COMP542 – Machine Learning – IT Assignment Help

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Assignment Task

1. K-Mean++. Implement K-Mean++  clustering algorithm in python as follows:

– Read    input    file    ‘as4_1.txt’    given    in    the    Canvas    course    website. The    file    is    composed    of    X    and    Y    values    in    the    first    and    second    columns    and    label    in    the    third    column.

– Create myInit() that    places    the    initial    k centroids    far    away    from    each    other    in    the    

4    steps    as    shown    below:

1. Randomly    select    the    first    centroid    from    the    data    points

2. For    each    data    point    compute    its    distance    from    the    nearest,    previously    chosen    centroid

Use    following    Euclidean distance    function: import numpy as np def euclidean2D(point1, point2):

 x1 = point1[0]

 x2 = point2[0]

 y1 = point1[1]

 y2 = point2[1] return np.sqrt((x1 – x2)**2 + (y1 – y2)**2)

3. Select    the    point    having    maximum    distance    from    the    nearest    centroid    as    the    next    centroid

4. Repeat  steps    2    and    3    until    k centroids    have    been    sampled

– Create    myAssign() that    assigns    each    example    to    the    nearest    centroid

– Create    myCentroid() that    calculates    a    new    centroid    of    all    points    that    are    assigned    to    the    same    centroid.

– Create    myUpdateCentroid() that    moves    the    centroids    to    the    center    of    the    examples    that    were    assigned    to    it

– Create    myKmeanPlusPlus() that    initially    calls    myInit(),    and    then    repeats    to    call    myAssign(),    myCentroid(), and    myUpdateCentroids() until    the   cluster    assignments    do    not    change    or    a    user-defined    tolerance    or    maximum    number    of    iteration    is    reached.        myKmeanPlusPlus() should   ask    user    to    receive    the    following    arguments    and    use    the    same    variable    name    in    the    parenthesis:

1. The    number    of    clusters    (k)

2. Tolerance    (myTol)

3. Maximum    number    of    iterations    (myMax) myKmeanPlusPlus()returns    a    list    of new labels.

– Create    myPlot() that    visualizes    plot    of    clustering    result    in    different    colors    and    markers.    You    can    use    any    plot    method.

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  • Posted on : December 20th, 2019

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