In [1]:

    

import numpy as np
import cv2
import matplotlib.pyplot as plt
%matplotlib inline


    

In [2]:

    

plt.style.use('ggplot')


    

In [3]:

    

np.random.seed(42)


    

In [4]:

    

single_data_point = np.random.randint(0, 100, 2)
single_data_point


    

    
Out[4]:





array([51, 92])

In [5]:

    

single_label = np.random.randint(0, 2)
single_label


    

    
Out[5]:





0

In [6]:

    

def generate_data(num_samples, num_features=2):
    '''Randomly generates a number of data points'''
    data_size = (num_samples, num_features)
    data = np.random.randint(0,100, size=data_size)
    labels_size = (num_samples, 1)
    labels = np.random.randint(0, 2, size=labels_size)
    # OpenCV can be a bit finicky when it comes to datatypes
    # make sure to always convert your data points
    # to np.float32
    return data.astype(np.float32), labels


    

In [7]:

    

train_data, labels = generate_data(11)
train_data


    

    
Out[7]:





array([[71., 60.],
       [20., 82.],
       [86., 74.],
       [74., 87.],
       [99., 23.],
       [ 2., 21.],
       [52.,  1.],
       [87., 29.],
       [37.,  1.],
       [63., 59.],
       [20., 32.]], dtype=float32)

In [8]:

    

train_data[0], labels[0]


    

    
Out[8]:





(array([71., 60.], dtype=float32), array([1]))

In [9]:

    

plt.plot(train_data[0,0], train_data[0,1], 'sb')
plt.xlabel('x coordinate')
plt.ylabel('y coordinate')


    

    
Out[9]:





<matplotlib.text.Text at 0x1e5ea731400>






    

In [10]:

    

def plot_data(all_blue, all_red):
    plt.scatter(all_blue[:,0], all_blue[:,1],\
               c='b', marker='s', s=180)
    plt.scatter(all_red[:,0], all_red[:,1],\
                c='r', marker='^', s=180)
    plt.xlabel('x coordinate (feature 1)')
    plt.ylabel('y coordinate (feature 2)')


    

In [11]:

    

labels.ravel() == 0


    

    
Out[11]:





array([False, False, False,  True, False,  True,  True,  True,  True,
        True, False])

In [12]:

    

blue = train_data[labels.ravel() == 0]


    

In [13]:

    

red = train_data[labels.ravel() == 1]


    

In [14]:

    

plot_data(blue, red)


    

In [15]:

    

knn = cv2.ml.KNearest_create()


    

In [16]:

    

knn.train(train_data, cv2.ml.ROW_SAMPLE, labels)


    

    
Out[16]:





True

In [17]:

    

newcomer, _ = generate_data(1)


    

In [18]:

    

plot_data(blue, red)
plt.plot(newcomer[0,0], newcomer[0,1], 'go',\
        markersize=14);


    

In [19]:

    

ret, results, neighbor, dist = knn.findNearest(newcomer,1)
print('predicted label:\t', results)
print("Neighbor's label:\t", neighbor)
print('Distance to neighbor:\t', dist)


    

    




predicted label:	 [[1.]]
Neighbor's label:	 [[1.]]
Distance to neighbor:	 [[250.]]

In [20]:

    

ret, results, neighbors, dist = knn.findNearest(newcomer,7)
print("Predicted label:\t", results)
print("Neighbors' labels:\t", neighbors)
print("Distance to neighbors:\t", dist)


    

    




Predicted label:	 [[0.]]
Neighbors' labels:	 [[1. 1. 0. 0. 0. 1. 0.]]
Distance to neighbors:	 [[ 250.  401.  784.  916. 1073. 1360. 4885.]]

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