
import warnings

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.metrics import pairwise_distances

warnings.filterwarnings('''ignore''')

UpperCAmelCase__				:  Any       					=				'''K-MEANS-CLUST/ '''



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE=None )	->     int:
			if seed is not None:  # useful for obtaining consistent results
						np.random.seed(__SCREAMING_SNAKE_CASE )
			UpperCamelCase__    :		Optional[Any]     =				data.shape[0]  # number of data points

			# Pick K indices from range [0, N).
			UpperCamelCase__    :		Any     =				np.random.randint(0				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )

			# Keep centroids as dense format, as many entries will be nonzero due to averaging.
			# As long as at least one document in a cluster contains a word,
			# it will carry a nonzero weight in the TF-IDF vector of the centroid.
			UpperCamelCase__    :		List[Any]     =				data[rand_indices, :]

			return centroids



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     Optional[int]:
			return pairwise_distances(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    metric='euclidean' )



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     str:
			# Compute distances between each data point and the set of centroids:
			# Fill in the blank (RHS only)
			UpperCamelCase__    :		List[Any]     =				centroid_pairwise_dist(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )

			# Compute cluster assignments for each data point:
			# Fill in the blank (RHS only)
			UpperCamelCase__    :		Optional[Any]     =				np.argmin(__SCREAMING_SNAKE_CASE				,    axis=1 )

			return cluster_assignment



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     int:
			UpperCamelCase__    :		Union[str, Any]     =				[]
			for i in range(__SCREAMING_SNAKE_CASE ):
						# Select all data points that belong to cluster i. Fill in the blank (RHS only)
						UpperCamelCase__    :		int     =				data[cluster_assignment == i]
						# Compute the mean of the data points. Fill in the blank (RHS only)
						UpperCamelCase__    :		Optional[int]     =				member_data_points.mean(axis=0 )
						new_centroids.append(__SCREAMING_SNAKE_CASE )
			UpperCamelCase__    :		List[Any]     =				np.array(__SCREAMING_SNAKE_CASE )

			return new_centroids



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     Optional[int]:
			UpperCamelCase__    :		Optional[Any]     =				0.0
			for i in range(__SCREAMING_SNAKE_CASE ):
						# Select all data points that belong to cluster i. Fill in the blank (RHS only)
						UpperCamelCase__    :		Optional[int]     =				data[cluster_assignment == i, :]

						if member_data_points.shape[0] > 0:  # check if i-th cluster is non-empty
									# Compute distances from centroid to data points (RHS only)
									UpperCamelCase__    :		str     =				pairwise_distances(
									    __SCREAMING_SNAKE_CASE				,    [centroids[i]]				,    metric='euclidean' )
									UpperCamelCase__    :		Any     =				distances**2
									heterogeneity += np.sum(__SCREAMING_SNAKE_CASE )

			return heterogeneity



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     int:
			plt.figure(figsize=(7, 4) )
			plt.plot(__SCREAMING_SNAKE_CASE				,    linewidth=4 )
			plt.xlabel('# Iterations' )
			plt.ylabel('Heterogeneity' )
			plt.title(F"""Heterogeneity of clustering over time, K={k:d}""" )
			plt.rcParams.update({'font.size': 16} )
			plt.show()



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE=500				,    __SCREAMING_SNAKE_CASE=None				,    __SCREAMING_SNAKE_CASE=False )	->     List[Any]:
			UpperCamelCase__    :		str     =				initial_centroids[:]
			UpperCamelCase__    :		str     =				None

			for itr in range(__SCREAMING_SNAKE_CASE ):
						if verbose:
									print(__SCREAMING_SNAKE_CASE				,    end='' )

						# 1. Make cluster assignments using nearest centroids
						UpperCamelCase__    :		Tuple     =				assign_clusters(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )

						# 2. Compute a new centroid for each of the k clusters, averaging all data
						#    points assigned to that cluster.
						UpperCamelCase__    :		Optional[int]     =				revise_centroids(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )

						# Check for convergence: if none of the assignments changed, stop
						if (
						    prev_cluster_assignment is not None
						    and (prev_cluster_assignment == cluster_assignment).all()
						):
									break

						# Print number of new assignments
						if prev_cluster_assignment is not None:
									UpperCamelCase__    :		Dict     =				np.sum(prev_cluster_assignment != cluster_assignment )
									if verbose:
												print(
												    F"""    {num_changed:5d} elements changed their cluster assignment.""" )

        # Record heterogeneity convergence metric
						if record_heterogeneity is not None:
									# YOUR CODE HERE
									UpperCamelCase__    :		Dict     =				compute_heterogeneity(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )
									record_heterogeneity.append(__SCREAMING_SNAKE_CASE )

