def test_gmm_best_segment(self):
"""
Calculate the best segment
generated by the GMM and
compare the subsequent likelihood
of a reference segmentation.
Note: this test will take a while
to run.
returns:
best_seg = np.ndarray[np.ndarray[float]]
"""
image_file = 'images/party_spock.png'
image_matrix = image_to_matrix(image_file)
image_matrix_flat = flatten_image_matrix(image_matrix)
num_components = 3
gmm = GaussianMixtureModel(image_matrix, num_components)
gmm.initialize_training()
iters = 10
# generate best segment from 10 iterations
# and extract its likelihood
best_seg = gmm.best_segment(iters)
matrix_to_image(best_seg, 'images/best_segment_spock.png')
best_likelihood = gmm.likelihood()
# extract likelihood from reference image
ref_image_file = 'images/party_spock%d_baseline.png' % num_components
ref_image = image_to_matrix(ref_image_file, grays=True)
gmm_ref = GaussianMixtureModel(ref_image, num_components)
ref_vals = ref_image.flatten()
ref_means = list(set(ref_vals))
ref_variances = np.zeros(num_components)
ref_mixing = np.zeros(num_components)
for i in range(num_components):
relevant_vals = ref_vals[ref_vals == ref_means[i]]
ref_mixing[i] = float(len(relevant_vals)) / float(len(ref_vals))
ref_mask = ref_vals == ref_means[i]
ref_variances[i] = np.mean(
(image_matrix_flat[ref_mask] - ref_means[i])**2)
gmm_ref.means = ref_means
gmm_ref.variances = ref_variances
gmm_ref.mixing_coefficients = ref_mixing
ref_likelihood = gmm_ref.likelihood()
# compare best likelihood and reference likelihood
likelihood_diff = best_likelihood - ref_likelihood
print "Reference"
print gmm_ref.means
print gmm_ref.variances
print gmm_ref.mixing_coefficients
print best_likelihood
print ref_likelihood
print likelihood_diff
likelihood_thresh = 8e4
self.assertTrue(likelihood_diff >= likelihood_thresh,
msg=("Image segmentation failed to improve baseline "
"by at least %.2f" % likelihood_thresh))
def test_gmm_segment(self, train_model, segment):
"""
Apply the trained GMM
to unsegmented image and
generate a segmented image.
returns:
segmented_matrix = numpy.ndarray[numpy.ndarray[float]]
"""
image_file = 'images/bird_color_24.png'
image_matrix = image_to_matrix(image_file).reshape(-1, 3)
num_components = 5
MU, SIGMA, PI, r = train_model(image_matrix, num_components,
convergence_function=default_convergence)
segment = segment(image_matrix, MU, num_components, r)
segment_num_components = len(np.unique(segment, axis=0))
self.assertTrue(segment_num_components == r.shape[0],
msg="Incorrect number of image segments produced")
segment_sort = np.sort(np.unique(segment, axis=0), axis=0)
mu_sort = np.sort(MU, axis=0)
self.assertTrue((segment_sort == mu_sort).all(),
msg="Incorrect segment values. Should be be MU values")
print_success_message()
def test_gmm_best_segment(self, best_segment):
"""
Calculate the best segment
generated by the GMM and
compare the subsequent likelihood
of a reference segmentation.
Note: this test will take a while
to run.
returns:
best_seg = np.ndarray[np.ndarray[float]]
"""
image_file = 'images/bird_color_24.png'
original_image_matrix = image_to_matrix(image_file)
image_matrix = original_image_matrix.reshape(-1, 3)
num_components = 3
iters = 10
# generate best segment from 10 iterations
# and extract its likelihood
best_likelihood, best_seg = best_segment(image_matrix, num_components, iters)
ref_likelihood = 40000
# # compare best likelihood and reference likelihood
likelihood_diff = best_likelihood - ref_likelihood
likelihood_thresh = 5000
self.assertTrue(likelihood_diff >= likelihood_thresh,
msg=("Image segmentation failed to improve baseline "
"by at least %.2f" % likelihood_thresh))
print_success_message()
def test_gmm_covariance(self, compute_sigma):
''' Testing implementation of covariance matrix
computation explicitly'''
image_file = 'images/bird_color_24.png'
image_matrix = image_to_matrix(image_file)
image_matrix = image_matrix.reshape(-1, 3)
m, n = image_matrix.shape
num_components = 5
MU = np.array([[0.64705884, 0.7490196, 0.7058824 ],
[0.98039216, 0.3019608, 0.14509805],
[0.3764706, 0.39215687, 0.28627452],
[0.2784314, 0.26666668, 0.23921569],
[0.16078432, 0.15294118, 0.30588236]])
SIGMA = np.array([[[ 0.15471499, 0.11200016, 0.04393127],
[ 0.11200016, 0.22953323, 0.16426138],
[ 0.04393127, 0.16426138, 0.19807944]],
[[ 0.38481037, 0.0204306, -0.12658471],
[ 0.0204306, 0.06127004, 0.02783406],
[-0.12658471, 0.02783406, 0.12057389]],
[[ 0.13134574, 0.03346525, -0.01198761],
[ 0.03346525, 0.06303026, 0.01652109],
[-0.01198761, 0.01652109, 0.08084702]],
[[ 0.15901856, 0.05194932, 0.00383432],
[ 0.05194932, 0.06501033, 0.02864332],
[ 0.00383432, 0.02864332, 0.08966025]],
[[ 0.21760082, 0.09526375, -0.00290086],
[ 0.09526375, 0.09400968, 0.02967787],
[-0.00290086, 0.02967787, 0.07848203]]])
self.assertTrue(np.allclose(SIGMA, compute_sigma(image_matrix, MU)),
msg="Incorrect covariance matrix.")
