

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')

a_         =      'K-MEANS-CLUST/ '

def 	lowerCamelCase__	(   _a							,     _a							,     _a=None):
	if seed is not None:  # useful for obtaining consistent results
		np.random.seed(_a)
	SCREAMING_SNAKE_CASE				: Dict			       =    data.shape[0]  # number of data points

	# Pick K indices from range [0, N).
	SCREAMING_SNAKE_CASE				: List[Any]			       =    np.random.randint(0							,     _a							,     _a)

	# 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.
	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    data[rand_indices, :]

	return centroids

def 	lowerCamelCase__	(   _a							,     _a):
	return pairwise_distances(_a							,     _a							,     metric="euclidean")

def 	lowerCamelCase__	(   _a							,     _a):
	# Compute distances between each data point and the set of centroids:
	# Fill in the blank (RHS only)
	SCREAMING_SNAKE_CASE				: List[Any]			       =    centroid_pairwise_dist(_a							,     _a)

	# Compute cluster assignments for each data point:
	# Fill in the blank (RHS only)
	SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    np.argmin(_a							,     axis=1)

	return cluster_assignment

def 	lowerCamelCase__	(   _a							,     _a							,     _a):
	SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    []
	for i in range(_a):
		# Select all data points that belong to cluster i. Fill in the blank (RHS only)
		SCREAMING_SNAKE_CASE				: Dict			       =    data[cluster_assignment == i]
		# Compute the mean of the data points. Fill in the blank (RHS only)
		SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    member_data_points.mean(axis=0)
		new_centroids.append(_a)
	SCREAMING_SNAKE_CASE				: int			       =    np.array(_a)

	return new_centroids

def 	lowerCamelCase__	(   _a							,     _a							,     _a							,     _a):
	SCREAMING_SNAKE_CASE				: Optional[int]			       =    0.0
	for i in range(_a):
		# Select all data points that belong to cluster i. Fill in the blank (RHS only)
		SCREAMING_SNAKE_CASE				: Any			       =    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)
			SCREAMING_SNAKE_CASE				: int			       =    pairwise_distances(
			    _a							,     [centroids[i]]							,     metric="euclidean")
			SCREAMING_SNAKE_CASE				: Optional[int]			       =    distances**2
			heterogeneity += np.sum(_a)

	return heterogeneity

def 	lowerCamelCase__	(   _a							,     _a):
	plt.figure(figsize=(7, 4))
	plt.plot(_a							,     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 	lowerCamelCase__	(   _a							,     _a							,     _a							,     _a=500							,     _a=None							,     _a=False):
	SCREAMING_SNAKE_CASE				: str			       =    initial_centroids[:]
	SCREAMING_SNAKE_CASE				: str			       =    None

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

		# 1. Make cluster assignments using nearest centroids
		SCREAMING_SNAKE_CASE				: Any			       =    assign_clusters(_a							,     _a)

		# 2. Compute a new centroid for each of the k clusters, averaging all data
		#    points assigned to that cluster.
		SCREAMING_SNAKE_CASE				: List[str]			       =    revise_centroids(_a							,     _a							,     _a)

		# 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:
			SCREAMING_SNAKE_CASE				: 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
			SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    compute_heterogeneity(_a							,     _a							,     _a							,     _a)
			record_heterogeneity.append(_a)

		SCREAMING_SNAKE_CASE				: Any			       =    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

