
"""simple docstring"""





class 						SCREAMING_SNAKE_CASE_ :




		"""simple docstring"""





		def __init__(     self							,  lowerCAmelCase__ = ""							,  lowerCAmelCase__ = False):
				# Mapping from the first character of the prefix of the node
				__SCREAMING_SNAKE_CASE            = {}

				# A node will be a leaf if the tree contains its word
				__SCREAMING_SNAKE_CASE            = is_leaf

				__SCREAMING_SNAKE_CASE            = prefix




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = 0
				for q, w in zip(self.prefix							,  lowerCAmelCase__):
						if q != w:
								break

						x += 1

				return self.prefix[:x], self.prefix[x:], word[x:]




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				for word in words:
						self.insert(lowerCAmelCase__)




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				# Case 1: If the word is the prefix of the node
				# Solution: We set the current node as leaf
				if self.prefix == word:
						__SCREAMING_SNAKE_CASE            = True

				# Case 2: The node has no edges that have a prefix to the word
				# Solution: We create an edge from the current node to a new one
				# containing the word
				elif word[0] not in self.nodes:
						__SCREAMING_SNAKE_CASE            = RadixNode(prefix=lowerCAmelCase__							,  is_leaf=lowerCAmelCase__)

				else:
						__SCREAMING_SNAKE_CASE            = self.nodes[word[0]]
						__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = incoming_node.match(
						    lowerCAmelCase__)

						# Case 3: The node prefix is equal to the matching
						# Solution: We insert remaining word on the next node
						if remaining_prefix == "":
								self.nodes[matching_string[0]].insert(lowerCAmelCase__)

						# Case 4: The word is greater equal to the matching
						# Solution: Create a node in between both nodes, change
						# prefixes and add the new node for the remaining word
						else:
								__SCREAMING_SNAKE_CASE            = remaining_prefix

								__SCREAMING_SNAKE_CASE            = self.nodes[matching_string[0]]
								__SCREAMING_SNAKE_CASE            = RadixNode(lowerCAmelCase__							,  lowerCAmelCase__)
								__SCREAMING_SNAKE_CASE            = aux_node

								if remaining_word == "":
										__SCREAMING_SNAKE_CASE            = True
								else:
										self.nodes[matching_string[0]].insert(lowerCAmelCase__)




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = self.nodes.get(word[0]							,  lowerCAmelCase__)
				if not incoming_node:
						return False
				else:
						__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = incoming_node.match(
						    lowerCAmelCase__)
						# If there is remaining prefix, the word can't be on the tree
						if remaining_prefix != "":
								return False
						# This applies when the word and the prefix are equal
						elif remaining_word == "":
								return incoming_node.is_leaf
						# We have word remaining so we check the next node
						else:
								return incoming_node.find(lowerCAmelCase__)




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = self.nodes.get(word[0]							,  lowerCAmelCase__)
				if not incoming_node:
						return False
				else:
						__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = incoming_node.match(
						    lowerCAmelCase__)
						# If there is remaining prefix, the word can't be on the tree
						if remaining_prefix != "":
								return False
						# We have word remaining so we check the next node
						elif remaining_word != "":
								return incoming_node.delete(lowerCAmelCase__)
						else:
								# If it is not a leaf, we don't have to delete
								if not incoming_node.is_leaf:
										return False
								else:
										# We delete the nodes if no edges go from it
										if len(incoming_node.nodes) == 0:
												del self.nodes[word[0]]
												# We merge the current node with its only child
												if len(self.nodes) == 1 and not self.is_leaf:
														__SCREAMING_SNAKE_CASE            = list(self.nodes.values())[0]
														__SCREAMING_SNAKE_CASE            = merging_node.is_leaf
														self.prefix += merging_node.prefix
														__SCREAMING_SNAKE_CASE            = merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
										elif len(incoming_node.nodes) > 1:
												__SCREAMING_SNAKE_CASE            = False
										# If there is 1 edge, we merge it with its child
										else:
												__SCREAMING_SNAKE_CASE            = list(incoming_node.nodes.values())[0]
												__SCREAMING_SNAKE_CASE            = merging_node.is_leaf
												incoming_node.prefix += merging_node.prefix
												__SCREAMING_SNAKE_CASE            = merging_node.nodes

