mean_shift_from_scratch.py

import matplotlib.pyplot as plt
from matplotlib import style
style.use('ggplot')
import numpy as np

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

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

class MeanShift:
	def __init__(self, radius=4):
		self.radius = radius

	def fit(self, data):
		centroids = {}

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

		while True:
			new_centroids = []

			for i in centroids:
				in_bandwidth = []
				centroid = centroids[i]

				for featureset in data:
					if np.linalg.norm(featureset-centroid) < self.radius:
						in_bandwidth.append(featureset)

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


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

			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(centroids[i], prev_centroids[i]):
					optimized = False

				if not optimized:
					break

			if optimized:
				break

		self.centroids = centroids

	def predict(self, data):
		pass

clf = MeanShift()
clf.fit(X)

centroids = clf.centroids

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

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

plt.show()