
'''simple docstring'''
from random import shuffle

import tensorflow as tf
from numpy import array

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    int     			=						int(__lowerCAmelCase       )
    assert noofclusters < len(__lowerCAmelCase       )

    # Find out the dimensionality
    snake_case_ :    int     			=						len(vectors[0]       )

    # Will help select random centroids from among the available vectors
    snake_case_ :    List[Any]     			=						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.

    snake_case_ :    Union[str, Any]     			=						tf.Graph()

    with graph.as_default():
        # SESSION OF COMPUTATION

        snake_case_ :    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
        snake_case_ :    Optional[int]     			=						[
            tf.Variable(vectors[vector_indices[i]]       ) for i in range(__lowerCAmelCase       )
        ]
        ##These nodes will assign the centroid Variables the appropriate
        ##values
        snake_case_ :    Union[str, Any]     			=						tf.placeholder("float64",		[dim]       )
        snake_case_ :    Dict     			=						[]
        for centroid in centroids:
            cent_assigns.append(tf.assign(__lowerCAmelCase,		__lowerCAmelCase       )       )

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

        ##Node for computing Euclidean distances
        # Placeholders for input
        snake_case_ :    Dict     			=						tf.placeholder("float",		[dim]       )
        snake_case_ :    Tuple     			=						tf.placeholder("float",		[dim]       )
        snake_case_ :    Optional[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
        snake_case_ :    Optional[Any]     			=						tf.placeholder("float",		[noofclusters]       )
        snake_case_ :    Any     			=						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.
        snake_case_ :    Any     			=						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.
        snake_case_ :    Union[str, Any]     			=						1_0_0
        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       )       ):
                snake_case_ :    Dict     			=						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.
                snake_case_ :    int     			=						[
                    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
                snake_case_ :    Optional[Any]     			=						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
                snake_case_ :    Any     			=						[
                    vectors[i]
                    for i in range(len(__lowerCAmelCase       )       )
                    if sess.run(assignments[i]       ) == cluster_n
                ]
                # Compute new centroid location
                snake_case_ :    Optional[int]     			=						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
        snake_case_ :    Dict     			=						sess.run(__lowerCAmelCase       )
        snake_case_ :    List[str]     			=						sess.run(__lowerCAmelCase       )
        return centroids, assignments






'''simple docstring'''
from __future__ import annotations

from cmath import sqrt

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    if a == 0:
        raise ValueError("Coefficient 'a' must not be zero."       )
    snake_case_ :    Union[str, Any]     			=						b * b - 4 * a * c

    snake_case_ :    List[str]     			=						(-b + sqrt(__SCREAMING_SNAKE_CASE       )) / (2 * a)
    snake_case_ :    int     			=						(-b - sqrt(__SCREAMING_SNAKE_CASE       )) / (2 * a)

    return (
        root_a.real if not root_a.imag else root_a,
        root_a.real if not root_a.imag else root_a,
    )

def 						UpperCamelCase_     (	):
    """simple docstring"""






    snake_case_			, snake_case_ :    str     			=						quadratic_roots(a=5,		b=6,		c=1       )
    print(f'The solutions are: {solutiona} and {solutiona}'       )


if __name__ == "__main__":
  main()




