





from random import shuffle

import tensorflow as tf
from numpy import array





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Optional[int]:




    """simple docstring"""




    _A     =							int(_SCREAMING_SNAKE_CASE				)
    assert noofclusters < len(_SCREAMING_SNAKE_CASE				)

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

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

    _A     =							tf.Graph()

    with graph.as_default():
        # SESSION OF COMPUTATION

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

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

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

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

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

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












__A							:			Dict         =			"Alexander Joslin"

import operator as op

from .stack import Stack





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> int:




    """simple docstring"""




    _A     =							{'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}

    _A     =							Stack()
    _A     =							Stack()

    for i in equation:
        if i.isdigit():
            # RULE 1
            operand_stack.push(int(_SCREAMING_SNAKE_CASE				)				)
        elif i in operators:
            # RULE 2
            operator_stack.push(_SCREAMING_SNAKE_CASE				)
        elif i == ")":
            # RULE 4
            _A     =							operator_stack.peek()
            operator_stack.pop()
            _A     =							operand_stack.peek()
            operand_stack.pop()
            _A     =							operand_stack.peek()
            operand_stack.pop()

            _A     =							operators[opr](_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
            operand_stack.push(_SCREAMING_SNAKE_CASE				)

    # RULE 5
    return operand_stack.peek()


if __name__ == "__main__":
 __A							:			Any         =			"(5 + ((4 * 2) * (2 + 3)))"
 # answer = 45
 print(f"{equation} = {dijkstras_two_stack_algorithm(equation)}")





