





'''simple docstring'''

from random import shuffle

import tensorflow as tf
from numpy import array





def lowerCamelCase		(	__lowerCamelCase					:       List[str]   ,			__lowerCamelCase					:       Any       )		->Any:
    _SCREAMING_SNAKE_CASE   							=     int(_snake_case       )
    assert noofclusters < len(_snake_case       )

    # Find out the dimensionality
    _SCREAMING_SNAKE_CASE   							=     len(vectors[0]       )

    # Will help select random centroids from among the available vectors
    _SCREAMING_SNAKE_CASE   							=     list(range(len(_snake_case       )       )       )
    shuffle(_snake_case       )

    # 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.

    _SCREAMING_SNAKE_CASE   							=     tf.Graph()

    with graph.as_default():
        # SESSION OF COMPUTATION

        _SCREAMING_SNAKE_CASE   							=     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
        _SCREAMING_SNAKE_CASE   							=     [
            tf.Variable(vectors[vector_indices[i]]       ) for i in range(_snake_case       )
        ]
        ##These nodes will assign the centroid Variables the appropriate
        ##values
        _SCREAMING_SNAKE_CASE   							=     tf.placeholder("""float64"""   ,			[dim]       )
        _SCREAMING_SNAKE_CASE   							=     []
        for centroid in centroids:
            cent_assigns.append(tf.assign(_snake_case   ,			_snake_case       )       )

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

        ##Now lets construct the node that will compute the mean
        # The placeholder for the input
        _SCREAMING_SNAKE_CASE   							=     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
        _SCREAMING_SNAKE_CASE   							=     tf.reduce_mean(_snake_case   ,			0       )

        ##Node for computing Euclidean distances
        # Placeholders for input
        _SCREAMING_SNAKE_CASE   							=     tf.placeholder("""float"""   ,			[dim]       )
        _SCREAMING_SNAKE_CASE   							=     tf.placeholder("""float"""   ,			[dim]       )
        _SCREAMING_SNAKE_CASE   							=     tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(_snake_case   ,			_snake_case       )   ,			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
        _SCREAMING_SNAKE_CASE   							=     tf.placeholder("""float"""   ,			[noofclusters]       )
        _SCREAMING_SNAKE_CASE   							=     tf.argmin(_snake_case   ,			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.
        _SCREAMING_SNAKE_CASE   							=     tf.initialize_all_variables()

        # Initialize all variables
        sess.run(_snake_case       )

        ##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.
        _SCREAMING_SNAKE_CASE   							=     100
        for _ in range(_snake_case       ):
            ##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(_snake_case       )       ):
                _SCREAMING_SNAKE_CASE   							=     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.
                _SCREAMING_SNAKE_CASE   							=     [
                    sess.run(_snake_case   ,			feed_dict={va: vect, va: sess.run(_snake_case       )}       )
                    for centroid in centroids
                ]
                # Now use the cluster assignment node, with the distances
                # as the input
                _SCREAMING_SNAKE_CASE   							=     sess.run(
                    _snake_case   ,			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(_snake_case       ):
                # Collect all the vectors assigned to this cluster
                _SCREAMING_SNAKE_CASE   							=     [
                    vectors[i]
                    for i in range(len(_snake_case       )       )
                    if sess.run(assignments[i]       ) == cluster_n
                ]
                # Compute new centroid location
                _SCREAMING_SNAKE_CASE   							=     sess.run(
                    _snake_case   ,			feed_dict={mean_input: array(_snake_case       )}       )
                # Assign value to appropriate variable
                sess.run(
                    cent_assigns[cluster_n]   ,			feed_dict={centroid_value: new_location}       )

        # Return centroids and assignments
        _SCREAMING_SNAKE_CASE   							=     sess.run(_snake_case       )
        _SCREAMING_SNAKE_CASE   							=     sess.run(_snake_case       )
        return centroids, assignments





"""simple docstring"""



def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    return int((input_a, input_a).count(0 ) == 0 )
def 					lowerCamelCase   (     ):
    assert and_gate(0    ,0 ) == 0
    assert and_gate(0    ,1 ) == 0
    assert and_gate(1    ,0 ) == 0
    assert and_gate(1    ,1 ) == 1


if __name__ == "__main__":
    test_and_gate()
    print(and_gate(1, 0))
    print(and_gate(0, 0))
    print(and_gate(0, 1))
    print(and_gate(1, 1))

