In [65]:

    

%matplotlib inline
import numpy as np
from matplotlib import pyplot as plt


    

In [66]:

    

a = np.arange(9)
a


    

    
Out[66]:





array([0, 1, 2, 3, 4, 5, 6, 7, 8])

In [67]:

    

b = np.arange(3,21,2)
b


    

    
Out[67]:





array([ 3,  5,  7,  9, 11, 13, 15, 17, 19])

In [68]:

    

plt.gca().set_aspect('equal')
plt.scatter(a,b)


    

    
Out[68]:





<matplotlib.collections.PathCollection at 0x7fad185febd0>






    

In [69]:

    

pts = np.random.uniform(size=(2,200))


    

In [70]:

    

x = pts[0,:]
y = pts[1,:]

plt.scatter(x,y)


    

    
Out[70]:





<matplotlib.collections.PathCollection at 0x7fad18686290>






    

In [71]:

    

from sklearn.datasets import make_moons

# make classification problem
X, y = make_moons(n_samples=500, noise=0.3)


    

In [72]:

    

# represent the coordinates in several convenient ways
I = X[:,0]
J = X[:,1]

plt.gca().set_aspect('equal')
plt.scatter(I, J, c=y, cmap='cool')


    

    
Out[72]:





<matplotlib.collections.PathCollection at 0x7fad184fbfd0>






    

In [73]:

    

from sklearn.neighbors import KNeighborsClassifier

model = KNeighborsClassifier(n_neighbors=7,metric='euclidean')
model.fit(X,y)


    

    
Out[73]:





KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='euclidean',
           metric_params=None, n_jobs=1, n_neighbors=7, p=2,
           weights='uniform')

In [74]:

    

resolution = 600

i = np.linspace(-3,3,resolution)

j, k = np.meshgrid(i,i)
pts = np.dstack((j,k)).reshape(-1,2)
pts


    

    
Out[74]:





array([[-3.        , -3.        ],
       [-2.98998331, -3.        ],
       [-2.97996661, -3.        ],
       ..., 
       [ 2.97996661,  3.        ],
       [ 2.98998331,  3.        ],
       [ 3.        ,  3.        ]])

In [75]:

    

y


    

    
Out[75]:





array([0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1,
       1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1,
       0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0,
       1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1,
       1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0,
       0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0,
       1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0,
       1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0,
       1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
       1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0,
       0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0,
       1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0,
       1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1,
       0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1,
       0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0,
       0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0,
       0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1,
       1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1,
       1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0,
       1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0,
       1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1,
       1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1])

In [76]:

    

py = model.predict(pts)
py.shape
py_img = py.reshape((resolution,resolution))

plt.scatter(I, J, c=y, cmap='cool')
plt.imshow(py_img,origin='lower',cmap='cool',extent=[-3,3,-3,3])


    

    
Out[76]:





<matplotlib.image.AxesImage at 0x7fad187d0290>






    

In [76]: