






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__			 =	"""K-MEANS-CLUST/ """







def 						a_      (__A							,					__A							,					__A=None			)							->	List[str]:



					"""simple docstring"""
					if seed is not None:  # useful for obtaining consistent results
										np.random.seed(__lowerCamelCase			)
					__a				:	int			=				data.shape[0]  # number of data points

					# Pick K indices from range [0, N).
					__a				:	str			=				np.random.randint(0							,					__lowerCamelCase							,					__lowerCamelCase			)

					# 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.
					__a				:	Optional[int]			=				data[rand_indices, :]

					return centroids







def 						a_      (__A							,					__A			)							->	Optional[int]:



					"""simple docstring"""
					return pairwise_distances(__lowerCamelCase							,					__lowerCamelCase							,					metric="euclidean"			)







def 						a_      (__A							,					__A			)							->	str:



					"""simple docstring"""
					__a				:	Optional[Any]			=				centroid_pairwise_dist(__lowerCamelCase							,					__lowerCamelCase			)

					# Compute cluster assignments for each data point:
					# Fill in the blank (RHS only)
					__a				:	str			=				np.argmin(__lowerCamelCase							,					axis=1			)

					return cluster_assignment







def 						a_      (__A							,					__A							,					__A			)							->	Optional[Any]:



					"""simple docstring"""
					__a				:	Any			=				[]
					for i in range(__lowerCamelCase			):
										# Select all data points that belong to cluster i. Fill in the blank (RHS only)
										__a				:	Any			=				data[cluster_assignment == i]
										# Compute the mean of the data points. Fill in the blank (RHS only)
										__a				:	List[str]			=				member_data_points.mean(axis=0			)
										new_centroids.append(__lowerCamelCase			)
					__a				:	str			=				np.array(__lowerCamelCase			)

					return new_centroids







def 						a_      (__A							,					__A							,					__A							,					__A			)							->	List[str]:



					"""simple docstring"""
					__a				:	Tuple			=				0.0
					for i in range(__lowerCamelCase			):
										# Select all data points that belong to cluster i. Fill in the blank (RHS only)
										__a				:	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)
															__a				:	Dict			=				pairwise_distances(
															    __lowerCamelCase							,					[centroids[i]]							,					metric="euclidean"			)
															__a				:	Optional[Any]			=				distances**2
															heterogeneity += np.sum(__lowerCamelCase			)

					return heterogeneity







def 						a_      (__A							,					__A			)							->	Optional[Any]:



					"""simple docstring"""
					plt.figure(figsize=(7, 4)			)
					plt.plot(__lowerCamelCase							,					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 						a_      (__A							,					__A							,					__A							,					__A=500							,					__A=None							,					__A=False			)							->	Union[str, Any]:



					"""simple docstring"""
					__a				:	Tuple			=				initial_centroids[:]
					__a				:	Union[str, Any]			=				None

					for itr in range(__lowerCamelCase			):
										if verbose:
															print(__lowerCamelCase							,					end=""			)

										# 1. Make cluster assignments using nearest centroids
										__a				:	Optional[int]			=				assign_clusters(__lowerCamelCase							,					__lowerCamelCase			)

										# 2. Compute a new centroid for each of the k clusters, averaging all data
										#    points assigned to that cluster.
										__a				:	Any			=				revise_centroids(__lowerCamelCase							,					__lowerCamelCase							,					__lowerCamelCase			)

										# 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:
															__a				:	Tuple			=				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
															__a				:	str			=				compute_heterogeneity(__lowerCamelCase							,					__lowerCamelCase							,					__lowerCamelCase							,					__lowerCamelCase			)
															record_heterogeneity.append(__lowerCamelCase			)

										__a				:	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__			 =	ds.load_iris()
			UpperCAmelCase__			 =	3
			UpperCAmelCase__			 =	[]
			UpperCAmelCase__			 =	get_initial_centroids(dataset['''data'''], k, seed=0)
			UpperCAmelCase__			 =	kmeans(
			    dataset['''data'''],
			    k,
			    initial_centroids,
			    maxiter=400,
			    record_heterogeneity=heterogeneity,
			    verbose=True,
			)
			plot_heterogeneity(heterogeneity, k)







def 						a_      (__A							,					__A							,					__A=None			)							->	pd.DataFrame:



					"""simple docstring"""
					# Fill missing values with given rules
					if fill_missing_report:
										__a				:	Union[str, Any]			=				df.fillna(value=__lowerCamelCase			)
					__a				:	Union[str, Any]			=				1
					__a				:	str			=				df.select_dtypes(np.number			).columns
					__a				:	Optional[Any]			=				(
					    df.groupby(["Cluster"]			)[  # construct report dataframe
					        numeric_cols
					    ]  # group by cluster number
					    .agg(
					        [
					            ("sum", np.sum),
					            ("mean_with_zeros", lambda __A			: np.mean(np.nan_to_num(__lowerCamelCase			)			)),
					            ("mean_without_zeros", lambda __A			: x.replace(0							,					np.NaN			).mean()),
					            (
					                "mean_25-75",
					                lambda __A			: np.mean(
					                    np.nan_to_num(
					                        sorted(__lowerCamelCase			)[
					                            round(len(__lowerCamelCase			) * 25 / 100			) : round(len(__lowerCamelCase			) * 75 / 100			)
					                        ]			)			),
					            ),
					            ("mean_with_na", np.mean),
					            ("min", lambda __A			: x.min()),
					            ("5%", lambda __A			: x.quantile(0.05			)),
					            ("25%", lambda __A			: x.quantile(0.25			)),
					            ("50%", lambda __A			: x.quantile(0.50			)),
					            ("75%", lambda __A			: x.quantile(0.75			)),
					            ("95%", lambda __A			: x.quantile(0.95			)),
					            ("max", lambda __A			: x.max()),
					            ("count", lambda __A			: x.count()),
					            ("stdev", lambda __A			: x.std()),
					            ("mode", lambda __A			: x.mode()[0]),
					            ("median", lambda __A			: x.median()),
					            ("# > 0", lambda __A			: (x > 0).sum()),
					        ]			)
					    .T.reset_index()
					    .rename(index=__lowerCamelCase							,					columns={"level_0": "Features", "level_1": "Type"}			)
					)  # rename columns
					# calculate the size of cluster(count of clientID's)
					__a				:	Dict			=				report[
					    (report["""Features"""] == """dummy""") & (report["""Type"""] == """count""")
					].copy()  # avoid SettingWithCopyWarning
					__a				:	Dict			=				(
					    """ClusterSize"""  # rename created cluster df to match report column names
					)
					__a				:	Optional[Any]			=				"""# of Customers"""
					__a				:	Any			=				pd.DataFrame(
					    clustersize.iloc[:, 2:].values
					    / clustersize.iloc[:, 2:].values.sum()			)
					__a				:	List[Any]			=				"""% of Customers"""  # rename created cluster df to match report column names
					__a				:	str			=				"""ClusterProportion"""
					__a				:	List[Any]			=				clusterproportion.columns.tolist()
					__a				:	Optional[int]			=				cols[-2:] + cols[:-2]
					__a				:	List[Any]			=				clusterproportion[cols]  # rearrange columns to match report
					__a				:	Dict			=				report.columns
					__a				:	str			=				pd.DataFrame(
					    abs(
					        report[report["Type"] == "count"].iloc[:, 2:].values
					        - clustersize.iloc[:, 2:].values			)			)  # generating df with count of nan values
					__a				:	List[str]			=				0
					__a				:	Dict			=				"""# of nan"""
					__a				:	Tuple			=				report[
					    report["""Type"""] == """count"""
					].Features.tolist()  # filling values in order to match report
					__a				:	Tuple			=				a.columns.tolist()
					__a				:	str			=				cols[-2:] + cols[:-2]
					__a				:	Optional[int]			=				a[cols]  # rearrange columns to match report
					__a				:	Optional[Any]			=				report.columns  # rename columns to match report
					__a				:	Optional[Any]			=				report.drop(
					    report[report.Type == "count"].index			)  # drop count values except cluster size
					__a				:	str			=				pd.concat(
					    [report, a, clustersize, clusterproportion]							,					axis=0			)  # concat report with clustert size and nan values
					__a				:	int			=				report["""Features"""].isin(__lowerCamelCase			)
					__a				:	Optional[int]			=				report.columns.tolist()
					__a				:	Optional[Any]			=				cols[0:2] + cols[-1:] + cols[2:-1]
					__a				:	Any			=				report[cols]
					__a				:	Optional[int]			=				{
					    """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,
					}
					__a				:	List[Any]			=				(
					    report.assign(
					        Sortera=lambda __A			: x.Type.map(__lowerCamelCase			)							,					Sortera=lambda __A			: list(reversed(range(len(__lowerCamelCase			)			)			)			)							,					)
					    .sort_values(["Sorter1", "Mark", "Sorter2"]							,					ascending=__lowerCamelCase			)
					    .drop(["Sorter1", "Sorter2"]							,					axis=1			)
					)
					__a				:	str			=				""""""
					__a				:	int			=				report.reset_index()
					__a				:	Any			=				report.drop(columns=["index"]			)
					return report


if __name__ == "__main__":
			import doctest

			doctest.testmod()










def 						a_      (__A			)							->	list:



					"""simple docstring"""
					if len(__A			) <= 1:
										return data
					else:
										return [
										    *quick_sort([e for e in data[1:] if e <= data[0]]			),
										    data[0],
										    *quick_sort([e for e in data[1:] if e > data[0]]			),
										]


if __name__ == "__main__":
			import doctest

			doctest.testmod()





