def test_discrete_handling():
variances = [[[5.320099817666392, 0.6070740587168416],
[0.6070740587168416, 2.91511327850999]],
[[2.4071373546900015, 0.22786169765608927],
[0.22786169765608927, 2.4736622912606343]],
[[3.065924316519349, 0.9318565801601824],
[0.9318565801601824, 3.269782301092388]]]
means = [[2.3804013106244195, -5.959479465161133],
[0.4759797740245023, -2.7971912319791215],
[-3.137589475680539, -5.78236605039465]]
weights = [0.2931922485762746, 0.33288265886527635, 0.3739250925584491]
mix = GM(means, variances, weights)
delta = 0.1
bounds = [-9.6, -3.6, 4, 3.6]
discrete_mix = mix.discretize2D(low=[bounds[0], bounds[1]],
high=[bounds[2], bounds[3]],
delta=delta)
flat_belief = discrete_dehydrate(discrete_mix)
# print(flat_belief)
shapes = [
int((bounds[2] - bounds[0]) / delta),
int((bounds[3] - bounds[1]) / delta)
]
print(shapes)
unflat_belief = discrete_rehydrate(flat_belief, shapes)
print(discrete_mix == unflat_belief)
def testRectangleModel():
pz = Softmax();
pz.buildRectangleModel([[2,2],[3,4]],1);
#print('Plotting Observation Model');
#pz.plot2D(low=[0,0],high=[10,5],vis=True);
prior = GM();
for i in range(0,10):
for j in range(0,5):
prior.addG(Gaussian([i,j],[[1,0],[0,1]],1));
# prior.addG(Gaussian([4,3],[[1,0],[0,1]],1));
# prior.addG(Gaussian([7,2],[[4,1],[1,4]],3))
prior.normalizeWeights();
dela = 0.1;
x, y = np.mgrid[0:10:dela, 0:5:dela]
fig,axarr = plt.subplots(6);
axarr[0].contourf(x,y,prior.discretize2D(low=[0,0],high=[10,5],delta=dela));
axarr[0].set_title('Prior');
titles = ['Inside','Left','Right','Up','Down'];
for i in range(0,5):
post = pz.runVBND(prior,i);
c = post.discretize2D(low=[0,0],high=[10,5],delta=dela);
axarr[i+1].contourf(x,y,c,cmap='viridis');
axarr[i+1].set_title('Post: ' + titles[i]);
plt.show();
def testGeneralModel():
pz = Softmax()
pz.buildGeneralModel(2, 4, [[1, 0], [2, 0], [3, 0]],
np.matrix([-1, 1, -1, 1, 1, -1, 0, -1, -1]).T)
#print('Plotting Observation Model');
#pz.plot2D(low=[0,0],high=[10,5],vis=True);
prior = GM()
for i in range(0, 10):
for j in range(0, 5):
prior.addG(Gaussian([i, j], [[1, 0], [0, 1]], 1))
# prior.addG(Gaussian([4,3],[[1,0],[0,1]],1));
# prior.addG(Gaussian([7,2],[[4,1],[1,4]],3))
prior.normalizeWeights()
dela = 0.1
x, y = np.mgrid[0:10:dela, 0:5:dela]
fig, axarr = plt.subplots(5)
axarr[0].contourf(x, y,
prior.discretize2D(low=[0, 0], high=[10, 5], delta=dela))
axarr[0].set_title('Prior')
titles = ['Inside', 'Left', 'Right', 'Down']
for i in range(0, 4):
post = pz.runVBND(prior, i)
c = post.discretize2D(low=[0, 0], high=[10, 5], delta=dela)
axarr[i + 1].contourf(x, y, c, cmap='viridis')
axarr[i + 1].set_title('Post: ' + titles[i])
plt.show()
def buildRectangleModel():
#Specify the lower left and upper right points
recBounds = [[2, 2], [3, 4]]
#recBounds = [[1,1],[8,4]];
B = np.matrix([
-1, 0, recBounds[0][0], 1, 0, -recBounds[1][0], 0, 1, -recBounds[1][1],
0, -1, recBounds[0][1]
]).T
M = np.zeros(shape=(12, 15))
#Boundry: Left|Near
rowSB = 0
classNum1 = 1
classNum2 = 0
for i in range(0, 3):
M[3 * rowSB + i, 3 * classNum2 + i] = -1
M[3 * rowSB + i, 3 * classNum1 + i] = 1
#Boundry: Right|Near
rowSB = 1
classNum1 = 2
classNum2 = 0
for i in range(0, 3):
M[3 * rowSB + i, 3 * classNum2 + i] = -1
M[3 * rowSB + i, 3 * classNum1 + i] = 1
#Boundry: Up|Near
rowSB = 2
classNum1 = 3
classNum2 = 0
for i in range(0, 3):
M[3 * rowSB + i, 3 * classNum2 + i] = -1
M[3 * rowSB + i, 3 * classNum1 + i] = 1
#Boundry: Down|Near
rowSB = 3
classNum1 = 4
classNum2 = 0
for i in range(0, 3):
M[3 * rowSB + i, 3 * classNum2 + i] = -1
M[3 * rowSB + i, 3 * classNum1 + i] = 1
A = np.hstack((M, B))
#print(np.linalg.matrix_rank(A))
#print(np.linalg.matrix_rank(M))
Theta = linalg.lstsq(M, B)[0].tolist()
weight = []
bias = []
for i in range(0, len(Theta) // 3):
weight.append([Theta[3 * i][0], Theta[3 * i + 1][0]])
bias.append(Theta[3 * i + 2][0])
steep = 1
weight = (np.array(weight) * steep).tolist()
bias = (np.array(bias) * steep).tolist()
pz = Softmax(weight, bias)
#print('Plotting Observation Model');
#pz.plot2D(low=[0,0],high=[10,5],vis=True);
prior = GM()
for i in range(0, 10):
for j in range(0, 5):
prior.addG(Gaussian([i, j], [[1, 0], [0, 1]], 1))
# prior.addG(Gaussian([4,3],[[1,0],[0,1]],1));
# prior.addG(Gaussian([7,2],[[4,1],[1,4]],3))
prior.normalizeWeights()
dela = 0.1
x, y = np.mgrid[0:10:dela, 0:5:dela]
fig, axarr = plt.subplots(6)
axarr[0].contourf(x, y,
prior.discretize2D(low=[0, 0], high=[10, 5], delta=dela))
axarr[0].set_title('Prior')
titles = ['Inside', 'Left', 'Right', 'Up', 'Down']
for i in range(0, 5):
post = pz.runVBND(prior, i)
c = post.discretize2D(low=[0, 0], high=[10, 5], delta=dela)
axarr[i + 1].contourf(x, y, c, cmap='viridis')
axarr[i + 1].set_title('Post: ' + titles[i])
plt.show()
