



"""simple docstring"""
from random import shuffle

import tensorflow as tf
from numpy import array



def 							lowerCAmelCase_    (				SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__						)      ->   str:
		a_  :   str			    =  int(_lowerCamelCase						)
		assert noofclusters < len(_lowerCamelCase						)

		# Find out the dimensionality
		a_  :   Tuple			    =  len(vectors[0]						)

		# Will help select random centroids from among the available vectors
		a_  :   Dict			    =  list(range(len(_lowerCamelCase						)						)						)
		shuffle(_lowerCamelCase						)

		# GRAPH OF COMPUTATION
		# We initialize a new graph and set it as the default during each run
		# of this algorithm. This ensures that as this function is called
		# multiple times, the default graph doesn't keep getting crowded with
		# unused ops and Variables from previous function calls.

		a_  :   Dict			    =  tf.Graph()

		with graph.as_default():
				# SESSION OF COMPUTATION

				a_  :   List[Any]			    =  tf.Session()

				##CONSTRUCTING THE ELEMENTS OF COMPUTATION

				##First lets ensure we have a Variable vector for each centroid,
				##initialized to one of the vectors from the available data points
				a_  :   List[Any]			    =  [
				    tf.Variable(vectors[vector_indices[i]]						) for i in range(_lowerCamelCase						)
				]
				##These nodes will assign the centroid Variables the appropriate
				##values
				a_  :   Dict			    =  tf.placeholder("float64",			[dim]						)
				a_  :   Optional[int]			    =  []
				for centroid in centroids:
						cent_assigns.append(tf.assign(_lowerCamelCase,			_lowerCamelCase						)						)

				##Variables for cluster assignments of individual vectors(initialized
				##to 0 at first)
				a_  :   Dict			    =  [tf.Variable(0						) for i in range(len(_lowerCamelCase						)						)]
				##These nodes will assign an assignment Variable the appropriate
				##value
				a_  :   Optional[Any]			    =  tf.placeholder("int32"						)
				a_  :   Any			    =  []
				for assignment in assignments:
						cluster_assigns.append(tf.assign(_lowerCamelCase,			_lowerCamelCase						)						)

				##Now lets construct the node that will compute the mean
				# The placeholder for the input
				a_  :   Union[str, Any]			    =  tf.placeholder("float",			[None, dim]						)
				# The Node/op takes the input and computes a mean along the 0th
				# dimension, i.e. the list of input vectors
				a_  :   Optional[int]			    =  tf.reduce_mean(_lowerCamelCase,			0						)

				##Node for computing Euclidean distances
				# Placeholders for input
				a_  :   int			    =  tf.placeholder("float",			[dim]						)
				a_  :   Optional[int]			    =  tf.placeholder("float",			[dim]						)
				a_  :   int			    =  tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(_lowerCamelCase,			_lowerCamelCase						),			2						)						)						)

				##This node will figure out which cluster to assign a vector to,
				##based on Euclidean distances of the vector from the centroids.
				# Placeholder for input
				a_  :   Optional[Any]			    =  tf.placeholder("float",			[noofclusters]						)
				a_  :   Optional[int]			    =  tf.argmin(_lowerCamelCase,			0						)

				##INITIALIZING STATE VARIABLES

				##This will help initialization of all Variables defined with respect
				##to the graph. The Variable-initializer should be defined after
				##all the Variables have been constructed, so that each of them
				##will be included in the initialization.
				a_  :   str			    =  tf.initialize_all_variables()

				# Initialize all variables
				sess.run(_lowerCamelCase						)

				##CLUSTERING ITERATIONS

				# Now perform the Expectation-Maximization steps of K-Means clustering
				# iterations. To keep things simple, we will only do a set number of
				# iterations, instead of using a Stopping Criterion.
				a_  :   int			    =  100
				for _ in range(_lowerCamelCase						):
						##EXPECTATION STEP
						##Based on the centroid locations till last iteration, compute
						##the _expected_ centroid assignments.
						# Iterate over each vector
						for vector_n in range(len(_lowerCamelCase						)						):
								a_  :   Optional[int]			    =  vectors[vector_n]
								# Compute Euclidean distance between this vector and each
								# centroid. Remember that this list cannot be named
								#'centroid_distances', since that is the input to the
								# cluster assignment node.
								a_  :   Union[str, Any]			    =  [
								    sess.run(_lowerCamelCase,			feed_dict={va: vect, va: sess.run(_lowerCamelCase						)}						)
								    for centroid in centroids
								]
								# Now use the cluster assignment node, with the distances
								# as the input
								a_  :   Dict			    =  sess.run(
								    _lowerCamelCase,			feed_dict={centroid_distances: distances}						)
								# Now assign the value to the appropriate state variable
								sess.run(
								    cluster_assigns[vector_n],			feed_dict={assignment_value: assignment}						)

						##MAXIMIZATION STEP
						# Based on the expected state computed from the Expectation Step,
						# compute the locations of the centroids so as to maximize the
						# overall objective of minimizing within-cluster Sum-of-Squares
						for cluster_n in range(_lowerCamelCase						):
								# Collect all the vectors assigned to this cluster
								a_  :   str			    =  [
								    vectors[i]
								    for i in range(len(_lowerCamelCase						)						)
								    if sess.run(assignments[i]						) == cluster_n
								]
								# Compute new centroid location
								a_  :   str			    =  sess.run(
								    _lowerCamelCase,			feed_dict={mean_input: array(_lowerCamelCase						)}						)
								# Assign value to appropriate variable
								sess.run(
								    cent_assigns[cluster_n],			feed_dict={centroid_value: new_location}						)

        # Return centroids and assignments
				a_  :   Tuple			    =  sess.run(_lowerCamelCase						)
				a_  :   str			    =  sess.run(_lowerCamelCase						)
				return centroids, assignments






'''simple docstring'''






from sklearn.metrics import matthews_corrcoef

import datasets


__UpperCamelCase					    =	"\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction.  The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n"

__UpperCamelCase					    =	"\nArgs:\n    predictions (list of int): Predicted labels, as returned by a model.\n    references (list of int): Ground truth labels.\n    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n    matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n    Example 1, a basic example with only predictions and references as inputs:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.54\n\n    Example 2, the same example as above, but also including sample weights:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.1\n\n    Example 3, the same example as above, but with sample weights that cause a negative correlation:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])\n        >>> print(round(results['matthews_correlation'], 2))\n        -0.25\n"

__UpperCamelCase					    =	"\\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n"




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,					_KWARGS_DESCRIPTION		)
class  _A (					datasets.Metric		):

		def 	lowercase__     (   self					:   Tuple			)					->    Dict:


					"""simple docstring"""

					return datasets.MetricInfo(
					    description=_DESCRIPTION  ,      citation=_CITATION  ,      inputs_description=_KWARGS_DESCRIPTION  ,      features=datasets.Features(
					        {
					            """predictions""": datasets.Value("""int32"""			),
					            """references""": datasets.Value("""int32"""			),
					        }			)  ,      reference_urls=[
					        """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
					    ]  ,      )

		def 	lowercase__     (   self					:   List[Any]  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   Union[str, Any]=None			)					->    Optional[int]:


					"""simple docstring"""

					return {
					    "matthews_correlation": float(matthews_corrcoef(__magic_name__  ,      __magic_name__  ,      sample_weight=__magic_name__			)			),
					}