print_success_message()
def BIC_likelihood_model_test():
"""Test to compare the
models with the lowest BIC
and the highest likelihood.
returns:
min_BIC_model = GaussianMixtureModel
max_likelihood_model = GaussianMixtureModel
for testing purposes:
comp_means = [
[0.023529412, 0.1254902],
[0.023529412, 0.1254902, 0.20392157],
[0.023529412, 0.1254902, 0.20392157, 0.36078432],
[0.023529412, 0.1254902, 0.20392157, 0.36078432, 0.59215689],
[0.023529412, 0.1254902, 0.20392157, 0.36078432, 0.59215689,
0.71372563],
[0.023529412, 0.1254902, 0.20392157, 0.36078432, 0.59215689,
0.71372563, 0.964706]
]
"""
# TODO: finish this method
image_file = 'images/party_spock.png'
image_matrix = image_to_matrix(image_file)
comp_means = [[0.023529412, 0.1254902],
[0.023529412, 0.1254902, 0.20392157],
[0.023529412, 0.1254902, 0.20392157, 0.36078432],
[0.023529412, 0.1254902, 0.20392157, 0.36078432, 0.59215689],
[
0.023529412, 0.1254902, 0.20392157, 0.36078432,
0.59215689, 0.71372563
],
[
0.023529412, 0.1254902, 0.20392157, 0.36078432,
0.59215689, 0.71372563, 0.964706
]]
max_likelihood = float('-inf')
min_BIC = float('inf')
max_likelihood_model = None
min_BIC_model = None
for i, num_components in enumerate(xrange(2, 8)):
model = GaussianMixtureModel(image_matrix, num_components)
model.initialize_training()
model.means = comp_means[i]
model.train_model()
likelihood = model.likelihood()
if max_likelihood < likelihood:
max_likelihood = likelihood
max_likelihood_model = model
#max_component = num_components
BIC = bayes_info_criterion(model)
if BIC < min_BIC:
min_BIC = BIC
min_BIC_model = model
#bic_component = num_components
#print 'Max', max_component, 'Bic', bic_component
return min_BIC_model, max_likelihood_model
def test_k_means(self):
"""
Testing your implementation
of k-means on the segmented
bird_color_24 reference images.
"""
k_min = 2
k_max = 6
image_dir = 'images/'
image_name = 'bird_color_24.png'
image_values = image_to_matrix(image_dir + image_name)
# initial mean for each k value
initial_means = [
np.array([[0.90980393, 0.8392157, 0.65098041],
[0.83137256, 0.80784315, 0.69411767]]),
np.array([[0.90980393, 0.8392157, 0.65098041],
[0.83137256, 0.80784315, 0.69411767],
[0.67450982, 0.52941179, 0.25490198]]),
np.array([[0.90980393, 0.8392157, 0.65098041],
[0.83137256, 0.80784315, 0.69411767],
[0.67450982, 0.52941179, 0.25490198],
[0.86666667, 0.8392157, 0.70588237]]),
np.array([[0.90980393, 0.8392157, 0.65098041],
[0.83137256, 0.80784315, 0.69411767],
[0.67450982, 0.52941179, 0.25490198],
[0.86666667, 0.8392157, 0.70588237], [0, 0, 0]]),
np.array([[0.90980393, 0.8392157, 0.65098041],
[0.83137256, 0.80784315, 0.69411767],
[0.67450982, 0.52941179, 0.25490198],
[0.86666667, 0.8392157, 0.70588237], [0, 0, 0],
[0.8392157, 0.80392158, 0.63921571]]),
]
# test different k values to find best
for k in range(k_min, k_max + 1):
updated_values = k_means_cluster(image_values, k,
initial_means[k - k_min])
ref_image = image_dir + 'k%d_%s' % (k, image_name)
ref_values = image_to_matrix(ref_image)
dist = image_difference(updated_values, ref_values)
self.assertEqual(int(dist),
0,
msg=("Clustering for %d clusters" +
"produced unrealistic image segmentation.") %
k)
def BIC_likelihood_model_test():
"""Test to compare the
models with the lowest BIC
and the highest likelihood.
