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Author: n0obcoder

Diff for: kMeans/kMeansClustering.py

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+import numpy as np
+import cv2
+import os,sys,glob,pdb
+from matplotlib import pyplot as plt
+
+disp = True
+
+###
+#numpy.random.normal(loc=0.0, scale=1.0, size=None)
+###
+############################# DATA GENERATION #############################
+def gen_data(data_points,loc):
+    #print('data_points: ', data_points)
+    #print('loc: ', loc)
+    x_center = loc[0]
+    y_center = loc[1]
+    x = np.random.normal(size= data_points, loc= x_center)*100
+    y = np.random.normal(size= data_points, loc= y_center)*100
+    data= np.vstack((x,y))
+    #print(x.shape)
+    #print(y.shape)
+    #print(data.shape)
+    #k=0
+    #print(x[k])
+    #print(y[k])
+    #print(data)
+    #print(x.shape,y.shape)
+    #exit()
+    data = data.astype(np.int32)
+    x,y = data
+    #print('x.shape: ', x.shape)
+    #print('y.shape', y.shape)
+    #print(x)
+    #pdb.set_trace()
+    return x, y
+
+results_dir= 'results'
+if not os.path.exists(results_dir):
+    os.mkdir(results_dir)
+
+#...................................
+x_data = np.array((),dtype=np.int32)
+y_data = np.array((),dtype=np.int32)
+#...................................
+x, y = gen_data(100,[5,5]) #plt.hist(x) shows that is a normal distribution
+plt.scatter(x, y)
+x_data = np.hstack((x_data, x))
+y_data = np.hstack((y_data, y))
+#...................................
+x, y = gen_data(50,[-2,4]) 
+plt.scatter(x, y)
+x_data = np.hstack((x_data, x))
+y_data = np.hstack((y_data, y))
+#...................................
+x, y = gen_data(300,[2,3]) 
+plt.scatter(x, y)
+x_data = np.hstack((x_data, x))
+y_data = np.hstack((y_data, y))
+#...................................
+#plt.scatter(x_data,y_data)
+plt.title('k-Means Clustering')
+plt.xlabel('Some Feature x')
+plt.ylabel('Some Feature y')
+plt.grid()
+plt.savefig(results_dir + '/generated_data.jpg')
+plt.show()
+print('>>>>>>>>>>>>>>>>>>>>>>>>>> DATA POINTS GENERATED')
+    
+############################# k-Means #############################
+
+#find out the extremes of our data space
+
+x_min= np.min(x_data)
+x_max= np.max(x_data)
+y_min= np.min(y_data)
+y_max= np.max(y_data)
+print('x_min, x_max, y_min, y_max: ', x_min, x_max, y_min, y_max)
+
+#set the value of 'k'
+color_list = ['Blue', 'Green', 'Yellow', 'Pink', 'Purple']
+k= 3 #you can change the value of k till 5 because the color_list that i have defined has only 5 colors. you can add more colors to the color_list and then change k to higher values.