										return True




		def 	snake_case_     (     self							,  lowerCAmelCase__ = 0):
				if self.prefix != "":
						print("""-""" * height							,  self.prefix							,  """  (leaf)""" if self.is_leaf else """""")

				for value in self.nodes.values():
						value.print_tree(height + 1)



def 						_lowerCAmelCase				(				):
		__SCREAMING_SNAKE_CASE            = """banana bananas bandana band apple all beast""".split()
		__SCREAMING_SNAKE_CASE            = RadixNode()
		root.insert_many(a_				)

		assert all(root.find(a_				) for word in words				)
		assert not root.find("""bandanas"""				)
		assert not root.find("""apps"""				)
		root.delete("""all"""				)
		assert not root.find("""all"""				)
		root.delete("""banana"""				)
		assert not root.find("""banana"""				)
		assert root.find("""bananas"""				)

		return True



def 						_lowerCAmelCase				(				):
		assert test_trie()



def 						_lowerCAmelCase				(				):
		__SCREAMING_SNAKE_CASE            = RadixNode()
		__SCREAMING_SNAKE_CASE            = """banana bananas bandanas bandana band apple all beast""".split()
		root.insert_many(a_				)

		print("""Words:"""  ,				a_				)
		print("""Tree:"""				)
		root.print_tree()


if __name__ == "__main__":
							main()












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 						__UpperCAmelCase			(     a_							,     a_							,     a_							,     a_							,     a_):
							snake_case_				=							np.array([[1, item, train_mtch[i]] for i, item in enumerate(a_)])
							snake_case_				=							np.array(a_)
							snake_case_				=							np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()							,     a_))							,     x.transpose())							,     a_)
							return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2])





def 						__UpperCAmelCase			(     a_							,     a_							,     a_):
							snake_case_				=							(1, 2, 1)
							snake_case_				=							(1, 1, 0, 7)
							snake_case_				=							SARIMAX(
							    a_							,     exog=a_							,     order=a_							,     seasonal_order=a_)
							snake_case_				=							model.fit(disp=a_							,     maxiter=6_00							,     method='nm')
							snake_case_				=							model_fit.predict(1							,     len(a_)							,     exog=[test_match])
							return result[0]





def 						__UpperCAmelCase			(     a_							,     a_							,     a_):
							snake_case_				=							SVR(kernel='rbf'							,     C=1							,     gamma=0.1							,     epsilon=0.1)
							regressor.fit(a_							,     a_)
							snake_case_				=							regressor.predict(a_)
							return y_pred[0]





def 						__UpperCAmelCase			(     a_):
							train_user.sort()
							snake_case_				=							np.percentile(a_							,     25)
							snake_case_				=							np.percentile(a_							,     75)
							snake_case_				=							qa - qa
							snake_case_				=							qa - (iqr * 0.1)
							return low_lim





def 						__UpperCAmelCase			(     a_							,     a_):
							snake_case_				=							0
							snake_case_				=							0
							for i in list_vote:
														if i > actual_result:
																					snake_case_				=							not_safe + 1
														else:
																					if abs(abs(a_) - abs(a_)) <= 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)
	lowercase  							=					[[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
	lowercase  							=					pd.DataFrame(
	    data_input, columns=["total_user", "total_even", "days"]
	)
	lowercase  							=					Normalizer().fit_transform(data_input_df.values)
	# split data
	lowercase  							=					normalize_df[:, 2].tolist()
	lowercase  							=					normalize_df[:, 0].tolist()
	lowercase  							=					normalize_df[:, 1].tolist()

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

	# for linear regression & sarimax
	lowercase  							=					total_date[: len(total_date) - 1]
	lowercase  							=					total_user[: len(total_user) - 1]
	lowercase  							=					total_match[: len(total_match) - 1]

	lowercase  							=					total_date[len(total_date) - 1 :]
	lowercase  							=					total_user[len(total_user) - 1 :]
	lowercase  							=					total_match[len(total_match) - 1 :]

	# voting system with forecasting
	lowercase  							=					[
	    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
	lowercase  							=					"" if data_safety_checker(res_vote, tst_user) else "not "
	print("Today's data is {not_str}safe.")