returns:
min_BIC_model = GaussianMixtureModel
max_likelihood_model = GaussianMixtureModel
"""
# TODO: finish this method
comp_means = [[0.023529412, 0.1254902],
[0.023529412, 0.1254902, 0.20392157],
[0.023529412, 0.1254902, 0.20392157, 0.36078432],
[0.023529412, 0.1254902, 0.20392157, 0.36078432, 0.59215689],
[
0.023529412, 0.1254902, 0.20392157, 0.36078432,
0.59215689, 0.71372563
],
[
0.023529412, 0.1254902, 0.20392157, 0.36078432,
0.59215689, 0.71372563, 0.964706
]]
image_file = 'images/party_spock.png'
image_matrix = image_to_matrix(image_file)
# first test original model
max_likelihood_model = GaussianMixtureModel(image_matrix,
len(comp_means[0]))
max_likelihood_model.initialize_training()
max_likelihood_model.means = np.copy(comp_means[0])
max_likelihood_model.train_model()
max_like = max_likelihood_model.likelihood()
min_BIC_model = GaussianMixtureModel(image_matrix, len(comp_means[0]))
min_BIC_model.initialize_training()
min_BIC_model.means = np.copy(comp_means[0])
min_BIC_model.train_model()
BIC = bayes_info_criterion(min_BIC_model)
for i in range(1, len(comp_means)):
gmm = GaussianMixtureModel(image_matrix, len(comp_means[i]))
gmm.initialize_training()
gmm.means = np.copy(comp_means[i])
gmm.train_model()
if bayes_info_criterion(gmm) < BIC:
min_BIC_model = gmm
BIC = bayes_info_criterion(gmm)
if gmm.likelihood() > max_like:
max_likelihood_model = gmm
max_like = gmm.likelihood()
return min_BIC_model, max_likelihood_model
def test_initial_means(self, initial_means):
image_file = 'images/bird_color_24.png'
image_values = image_to_matrix(image_file).reshape(-1, 3)
m, n = image_values.shape
for k in range(1, 10):
means = initial_means(image_values, k)
self.assertEqual(means.shape, (k, n),
msg=("Initialization for %d dimensional array "
"with %d clusters returned an matrix of an incompatible dimension.") % (n, k))
for mean in means:
self.assertTrue(any(np.equal(image_values, mean).all(1)),
msg=("Means should be points from given array"))
print_success_message()
def test_gmm_train(self, train_model, likelihood):
"""Test the training
procedure for GMM.
returns:
gmm = GaussianMixtureModel
"""
image_file = 'images/bird_color_24.png'
image_matrix = image_to_matrix(image_file)
image_matrix = image_matrix.reshape(-1, 3)
num_components = 5
m, n = image_matrix.shape
means = np.array([[0.34901962, 0.3647059, 0.30588236],
[0.9882353, 0.3254902, 0.19607843],
[1., 0.6117647, 0.5019608],
[0.37254903, 0.3882353, 0.2901961],
[0.3529412, 0.40784314, 1.]])
covariances = np.array([[[0.13715639, 0.03524152, -0.01240736],
[0.03524152, 0.06077217, 0.01898307],
[-0.01240736, 0.01898307, 0.07848206]],
[[0.3929004, 0.03238055, -0.10174976],
[0.03238055, 0.06016063, 0.02226048],
[-0.10174976, 0.02226048, 0.10162983]],
[[0.40526569, 0.18437279, 0.05891556],
[0.18437279, 0.13535137, 0.0603222],
[0.05891556, 0.0603222, 0.09712359]],
[[0.13208355, 0.03362673, -0.01208926],
[0.03362673, 0.06261538, 0.01699577],
[-0.01208926, 0.01699577, 0.08031248]],
[[0.13623408, 0.03036055, -0.09287403],
[0.03036055, 0.06499729, 0.06576895],
[-0.09287403, 0.06576895, 0.49017089]]])
pis = np.array([0.2, 0.2, 0.2, 0.2, 0.2])
initial_lkl = likelihood(image_matrix, pis, means, covariances, num_components)
MU, SIGMA, PI, r = train_model(image_matrix, num_components,
convergence_function=default_convergence,
initial_values=(means, covariances, pis))
final_lkl = likelihood(image_matrix, PI, MU, SIGMA, num_components)
likelihood_difference = final_lkl - initial_lkl
likelihood_thresh = 90000
diff_check = likelihood_difference >= likelihood_thresh
self.assertTrue(diff_check, msg=("Model likelihood increased by less"
" than %d for a two-mean mixture" % likelihood_thresh))
print_success_message()
def BIC_likelihood_model_test():
"""Test to compare the
models with the lowest BIC
and the highest likelihood.