						UpperCamelCase__    :		str     =				cluster_assignment[:]

			return centroids, cluster_assignment


# Mock test below
if False:  # change to true to run this test case.
							from sklearn import datasets as ds

							UpperCAmelCase__				:  List[str]       					=				ds.load_iris()
							UpperCAmelCase__				:  List[Any]       					=				3
							UpperCAmelCase__				:  Optional[int]       					=				[]
							UpperCAmelCase__				:  Any       					=				get_initial_centroids(dataset['''data'''], k, seed=0)
							UpperCAmelCase__					,       UpperCAmelCase__				:  Union[str, Any]       					=				kmeans(
							    dataset['''data'''],
							    k,
							    initial_centroids,
							    maxiter=400,
							    record_heterogeneity=heterogeneity,
							    verbose=True,
							)
							plot_heterogeneity(heterogeneity, k)



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE=None )	->     pd.DataFrame:
			# Fill missing values with given rules
			if fill_missing_report:
						UpperCamelCase__    :		List[Any]     =				df.fillna(value=__SCREAMING_SNAKE_CASE )
			UpperCamelCase__    :		Optional[int]     =				1
			UpperCamelCase__    :		Union[str, Any]     =				df.select_dtypes(np.number ).columns
			UpperCamelCase__    :		List[Any]     =				(
			    df.groupby(['Cluster'] )[  # construct report dataframe
			        numeric_cols
			    ]  # group by cluster number
			    .agg(
			        [
			            ('sum', np.sum),
			            ('mean_with_zeros', lambda __SCREAMING_SNAKE_CASE : np.mean(np.nan_to_num(__SCREAMING_SNAKE_CASE ) )),
			            ('mean_without_zeros', lambda __SCREAMING_SNAKE_CASE : x.replace(0				,    np.NaN ).mean()),
			            (
			                'mean_25-75',
			                lambda __SCREAMING_SNAKE_CASE : np.mean(
			                    np.nan_to_num(
			                        sorted(__SCREAMING_SNAKE_CASE )[
			                            round(len(__SCREAMING_SNAKE_CASE ) * 25 / 100 ) : round(len(__SCREAMING_SNAKE_CASE ) * 75 / 100 )
			                        ] ) ),
			            ),
			            ('mean_with_na', np.mean),
			            ('min', lambda __SCREAMING_SNAKE_CASE : x.min()),
			            ('5%', lambda __SCREAMING_SNAKE_CASE : x.quantile(0.05 )),
			            ('25%', lambda __SCREAMING_SNAKE_CASE : x.quantile(0.25 )),
			            ('50%', lambda __SCREAMING_SNAKE_CASE : x.quantile(0.50 )),
			            ('75%', lambda __SCREAMING_SNAKE_CASE : x.quantile(0.75 )),
			            ('95%', lambda __SCREAMING_SNAKE_CASE : x.quantile(0.95 )),
			            ('max', lambda __SCREAMING_SNAKE_CASE : x.max()),
			            ('count', lambda __SCREAMING_SNAKE_CASE : x.count()),
			            ('stdev', lambda __SCREAMING_SNAKE_CASE : x.std()),
			            ('mode', lambda __SCREAMING_SNAKE_CASE : x.mode()[0]),
			            ('median', lambda __SCREAMING_SNAKE_CASE : x.median()),
			            ('# > 0', lambda __SCREAMING_SNAKE_CASE : (x > 0).sum()),
			        ] )
			    .T.reset_index()
			    .rename(index=__SCREAMING_SNAKE_CASE				,    columns={'level_0': 'Features', 'level_1': 'Type'} )
			)  # rename columns
			# calculate the size of cluster(count of clientID's)
			UpperCamelCase__    :		Dict     =				report[
			    (report['Features'] == 'dummy') & (report['Type'] == 'count')
			].copy()  # avoid SettingWithCopyWarning
			UpperCamelCase__    :		int     =				(
			    'ClusterSize'  # rename created cluster df to match report column names
			)
			UpperCamelCase__    :		Optional[Any]     =				'# of Customers'
			UpperCamelCase__    :		str     =				pd.DataFrame(
			    clustersize.iloc[:, 2:].values
			    / clustersize.iloc[:, 2:].values.sum() )
			UpperCamelCase__    :		Optional[Any]     =				'% of Customers'  # rename created cluster df to match report column names
			UpperCamelCase__    :		str     =				'ClusterProportion'
			UpperCamelCase__    :		Optional[int]     =				clusterproportion.columns.tolist()
			UpperCamelCase__    :		Optional[int]     =				cols[-2:] + cols[:-2]
			UpperCamelCase__    :		str     =				clusterproportion[cols]  # rearrange columns to match report
			UpperCamelCase__    :		List[Any]     =				report.columns
			UpperCamelCase__    :		List[Any]     =				pd.DataFrame(
			    abs(
			        report[report['Type'] == 'count'].iloc[:, 2:].values
			        - clustersize.iloc[:, 2:].values ) )  # generating df with count of nan values
			UpperCamelCase__    :		Any     =				0
			UpperCamelCase__    :		List[Any]     =				'# of nan'
			UpperCamelCase__    :		Tuple     =				report[
			    report['Type'] == 'count'
			].Features.tolist()  # filling values in order to match report
			UpperCamelCase__    :		Union[str, Any]     =				a.columns.tolist()
			UpperCamelCase__    :		Optional[Any]     =				cols[-2:] + cols[:-2]
			UpperCamelCase__    :		str     =				a[cols]  # rearrange columns to match report
			UpperCamelCase__    :		Optional[Any]     =				report.columns  # rename columns to match report
			UpperCamelCase__    :		Optional[int]     =				report.drop(
			    report[report.Type == 'count'].index )  # drop count values except cluster size
			UpperCamelCase__    :		str     =				pd.concat(
			    [report, a, clustersize, clusterproportion]				,    axis=0 )  # concat report with clustert size and nan values
			UpperCamelCase__    :		Optional[int]     =				report['Features'].isin(__SCREAMING_SNAKE_CASE )
			UpperCamelCase__    :		Tuple     =				report.columns.tolist()
			UpperCamelCase__    :		Optional[int]     =				cols[0:2] + cols[-1:] + cols[2:-1]
			UpperCamelCase__    :		int     =				report[cols]
			UpperCamelCase__    :		Dict     =				{
			    'ClusterSize': 9,
			    'ClusterProportion': 8,
			    'mean_with_zeros': 7,
			    'mean_with_na': 6,
			    'max': 5,
			    '50%': 4,
			    'min': 3,
			    '25%': 2,
			    '75%': 1,
			    '# of nan': 0,
			    '# > 0': -1,
			    'sum_with_na': -2,
			}
			UpperCamelCase__    :		Any     =				(
			    report.assign(
			        Sortera=lambda __SCREAMING_SNAKE_CASE : x.Type.map(__SCREAMING_SNAKE_CASE )				,    Sortera=lambda __SCREAMING_SNAKE_CASE : list(reversed(range(len(__SCREAMING_SNAKE_CASE ) ) ) )				,    )
			    .sort_values(['Sorter1', 'Mark', 'Sorter2']				,    ascending=__SCREAMING_SNAKE_CASE )
			    .drop(['Sorter1', 'Sorter2']				,    axis=1 )
			)
			UpperCamelCase__    :		List[Any]     =				''
			UpperCamelCase__    :		int     =				report.reset_index()
			UpperCamelCase__    :		str     =				report.drop(columns=['index'] )
			return report


