Machine-Learning/kmean_from_scratch-1.py at master · Mohit54/Machine-Learning · GitHub

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'''1. Choose random value for K
2. Randomly choose K featuresets to start as your centroids
3. Calculate distance between all featyres and centroids
4. classify other featuresets as same as closest centroids
5. Take mean of each class, make that mean new centroid
6. Repeat 3-5 until optimized(centroids remain same and not change)'''

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],])

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

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


class K_Means:
    def __init__(self, k=2, tol=0.001, max_iter=300):
        self.k = k
        self.tol = tol
        self.max_iter = max_iter

    def fit(self,data):

        self.centroids = {}

        for i in range(self.k):
            self.centroids[i] = data[i]

        for i in range(self.max_iter):
            self.classifications = {}

            for i in range(self.k):
                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)

            prev_centroids = dict(self.centroids)

            for classification in self.classifications:
                self.centroids[classification] = np.average(self.classifications[classification],axis=0)

            optimized = True

            for c in self.centroids:
                original_centroid = prev_centroids[c]
                current_centroid = self.centroids[c]
                if np.sum((current_centroid-original_centroid)/original_centroid*100.0) > self.tol:
                    print(np.sum((current_centroid-original_centroid)/original_centroid*100.0))
                    optimized = False

            if optimized:
                break

    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 = K_Means()
clf.fit(X)

for centroid in clf.centroids:
    plt.scatter(clf.centroids[centroid][0], clf.centroids[centroid][1],
                marker="o", color="k", s=150, linewidths=5)

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, linewidths=5)

unknowns = np.array([[1,3],
                     [8,9],
                     [0,3],
                     [5,4],
                     [6,4],])

for unknown in unknowns:
    classification = clf.predict(unknown)
    plt.scatter(unknown[0], unknown[1], marker="*", color=colors[classification], s=150, linewidths=5)


plt.show()