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'''
  					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__		:       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 typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  SCREAMING_SNAKE_CASE__		:       Any   							=							{"""configuration_dinat""": ["""DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DinatConfig"""]}
  
  
  try:
        if not is_torch_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        SCREAMING_SNAKE_CASE__		:       str   							=							[
            """DINAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
            """DinatForImageClassification""",
            """DinatModel""",
            """DinatPreTrainedModel""",
            """DinatBackbone""",
        ]
  
  if TYPE_CHECKING:
        from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig
  
        try:
              if not is_torch_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_dinat import (
                  DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DinatBackbone,
                  DinatForImageClassification,
                  DinatModel,
                  DinatPreTrainedModel,
              )
  
  else:
        import sys
  
        SCREAMING_SNAKE_CASE__		:       Dict   							=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  