



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     (						__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase=None      )			->			Optional[int]:



						'''simple docstring'''






						if seed is not None:  # useful for obtaining consistent results
												np.random.seed(__UpperCAmelCase      )
						UpperCAmelCase_					=	data.shape[0]  # number of data points

						# Pick K indices from range [0, N).
						UpperCAmelCase_					=	np.random.randint(0				,   __UpperCAmelCase				,   __UpperCAmelCase      )

						# 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_					=	data[rand_indices, :]

						return centroids







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			Union[str, Any]:



						'''simple docstring'''






						return pairwise_distances(__UpperCAmelCase				,   __UpperCAmelCase				,   metric='''euclidean'''      )







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			int:



						'''simple docstring'''






						UpperCAmelCase_					=	centroid_pairwise_dist(__UpperCAmelCase				,   __UpperCAmelCase      )

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

						return cluster_assignment







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )			->			str:



						'''simple docstring'''






						UpperCAmelCase_					=	[]
						for i in range(__UpperCAmelCase      ):
												# Select all data points that belong to cluster i. Fill in the blank (RHS only)
												UpperCAmelCase_					=	data[cluster_assignment == i]
												# Compute the mean of the data points. Fill in the blank (RHS only)
												UpperCAmelCase_					=	member_data_points.mean(axis=0      )
												new_centroids.append(__UpperCAmelCase      )
						UpperCAmelCase_					=	np.array(__UpperCAmelCase      )

						return new_centroids







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )			->			List[str]:



						'''simple docstring'''






						UpperCAmelCase_					=	0.0
						for i in range(__UpperCAmelCase      ):
												# Select all data points that belong to cluster i. Fill in the blank (RHS only)
												UpperCAmelCase_					=	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_					=	pairwise_distances(
																		    __UpperCAmelCase				,   [centroids[i]]				,   metric='''euclidean'''      )
																		UpperCAmelCase_					=	distances**2
																		heterogeneity += np.sum(__UpperCAmelCase      )

						return heterogeneity







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			List[str]:



						'''simple docstring'''






						plt.figure(figsize=(7, 4)      )
						plt.plot(__UpperCAmelCase				,   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     (						__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase=500				,   __UpperCAmelCase=None				,   __UpperCAmelCase=False      )			->			Optional[int]:



						'''simple docstring'''






						UpperCAmelCase_					=	initial_centroids[:]
						UpperCAmelCase_					=	None

						for itr in range(__UpperCAmelCase      ):
												if verbose:
																		print(__UpperCAmelCase				,   end=''''''      )

												# 1. Make cluster assignments using nearest centroids
												UpperCAmelCase_					=	assign_clusters(__UpperCAmelCase				,   __UpperCAmelCase      )

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

												# 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_					=	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_					=	compute_heterogeneity(__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )
																		record_heterogeneity.append(__UpperCAmelCase      )

												UpperCAmelCase_					=	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_			,     UpperCamelCase_      			=  kmeans(
							    dataset["data"],
							    k,
							    initial_centroids,
							    maxiter=400,
							    record_heterogeneity=heterogeneity,
							    verbose=True,
							)
							plot_heterogeneity(heterogeneity, k)







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase=None      )			->			pd.DataFrame:



						'''simple docstring'''






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


if __name__ == "__main__":
							import doctest

							doctest.testmod()










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase_      			=  {
    "configuration_mobilenet_v1": [
        "MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileNetV1Config",
        "MobileNetV1OnnxConfig",
    ],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_      			=  ["MobileNetV1FeatureExtractor"]
							UpperCamelCase_      			=  ["MobileNetV1ImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_      			=  [
							    "MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "MobileNetV1ForImageClassification",
							    "MobileNetV1Model",
							    "MobileNetV1PreTrainedModel",
							    "load_tf_weights_in_mobilenet_v1",
							]


if TYPE_CHECKING:
							from .configuration_mobilenet_va import (
							    MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    MobileNetVaConfig,
							    MobileNetVaOnnxConfig,
							)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
														from .image_processing_mobilenet_va import MobileNetVaImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mobilenet_va import (
														    MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MobileNetVaForImageClassification,
														    MobileNetVaModel,
														    MobileNetVaPreTrainedModel,
														    load_tf_weights_in_mobilenet_va,
														)


else:
							import sys

							UpperCamelCase_      			=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





