Create Clustering by Kmeans

Clustering by Kmeans

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+# importing dependencies 
+import numpy as np 
+import pandas as pd 
+import matplotlib.pyplot as plt 
+import matplotlib.cm as cm
+import sys 
+from statistics import mean 
+
+# creating data 
+mean_01 = np.array([0.0, 0.0]) 
+cov_01 = np.array([[1, 0.3], [0.3, 1]]) 
+dist_01 = np.random.multivariate_normal(mean_01, cov_01, 10) 
+
+mean_02 = np.array([6.0, 7.0]) 
+cov_02 = np.array([[1.5, 0.3], [0.3, 1]]) 
+dist_02 = np.random.multivariate_normal(mean_02, cov_02, 10) 
+
+mean_03 = np.array([7.0, -5.0]) 
+cov_03 = np.array([[1.2, 0.5], [0.5, 1,3]]) 
+dist_03 = np.random.multivariate_normal(mean_03, cov_01, 10) 
+
+mean_04 = np.array([2.0, -7.0]) 
+cov_04 = np.array([[1.2, 0.5], [0.5, 1,3]]) 
+dist_04 = np.random.multivariate_normal(mean_04, cov_01, 10) 
+
+data = np.vstack((dist_01, dist_02, dist_03, dist_04)) 
+np.random.shuffle(data) 
+
+
+# function to compute euclidean distance 
+def distance(p1, p2): 
+    return np.sum((p1 - p2)**2) 
+
+def initialize(data, no_of_clusters): 
+    ''' 
+    intialized the centroids for K-means++ 
+    inputs: 
+        data - numpy array of data points having shape (200, 2) 
+
+    '''
+    ## initialize the centroids list and add 
+    ## a randomly selected data point to the list 
+    centroids = [] 
+    centroids.append(data[np.random.randint( 
+            data.shape[0]), :].tolist())
+    temp_data=data.tolist()
+    temp_data.append('dummy')
+
+
+    ## compute remaining no_of_clusters - 1 centroids 
+    for c_id in range(no_of_clusters-1): 
+
+        ## initialize a list to store distances of data 
+        ## points from nearest centroid 
+        dist = [] 
+        for i in range(data.shape[0]):
+            if [data[i,:].tolist()]*len(centroids)!=centroids:
+                temp_sum=1
+                for j in range(c_id+1):
+                    #dist.append(distance(data[i,:],centroids[c_id]))
+                    temp_sum *= distance(data[i,:],centroids[j])
+                dist.append(temp_sum)
+            else:
+                dist.append(0)
+        pdf=(dist/sum(dist)).tolist()
+        pdf.append(0)
+        next_centroid = np.random.choice(temp_data,p=pdf)
+        centroids.append(next_centroid)
+    return centroids 
+
+def classify_a_point(point, groups):
+    index=-1
+    dist=[]
+    for i in range(len(groups)):
+        temp_dist=[]
+        for j in range(len(groups[i])):
+            temp_dist.append(distance(point,groups[i][j]))
+        dist.append((mean(temp_dist),i))
+    m=sys.maxsize
+    for d in dist:
+        if d[0]<m:
+            m=d[0]
+            index=d[1]
+
+    return index
+
+def cluster(data, no_of_clusters):
+    groups=initialize(data, no_of_clusters)
+    groups=[[element] for element in groups]
+    for i in range(data.shape[0]):
+        group_no = classify_a_point(data[i,:], groups)
+        groups[group_no].append(data[i,:])
+    return groups
+
+def plot_clusters(groups):
+    #colors = cm.rainbow(np.linspace(0, 1, len(groups)))
+    plt.scatter(*zip(*groups[0]),[6], 'r')
+    plt.scatter(*zip(*groups[1]),[6], 'b')
+    plt.scatter(*zip(*groups[2]),[6], 'g')
+    plt.scatter(*zip(*groups[3]),[6], 'c')
+    plt.show()
+
+
+plot_clusters(cluster(data, 4))