In [1]:

    

from numpy import *
from PIL import *
import pickle
from pylab import *
import os


    

In [2]:

    

import pickle
import bayes
import imtools


    

In [3]:

    

with open('points_normal.pkl', 'r') as f:
    class_1 = pickle.load(f)
    class_2 = pickle.load(f)
    labels = pickle.load(f)

bc = bayes.BayesClassifier()
bc.train([class_1, class_2], [1, -1])

with open('points_normal_test.pkl', 'r') as f:
    class_1 = pickle.load(f)
    class_2 = pickle.load(f)
    labels = pickle.load(f)

print bc.classify(class_1[:10])[0]
print bc.classify(class_2[:10])[0]

# draw points and boundary line
def classify(x, y, bc=bc):
    points = vstack((x, y))
    return bc.classify(points.T)[0]

imtools.plot_2D_boundary([-6, 6, -6, 6], [class_1, class_2], classify, [1, -1])
show()


    

    




[1 1 1 1 1 1 1 1 1 1]
[-1 -1 -1 -1 -1 -1 -1 -1 -1 -1]






    

In [48]:

    

with open('points_normal.pkl', 'r') as f:
    class_1 = pickle.load(f)
    class_2 = pickle.load(f)
    labels = pickle.load(f)


    

In [49]:

    

import heurisitc
poisson = reload(poisson)
bcp = poisson.BayesClassifier()


    

In [50]:

    

bcp.train([class_1, class_2], [1, -1])


    

In [51]:

    

with open('points_normal_test.pkl', 'r') as f:
    class_1 = pickle.load(f)
    class_2 = pickle.load(f)
    labels = pickle.load(f)


    

In [52]:

    

print bcp.classify(class_1[:10])[0]
print bcp.classify(class_2[:10])[0]


    

    




[1 1 1 1 1 1 1 1 1 1]
[-1 -1 -1 -1 -1 -1 -1 -1 -1 -1]

In [53]:

    

def classify(x, y, bc=bcp):
    points = vstack((x, y))
    return bc.classify(points.T)[0]

imtools.plot_2D_boundary([-6, 6, -6, 6], [class_1, class_2], classify, [1, -1])
show()


    

In [45]:

    

with open('points_ring.pkl', 'r') as f:
    class_1 = pickle.load(f)
    class_2 = pickle.load(f)
    labels = pickle.load(f)

bc = bayes.BayesClassifier()
bc.train([class_1, class_2], [1, -1])

with open('points_ring_test.pkl', 'r') as f:
    class_1 = pickle.load(f)
    class_2 = pickle.load(f)
    labels = pickle.load(f)

def classify(x, y, bc=bc):
    points = vstack((x, y))
    return bc.classify(points.T)[0]

imtools.plot_2D_boundary([-6, 6, -6, 6], [class_1, class_2], classify, [1, -1])
show()


    

In [47]:

    

import poisson
poisson = reload(poisson)

with open('points_ring.pkl', 'r') as f:
    class_1 = pickle.load(f)
    class_2 = pickle.load(f)
    labels = pickle.load(f)

bcp = poisson.BayesClassifier()
bcp.train([class_1, class_2], [1, -1])

with open('points_ring_test.pkl', 'r') as f:
    class_1 = pickle.load(f)
    class_2 = pickle.load(f)
    labels = pickle.load(f)

def classify(x, y, bc=bcp):
    points = vstack((x, y))
    return bc.classify(points.T)[0]

imtools.plot_2D_boundary([-6, 6, -6, 6], [class_1, class_2], classify, [1, -1])
show()


    

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