def compress(self, X, n_components, n_neighbours):
n = X.shape[0]
k = self.k
numNeighbours = self.numNeighbours
# find the distances to every other point
euclD = utils.euclidean_dist_squared(X, X)
euclD = np.sqrt(euclD)
knnD = np.zeros((n, n))
# get the KNN of point i
for i in range(n):
# finds numNeighbours smallest distances from obj_i
# +1 because it will always select itself as (distance of 0), and distances are non-negative
minIndexes = np.argsort(euclD[i])[:numNeighbours + 1]
for index in minIndexes:
# add distances of KNN_i to the distance matrix
knnD[i, index] = euclD[i, index]
D = np.zeros((n, n))
# get distance of every other path using only KNN
for i in range(n):
for j in range(n):
if i != j:
D[i, j] = utils.dijkstra(knnD, i, j)
Z = AlternativePCA(k).fit(X).compress(X)
z = find_min(self._fun_obj_z, Z.flatten(), 500, False, D)
Z = z.reshape(n, k)
return Z
ax.scatter(Z[:, 0], Z[:, 1])
for i in range(n):
ax.annotate(animals[i], (Z[i, 0], Z[i, 1]))
utils.savefig('q2_2_PCA_animals.png')
elif question == '3.1':
X = load_dataset('highway.pkl')['X'].astype(float) / 255
n, d = X.shape
print(n, d)
h, w = 64, 64 # height and width of each image
k = 5 # number of PCs
threshold = 0.1 # threshold for being considered "foreground"
model = AlternativePCA(k=k)
model.fit(X)
Z = model.compress(X)
Xhat_pca = model.expand(Z)
model = RobustPCA(k=k)
model.fit(X)
Z = model.compress(X)
Xhat_robust = model.expand(Z)
fig, ax = plt.subplots(2, 3)
for i in range(10):
ax[0, 0].set_title('$X$')
ax[0, 0].imshow(X[i].reshape(h, w).T, cmap='gray')
ax[0, 1].set_title('$\hat{X}$ (L2)')
io_args = parser.parse_args()
task = io_args.task
if task == '1':
X = load_dataset('highway.pkl')['X'].astype(float) / 255
#print(X[0].reshape(64,64).T)
n, d = X.shape
print(n, d)
h, w = 64, 64 # height and width of each image
k = 5 # number of PCs
threshold = 0.1 # threshold for being considered "foreground"
#threshold = 0.07
model = AlternativePCA(k=k)
model.fit(X)
Z = model.compress(X)
Xhat_pca = model.expand(Z)
model = RobustPCA(k=k)
model.fit(X)
Z = model.compress(X)
Xhat_robust = model.expand(Z)
fig, ax = plt.subplots(2, 3)
for i in range(10):
ax[0, 0].set_title('$X$')
ax[0, 0].imshow(X[i].reshape(h, w).T, cmap='gray')
ax[0, 1].set_title('$\hat{X}$ (L2)')