
'''simple docstring'''

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 	_UpperCAmelCase				(  _lowerCamelCase						:       List[str]  ,      _lowerCamelCase						:       Dict  ,      _lowerCamelCase						:       Any=None		)    ->						List[str]:
  if seed is not None:  # useful for obtaining consistent results
    np.random.seed(__lowerCamelCase		)
  _lowerCAmelCase       :  int   				=					data.shape[0]  # number of data points

  # Pick K indices from range [0, N).
  _lowerCAmelCase       :  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.
  _lowerCAmelCase       :  Optional[int]   				=					data[rand_indices, :]

  return centroids


def 	_UpperCAmelCase				(  _lowerCamelCase						:       List[Any]  ,      _lowerCamelCase						:       Optional[int]		)    ->						Optional[int]:
  return pairwise_distances(__lowerCamelCase  ,      __lowerCamelCase  ,      metric="""euclidean"""		)


def 	_UpperCAmelCase				(  _lowerCamelCase						:       Union[str, Any]  ,      _lowerCamelCase						:       Optional[Any]		)    ->						str:
  _lowerCAmelCase       :  Optional[Any]   				=					centroid_pairwise_dist(__lowerCamelCase  ,      __lowerCamelCase		)

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

  return cluster_assignment


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

  return new_centroids


def 	_UpperCAmelCase				(  _lowerCamelCase						:       Any  ,      _lowerCamelCase						:       Any  ,      _lowerCamelCase						:       Dict  ,      _lowerCamelCase						:       Union[str, Any]		)    ->						List[str]:
  _lowerCAmelCase       :  Tuple   				=					0.0
  for i in range(__lowerCamelCase		):
    # Select all data points that belong to cluster i. Fill in the blank (RHS only)
    _lowerCAmelCase       :  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)
      _lowerCAmelCase       :  Dict   				=					pairwise_distances(
          __lowerCamelCase  ,      [centroids[i]]  ,      metric="""euclidean"""		)
      _lowerCAmelCase       :  Optional[Any]   				=					distances**2
      heterogeneity += np.sum(__lowerCamelCase		)

  return heterogeneity


def 	_UpperCAmelCase				(  _lowerCamelCase						:       List[str]  ,      _lowerCamelCase						:       Union[str, Any]		)    ->						Optional[Any]:
  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 	_UpperCAmelCase				(  _lowerCamelCase						:       List[Any]  ,      _lowerCamelCase						:       List[str]  ,      _lowerCamelCase						:       Dict  ,      _lowerCamelCase						:       int=5_00  ,      _lowerCamelCase						:       Optional[int]=None  ,      _lowerCamelCase						:       int=False		)    ->						Union[str, Any]:
  _lowerCAmelCase       :  Tuple   				=					initial_centroids[:]
  _lowerCAmelCase       :  Union[str, Any]   				=					None

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

    # 1. Make cluster assignments using nearest centroids
    _lowerCAmelCase       :  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.
    _lowerCAmelCase       :  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:
      _lowerCAmelCase       :  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
      _lowerCAmelCase       :  str   				=					compute_heterogeneity(__lowerCamelCase  ,      __lowerCamelCase  ,      __lowerCamelCase  ,      __lowerCamelCase		)
      record_heterogeneity.append(__lowerCamelCase		)

    _lowerCAmelCase       :  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=4_00,
         record_heterogeneity=heterogeneity,
         verbose=True,
     )
     plot_heterogeneity(heterogeneity, k)


