code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  import numpy as np
  
  
  
  
  
  
  
  class 					__lowercase					:
  
  
  
  		"""simple docstring"""
  
  		def __init__(   self		,			A=5		,			A=5  )     ->   List[Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     depth
  					lowerCamelCase								=     0
  					lowerCamelCase								=     None
  					lowerCamelCase								=     None
  					lowerCamelCase								=     min_leaf_size
  					lowerCamelCase								=     None
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A		,			A  )     ->   Tuple:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					if labels.ndim != 1:
  								print("""Error: Input labels must be one dimensional"""  )
  
  					return np.mean((labels - prediction) ** 2  )
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A		,			A  )     ->   str:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					if x.ndim != 1:
  								print("""Error: Input data set must be one dimensional"""  )
  								return
  					if len(lowerCamelCase_  ) != len(lowerCamelCase_  ):
  								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(lowerCamelCase_  ) < 2 * self.min_leaf_size:
  								lowerCamelCase								=     np.mean(lowerCamelCase_  )
  								return
  
  					if self.depth == 1:
  								lowerCamelCase								=     np.mean(lowerCamelCase_  )
  								return
  
  					lowerCamelCase								=     0
  					lowerCamelCase								=     self.mean_squared_error(lowerCamelCase_		,			np.mean(lowerCamelCase_  )  ) * 2
  					for i in range(len(lowerCamelCase_  )  ):
  								if len(x[:i]  ) < self.min_leaf_size:
  											continue
  								elif len(x[i:]  ) < self.min_leaf_size:
  											continue
  								else:
  											lowerCamelCase								=     self.mean_squared_error(x[:i]		,			np.mean(y[:i]  )  )
  											lowerCamelCase								=     self.mean_squared_error(x[i:]		,			np.mean(y[i:]  )  )
  											lowerCamelCase								=     error_left + error_right
  											if error < min_error:
  														lowerCamelCase								=     i
  														lowerCamelCase								=     error
  
  					if best_split != 0:
  								lowerCamelCase								=     x[:best_split]
  								lowerCamelCase								=     y[:best_split]
  								lowerCamelCase								=     x[best_split:]
  								lowerCamelCase								=     y[best_split:]
  
  								lowerCamelCase								=     x[best_split]
  								lowerCamelCase								=     DecisionTree(
  								    depth=self.depth - 1		,			min_leaf_size=self.min_leaf_size  )
  								lowerCamelCase								=     DecisionTree(
  								    depth=self.depth - 1		,			min_leaf_size=self.min_leaf_size  )
  								self.left.train(lowerCamelCase_		,			lowerCamelCase_  )
  								self.right.train(lowerCamelCase_		,			lowerCamelCase_  )
  					else:
  								lowerCamelCase								=     np.mean(lowerCamelCase_  )
  
  					return
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A  )     ->   int:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					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(lowerCamelCase_  )
  								else:
  											return self.left.predict(lowerCamelCase_  )
  					else:
  								print("""Error: Decision tree not yet trained"""  )
  								return None
  
  
  
  
  
  
  
  class 					__lowercase					:
  
  
  
  		"""simple docstring"""
  
  		@staticmethod
  		def 				__A      (   A		,			A  )     ->   Tuple:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     float(0  )
  					for label in labels:
  								squared_error_sum += (label - prediction) ** 2
  
  					return float(squared_error_sum / labels.size  )
  
  
  
  
  
  
  def   __lowerCamelCase	(       ):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			lowerCamelCase								=     np.arange(-1.0     ,       1.0     ,       0.0_0_5			)
  			lowerCamelCase								=     np.sin(a_			)
  
  			lowerCamelCase								=     DecisionTree(depth=10     ,       min_leaf_size=10			)
  			tree.train(a_     ,       a_			)
  
  			lowerCamelCase								=     (np.random.rand(10			) * 2) - 1
  			lowerCamelCase								=     np.array([tree.predict(a_			) for x in test_cases]			)
  			lowerCamelCase								=     np.mean((predictions - test_cases) ** 2			)
  
  			print("""Test values: """ + str(a_			)			)
  			print("""Predictions: """ + str(a_			)			)
  			print("""Average error: """ + str(a_			)			)
  
  
  if __name__ == "__main__":
  		main()
  		import doctest
  
  		doctest.testmod(name="mean_squarred_error", verbose=True)
  
  
style_context: 
  
  
  import math
  
  import qiskit
  from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
  
  
  
  
  
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	int = 1     ,       lowerCamelCase__		:	int = 1			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			if isinstance(lowerCamelCase__     ,       lowerCamelCase__			) or isinstance(lowerCamelCase__     ,       lowerCamelCase__			):
  						raise TypeError("""inputs must be integers."""			)
  			if (bit_a < 0) or (bit_a < 0):
  						raise ValueError("""inputs must be positive."""			)
  			if (math.floor(lowerCamelCase__			) != bit_a) or (math.floor(lowerCamelCase__			) != bit_a):
  						raise ValueError("""inputs must be exact integers."""			)
  			if (bit_a > 1) or (bit_a > 1):
  						raise ValueError("""inputs must be less or equal to 1."""			)
  
  			# build registers
  			lowerCamelCase								=     QuantumRegister(2     ,       """qr"""			)
  			lowerCamelCase								=     ClassicalRegister(2     ,       """cr"""			)
  
  			lowerCamelCase								=     QuantumCircuit(lowerCamelCase__     ,       lowerCamelCase__			)
  
  			# entanglement the qubits
  			quantum_circuit.h(1			)
  			quantum_circuit.cx(1     ,       0			)
  
  			# send the information
  			lowerCamelCase								=     str(lowerCamelCase__			) + str(lowerCamelCase__			)
  
  			if c_information == "11":
  						quantum_circuit.x(1			)
  						quantum_circuit.z(1			)
  			elif c_information == "10":
  						quantum_circuit.z(1			)
  			elif c_information == "01":
  						quantum_circuit.x(1			)
  			else:
  						quantum_circuit.i(1			)
  
  			# unentangled the circuit
  			quantum_circuit.cx(1     ,       0			)
  			quantum_circuit.h(1			)
  
  			# measure the circuit
  			quantum_circuit.measure(lowerCamelCase__     ,       lowerCamelCase__			)
  
  			lowerCamelCase								=     Aer.get_backend("""aer_simulator"""			)
  			lowerCamelCase								=     execute(lowerCamelCase__     ,       lowerCamelCase__     ,       shots=1000			)
  
  			return job.result().get_counts(lowerCamelCase__			)
  
  
  if __name__ == "__main__":
  		print(f"""Counts for classical state send: {superdense_coding(1,1)}""")
  
  