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

def 	lowerCamelCase__	(   _a							,     _a							,     _a=None):
	# Fill missing values with given rules
	if fill_missing_report:
		SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    df.fillna(value=_a)
	SCREAMING_SNAKE_CASE				: int			       =    1
	SCREAMING_SNAKE_CASE				: Dict			       =    df.select_dtypes(np.number).columns
	SCREAMING_SNAKE_CASE				: List[str]			       =    (
	    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(_a))),
	            ("mean_without_zeros", lambda _a: x.replace(0							,     np.NaN).mean()),
	            (
	                "mean_25-75",
	                lambda _a: np.mean(
	                    np.nan_to_num(
	                        sorted(_a)[
	                            round(len(_a) * 25 / 100) : round(len(_a) * 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=_a							,     columns={"level_0": "Features", "level_1": "Type"})
	)  # rename columns
	# calculate the size of cluster(count of clientID's)
	SCREAMING_SNAKE_CASE				: List[str]			       =    report[
	    (report["Features"] == "dummy") & (report["Type"] == "count")
	].copy()  # avoid SettingWithCopyWarning
	SCREAMING_SNAKE_CASE				: Tuple			       =    (
	    "ClusterSize"  # rename created cluster df to match report column names
	)
	SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    "# of Customers"
	SCREAMING_SNAKE_CASE				: List[str]			       =    pd.DataFrame(
	    clustersize.iloc[:, 2:].values
	    / clustersize.iloc[:, 2:].values.sum())
	SCREAMING_SNAKE_CASE				: Dict			       =    "% of Customers"  # rename created cluster df to match report column names
	SCREAMING_SNAKE_CASE				: int			       =    "ClusterProportion"
	SCREAMING_SNAKE_CASE				: int			       =    clusterproportion.columns.tolist()
	SCREAMING_SNAKE_CASE				: int			       =    cols[-2:] + cols[:-2]
	SCREAMING_SNAKE_CASE				: List[Any]			       =    clusterproportion[cols]  # rearrange columns to match report
	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    report.columns
	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    pd.DataFrame(
	    abs(
	        report[report["Type"] == "count"].iloc[:, 2:].values
	        - clustersize.iloc[:, 2:].values))  # generating df with count of nan values
	SCREAMING_SNAKE_CASE				: List[str]			       =    0
	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    "# of nan"
	SCREAMING_SNAKE_CASE				: Tuple			       =    report[
	    report["Type"] == "count"
	].Features.tolist()  # filling values in order to match report
	SCREAMING_SNAKE_CASE				: Tuple			       =    a.columns.tolist()
	SCREAMING_SNAKE_CASE				: Tuple			       =    cols[-2:] + cols[:-2]
	SCREAMING_SNAKE_CASE				: List[str]			       =    a[cols]  # rearrange columns to match report
	SCREAMING_SNAKE_CASE				: Dict			       =    report.columns  # rename columns to match report
	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    report.drop(
	    report[report.Type == "count"].index)  # drop count values except cluster size
	SCREAMING_SNAKE_CASE				: Any			       =    pd.concat(
	    [report, a, clustersize, clusterproportion]							,     axis=0)  # concat report with clustert size and nan values
	SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    report["Features"].isin(_a)
	SCREAMING_SNAKE_CASE				: Optional[int]			       =    report.columns.tolist()
	SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    cols[0:2] + cols[-1:] + cols[2:-1]
	SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    report[cols]
	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    {
	    "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,
	}
	SCREAMING_SNAKE_CASE				: Dict			       =    (
	    report.assign(
	        Sortera=lambda _a: x.Type.map(_a)							,     Sortera=lambda _a: list(reversed(range(len(_a))))							,     )
	    .sort_values(["Sorter1", "Mark", "Sorter2"]							,     ascending=_a)
	    .drop(["Sorter1", "Sorter2"]							,     axis=1)
	)
	SCREAMING_SNAKE_CASE				: List[str]			       =    ""
	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    report.reset_index()
	SCREAMING_SNAKE_CASE				: Tuple			       =    report.drop(columns=["index"])
	return report


if __name__ == "__main__":
			import doctest

			doctest.testmod()


from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(     self							:	Optional[int] , a							:	Any		)							->    None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[str]			       =    vocab
				SCREAMING_SNAKE_CASE				: str			       =    normalizers.BertNormalizer(
				    clean_text=a , handle_chinese_chars=a , strip_accents=a , lowercase=a , )
				try:
					import rjieba
				except ImportError:
					raise ImportError(
					    "You need to install rjieba to use RoFormerTokenizer. "
					    "See https://pypi.org/project/rjieba/ for installation."		)
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    rjieba
			def 							__UpperCamelCase	(     self							:	Optional[int] , a							:	int , a							:	NormalizedString		)							->    List[NormalizedString]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Dict			       =    []

				# this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
				for token, start, end in self.jieba.tokenize(str(a		) , hmm=a		):
					if token in self.vocab:
						splits.append(normalized_string[start:end]		)
					else:
						SCREAMING_SNAKE_CASE				: Any			       =    self.normalizers.normalize_str(a		).split()
						for token in token_list:
							if token:
								SCREAMING_SNAKE_CASE				: Optional[int]			       =    start + len(a		)
								splits.append(normalized_string[start:end]		)
								SCREAMING_SNAKE_CASE				: List[Any]			       =    end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

				return splits






			def 							__UpperCamelCase	(     self							:	Optional[int] , a							:	PreTokenizedString		)							->    Tuple:

				"""simple docstring"""

				pretok.split(self.jieba_split		)