+
+#randomly initialize k number of center points
+kcenters = []
+
+for i in range(k):
+
+    x_center= np.random.randint(x_min, x_max + 1)
+    y_center= np.random.randint(y_min, y_max + 1)
+    center = [x_center, y_center]
+    kcenters.append(center)
+    
+    print('k center point {} initialized at ({}, {})'.format(i+1, x_center, y_center))
+
+print('kcenters: ', kcenters)
+
+def display_kcenters(kcenters):
+    for i in range(len(kcenters)):
+        plt.scatter(kcenters[i][0], kcenters[i][1], color='Red')
+    #plt.show()
+    
+initial_data_point_color = 'Gray'    
+display_kcenters(kcenters)
+plt.scatter(x_data, y_data, color= initial_data_point_color)
+plt.title('before kMeans Clustering')
+plt.xlabel('Some Feature x')
+plt.ylabel('Some Feature y')
+plt.grid()
+plt.savefig(results_dir + '/before_kmeans.jpg')
+plt.show()
+
+color_info_list = [initial_data_point_color]*len(x_data)
+
+#print(color_info_list)
+#print(len(color_info_list))
+
+print('>>>>>>>>>>>>>>>>>>>>>>>>>> KCENTERS INITIALIZED')
+
+def assign_cluster(x, y, kcenters):
+    distances = []
+    for i in range(len(kcenters)):
+        #print(i)
+        dist = np.sqrt(np.square(x - kcenters[i][0]) + np.square(y - kcenters[i][1]))
+        #print(dist)
+        distances.append(dist)
+    distances = np.array(distances)
+    #print(distances)
+    #print(type(distances))
+    
+    return np.argmin(distances)
+
+def get_mean_shit_value(old_point, new_point):
+    #pdb.set_trace()
+    mean_shift_value = np.sqrt(  np.square(old_point[0] - new_point[0]) + np.square(old_point[1] - new_point[1])  )
+    return mean_shift_value
+
+
+iterations = 100
+stop=[False]*k
+
+for n in range(iterations):
+    #iterate over the data points and assign them to one of the k clusters
+    print('iteration number {}'.format(n))
+    
+    for i, (x, y) in enumerate(zip(x_data, y_data)):
+        
+        #print(type(x)) #<type 'numpy.int32'>
+        cluster_index= assign_cluster(x, y, kcenters)
+        
+        color_info_list[i] = color_list[cluster_index]
+        #print('point {} goes {}'.format(i+1, color_list[cluster_index]))
+        
+
+    plt.scatter(x_data, y_data, color= color_info_list)
+    display_kcenters(kcenters)
+    plt.title('k-Means iter {}'.format(n))
+    plt.xlabel('Some Feature x')
+    plt.ylabel('Some Feature y')
+    plt.grid()
+    plt.savefig(results_dir + '/iter{}.jpg'.format(n))
+    plt.show()
+
+    #calculate the new position of the centers
+
+    for i in range(len(kcenters)):
+        
+        x_coords_list=[]
+        y_coords_list= []
+        for j in range(len(x_data)):
+            if color_info_list[j] == color_list[i]:
+                x_coords_list.append(x_data[j])
+                y_coords_list.append(y_data[j])
+        
+        x_mean = np.mean(x_coords_list).astype(np.int32)
+        y_mean = np.mean(y_coords_list).astype(np.int32)
+        #print(type(x_mean))
+        #print('kcenters[{}] shifted from {} to {}'.format(i, kcenters[i],[x_mean,y_mean]))
+        
+        mean_shift_value = get_mean_shit_value(kcenters[i], [x_mean, y_mean])
+        if disp:
+            print('mean_shift_value: ', mean_shift_value)
+        
+        if mean_shift_value == 0:
+            stop[i] = True
+        #print(stop)
+        #update the center
+        kcenters[i] = [x_mean, y_mean]
+    
+    if disp:
+        print('...................................')
+
+
+    #check if all the cluster centers have converged
+    if sum(stop) == k:
+        print('all the {} cluster centers have converged on the {}th iteration'.format(k, n))
+        exit()
+
+
+
+'''
+data = np.array([
+    [1, 2],
+    [2, 3],
+    [3, 6],
+])
+x, y = data.T
+plt.scatter(x,y)
+'''
+
+'''
+def save_plt(m,c,epoch_id):
+    
+    
+    # Plot
+    plt.scatter(x_list, y_list, color = 'cyan')#, s=area, c=colors, alpha=0.5)
+    plt.title('fake data')
+    plt.xlabel('x')
+    plt.ylabel('y')
+    plt.grid()
+    #plt.show()
+
+    x_temp = x_max - x_min
+    line_coord1 = [x_min , x_max ]
+    line_coord2 = [x_min*m_fake + c_fake, m_fake*x_max + c_fake]
+    plt.plot(line_coord1, line_coord2 , color = 'green')#, 'k-')    
+    
+    line_coord1 = [x_min , x_max]
+    line_coord2 = [x_min*m + c , m*x_max + c]
+    plt.plot(line_coord1, line_coord2 , color = 'pink')#, 'k-')
+    plt.savefig('result/res{}.png'.format(epoch_id))
+    #plt.show()    
+'''