



import numpy as np
import qiskit


def 						UpperCAmelCase__   (   UpperCAmelCase__			:str			,       UpperCAmelCase__			:int       ):






 '''simple docstring'''

 a							=		qiskit.QuantumCircuit(num_qubits + 1       )

 # First, let's deal with the case in which oracle is balanced
 if case == "balanced":
  # First generate a random number that tells us which CNOTs to
  # wrap in X-gates:
  a							=		np.random.randint(1			,       2**num_qubits       )
  # Next, format 'b' as a binary string of length 'n', padded with zeros:
  a							=		format(UpperCAmelCase__			,       F"""0{num_qubits}b"""       )
  # Next, we place the first X-gates. Each digit in our binary string
  # corresponds to a qubit, if the digit is 0, we do nothing, if it's 1
  # we apply an X-gate to that qubit:
  for index, bit in enumerate(UpperCAmelCase__       ):
   if bit == "1":
    oracle_qc.x(UpperCAmelCase__       )
        # Do the controlled-NOT gates for each qubit, using the output qubit
        # as the target:
  for index in range(UpperCAmelCase__       ):
   oracle_qc.cx(UpperCAmelCase__			,       UpperCAmelCase__       )
  # Next, place the final X-gates
  for index, bit in enumerate(UpperCAmelCase__       ):
   if bit == "1":
    oracle_qc.x(UpperCAmelCase__       )

    # Case in which oracle is constant
 if case == "constant":
  # First decide what the fixed output of the oracle will be
  # (either always 0 or always 1)
  a							=		np.random.randint(2       )
  if output == 1:
   oracle_qc.x(UpperCAmelCase__       )

 a							=		oracle_qc.to_gate()
 a							=		"Oracle"  # To show when we display the circuit
 return oracle_gate


def 						UpperCAmelCase__   (   UpperCAmelCase__			:qiskit.QuantumCircuit			,       UpperCAmelCase__			:int       ):






 '''simple docstring'''

 a							=		qiskit.QuantumCircuit(num_qubits + 1			,       UpperCAmelCase__       )
 # Set up the output qubit:
 dj_circuit.x(UpperCAmelCase__       )
 dj_circuit.h(UpperCAmelCase__       )
 # And set up the input register:
 for qubit in range(UpperCAmelCase__       ):
  dj_circuit.h(UpperCAmelCase__       )
 # Let's append the oracle gate to our circuit:
 dj_circuit.append(UpperCAmelCase__			,       range(num_qubits + 1       )       )
 # Finally, perform the H-gates again and measure:
 for qubit in range(UpperCAmelCase__       ):
  dj_circuit.h(UpperCAmelCase__       )

 for i in range(UpperCAmelCase__       ):
  dj_circuit.measure(UpperCAmelCase__			,       UpperCAmelCase__       )

 return dj_circuit


def 						UpperCAmelCase__   (   UpperCAmelCase__			:str			,       UpperCAmelCase__			:int       ):






 '''simple docstring'''

 a							=		qiskit.Aer.get_backend("aer_simulator"       )

 a							=		dj_oracle(UpperCAmelCase__			,       UpperCAmelCase__       )
 a							=		dj_algorithm(UpperCAmelCase__			,       UpperCAmelCase__       )

 # Execute the circuit on the simulator
 a							=		qiskit.execute(UpperCAmelCase__			,       UpperCAmelCase__			,       shots=10_00       )

 # Return the histogram data of the results of the experiment.
 return job.result().get_counts(UpperCAmelCase__       )


if __name__ == "__main__":
     print(F"""Deutsch Jozsa - Constant Oracle: {deutsch_jozsa('constant', 3)}""")
     print(F"""Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa('balanced', 3)}""")




import numpy as np



class     lowercase_       :



      def __init__(     self :							Any     ,   A__ :							Optional[int]=5     ,   A__ :							Any=5						)       ->	Any:
        _snake_case		=				depth
        _snake_case		=				0
        _snake_case		=				None
        _snake_case		=				None
        _snake_case		=				min_leaf_size
        _snake_case		=				None



      def 		UpperCamelCase_						(     self :							List[Any]     ,   A__ :							Tuple     ,   A__ :							Union[str, Any]						)       ->	int:
        if labels.ndim != 1:
          print('''Error: Input labels must be one dimensional'''						)

        return np.mean((labels - prediction) ** 2						)



      def 		UpperCamelCase_						(     self :							str     ,   A__ :							str     ,   A__ :							int						)       ->	Optional[Any]:
        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:
          _snake_case		=				np.mean(A__						)
          return

        if self.depth == 1:
          _snake_case		=				np.mean(A__						)
          return

        _snake_case		=				0
        _snake_case		=				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:
            _snake_case		=				self.mean_squared_error(x[:i]     ,   np.mean(y[:i]						)						)
            _snake_case		=				self.mean_squared_error(x[i:]     ,   np.mean(y[i:]						)						)
            _snake_case		=				error_left + error_right
            if error < min_error:
              _snake_case		=				i
              _snake_case		=				error

        if best_split != 0:
          _snake_case		=				x[:best_split]
          _snake_case		=				y[:best_split]
          _snake_case		=				x[best_split:]
          _snake_case		=				y[best_split:]

          _snake_case		=				x[best_split]
          _snake_case		=				DecisionTree(
              depth=self.depth - 1     ,   min_leaf_size=self.min_leaf_size						)
          _snake_case		=				DecisionTree(
              depth=self.depth - 1     ,   min_leaf_size=self.min_leaf_size						)
          self.left.train(A__     ,   A__						)
          self.right.train(A__     ,   A__						)
        else:
          _snake_case		=				np.mean(A__						)

        return







      def 		UpperCamelCase_						(     self :							Optional[int]     ,   A__ :							Tuple						)       ->	Any:
        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     lowercase_       :



      @staticmethod
      def 		UpperCamelCase_						(     A__ :							Union[str, Any]     ,   A__ :							Union[str, Any]						)       ->	int:
        _snake_case		=				float(0						)
        for label in labels:
          squared_error_sum += (label - prediction) ** 2

        return float(squared_error_sum / labels.size						)



def  snake_case_()				->      Any:



  """simple docstring"""






  _snake_case		=				np.arange(-1.0					,			1.0					,			0.005	)
  _snake_case		=				np.sin(_UpperCamelCase	)

  _snake_case		=				DecisionTree(depth=10					,			min_leaf_size=10	)
  tree.train(_UpperCamelCase					,			_UpperCamelCase	)

  _snake_case		=				(np.random.rand(10	) * 2) - 1
  _snake_case		=				np.array([tree.predict(_UpperCamelCase	) for x in test_cases]	)
  _snake_case		=				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)