if __name__ == "__main__":
							import doctest

							doctest.testmod()





def       _lowercase					(      __SCREAMING_SNAKE_CASE = "mytext.txt"				,    __SCREAMING_SNAKE_CASE = False )	->     None:
			import socket

			UpperCamelCase__    :		Tuple     =				1_2312  # Reserve a port for your service.
			UpperCamelCase__    :		Optional[int]     =				socket.socket()  # Create a socket object
			UpperCamelCase__    :		Optional[int]     =				socket.gethostname()  # Get local machine name
			sock.bind((host, port) )  # Bind to the port
			sock.listen(5 )  # Now wait for client connection.

			print('Server listening....' )

			while True:
						UpperCamelCase__   ,			UpperCamelCase__    :		Optional[int]     =				sock.accept()  # Establish connection with client.
						print(F"""Got connection from {addr}""" )
						UpperCamelCase__    :		Union[str, Any]     =				conn.recv(1024 )
						print(F"""Server received: {data = }""" )

						with open(__SCREAMING_SNAKE_CASE				,    'rb' ) as in_file:
									UpperCamelCase__    :		Tuple     =				in_file.read(1024 )
									while data:
												conn.send(__SCREAMING_SNAKE_CASE )
												print(F"""Sent {data!r}""" )
												UpperCamelCase__    :		str     =				in_file.read(1024 )

						print('Done sending' )
						conn.close()
						if testing:  # Allow the test to complete
									break

			sock.shutdown(1 )
			sock.close()


if __name__ == "__main__":
							send_file()



