code: 
  
  
  
  
  
  
  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""")
  
  SCREAMING_SNAKE_CASE__		:       str   							=							"""K-MEANS-CLUST/ """
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase=None					)   -> List[str]:
  
       '''simple docstring'''
       if seed is not None:  # useful for obtaining consistent results
            np.random.seed(__lowerCamelCase					)
       UpperCAmelCase__			:     int										= data.shape[0]  # number of data points
  
       # Pick K indices from range [0, N).
       UpperCAmelCase__			:     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.
       UpperCAmelCase__			:     Optional[int]										= data[rand_indices, :]
  
       return centroids
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> Optional[int]:
  
       '''simple docstring'''
       return pairwise_distances(__lowerCamelCase   ,       __lowerCamelCase   ,       metric="""euclidean"""					)
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> str:
  
       '''simple docstring'''
       # Compute distances between each data point and the set of centroids:
       # Fill in the blank (RHS only)
       UpperCAmelCase__			:     Optional[Any]										= centroid_pairwise_dist(__lowerCamelCase   ,       __lowerCamelCase					)
  
       # Compute cluster assignments for each data point:
       # Fill in the blank (RHS only)
       UpperCAmelCase__			:     str										= np.argmin(__lowerCamelCase   ,       axis=1					)
  
       return cluster_assignment
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase					)   -> Optional[Any]:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Any										= []
       for i in range(__lowerCamelCase					):
            # Select all data points that belong to cluster i. Fill in the blank (RHS only)
            UpperCAmelCase__			:     Any										= data[cluster_assignment == i]
            # Compute the mean of the data points. Fill in the blank (RHS only)
            UpperCAmelCase__			:     List[str]										= member_data_points.mean(axis=0					)
            new_centroids.append(__lowerCamelCase					)
       UpperCAmelCase__			:     str										= np.array(__lowerCamelCase					)
  
       return new_centroids
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase					)   -> List[str]:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Tuple										= 0.0
       for i in range(__lowerCamelCase					):
            # Select all data points that belong to cluster i. Fill in the blank (RHS only)
            UpperCAmelCase__			:     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__			:     Dict										= pairwise_distances(
                     __lowerCamelCase   ,       [centroids[i]]   ,       metric="""euclidean"""					)
                 UpperCAmelCase__			:     Optional[Any]										= distances**2
                 heterogeneity += np.sum(__lowerCamelCase					)
  
       return heterogeneity
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> 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    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase=500   ,       __lowerCamelCase=None   ,       __lowerCamelCase=False					)   -> Union[str, Any]:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Tuple										= initial_centroids[:]
       UpperCAmelCase__			:     Union[str, Any]										= None
  
       for itr in range(__lowerCamelCase					):
            if verbose:
                 print(__lowerCamelCase   ,       end=""""""					)
  
            # 1. Make cluster assignments using nearest centroids
            UpperCAmelCase__			:     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.
            UpperCAmelCase__			:     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:
                 UpperCAmelCase__			:     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
                 UpperCAmelCase__			:     str										= compute_heterogeneity(__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase					)
                 record_heterogeneity.append(__lowerCamelCase					)
  
            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
  
        SCREAMING_SNAKE_CASE__		:       Optional[int]   							=							ds.load_iris()
        SCREAMING_SNAKE_CASE__		:       Tuple   							=							3
        SCREAMING_SNAKE_CASE__		:       List[str]   							=							[]
        SCREAMING_SNAKE_CASE__		:       List[Any]   							=							get_initial_centroids(dataset["""data"""], k, seed=0)
        SCREAMING_SNAKE_CASE__							, SCREAMING_SNAKE_CASE__		:       Dict   							=							kmeans(
            dataset["""data"""],
            k,
            initial_centroids,
            maxiter=4_00,
            record_heterogeneity=heterogeneity,
            verbose=True,
        )
        plot_heterogeneity(heterogeneity, k)
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase=None					)   -> pd.DataFrame:
  
       '''simple docstring'''
       # Fill missing values with given rules
       if fill_missing_report:
            UpperCAmelCase__			:     Union[str, Any]										= df.fillna(value=__lowerCamelCase					)
       UpperCAmelCase__			:     Union[str, Any]										= 1
       UpperCAmelCase__			:     str										= df.select_dtypes(np.number					).columns
       UpperCAmelCase__			:     Optional[Any]										= (
           df.groupby(["""Cluster"""]					)[  # construct report dataframe
               numeric_cols
           ]  # group by cluster number
           .agg(
               [
                   ("""sum""", np.sum),
                   ("""mean_with_zeros""", lambda __lowerCamelCase					: np.mean(np.nan_to_num(__lowerCamelCase					)					)),
                   ("""mean_without_zeros""", lambda __lowerCamelCase					: x.replace(0   ,       np.NaN					).mean()),
                   (
                       """mean_25-75""",
                       lambda __lowerCamelCase					: 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 __lowerCamelCase					: x.min()),
                   ("""5%""", lambda __lowerCamelCase					: x.quantile(0.05					)),
                   ("""25%""", lambda __lowerCamelCase					: x.quantile(0.25					)),
                   ("""50%""", lambda __lowerCamelCase					: x.quantile(0.50					)),
                   ("""75%""", lambda __lowerCamelCase					: x.quantile(0.75					)),
                   ("""95%""", lambda __lowerCamelCase					: x.quantile(0.95					)),
                   ("""max""", lambda __lowerCamelCase					: x.max()),
                   ("""count""", lambda __lowerCamelCase					: x.count()),
                   ("""stdev""", lambda __lowerCamelCase					: x.std()),
                   ("""mode""", lambda __lowerCamelCase					: x.mode()[0]),
                   ("""median""", lambda __lowerCamelCase					: x.median()),
                   ("""# > 0""", lambda __lowerCamelCase					: (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)
       UpperCAmelCase__			:     Dict										= report[
           (report["""Features"""] == """dummy""") & (report["""Type"""] == """count""")
       ].copy()  # avoid SettingWithCopyWarning
       UpperCAmelCase__			:     Dict										= (
           """ClusterSize"""  # rename created cluster df to match report column names
       )
       UpperCAmelCase__			:     Optional[Any]										= """# of Customers"""
       UpperCAmelCase__			:     Any										= pd.DataFrame(
           clustersize.iloc[:, 2:].values
           / clustersize.iloc[:, 2:].values.sum()					)
       UpperCAmelCase__			:     List[Any]										= """% of Customers"""  # rename created cluster df to match report column names
       UpperCAmelCase__			:     str										= """ClusterProportion"""
       UpperCAmelCase__			:     List[Any]										= clusterproportion.columns.tolist()
       UpperCAmelCase__			:     Optional[int]										= cols[-2:] + cols[:-2]
       UpperCAmelCase__			:     List[Any]										= clusterproportion[cols]  # rearrange columns to match report
       UpperCAmelCase__			:     Dict										= report.columns
       UpperCAmelCase__			:     str										= pd.DataFrame(
           abs(
               report[report["""Type"""] == """count"""].iloc[:, 2:].values
               - clustersize.iloc[:, 2:].values					)					)  # generating df with count of nan values
       UpperCAmelCase__			:     List[str]										= 0
       UpperCAmelCase__			:     Dict										= """# of nan"""
       UpperCAmelCase__			:     Tuple										= report[
           report["""Type"""] == """count"""
       ].Features.tolist()  # filling values in order to match report
       UpperCAmelCase__			:     Tuple										= a.columns.tolist()
       UpperCAmelCase__			:     str										= cols[-2:] + cols[:-2]
       UpperCAmelCase__			:     Optional[int]										= a[cols]  # rearrange columns to match report
       UpperCAmelCase__			:     Optional[Any]										= report.columns  # rename columns to match report
       UpperCAmelCase__			:     Optional[Any]										= 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__			:     int										= report["""Features"""].isin(__lowerCamelCase					)
       UpperCAmelCase__			:     Optional[int]										= report.columns.tolist()
       UpperCAmelCase__			:     Optional[Any]										= cols[0:2] + cols[-1:] + cols[2:-1]
       UpperCAmelCase__			:     Any										= report[cols]
       UpperCAmelCase__			:     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,
       }
       UpperCAmelCase__			:     List[Any]										= (
           report.assign(
               Sortera=lambda __lowerCamelCase					: x.Type.map(__lowerCamelCase					)   ,       Sortera=lambda __lowerCamelCase					: list(reversed(range(len(__lowerCamelCase					)					)					)					)   ,       )
           .sort_values(["""Sorter1""", """Mark""", """Sorter2"""]   ,       ascending=__lowerCamelCase					)
           .drop(["""Sorter1""", """Sorter2"""]   ,       axis=1					)
       )
       UpperCAmelCase__			:     str										= """"""
       UpperCAmelCase__			:     int										= report.reset_index()
       UpperCAmelCase__			:     Any										= report.drop(columns=["""index"""]					)
       return report
  
  
  if __name__ == "__main__":
        import doctest
  
        doctest.testmod()
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from ..utils import requires_backends
  
  
  SCREAMING_SNAKE_CASE__		:       int   							=							None
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     :
  
  
  
  
       def __init__(							self  ,	*_lowerCAmelCase  ,	**_lowerCAmelCase		):
            requires_backends(self  ,	["""detectron2"""]		)
  
  
  
  
       @classmethod
       def      __UpperCAmelCase     (							cls  ,	*_lowerCAmelCase  ,	**_lowerCAmelCase		):
            requires_backends(cls  ,	["""detectron2"""]		)
  
  
  
  
  
  
  