returns:
min_BIC_model = GaussianMixtureModel
max_likelihood_model = GaussianMixtureModel
"""
comp_means = [
np.array([0.023529412, 0.1254902]),
np.array([0.023529412, 0.1254902, 0.20392157]),
np.array([0.023529412, 0.1254902, 0.20392157, 0.36078432]),
np.array([0.023529412, 0.1254902, 0.20392157, 0.36078432, 0.59215689]),
np.array([
0.023529412, 0.1254902, 0.20392157, 0.36078432, 0.59215689,
0.71372563
]),
np.array([
0.023529412, 0.1254902, 0.20392157, 0.36078432, 0.59215689,
0.71372563, 0.964706
])
]
img = image_to_matrix('images/party_spock.png')
max_likelihood = float("-inf")
min_BIC = float("inf")
max_likelihood_model = None
min_BIC_model = None
k = 2
for means in comp_means:
gmm = GaussianMixtureModel(np.copy(img), k)
gmm.initialize_training()
gmm.means = means
# gmm.train_model()
cur_likelihood = gmm.likelihood()
cur_BIC = bayes_info_criterion(gmm)
if max_likelihood < cur_likelihood:
max_likelihood = cur_likelihood
max_likelihood_model = gmm
if min_BIC > cur_BIC:
min_BIC = cur_BIC
min_BIC_model = gmm
k += 1
del comp_means
return min_BIC_model, max_likelihood_model
def BIC_likelihood_model_test():
"""Test to compare the
models with the lowest BIC
and the highest likelihood.
returns:
min_BIC_model = GaussianMixtureModel
max_likelihood_model = GaussianMixtureModel
for testing purposes:
"""
gmm_list = []
bic_list = []
likelihood_list = []
comp_means = [[0.023529412, 0.1254902],
[0.023529412, 0.1254902, 0.20392157],
[0.023529412, 0.1254902, 0.20392157, 0.36078432],
[0.023529412, 0.1254902, 0.20392157, 0.36078432, 0.59215689],
[
0.023529412, 0.1254902, 0.20392157, 0.36078432,
0.59215689, 0.71372563
],
[
0.023529412, 0.1254902, 0.20392157, 0.36078432,
0.59215689, 0.71372563, 0.964706
]]
image_file = 'images/party_spock.png'
image_matrix = image_to_matrix(image_file)
for i in range(0, 6):
num_components = i + 2
gmm = GaussianMixtureModel(image_matrix, num_components, comp_means[i])
gmm.initialize_training()
gmm.train_model()
likelihood = gmm.likelihood()
likelihood_list.append(likelihood)
bic = bayes_info_criterion(gmm)
bic_list.append(bic)
gmm_list.append(gmm)
print likelihood_list
print np.argmax(likelihood_list)
print bic_list
print np.argmin(bic_list)
min_BIC_model = gmm_list[np.argmin(bic_list)]
max_likelihood_model = gmm_list[np.argmax(likelihood_list)]
def test_gmm_likelihood(self, likelihood):
"""Testing the GMM method
for calculating the overall
model probability.
Should return -364370.
returns:
likelihood = float
"""
image_file = 'images/bird_color_24.png'
image_matrix = image_to_matrix(image_file)
image_matrix = image_matrix.reshape(-1, 3)
num_components = 5
m, n = image_matrix.shape
means = np.array([[0.34901962, 0.3647059, 0.30588236],
[0.9882353, 0.3254902, 0.19607843],
[1., 0.6117647, 0.5019608],
[0.37254903, 0.3882353, 0.2901961],
[0.3529412, 0.40784314, 1.]])
covariances = np.array([[[0.13715639, 0.03524152, -0.01240736],
[0.03524152, 0.06077217, 0.01898307],
[-0.01240736, 0.01898307, 0.07848206]],
[[0.3929004, 0.03238055, -0.10174976],
[0.03238055, 0.06016063, 0.02226048],
[-0.10174976, 0.02226048, 0.10162983]],
[[0.40526569, 0.18437279, 0.05891556],
[0.18437279, 0.13535137, 0.0603222],
[0.05891556, 0.0603222, 0.09712359]],
[[0.13208355, 0.03362673, -0.01208926],
[0.03362673, 0.06261538, 0.01699577],
[-0.01208926, 0.01699577, 0.08031248]],
[[0.13623408, 0.03036055, -0.09287403],
[0.03036055, 0.06499729, 0.06576895],
[-0.09287403, 0.06576895, 0.49017089]]])
pis = np.array([0.2, 0.2, 0.2, 0.2, 0.2])
lkl = likelihood(image_matrix, pis, means, covariances, num_components)
self.assertEqual(np.round(lkl), -23943.0,
msg="Incorrect likelihood value returned")
# expected_lkl =
print_success_message()
def test_gmm_e_step(self, E_step):
"""Testing the E-step implementation
returns:
r = numpy.ndarray[numpy.ndarray[float]]
"""
image_file = 'images/bird_color_24.png'
image_matrix = image_to_matrix(image_file)
image_matrix = image_matrix.reshape(-1, 3)
num_components = 5
m, n = image_matrix.shape
means = np.array([[0.34901962, 0.3647059, 0.30588236],
[0.9882353, 0.3254902, 0.19607843],
[1., 0.6117647, 0.5019608],
[0.37254903, 0.3882353, 0.2901961],
[0.3529412, 0.40784314, 1.]])
