

'''simple docstring'''


from random import shuffle

import tensorflow as tf
from numpy import array




def 	_a       (				__lowerCAmelCase		:      str   ,							__lowerCAmelCase		:      Optional[int]     ):

		"""simple docstring"""

		snake_case__						:       List[Any]       						=  int(__lowerCAmelCase     )
		assert noofclusters < len(__lowerCAmelCase     )

		# Find out the dimensionality
		snake_case__						:       str       						=  len(vectors[0]     )

		# Will help select random centroids from among the available vectors
		snake_case__						:       List[Any]       						=  list(range(len(__lowerCAmelCase     )     )     )
		shuffle(__lowerCAmelCase     )

		# GRAPH OF COMPUTATION
		# We initialize a new graph and set it as the default during each run
		# of this algorithm. This ensures that as this function is called
		# multiple times, the default graph doesn't keep getting crowded with
		# unused ops and Variables from previous function calls.

		snake_case__						:       Optional[Any]       						=  tf.Graph()

		with graph.as_default():
				# SESSION OF COMPUTATION

				snake_case__						:       str       						=  tf.Session()

				##CONSTRUCTING THE ELEMENTS OF COMPUTATION

				##First lets ensure we have a Variable vector for each centroid,
				##initialized to one of the vectors from the available data points
				snake_case__						:       Union[str, Any]       						=  [
				    tf.Variable(vectors[vector_indices[i]]     ) for i in range(__lowerCAmelCase     )
				]
				##These nodes will assign the centroid Variables the appropriate
				##values
				snake_case__						:       Any       						=  tf.placeholder('''float64'''   ,							[dim]     )
				snake_case__						:       int       						=  []
				for centroid in centroids:
						cent_assigns.append(tf.assign(__lowerCAmelCase   ,							__lowerCAmelCase     )     )

				##Variables for cluster assignments of individual vectors(initialized
				##to 0 at first)
				snake_case__						:       str       						=  [tf.Variable(0     ) for i in range(len(__lowerCAmelCase     )     )]
				##These nodes will assign an assignment Variable the appropriate
				##value
				snake_case__						:       Dict       						=  tf.placeholder('''int32'''     )
				snake_case__						:       Tuple       						=  []
				for assignment in assignments:
						cluster_assigns.append(tf.assign(__lowerCAmelCase   ,							__lowerCAmelCase     )     )

				##Now lets construct the node that will compute the mean
				# The placeholder for the input
				snake_case__						:       List[str]       						=  tf.placeholder('''float'''   ,							[None, dim]     )
				# The Node/op takes the input and computes a mean along the 0th
				# dimension, i.e. the list of input vectors
				snake_case__						:       List[str]       						=  tf.reduce_mean(__lowerCAmelCase   ,							0     )

				##Node for computing Euclidean distances
				# Placeholders for input
				snake_case__						:       Tuple       						=  tf.placeholder('''float'''   ,							[dim]     )
				snake_case__						:       int       						=  tf.placeholder('''float'''   ,							[dim]     )
				snake_case__						:       Optional[int]       						=  tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(__lowerCAmelCase   ,							__lowerCAmelCase     )   ,							2     )     )     )

				##This node will figure out which cluster to assign a vector to,
				##based on Euclidean distances of the vector from the centroids.
				# Placeholder for input
				snake_case__						:       Optional[int]       						=  tf.placeholder('''float'''   ,							[noofclusters]     )
				snake_case__						:       Dict       						=  tf.argmin(__lowerCAmelCase   ,							0     )

				##INITIALIZING STATE VARIABLES

				##This will help initialization of all Variables defined with respect
				##to the graph. The Variable-initializer should be defined after
				##all the Variables have been constructed, so that each of them
				##will be included in the initialization.
				snake_case__						:       List[Any]       						=  tf.initialize_all_variables()

				# Initialize all variables
				sess.run(__lowerCAmelCase     )

				##CLUSTERING ITERATIONS

				# Now perform the Expectation-Maximization steps of K-Means clustering
				# iterations. To keep things simple, we will only do a set number of
				# iterations, instead of using a Stopping Criterion.
				snake_case__						:       List[str]       						=  1_00
				for _ in range(__lowerCAmelCase     ):
						##EXPECTATION STEP
						##Based on the centroid locations till last iteration, compute
						##the _expected_ centroid assignments.
						# Iterate over each vector
						for vector_n in range(len(__lowerCAmelCase     )     ):
								snake_case__						:       Optional[int]       						=  vectors[vector_n]
								# Compute Euclidean distance between this vector and each
								# centroid. Remember that this list cannot be named
								#'centroid_distances', since that is the input to the
								# cluster assignment node.
								snake_case__						:       Union[str, Any]       						=  [
								    sess.run(__lowerCAmelCase   ,							feed_dict={va: vect, va: sess.run(__lowerCAmelCase     )}     )
								    for centroid in centroids
								]
								# Now use the cluster assignment node, with the distances
								# as the input
								snake_case__						:       str       						=  sess.run(
								    __lowerCAmelCase   ,							feed_dict={centroid_distances: distances}     )
								# Now assign the value to the appropriate state variable
								sess.run(
								    cluster_assigns[vector_n]   ,							feed_dict={assignment_value: assignment}     )

