





'''simple docstring'''






from random import shuffle

import tensorflow as tf
from numpy import array
def 							_lowerCamelCase(  UpperCamelCase__    : Tuple    ,       UpperCamelCase__    : List[Any]		)		->  List[Any]:
    A       :       List[str]							=						int(UpperCamelCase__		)
    assert noofclusters < len(UpperCamelCase__		)

    # Find out the dimensionality
    A       :       Dict							=						len(vectors[0]		)

    # Will help select random centroids from among the available vectors
    A       :       Union[str, Any]							=						list(range(len(UpperCamelCase__		)		)		)
    shuffle(UpperCamelCase__		)

    # 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       :       str							=						tf.Graph()

    with graph.as_default():
        # SESSION OF COMPUTATION

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

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

        ##Now lets construct the node that will compute the mean
        # The placeholder for the input
        A       :       str							=						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       :       str							=						tf.reduce_mean(UpperCamelCase__    ,       0		)

        ##Node for computing Euclidean distances
        # Placeholders for input
        A       :       str							=						tf.placeholder('''float'''    ,       [dim]		)
        A       :       Dict							=						tf.placeholder('''float'''    ,       [dim]		)
        A       :       Optional[int]							=						tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(UpperCamelCase__    ,       UpperCamelCase__		)    ,       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       :       int							=						tf.placeholder('''float'''    ,       [noofclusters]		)
        A       :       Dict							=						tf.argmin(UpperCamelCase__    ,       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       :       Any							=						tf.initialize_all_variables()

        # Initialize all variables
        sess.run(UpperCamelCase__		)

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

        # Return centroids and assignments
        A       :       str							=						sess.run(UpperCamelCase__		)
        A       :       List[str]							=						sess.run(UpperCamelCase__		)
        return centroids, assignments







'''simple docstring'''






import requests

snake_case_     =	"""https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""
def 							_lowerCamelCase(  UpperCamelCase__    : str		)		->  None:
    # fetching a list of articles in json format
    A       :       Any							=						requests.get(_NEWS_API + bbc_news_api_key		).json()
    # each article in the list is a dict
    for i, article in enumerate(bbc_news_page['''articles''']    ,       1		):
        print(F'''{i}.) {article['title']}'''		)


if __name__ == "__main__":
     fetch_bbc_news(bbc_news_api_key="""<Your BBC News API key goes here>""")