covariances = np.array([[[0.13715639, 0.03524152, -0.01240736],
[0.03524152, 0.06077217, 0.01898307],
[-0.01240736, 0.01898307, 0.07848206]],
[[0.3929004, 0.03238055, -0.10174976],
[0.03238055, 0.06016063, 0.02226048],
[-0.10174976, 0.02226048, 0.10162983]],
[[0.40526569, 0.18437279, 0.05891556],
[0.18437279, 0.13535137, 0.0603222],
[0.05891556, 0.0603222, 0.09712359]],
[[0.13208355, 0.03362673, -0.01208926],
[0.03362673, 0.06261538, 0.01699577],
[-0.01208926, 0.01699577, 0.08031248]],
[[0.13623408, 0.03036055, -0.09287403],
[0.03036055, 0.06499729, 0.06576895],
[-0.09287403, 0.06576895, 0.49017089]]])
pis = np.array([0.2, 0.2, 0.2, 0.2, 0.2])
r = E_step(image_matrix, means, covariances, pis, num_components)
expected_r_rows = np.array([25262.04787326, 18961.31887563, 14991.17253041, 24783.52164336, 14821.93907735])
self.assertEqual(round(r.sum()), m,
msg="Incorrect responsibility values, sum of all elements must be equal to m.")
self.assertTrue(np.allclose(r.sum(axis=0), 1),
msg="Incorrect responsibility values, columns are not normalized.")
self.assertTrue(np.allclose(r.sum(axis=1), expected_r_rows),
msg="Incorrect responsibility values, rows are not normalized.")
print_success_message()
def test_bayes_info(self, bayes_info_criterion):
"""
Test for your
implementation of
BIC on fixed GMM values.
Should be about 727045.
returns:
BIC = float
"""
image_file = 'images/bird_color_24.png'
image_matrix = image_to_matrix(image_file).reshape(-1, 3)
num_components = 5
means = np.array([[0.34901962, 0.3647059, 0.30588236],
[0.9882353, 0.3254902, 0.19607843],
[1., 0.6117647, 0.5019608],
[0.37254903, 0.3882353, 0.2901961],
[0.3529412, 0.40784314, 1.]])
covariances = np.array([[[0.13715639, 0.03524152, -0.01240736],
[0.03524152, 0.06077217, 0.01898307],
[-0.01240736, 0.01898307, 0.07848206]],
[[0.3929004, 0.03238055, -0.10174976],
[0.03238055, 0.06016063, 0.02226048],
[-0.10174976, 0.02226048, 0.10162983]],
[[0.40526569, 0.18437279, 0.05891556],
[0.18437279, 0.13535137, 0.0603222],
[0.05891556, 0.0603222, 0.09712359]],
[[0.13208355, 0.03362673, -0.01208926],
[0.03362673, 0.06261538, 0.01699577],
[-0.01208926, 0.01699577, 0.08031248]],
[[0.13623408, 0.03036055, -0.09287403],
[0.03036055, 0.06499729, 0.06576895],
[-0.09287403, 0.06576895, 0.49017089]]])
pis = np.array([0.2, 0.2, 0.2, 0.2, 0.2])
b_i_c = bayes_info_criterion(image_matrix , pis, means, covariances, num_components)
self.assertTrue(np.isclose(48500, b_i_c, atol=500),
msg="BIC calculation incorrect.")
print_success_message()
def test_gmm_segment(self):
"""
Apply the trained GMM
to unsegmented image and
generate a segmented image.
returns:
segmented_matrix = numpy.ndarray[numpy.ndarray[float]]
"""
image_file = 'images/party_spock.png'
image_matrix = image_to_matrix(image_file)
num_components = 3
gmm = GaussianMixtureModel(image_matrix, num_components)
gmm.initialize_training()
gmm.train_model()
segment = gmm.segment()
segment_num_components = len(np.unique(segment))
self.assertTrue(segment_num_components == num_components,
msg="Incorrect number of image segments produced")
def test_gmm_likelihood(self):
"""Testing the GMM method
for calculating the overall
model probability.
Should return -302844.
returns:
likelihood = float
"""
image_file = 'images/self_driving.png'
image_matrix = image_to_matrix(image_file)
num_components = 5
gmm = GaussianMixtureModel(image_matrix, num_components)
gmm.initialize_training()
gmm.means = np.array([0.03921569, 0.1764706, 0.06666667, 0.42745098, 0.2784314])
likelihood = gmm.likelihood()
self.assertEqual(round(likelihood), -302844,
msg="Incorrect model probability")
def test_gmm_initialization(self, initialize_parameters):
"""Testing the GMM method
for initializing the training"""
image_file = 'images/bird_color_24.png'
image_matrix = image_to_matrix(image_file)
image_matrix = image_matrix.reshape(-1, 3)
m, n = image_matrix.shape
num_components = 5
np.random.seed(0)
means, variances, mixing_coefficients = initialize_parameters(image_matrix, num_components)
self.assertTrue(variances.shape == (num_components, n, n),
msg="Incorrect variance dimensions")
self.assertTrue(means.shape == (num_components, n),
msg="Incorrect mean dimensions")
for mean in means:
self.assertTrue(any(np.equal(image_matrix, mean).all(1)),
msg=("Means should be points from given array"))
self.assertTrue(mixing_coefficients.sum() == 1,
msg="Incorrect mixing coefficients, make all coefficient sum to 1")
print_success_message()
def test_gmm_likelihood(self):
"""Testing the GMM method
for calculating the overall
model probability.
