(Ga, '''E''', '''F''')
  print(short_distance)  # E -- 3 --> F == 3
  
  __magic_name__		       =				dijkstra(Ga, '''E''', '''F''')
  print(short_distance)  # E -- 2 --> G -- 1 --> F == 3
  
  if __name__ == "__main__":
  			import doctest
  
  			doctest.testmod()
  
  
  
  
  