def main():
gmm = GMM(N=1000)
gmm.append(normalDist(array([0.,10.]),
array([[5.,0.],
[0.,3.]])),
0.4)
gmm.append(normalDist(array([-5.,-10.]),
array([[5.,0.],
[0.,5.]])),
0.3)
gmm.append(normalDist(array([15.,15.]),
array([[5.,0.],
[0.,5.]])))
(t,x) = gmm.sample().mixtures()
colors = [['blue', 'red', 'green'][int(label)] for label in t]
plt.scatter(x[:,0], x[:,1], color=colors)
cls = linear()
cls.fit(x,t,target=1)
print(cls.wml)
xlim = [min(x[:,0]), max(x[:,0])]
tics = arange(xlim[0]-3,xlim[1]+3,0.01)
plt.xlim(xlim[0]-3,xlim[1]+3)
plt.plot(*cls.cPlane(tics),
color='black')
plt.show()
def main():
gmm = GMM(N=1000)
gmm.append(normalDist(array([0., 10.]), array([[5., 0.], [0., 3.]])), 0.4)
gmm.append(normalDist(array([-5., -10.]), array([[5., 0.], [0., 5.]])),
0.3)
gmm.append(normalDist(array([15., 15.]), array([[5., 0.], [0., 5.]])))
(t, x) = gmm.sample().mixtures()
colors = [['blue', 'red', 'green'][int(label)] for label in t]
plt.scatter(x[:, 0], x[:, 1], color=colors)
cls = linear()
cls.fit(x, t, target=1)
print(cls.wml)
xlim = [min(x[:, 0]), max(x[:, 0])]
tics = arange(xlim[0] - 3, xlim[1] + 3, 0.01)
plt.xlim(xlim[0] - 3, xlim[1] + 3)
plt.plot(*cls.cPlane(tics), color='black')
plt.show()
def composeGMM(type=1):
if type == 1:
gmm = GMM(N=1000)
gmm.append(normalDist(array([-10.,5.]),
array([[5.,0.],
[0.,3.]])),
0.4)
gmm.append(normalDist(array([-5.,-10.]),
array([[5.,0.],
[0.,5.]])),
0.3)
gmm.append(normalDist(array([15.,15.]),
array([[5.,0.],
[0.,5.]])))
elif type == 2:
gmm = GMM(N=1000)
gmm.append(normalDist(array([0.,5.]),
array([[5.,0.],
[0.,3.]])),
0.3)
gmm.append(normalDist(array([-2.,-10.]),
array([[5.,0.],
[0.,5.]])),
0.3)
gmm.append(normalDist(array([-5.,15.]),
array([[5.,0.],
[0.,5.]])))
elif type == 3:
gmm = GMM(N=1000)
gmm.append(normalDist(array([5.,5.]),
array([[20.,0.],
[0.,3.]])),
0.5)
gmm.append(normalDist(array([-5.,-5.]),
array([[20.,0.],
[0.,3.]])))
return gmm
def composeGMM(type=1):
if type == 1:
gmm = GMM(N=1000)
gmm.append(normalDist(array([-10., 5.]), array([[5., 0.], [0., 3.]])),
0.4)
gmm.append(normalDist(array([-5., -10.]), array([[5., 0.], [0., 5.]])),
0.3)
gmm.append(normalDist(array([15., 15.]), array([[5., 0.], [0., 5.]])))
elif type == 2:
gmm = GMM(N=1000)
gmm.append(normalDist(array([0., 5.]), array([[5., 0.], [0., 3.]])),
0.3)
gmm.append(normalDist(array([-2., -10.]), array([[5., 0.], [0., 5.]])),
0.3)
gmm.append(normalDist(array([-5., 15.]), array([[5., 0.], [0., 5.]])))
elif type == 3:
gmm = GMM(N=1000)
gmm.append(normalDist(array([5., 5.]), array([[20., 0.], [0., 3.]])),
0.5)
gmm.append(normalDist(array([-5., -5.]), array([[20., 0.], [0., 3.]])))
return gmm