Should return -364370.
returns:
likelihood = float
"""
image_file = 'images/party_spock.png'
image_matrix = image_to_matrix(image_file)
num_components = 5
gmm = GaussianMixtureModel(image_matrix, num_components)
gmm.initialize_training()
gmm.means = [0.4627451, 0.10196079, 0.027450981,
0.011764706, 0.1254902]
likelihood = gmm.likelihood()
self.assertEqual(round(likelihood), -364370,
msg="Incorrect model probability")
def test_gmm_joint_prob(self):
"""Testing the GMM method
for calculating the joint
log probability of a given point.
Should return -0.9413.
returns:
joint_prob = float
"""
image_file = 'images/self_driving.png'
image_matrix = image_to_matrix(image_file)
num_components = 5
gmm = GaussianMixtureModel(image_matrix, num_components)
gmm.initialize_training()
gmm.means = np.array([0.03921569, 0.1764706, 0.06666667, 0.42745098, 0.2784314])
test_val = 0.03921569
joint_prob = gmm.joint_prob(test_val)
self.assertEqual(round(joint_prob, 4), -0.9413,
msg="Incorrect joint log probability")
def BIC_likelihood_model_test():
"""Test to compare the
models with the lowest BIC
and the highest likelihood.
returns:
min_BIC_model = GaussianMixtureModel
max_likelihood_model = GaussianMixtureModel
for testing purposes:
"""
comp_means = [[0.023529412, 0.1254902],
[0.023529412, 0.1254902, 0.20392157],
[0.023529412, 0.1254902, 0.20392157, 0.36078432],
[0.023529412, 0.1254902, 0.20392157, 0.36078432, 0.59215689],
[
0.023529412, 0.1254902, 0.20392157, 0.36078432,
0.59215689, 0.71372563
],
[
0.023529412, 0.1254902, 0.20392157, 0.36078432,
0.59215689, 0.71372563, 0.964706
]]
image_file = 'images/party_spock.png'
image_matrix = image_to_matrix(image_file)
bic = []
likelihood = []
for k in range(2, 8):
#print(k)
gmm = GaussianMixtureModel(image_matrix, k)
gmm.initialize_training()
gmm.means = np.copy(comp_means[k - 2])
gmm.train_model()
bic.append(bayes_info_criterion(gmm))
likelihood.append(gmm.likelihood())
print(likelihood)
min_BIC_model = np.argmin(bic) + 2
max_likelihood_model = np.argmax(likelihood) + 2
return min_BIC_model, max_likelihood_model
raise NotImplementedError()
def test_bayes_info(self):
"""
Test for your
implementation of
BIC on fixed GMM values.
Should be about 610317.
returns:
BIC = float
"""
image_file = 'images/self_driving.png'
image_matrix = image_to_matrix(image_file)
num_components = 3
initial_means = np.array([0.4627451, 0.10196079, 0.027450981])
gmm = GaussianMixtureModel(image_matrix, num_components)
gmm.initialize_training()
gmm.means = np.copy(initial_means)
b_i_c = bayes_info_criterion(gmm)
self.assertEqual(round(610317, -3), round(b_i_c, -3),
msg="BIC calculation incorrect.")
def test_gmm_joint_prob(self):
"""Testing the GMM method
for calculating the joint
log probability of a given point.
Should return -0.98196.
returns:
joint_prob = float
"""
image_file = 'images/party_spock.png'
image_matrix = image_to_matrix(image_file)
num_components = 5
gmm = GaussianMixtureModel(image_matrix, num_components)
gmm.initialize_training()
gmm.means = [0.4627451, 0.10196079, 0.027450981,
0.011764706, 0.1254902]
test_val = 0.4627451
joint_prob = gmm.joint_prob(test_val)
self.assertEqual(round(joint_prob, 4), -0.982,
msg="Incorrect joint log probability")
def test_gmm_prob(self, prob):
"""Testing the GMM method
for calculating the probability
of a given point belonging to a
component.
returns:
prob = float
"""
image_file = 'images/bird_color_24.png'
image_matrix = image_to_matrix(image_file)
image_matrix = image_matrix.reshape(-1, 3)
m, n = image_matrix.shape
mean = np.array([0.0627451, 0.10980392, 0.54901963])
covariance = np.array([[0.28756526, 0.13084501, -0.09662368],
[0.13084501, 0.11177602, -0.02345659],
[-0.09662368, -0.02345659, 0.11303925]])
p = prob(image_matrix[0], mean, covariance)
self.assertEqual(round(p, 5), 0.57693,
msg="Incorrect probability value returned.")
print_success_message()
def test_convergence_condition(self):
"""
Compare the performance of
the default convergence function
with the new convergence function.
