approximating a list with point of interests

Question

I want this list to get approximated into 6 values, as you see the values are spread throught with some varianace. I plotted in matplotlib, I get this. Now I have 6 points of interests with multiple values how can I aprroximate it to only 6 values

[(61, 148), (61, 149), (61, 150), (62, 147), (62, 148), (62, 149), (62, 150), (63, 147), (63, 148), (63, 149), (63, 150), (64, 147), (64, 148), (64, 149), (64, 150), (65, 147), (65, 148), (65, 149), (65, 150), (149, 436), (149, 437), (149, 438), (150, 366), (150, 367), (150, 368), (150, 436), (150, 437), (150, 438), (150, 439), (151, 366), (151, 367), (151, 368), (151, 436), (151, 437), (151, 438), (151, 439), (152, 366), (152, 367), (152, 368), (152, 436), (152, 437), (152, 438), (152, 439), (175, 147), (175, 148), (175, 149), (175, 150), (175, 264), (175, 265), (175, 266), (175, 267), (176, 147), (176, 148), (176, 149), (176, 150), (176, 264), (176, 265), (176, 266), (176, 267), (177, 147), (177, 148), (177, 149), (177, 150), (177, 264), (177, 265), (177, 266), (177, 267), (178, 147), (178, 148), (178, 149), (178, 264), (178, 265), (178, 266), (230, 366), (230, 367), (230, 368), (230, 369), (231, 366), (231, 367), (231, 368), (231, 369), (232, 366), (232, 367), (232, 368), (232, 369), (233, 366), (233, 367), (233, 368)]

the list has values like (61,148),(61,149), (62,149), (62,148)..... instead having these multiple values I want average it to a single value.....but It contains 6 different point of interests. So, I want to convert this 88 values to 6 values. — Perseus784, Aug 05 '17 at 03:36
You should use *k-means* clustering. You can use `scipy` for it. https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.vq.kmeans.html#scipy.cluster.vq.kmeans — alkasm, Aug 05 '17 at 04:44
Heirarchial clustering was much more apt to this situation because we don't know how many range of values a single point of interest may have. — Perseus784, Aug 07 '17 at 19:47

score 1 · Accepted Answer · answered Aug 05 '17 at 05:54

Using heirarchial clustering solved this problem. Set a default radius ran the same data points. It got reduced to 6 points.

#mean shift clustering.
#this lets the program decide number of groups involved in the given dataset
import numpy as np
import matplotlib.pyplot as plt
import random as r

#setting all the points as centroids
centroids = {}


def auto_cluster(radius,data):
    global centroids

    for i in range(len(data)):
        centroids[i] = data[i]

    while True:
        new_centroids=[]
        #checking all the points whether it is in radius and assign to to that centroid
        for j in centroids:
            in_radius=[]
            centroid=centroids[j]
            for point in data:
                if np.linalg.norm(point-centroid)<radius:
                    in_radius.append(point)
            #finding mean
            new_centroid=np.average(in_radius,axis=0)
            new_centroids.append(tuple(new_centroid))
        #collect all the final centroids for each grp
        uniques=sorted(list(set(new_centroids)))
        prev_centroids=dict(centroids)
        centroids={}
        #fil with new centroids
        for i in range(len(uniques)):
            centroids[i]=np.array(uniques[i])
        opt=True
        #chech whether the centroid is optimized
        for i in centroids:
            if not np.array_equal(centroids[i],prev_centroids[i]):
                opt=False
            if not opt:
                break
        if opt:break
    return centroids

if __name__=="__main__":
    data = [[61, 148], [61, 149], [61, 150], [62, 147], [62, 148], [62, 149], [62, 150], [63, 147], [63, 148],
            [63, 149], [63, 150], [64, 147], [64, 148], [64, 149], [64, 150], [65, 147], [65, 148], [65, 149],
            [65, 150], [149, 436], [149, 437], [149, 438], [150, 366], [150, 367], [150, 368], [150, 436], [150, 437],
            [150, 438], [150, 439], [151, 366], [151, 367], [151, 368], [151, 436], [151, 437], [151, 438], [151, 439],
            [152, 366], [152, 367], [152, 368], [152, 436], [152, 437], [152, 438], [152, 439], [175, 147], [175, 148],
            [175, 149], [175, 150], [175, 264], [175, 265], [175, 266], [175, 267], [176, 147], [176, 148], [176, 149],
            [176, 150], [176, 264], [176, 265], [176, 266], [176, 267], [177, 147], [177, 148], [177, 149], [177, 150],
            [177, 264], [177, 265], [177, 266], [177, 267], [178, 147], [178, 148], [178, 149], [178, 264], [178, 265],
            [178, 266], [230, 366], [230, 367], [230, 368], [230, 369], [231, 366], [231, 367], [231, 368], [231, 369],
            [232, 366], [232, 367], [232, 368], [232, 369], [233, 366], [233, 367], [233, 368]]
    data = np.array(data)
    centroids = {}

    cent = auto_cluster(radius=5,data=data)
    print centroids
    print(len(cent))  # no. of centroids
    # plots
    [plt.scatter(x[0], x[1], s=50, c='g') for x in data]
    for c in cent:
        plt.scatter(cent[c][0], cent[c][1], s=200, marker='*')
    plt.show()

approximating a list with point of interests

1 Answers1