						##MAXIMIZATION STEP
						# Based on the expected state computed from the Expectation Step,
						# compute the locations of the centroids so as to maximize the
						# overall objective of minimizing within-cluster Sum-of-Squares
						for cluster_n in range(__lowerCAmelCase     ):
								# Collect all the vectors assigned to this cluster
								snake_case__						:       Dict       						=  [
								    vectors[i]
								    for i in range(len(__lowerCAmelCase     )     )
								    if sess.run(assignments[i]     ) == cluster_n
								]
								# Compute new centroid location
								snake_case__						:       Union[str, Any]       						=  sess.run(
								    __lowerCAmelCase   ,							feed_dict={mean_input: array(__lowerCAmelCase     )}     )
								# Assign value to appropriate variable
								sess.run(
								    cent_assigns[cluster_n]   ,							feed_dict={centroid_value: new_location}     )

        # Return centroids and assignments
				snake_case__						:       List[str]       						=  sess.run(__lowerCAmelCase     )
				snake_case__						:       List[Any]       						=  sess.run(__lowerCAmelCase     )
				return centroids, assignments






'''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 	_a       (				__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list     ):

		"""simple docstring"""

		snake_case__						:       Union[str, Any]       						=  np.array([[1, item, train_mtch[i]] for i, item in enumerate(__lowerCAmelCase     )]     )
		snake_case__						:       Dict       						=  np.array(__lowerCAmelCase     )
		snake_case__						:       List[str]       						=  np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()   ,							__lowerCAmelCase     )     )   ,							x.transpose()     )   ,							__lowerCAmelCase     )
		return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]     )




def 	_a       (				__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list     ):

		"""simple docstring"""

		snake_case__						:       List[str]       						=  (1, 2, 1)
		snake_case__						:       Union[str, Any]       						=  (1, 1, 0, 7)
		snake_case__						:       List[str]       						=  SARIMAX(
		    __lowerCAmelCase   ,							exog=__lowerCAmelCase   ,							order=__lowerCAmelCase   ,							seasonal_order=__lowerCAmelCase     )
		snake_case__						:       str       						=  model.fit(disp=__lowerCAmelCase   ,							maxiter=6_00   ,							method='''nm'''     )
		snake_case__						:       List[str]       						=  model_fit.predict(1   ,							len(__lowerCAmelCase     )   ,							exog=[test_match]     )
		return result[0]




def 	_a       (				__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list     ):

		"""simple docstring"""

		snake_case__						:       List[Any]       						=  SVR(kernel='''rbf'''   ,							C=1   ,							gamma=0.1   ,							epsilon=0.1     )
		regressor.fit(__lowerCAmelCase   ,							__lowerCAmelCase     )
		snake_case__						:       Optional[Any]       						=  regressor.predict(__lowerCAmelCase     )
		return y_pred[0]




def 	_a       (				__lowerCAmelCase		:      list     ):

		"""simple docstring"""

		train_user.sort()
		snake_case__						:       int       						=  np.percentile(__lowerCAmelCase   ,							25     )
		snake_case__						:       List[str]       						=  np.percentile(__lowerCAmelCase   ,							75     )
		snake_case__						:       Dict       						=  qa - qa
		snake_case__						:       Optional[int]       						=  qa - (iqr * 0.1)
		return low_lim




def 	_a       (				__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      float     ):

		"""simple docstring"""

		snake_case__						:       int       						=  0
		snake_case__						:       List[Any]       						=  0
		for i in list_vote:
				if i > actual_result:
						snake_case__						:       Optional[Any]       						=  not_safe + 1
				else:
						if abs(abs(__lowerCAmelCase     ) - abs(__lowerCAmelCase     )     ) <= 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)
					lowerCAmelCase__						:       Optional[int]										=						[[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
					lowerCAmelCase__						:       List[Any]										=						pd.DataFrame(
					    data_input, columns=["""total_user""", """total_even""", """days"""]
					)
					lowerCAmelCase__						:       Union[str, Any]										=						Normalizer().fit_transform(data_input_df.values)
					# split data
					lowerCAmelCase__						:       Union[str, Any]										=						normalize_df[:, 2].tolist()
					lowerCAmelCase__						:       List[str]										=						normalize_df[:, 0].tolist()
					lowerCAmelCase__						:       List[str]										=						normalize_df[:, 1].tolist()

					# for svr (input variable = total date and total match)
					lowerCAmelCase__						:       Dict										=						normalize_df[:, [1, 2]].tolist()
					lowerCAmelCase__						:       Union[str, Any]										=						x[: len(x) - 1]
					lowerCAmelCase__						:       List[str]										=						x[len(x) - 1 :]

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

					lowerCAmelCase__						:       Optional[int]										=						total_date[len(total_date) - 1 :]
					lowerCAmelCase__						:       Union[str, Any]										=						total_user[len(total_user) - 1 :]
					lowerCAmelCase__						:       List[Any]										=						total_match[len(total_match) - 1 :]

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