return:
default_convergence_likelihood = float
new_convergence_likelihood = float
"""
image_file = 'images/party_spock.png'
image_matrix = image_to_matrix(image_file)
num_components = 3
initial_means = np.array([0.4627451, 0.10196079, 0.027450981])
# first test original model
gmm = GaussianMixtureModel(image_matrix, num_components)
gmm.initialize_training()
gmm.means = np.copy(initial_means)
gmm.train_model()
default_convergence_likelihood = gmm.likelihood()
# now test new convergence model
gmm_new = GaussianMixtureModelConvergence(image_matrix, num_components)
gmm_new.initialize_training()
gmm_new.means = np.copy(initial_means)
gmm_new.train_model()
new_convergence_likelihood = gmm_new.likelihood()
# test convergence difference
convergence_diff = new_convergence_likelihood - \
default_convergence_likelihood
convergence_thresh = 8200
print new_convergence_likelihood
print default_convergence_likelihood
self.assertTrue(convergence_diff >= convergence_thresh,
msg=("Likelihood difference between"
" the original and converged"
" models less than %.2f" % convergence_thresh))
def test_gmm_m_step(self, M_step):
"""Testing the M-step implementation
returns:
pi = numpy.ndarray[]
mu = numpy.ndarray[numpy.ndarray[float]]
sigma = numpy.ndarray[numpy.ndarray[numpy.ndarray[float]]]
"""
image_file = 'images/bird_color_24.png'
image_matrix = image_to_matrix(image_file)
image_matrix = image_matrix.reshape(-1, 3)
num_components = 3
r = np.array([[0.51660555, 0.52444999, 0.50810777, 0.51151982, 0.4997758,
0.51134715, 0.4997758, 0.49475051, 0.48168621, 0.47946386],
[0.10036031, 0.09948503, 0.1052672, 0.10687822, 0.11345191,
0.10697943, 0.11345191, 0.11705775, 0.11919758, 0.12314451],
[0.38303414, 0.37606498, 0.38662503, 0.38160197, 0.3867723,
0.38167342, 0.3867723, 0.38819173, 0.39911622, 0.39739164]])
mu, sigma, pi = M_step(image_matrix[:10], r, num_components)
expected_PI = np.array([0.50274825, 0.11052739, 0.38672437])
expected_MU = np.array([[0.12401373, 0.12246745, 0.11884939],
[0.12509098, 0.12350831, 0.12009721],
[0.1244816, 0.12288793, 0.11943994]])
expected_SIGMA = np.array([[[0.00014082, 0.00011489, 0.00013914],
[0.00011489, 0.00014875, 0.00013629],
[0.00013914, 0.00013629, 0.00017721]],
[[0.00014278, 0.00011441, 0.00014151],
[0.00011441, 0.00014355, 0.00013533],
[0.00014151, 0.00013533, 0.00018113]],
[[0.00014206, 0.0001155, 0.00014097],
[0.0001155, 0.00014746, 0.00013691],
[0.00014097, 0.00013691, 0.00018029]]])
self.assertTrue(np.allclose(pi, expected_PI),
msg="Incorrect new coefficient matrix.")
self.assertTrue(np.allclose(mu, expected_MU),
msg="Incorrect new means matrix.")
self.assertTrue(np.allclose(sigma, expected_SIGMA),
msg="Incorrect new covariance matrix.")
print_success_message()
def test_gmm_improvement(self, improved_initialization, initialize_parameters, train_model, likelihood):
"""
Tests whether the new mixture
model is actually an improvement
over the previous one: if the
new model has a higher likelihood
than the previous model for the
provided initial means.
returns:
original_segment = numpy.ndarray[numpy.ndarray[float]]
improved_segment = numpy.ndarray[numpy.ndarray[float]]
"""
image_file = 'images/bird_color_24.png'
image_matrix = image_to_matrix(image_file).reshape(-1, 3)
num_components = 5
np.random.seed(0)
initial_means, initial_sigma, initial_pi = initialize_parameters(image_matrix, num_components)
# first train original model with fixed means
reg_MU, reg_SIGMA, reg_PI, reg_r = train_model(image_matrix, num_components,
convergence_function=default_convergence,
initial_values=(initial_means, initial_sigma, initial_pi))
improved_params = improved_initialization(image_matrix, num_components)
# # then train improved model
imp_MU, imp_SIGMA, imp_PI, imp_r = train_model(image_matrix, num_components,
convergence_function=default_convergence,
initial_values=improved_params)
original_likelihood = likelihood(image_matrix, reg_PI, reg_MU, reg_SIGMA, num_components)
improved_likelihood = likelihood(image_matrix, imp_PI, imp_MU, imp_SIGMA, num_components)
# # then calculate likelihood difference
diff_thresh = 3e3
likelihood_diff = improved_likelihood - original_likelihood
self.assertTrue(likelihood_diff >= diff_thresh,
msg=("Model likelihood less than "
"%d higher than original model" % diff_thresh))
print_success_message()
def test_gmm_improvement(self):
"""
Tests whether the new mixture
model is actually an improvement
over the previous one: if the
new model has a higher likelihood
than the previous model for the
provided initial means.
