




'''simple docstring'''

import numpy



class 		SCREAMING_SNAKE_CASE__  :





			def __init__(      self						:    Any      ,		a_						:    numpy.ndarray      ,		a_						:    numpy.ndarray							):



						"""simple docstring"""




						__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 							A (      self						:    Union[str, Any]							):



						"""simple docstring"""




						__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 							A (      self						:    Optional[Any]							):



						"""simple docstring"""




						__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 							A (      self						:    Union[str, Any]      ,		a_						:    numpy.ndarray      ,		a_						:    int      ,		a_						:    bool							):



						"""simple docstring"""




						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 							A (      self						:    Optional[Any]      ,		a_						:    numpy.ndarray							):



						"""simple docstring"""




						__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 __UpperCAmelCase	(   _UpperCAmelCase		:   numpy.ndarray						)      ->      numpy.ndarray:
			return 1 / (1 + numpy.exp(-value						))


def __UpperCAmelCase	(   _UpperCAmelCase		:   numpy.ndarray						)      ->      numpy.ndarray:
			return (value) * (1 - (value))


def __UpperCAmelCase	(   )      ->      int:
			__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=_UpperCAmelCase		,				output_array=_UpperCAmelCase						)

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

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


if __name__ == "__main__":
						example()












"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    list  ,     _UpperCAmelCase					:    list  ,     _UpperCAmelCase					:    list  ,     _UpperCAmelCase					:    list  ,     _UpperCAmelCase					:    list			):
 lowerCAmelCase    				= np.array([[1, item, train_mtch[i]] for i, item in enumerate(_UpperCAmelCase			)]			)
 lowerCAmelCase    				= np.array(_UpperCAmelCase			)
 lowerCAmelCase    				= np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()  ,     _UpperCAmelCase			)			)  ,     x.transpose()			)  ,     _UpperCAmelCase			)
 return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]			)

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    list  ,     _UpperCAmelCase					:    list  ,     _UpperCAmelCase					:    list			):
 lowerCAmelCase    				= (1, 2, 1)
 lowerCAmelCase    				= (1, 1, 0, 7)
 lowerCAmelCase    				= SARIMAX(
     _UpperCAmelCase  ,     exog=_UpperCAmelCase  ,     order=_UpperCAmelCase  ,     seasonal_order=_UpperCAmelCase			)
 lowerCAmelCase    				= model.fit(disp=_UpperCAmelCase  ,     maxiter=600  ,     method='nm'			)
 lowerCAmelCase    				= model_fit.predict(1  ,     len(_UpperCAmelCase			)  ,     exog=[test_match]			)
 return result[0]

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    list  ,     _UpperCAmelCase					:    list  ,     _UpperCAmelCase					:    list			):
 lowerCAmelCase    				= SVR(kernel='rbf'  ,     C=1  ,     gamma=0.1  ,     epsilon=0.1			)
 regressor.fit(_UpperCAmelCase  ,     _UpperCAmelCase			)
 lowerCAmelCase    				= regressor.predict(_UpperCAmelCase			)
 return y_pred[0]

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    list			):
 train_user.sort()
 lowerCAmelCase    				= np.percentile(_UpperCAmelCase  ,     25			)
 lowerCAmelCase    				= np.percentile(_UpperCAmelCase  ,     75			)
 lowerCAmelCase    				= qa - qa
 lowerCAmelCase    				= qa - (iqr * 0.1)
 return low_lim

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    list  ,     _UpperCAmelCase					:    float			):
 lowerCAmelCase    				= 0
 lowerCAmelCase    				= 0
 for i in list_vote:
  if i > actual_result:
   lowerCAmelCase    				= not_safe + 1
  else:
   if abs(abs(_UpperCAmelCase			) - abs(_UpperCAmelCase			)			) <= 0.1:
    safe += 1
   else:
    not_safe += 1
 return safe > not_safe


if __name__ == "__main__":
       # data_input_df = pd.read_csv("ex_data.csv", header=None)
       __UpperCamelCase     :						Optional[Any]						       =  [[1_8231, 0.0, 1], [2_2621, 1.0, 2], [1_5675, 0.0, 3], [2_3583, 1.0, 4]]
       __UpperCamelCase     :						Any						       =  pd.DataFrame(
           data_input, columns=['''total_user''', '''total_even''', '''days''']
       )
       __UpperCamelCase     :						Dict						       =  Normalizer().fit_transform(data_input_df.values)
       # split data
       __UpperCamelCase     :						Dict						       =  normalize_df[:, 2].tolist()
       __UpperCamelCase     :						Union[str, Any]						       =  normalize_df[:, 0].tolist()
       __UpperCamelCase     :						List[str]						       =  normalize_df[:, 1].tolist()

       # for svr (input variable = total date and total match)
       __UpperCamelCase     :						Optional[int]						       =  normalize_df[:, [1, 2]].tolist()
       __UpperCamelCase     :						Tuple						       =  x[: len(x) - 1]
       __UpperCamelCase     :						Any						       =  x[len(x) - 1 :]

       # for linear regression & sarimax
       __UpperCamelCase     :						str						       =  total_date[: len(total_date) - 1]
       __UpperCamelCase     :						Union[str, Any]						       =  total_user[: len(total_user) - 1]
       __UpperCamelCase     :						List[Any]						       =  total_match[: len(total_match) - 1]

       __UpperCamelCase     :						Optional[Any]						       =  total_date[len(total_date) - 1 :]
       __UpperCamelCase     :						str						       =  total_user[len(total_user) - 1 :]
       __UpperCamelCase     :						str						       =  total_match[len(total_match) - 1 :]

       # voting system with forecasting
       __UpperCamelCase     :						Any						       =  [
           linear_regression_prediction(
               trn_date, trn_user, trn_match, tst_date, tst_match
           ),
           sarimax_predictor(trn_user, trn_match, tst_match),
           support_vector_regressor(x_train, x_test, trn_user),
       ]

       # check the safety of today's data
       __UpperCamelCase     :						List[str]						       =  '''''' if data_safety_checker(res_vote, tst_user) else '''not '''
       print('''Today\'s data is {not_str}safe.''')





