ml41.py

import matplotlib.pyplot as plt
from matplotlib import style
style.use('ggplot')
import numpy as np
from sklearn.datasets.samples_generator import make_blobs
import random


X, y = make_blobs(n_samples=50, centers=3, n_features=2)

# X = np.array([[1,2],
#               [1.5,1.8],
#               [5,8],
#               [8,8],
#               [1,0.6],
#               [9,11],
#               [8,2],
#               [10,2],
#               [9,3],])

# plt.scatter(X[:,0],X[:,1], s=150)
# plt.show()

colors = 10 * ['g', 'r', 'c', 'b', 'k']

class Mean_Shift:
    def __init__(self, radius=None, radius_norm_step=100):
        self.radius = radius
        self.radius_norm_step = radius_norm_step

    def fit(self, data):


        if self.radius == None:
            all_data_centroid = np.average(data, axis=0)
            all_data_norm = np.linalg.norm(all_data_centroid)
            self.radius = all_data_norm / self.radius_norm_step

        centroids = {}

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

        weights = [i for i in range(self.radius_norm_step)][::-1]

        while True:
            new_centroids = []
            for i in centroids:
                in_bandwidth = []
                centroid = centroids[i]

                for featureset in data:
                    distance = np.linalg.norm(featureset-centroid)
                    if distance == 0:
                        distance = 0.000000001
                    weight_index = int(distance/self.radius)
                    if weight_index > self.radius_norm_step-1:
                        weight_index = self.radius_norm_step-1
                    to_add = (weights[weight_index]**2)*[featureset]
                    in_bandwidth += to_add

                new_centroid = np.average(in_bandwidth, axis=0)
                new_centroids.append(tuple(new_centroid))

            uniques = sorted(list(set(new_centroids)))

            to_pop = []

            for i in uniques:
                if i in to_pop:
                    break
                for ii in uniques:
                    if i == ii:
                        pass
                    elif np.linalg.norm(np.array(i) - np.array(ii)) <= self.radius and ii not in to_pop:
                        to_pop.append(ii)
                        break

            for i in to_pop:
                uniques.remove(i)

            prev_centroids = dict(centroids)

            centroids = {}
            for i in range(len(uniques)):
                centroids[i] = np.array(uniques[i])

            optimized = True

            for i in centroids:
                if not np.array_equal(prev_centroids[i], centroids[i]):
                    optimized = False

                if not optimized:
                    break

            if optimized:
                break

        self.centroids = centroids

        self.classifications = {}

        for i in range(len(self.centroids)):
            self.classifications[i] = []

        for featureset in data:
            distances = [np.linalg.norm(featureset-self.centroids[centroid]) for centroid in self.centroids]
            classification = distances.index(min(distances))
            self.classifications[classification].append(featureset)


    def predict(self, data):
        distances = [np.linalg.norm(data-self.centroids[centroid]) for centroid in self.centroids]
        classification = distances.index(min(distances))
        return classification

clf = Mean_Shift()
clf.fit(X)

centroids = clf.centroids

for classification in clf.classifications:
    color = colors[classification]
    for featureset in clf.classifications[classification]:
        plt.scatter(featureset[0], featureset[1], marker='x', color=color, s=150, lw=5)

for c in centroids:
    plt.scatter(centroids[c][0], centroids[c][1], color='k', marker='*', s=150)

plt.show()