


"""simple docstring"""
from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap

def 				_snake_case      (   __snake_case							:      List[str]   ):




   """simple docstring"""







   return getitem, k

def 				_snake_case      (   __snake_case							:      List[Any]						, __snake_case							:      int   ):




   """simple docstring"""







   return setitem, k, v

def 				_snake_case      (   __snake_case							:      str   ):




   """simple docstring"""







   return delitem, k

def 				_snake_case      (   __snake_case							:      int						, __snake_case							:      Optional[Any]						, *__snake_case							:      Any   ):




   """simple docstring"""







   try:
      return fun(__snake_case						, *__snake_case   ), None
   except Exception as e:
      return None, e


UpperCAmelCase				=						(
    _set("""key_a""", """val_a"""),
    _set("""key_b""", """val_b"""),
)

UpperCAmelCase				=						[
    _set("""key_a""", """val_a"""),
    _set("""key_a""", """val_b"""),
]

UpperCAmelCase				=						[
    _set("""key_a""", """val_a"""),
    _set("""key_b""", """val_b"""),
    _del("""key_a"""),
    _del("""key_b"""),
    _set("""key_a""", """val_a"""),
    _del("""key_a"""),
]

UpperCAmelCase				=						[
    _get("""key_a"""),
    _del("""key_a"""),
    _set("""key_a""", """val_a"""),
    _del("""key_a"""),
    _del("""key_a"""),
    _get("""key_a"""),
]

UpperCAmelCase				=						[
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
]

UpperCAmelCase				=						[
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
    *[_del(x) for x in range(5)],
    _set("""key_a""", """val_b"""),
]

@pytest.mark.parametrize(
    """operations"""						, (
        pytest.param(_add_items						, id="""add items"""   ),
        pytest.param(_overwrite_items						, id="""overwrite items"""   ),
        pytest.param(_delete_items						, id="""delete items"""   ),
        pytest.param(_access_absent_items						, id="""access absent items"""   ),
        pytest.param(_add_with_resize_up						, id="""add with resize up"""   ),
        pytest.param(_add_with_resize_down						, id="""add with resize down"""   ),
    )						, )
def 				_snake_case      (   __snake_case							:      Any   ):




   """simple docstring"""







   _lowerCamelCase	:					str   =		HashMap(initial_block_size=4   )
   _lowerCamelCase	:					List[Any]   =		{}
   for _, (fun, *args) in enumerate(__snake_case   ):
      _lowerCamelCase   ,    _lowerCamelCase	:					List[str]   =		_run_operation(__snake_case						, __snake_case						, *__snake_case   )
      _lowerCamelCase   ,    _lowerCamelCase	:					Dict   =		_run_operation(__snake_case						, __snake_case						, *__snake_case   )
      assert my_res == py_res
      assert str(__snake_case   ) == str(__snake_case   )
      assert set(__snake_case   ) == set(__snake_case   )
      assert len(__snake_case   ) == len(__snake_case   )
      assert set(my.items()   ) == set(py.items()   )





def 				_snake_case      (   ):




   """simple docstring"""







   def is_public(__snake_case							:      str   ) -> bool:
      return not name.startswith("""_"""   )

   _lowerCamelCase	:					Any   =		{name for name in dir({}   ) if is_public(__snake_case   )}
   _lowerCamelCase	:					Tuple   =		{name for name in dir(HashMap()   ) if is_public(__snake_case   )}

   assert dict_public_names > hash_public_names







'''simple docstring'''


import numpy
class 						a_				:




	def __init__(       self     :  Any       ,		__lowerCAmelCase     :  numpy.ndarray       ,		__lowerCAmelCase     :  numpy.ndarray      ):
							__snake_case						 =   input_array

							# Random initial weights are assigned where first argument is the
							# number of nodes in previous layer and second argument is the
							# number of nodes in the next layer.

							# Random initial weights are assigned.
							# self.input_array.shape[1] is used to represent number of nodes in input layer.
							# First hidden layer consists of 4 nodes.
							__snake_case						 =   numpy.random.rand(
							    self.input_array.shape[1]       ,		4      )

							# Random initial values for the first hidden layer.
							# First hidden layer has 4 nodes.
							# Second hidden layer has 3 nodes.
							__snake_case						 =   numpy.random.rand(
							    4       ,		3      )

							# Random initial values for the second hidden layer.
							# Second hidden layer has 3 nodes.
							# Output layer has 1 node.
							__snake_case						 =   numpy.random.rand(3       ,		1      )

							# Real output values provided.
							__snake_case						 =   output_array

							# Predicted output values by the neural network.
							# Predicted_output array initially consists of zeroes.
							__snake_case						 =   numpy.zeros(output_array.shape      )




	def   lowercase__   (       self     :  Optional[Any]      ):
							__snake_case						 =   sigmoid(
							    numpy.dot(self.input_array       ,		self.input_layer_and_first_hidden_layer_weights      )      )

							# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
							# connecting the first hidden set of nodes with the second hidden set of nodes.
							__snake_case						 =   sigmoid(
							    numpy.dot(
							        self.layer_between_input_and_first_hidden_layer       ,		self.first_hidden_layer_and_second_hidden_layer_weights       ,		)      )

							# layer_between_second_hidden_layer_and_output is the layer connecting
							# second hidden layer with the output node.
							__snake_case						 =   sigmoid(
							    numpy.dot(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer       ,		self.second_hidden_layer_and_output_layer_weights       ,		)      )

							return self.layer_between_second_hidden_layer_and_output




	def   lowercase__   (       self     :  List[str]      ):
							__snake_case						 =   numpy.dot(
							    self.layer_between_first_hidden_layer_and_second_hidden_layer.T       ,		2
							    * (self.output_array - self.predicted_output)
							    * sigmoid_derivative(self.predicted_output      )       ,		)
							__snake_case						 =   numpy.dot(
							    self.layer_between_input_and_first_hidden_layer.T       ,		numpy.dot(
							        2
							        * (self.output_array - self.predicted_output)
							        * sigmoid_derivative(self.predicted_output      )       ,		self.second_hidden_layer_and_output_layer_weights.T       ,		)
							    * sigmoid_derivative(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer      )       ,		)
							__snake_case						 =   numpy.dot(
							    self.input_array.T       ,		numpy.dot(
							        numpy.dot(
							            2
							            * (self.output_array - self.predicted_output)
							            * sigmoid_derivative(self.predicted_output      )       ,		self.second_hidden_layer_and_output_layer_weights.T       ,		)
							        * sigmoid_derivative(
							            self.layer_between_first_hidden_layer_and_second_hidden_layer      )       ,		self.first_hidden_layer_and_second_hidden_layer_weights.T       ,		)
							    * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer      )       ,		)

							self.input_layer_and_first_hidden_layer_weights += (
							    updated_input_layer_and_first_hidden_layer_weights
							)
							self.first_hidden_layer_and_second_hidden_layer_weights += (
							    updated_first_hidden_layer_and_second_hidden_layer_weights
							)
							self.second_hidden_layer_and_output_layer_weights += (
							    updated_second_hidden_layer_and_output_layer_weights
							)




	def   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  numpy.ndarray       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  bool      ):
							for iteration in range(1       ,		iterations + 1      ):
													__snake_case						 =   self.feedforward()
													self.back_propagation()
													if give_loss:
																			__snake_case						 =   numpy.mean(numpy.square(output - self.feedforward()      )      )
																			print(F'Iteration {iteration} Loss: {loss}'      )




	def   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  numpy.ndarray      ):
							__snake_case						 =   input_arr

							__snake_case						 =   sigmoid(
							    numpy.dot(self.array       ,		self.input_layer_and_first_hidden_layer_weights      )      )

							__snake_case						 =   sigmoid(
							    numpy.dot(
							        self.layer_between_input_and_first_hidden_layer       ,		self.first_hidden_layer_and_second_hidden_layer_weights       ,		)      )

							__snake_case						 =   sigmoid(
							    numpy.dot(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer       ,		self.second_hidden_layer_and_output_layer_weights       ,		)      )

							return int(self.layer_between_second_hidden_layer_and_output > 0.6      )





def 						lowerCamelCase__   (				a							):
						return 1 / (1 + numpy.exp(-value							))
def 						lowerCamelCase__   (				a							):
						return (value) * (1 - (value))
def 						lowerCamelCase__   (				):
						__snake_case						 =   numpy.array(
						    (
						        [0, 0, 0],
						        [0, 0, 1],
						        [0, 1, 0],
						        [0, 1, 1],
						        [1, 0, 0],
						        [1, 0, 1],
						        [1, 1, 0],
						        [1, 1, 1],
						    )    ,	dtype=numpy.floataa    ,	)

						# True output values for the given input values.
						__snake_case						 =   numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])    ,	dtype=numpy.floataa							)

						# Calling neural network class.
						__snake_case						 =   TwoHiddenLayerNeuralNetwork(
						    input_array=a    ,	output_array=a							)

						# Calling training function.
						# Set give_loss to True if you want to see loss in every iteration.
						neural_network.train(output=a    ,	iterations=10    ,	give_loss=a							)

						return neural_network.predict(numpy.array(([1, 1, 1])    ,	dtype=numpy.floataa							)							)


if __name__ == "__main__":
	example()

