pklist=['points_normal.pkl','points_ring.pkl']
figure()
# load 2D points using Pickle
for i, pklfile in enumerate(pklist):
with open(pklfile, 'r') as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
# load test data using Pickle
with open(pklfile[:-4]+'_test.pkl', 'r') as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
model = knn.KnnClassifier(labels,vstack((class_1,class_2)))
# test on the first point
print model.classify(class_1[0])
#define function for plotting
def classify(x,y,model=model):
return array([model.classify([xx,yy]) for (xx,yy) in zip(x,y)])
# lot the classification boundary
subplot(1,2,i+1)
imtools.plot_2D_boundary([-6,6,-6,6],[class_1,class_2],classify,[1,-1])
titlename=pklfile[:-4]
title(titlename)
show()
# create SVM
prob = svm_problem(labels, samples)
param = svm_parameter('-t 2')
# train SVM on data
m = svm_train(prob, param)
# how did the training do?
res = svm_predict(labels, samples, m)
# load test data using Pickle
with open('../data/points_normal_test.pkl', 'r') as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
# convert to lists for libsvm
class_1 = map(list, class_1)
class_2 = map(list, class_2)
# define function for plotting
def predict(x, y, model=m):
return array(svm_predict([0] * len(x), map(list, zip(x, y)), model)[0])
# plot the classification boundary
imtools.plot_2D_boundary([-6, 6, -6, 6],
[array(class_1), array(class_2)], predict, [-1, 1])
show()