returns:
original_segment = numpy.ndarray[numpy.ndarray[float]]
improved_segment = numpy.ndarray[numpy.ndarray[float]]
"""
image_file = 'images/self_driving.png'
image_matrix = image_to_matrix(image_file)
num_components = 4
initial_means = np.array([0.4627451, 0.20392157, 0.36078432, 0.47254905])
# first train original model with fixed means
gmm = GaussianMixtureModel(image_matrix, num_components)
gmm.initialize_training()
gmm.means = np.copy(initial_means)
gmm.train_model()
original_segment = gmm.segment()
original_likelihood = gmm.likelihood()
# then train improved model
gmm_improved = GaussianMixtureModelImproved(image_matrix,
num_components)
gmm_improved.initialize_training()
gmm_improved.train_model()
improved_segment = gmm_improved.segment()
improved_likelihood = gmm_improved.likelihood()
# then calculate likelihood difference
diff_thresh = 2.2e3
likelihood_diff = improved_likelihood - original_likelihood
print(improved_likelihood, original_likelihood, likelihood_diff)
self.assertTrue(likelihood_diff >= diff_thresh,
msg=("Model likelihood less than "
"%d higher than original model" % diff_thresh))
def test_convergence_condition(self):
"""
Compare the performance of
the default convergence function
with the new convergence function.
return:
default_convergence_likelihood = float
new_convergence_likelihood = float
"""
image_file = 'images/self_driving.png'
image_matrix = image_to_matrix(image_file)
num_components = 4
initial_means = np.array([0.25882354, 0.05686277, 0.75686275, 0.3882353])
# first test original model
gmm = GaussianMixtureModel(image_matrix, num_components)
gmm.initialize_training()
gmm.means = np.copy(initial_means)
gmm.train_model()
default_convergence_likelihood = gmm.likelihood()
# now test new convergence model
gmm_new = GaussianMixtureModelConvergence(image_matrix, num_components)
gmm_new.initialize_training()
gmm_new.means = np.copy(initial_means)
gmm_new.train_model()
new_convergence_likelihood = gmm_new.likelihood()
# test convergence difference
convergence_diff = new_convergence_likelihood - \
default_convergence_likelihood
convergence_thresh = 1800
self.assertTrue(convergence_diff >= convergence_thresh,
msg=("Likelihood difference between"
" the original and converged"
" models less than %.2f" % convergence_thresh))
def test_convergence_condition(self, improved_initialization, train_model_improved, initialize_parameters,
train_model, likelihood, conv_check):
"""
Compare the performance of
the default convergence function
with the new convergence function.
return:
default_convergence_likelihood = float
new_convergence_likelihood = float
"""
image_file = 'images/bird_color_24.png'
image_matrix = image_to_matrix(image_file).reshape(-1, 3)
num_components = 5
initial_means, initial_sigma, initial_pi = initialize_parameters(image_matrix, num_components)
# first train original model with fixed means
reg_MU, reg_SIGMA, reg_PI, reg_r = train_model(image_matrix, num_components,
convergence_function=default_convergence,
initial_values=(initial_means, initial_sigma, initial_pi))
improved_params = improved_initialization(image_matrix, num_components)
# # then train improved model
imp_MU, imp_SIGMA, imp_PI, imp_r = train_model_improved(image_matrix, num_components,
convergence_function=conv_check,
initial_values=improved_params)
default_convergence_likelihood = likelihood(image_matrix, reg_PI, reg_MU, reg_SIGMA, num_components)
new_convergence_likelihood = likelihood(image_matrix, imp_PI, imp_MU, imp_SIGMA, num_components)
# # test convergence difference
convergence_diff = new_convergence_likelihood - \
default_convergence_likelihood
convergence_thresh = 5000
self.assertTrue(convergence_diff >= convergence_thresh,
msg=("Likelihood difference between"
" the original and converged"
" models less than %.2f" % convergence_thresh))
print_success_message()
def BIC_likelihood_model_test():
"""Test to compare the
models with the lowest BIC
and the highest likelihood.
returns:
min_BIC_model = GaussianMixtureModel
max_likelihood_model = GaussianMixtureModel
"""
# TODO: finish this method
image_file = 'images/party_spock.png'
image_matrix = image_to_matrix(image_file)
comp_means = [[0.023529412, 0.1254902],
[0.023529412, 0.1254902, 0.20392157],
[0.023529412, 0.1254902, 0.20392157, 0.36078432],
[0.023529412, 0.1254902, 0.20392157, 0.36078432, 0.59215689],
[
0.023529412, 0.1254902, 0.20392157, 0.36078432,
0.59215689, 0.71372563
],
[
0.023529412, 0.1254902, 0.20392157, 0.36078432,
0.59215689, 0.71372563, 0.964706
]]
models = []
BICs = []
likelihoods = []
for components in comp_means:
gmm = GaussianMixtureModel(image_matrix, len(components))
gmm.initialize_training()
gmm.means = np.copy(components)
models.append(gmm)
BICs.append(bayes_info_criterion(gmm))
likelihoods.append(gmm.likelihood())
min_BIC_model = models[BICs.index(min(BICs))]
max_likelihood_model = models[likelihoods.index(min(likelihoods))]
return min_BIC_model, max_likelihood_model