y = np.exp(-0.5*np.diag(np.dot(x, np.dot(S, x.T))))
return y*(2*np.pi)**(-d/2.0)/(np.sqrt(np.prod(v))+1e-6)
if __name__ == '__main__':
# with open('points_ring.pkl', 'r') as f:
with open('points_normal.pkl', 'r') as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
bc = BayesClassifier()
bc.train([class_1, class_2], [1, -1])
with open('points_normal_test.pkl', 'r') as f:
# with open('points_ring_test.pkl', 'r') as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
print bc.classify(class_2[:10])[0]
def classify(x, y, bc=bc):
points = np.vstack((x, y))
return bc.classify(points.T)[0]
img_tools.plot_2D_boundary([-6, 6, -6, 6], [class_1, class_2], classify, [1, -1])
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
class_1 = map(list, class_1)
class_2 = map(list, class_2)
labels = list(labels)
samples = class_1 + class_2
prob = svm.svm_problem(labels, samples)
param = svm.svm_parameter('-t 2')
m = svm.svm_train(prob, param)
res = svm.svm_predict(labels, samples, m)
with open('points_ring_test.pkl', 'r') as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
class_1 = map(list, class_1)
class_2 = map(list, class_2)
def predict(x, y, model=m):
result = zip(x, y)
result = map(list, result)
return np.array(svm.svm_predict([0] * len(x), result, model)[0])
img_tools.plot_2D_boundary(
[-6, 6, -6, 6], [np.array(class_1), np.array(class_2)], predict, [-1, 1])
angle = 2 * np.pi * np.random.randn(n, 1)
testt = r * np.cos(angle)
class_2 = np.hstack((r * np.cos(angle), r * np.sin(angle)))
labels = np.hstack((np.ones(n), -1 * np.ones(n)))
with open('points_ring.pkl', 'w') as f:
pickle.dump(class_1, f)
pickle.dump(class_2, f)
pickle.dump(labels, f)
if __name__ == '__main__':
# generate_data()
# with open('points_normal.pkl', 'r') as f:
with open('points_ring.pkl', 'r') as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
model = KnnClassifier(labels, np.vstack((class_1, class_2)))
# with open('points_normal_test.pkl', 'r') as f:
with open('points_ring_test.pkl', 'r') as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
print model.classify(class_1[0])
def classify(x, y, models=model):
return np.array([models.classify([xx, yy]) for (xx, yy) in zip(x, y)])
plot_2D_boundary([-6, 6, -6, 6], [class_1, class_2], classify, [1, -1])
with open('points_ring.pkl', 'r') as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
class_1 = map(list, class_1)
class_2 = map(list, class_2)
labels = list(labels)
samples = class_1+class_2
prob = svm.svm_problem(labels, samples)
param = svm.svm_parameter('-t 2')
m = svm.svm_train(prob, param)
res = svm.svm_predict(labels, samples, m)
with open('points_ring_test.pkl', 'r') as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
class_1 = map(list, class_1)
class_2 = map(list, class_2)
def predict(x, y, model=m):
result = zip(x, y)
result = map(list, result)
return np.array(svm.svm_predict([0]*len(x), result, model)[0])
img_tools.plot_2D_boundary([-6, 6, -6, 6], [np.array(class_1), np.array(class_2)], predict, [-1, 1])
angle = 2 * np.pi * np.random.randn(n, 1)
testt = r * np.cos(angle)
class_2 = np.hstack((r * np.cos(angle), r * np.sin(angle)))
labels = np.hstack((np.ones(n), -1 * np.ones(n)))
with open("points_ring.pkl", "w") as f:
pickle.dump(class_1, f)
pickle.dump(class_2, f)
pickle.dump(labels, f)
if __name__ == "__main__":
# generate_data()
# with open('points_normal.pkl', 'r') as f:
with open("points_ring.pkl", "r") as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
model = KnnClassifier(labels, np.vstack((class_1, class_2)))
# with open('points_normal_test.pkl', 'r') as f:
with open("points_ring_test.pkl", "r") as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
print model.classify(class_1[0])
def classify(x, y, models=model):
return np.array([models.classify([xx, yy]) for (xx, yy) in zip(x, y)])
plot_2D_boundary([-6, 6, -6, 6], [class_1, class_2], classify, [1, -1])