def 	_UpperCAmelCase				(  _lowerCamelCase						:       int  ,      _lowerCamelCase						:       str  ,      _lowerCamelCase						:       Any=None		)    ->						pd.DataFrame:
  # Fill missing values with given rules
  if fill_missing_report:
    _lowerCAmelCase       :  Union[str, Any]   				=					df.fillna(value=__lowerCamelCase		)
  _lowerCAmelCase       :  Union[str, Any]   				=					1
  _lowerCAmelCase       :  str   				=					df.select_dtypes(np.number		).columns
  _lowerCAmelCase       :  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 / 1_00		) : round(len(__lowerCamelCase		) * 75 / 1_00		)
                          ]		)		),
              ),
              ("""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)
  _lowerCAmelCase       :  Dict   				=					report[
      (report["""Features"""] == """dummy""") & (report["""Type"""] == """count""")
  ].copy()  # avoid SettingWithCopyWarning
  _lowerCAmelCase       :  Dict   				=					(
      """ClusterSize"""  # rename created cluster df to match report column names
  )
  _lowerCAmelCase       :  Optional[Any]   				=					"""# of Customers"""
  _lowerCAmelCase       :  Any   				=					pd.DataFrame(
      clustersize.iloc[:, 2:].values
      / clustersize.iloc[:, 2:].values.sum()		)
  _lowerCAmelCase       :  List[Any]   				=					"""% of Customers"""  # rename created cluster df to match report column names
  _lowerCAmelCase       :  str   				=					"""ClusterProportion"""
  _lowerCAmelCase       :  List[Any]   				=					clusterproportion.columns.tolist()
  _lowerCAmelCase       :  Optional[int]   				=					cols[-2:] + cols[:-2]
  _lowerCAmelCase       :  List[Any]   				=					clusterproportion[cols]  # rearrange columns to match report
  _lowerCAmelCase       :  Dict   				=					report.columns
  _lowerCAmelCase       :  str   				=					pd.DataFrame(
      abs(
          report[report["""Type"""] == """count"""].iloc[:, 2:].values
          - clustersize.iloc[:, 2:].values		)		)  # generating df with count of nan values
  _lowerCAmelCase       :  List[str]   				=					0
  _lowerCAmelCase       :  Dict   				=					"""# of nan"""
  _lowerCAmelCase       :  Tuple   				=					report[
      report["""Type"""] == """count"""
  ].Features.tolist()  # filling values in order to match report
  _lowerCAmelCase       :  Tuple   				=					a.columns.tolist()
  _lowerCAmelCase       :  str   				=					cols[-2:] + cols[:-2]
  _lowerCAmelCase       :  Optional[int]   				=					a[cols]  # rearrange columns to match report
  _lowerCAmelCase       :  Optional[Any]   				=					report.columns  # rename columns to match report
  _lowerCAmelCase       :  Optional[Any]   				=					report.drop(
      report[report.Type == """count"""].index		)  # drop count values except cluster size
  _lowerCAmelCase       :  str   				=					pd.concat(
      [report, a, clustersize, clusterproportion]  ,      axis=0		)  # concat report with clustert size and nan values
  _lowerCAmelCase       :  int   				=					report["""Features"""].isin(__lowerCamelCase		)
  _lowerCAmelCase       :  Optional[int]   				=					report.columns.tolist()
  _lowerCAmelCase       :  Optional[Any]   				=					cols[0:2] + cols[-1:] + cols[2:-1]
  _lowerCAmelCase       :  Any   				=					report[cols]
  _lowerCAmelCase       :  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,
  }
  _lowerCAmelCase       :  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		)
  )
  _lowerCAmelCase       :  str   				=					""""""
  _lowerCAmelCase       :  int   				=					report.reset_index()
  _lowerCAmelCase       :  Any   				=					report.drop(columns=["""index"""]		)
  return report


if __name__ == "__main__":
     import doctest

     doctest.testmod()




'''simple docstring'''

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like


def 	_UpperCAmelCase				(  _lowerCamelCase						:       str  ,      _lowerCamelCase						:       List[Any]=3		)    ->						Union[str, Any]:
  _lowerCAmelCase       :  str   				=					int(mask / 2		)
  # copy image size
  _lowerCAmelCase       :  Dict   				=					zeros_like(_lowerCamelCase		)
  for i in range(_lowerCamelCase  ,      gray_img.shape[0] - bd		):
    for j in range(_lowerCamelCase  ,      gray_img.shape[1] - bd		):
      # get mask according with mask
      _lowerCAmelCase       :  Union[str, Any]   				=					ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]		)
      # calculate mask median
      _lowerCAmelCase       :  str   				=					sort(_lowerCamelCase		)[inta(divide((multiply(_lowerCamelCase  ,      _lowerCamelCase		))  ,      2		) + 1		)]
      _lowerCAmelCase       :  Dict   				=					median
  return median_img


if __name__ == "__main__":
     # read original image
     UpperCamelCase_     			=       imread("""../image_data/lena.jpg""")
     # turn image in gray scale value
     UpperCamelCase_     			=       cvtColor(img, COLOR_BGR2GRAY)

     # get values with two different mask size
     UpperCamelCase_     			=       median_filter(gray, 3)
     UpperCamelCase_     			=       median_filter(gray, 5)

     # show result images
     imshow("""median filter with 3x3 mask""", medianaxa)
     imshow("""median filter with 5x5 mask""", medianaxa)
     waitKey(0)


