def MVU_slack(datafile, dim=3):
# takes in a pickled matrix of points - outputs a MVU embedding
fp = open(datafile)
pts = pickle.load(fp)
ans = pickle.load(fp) # latent space coordinates
size = len(pts)
k = len(ans[0]) # the number of latent dimensions
# mean center coordinates
m = np.mean(pts, axis=0)
pts = pts - m
# TODO: move graph cluster algorithm to own file - write in C?
# compute the distance matrix and cluster
Y = hc.squareform(hc.pdist(pts, 'euclidean'))
res = cluster_graph(Y, fnc='k', size=8)
x, y = np.nonzero(res & (Y != 0)) # indices of nearest neighbors
# generate data to write problem in SPDA format
# TODO: add slack variable block
indx = []
for (i, j) in zip(x, y):
if i <= j:
indx.append((i, j))
m = len(indx) + 1
nblocks = 2
c = [0.0]
for (i, j) in indx:
c.append(Y[i, j]**2)
write_spda_file_slack("../ds/sdp.dat", m, nblocks, size, c, indx, .01)
# TODO: add some error checking
os.system("csdp ../ds/sdp.dat ../ds/sdp.sol")
y, Z, X = read_sol_file_slack("../ds/sdp.sol", size)
# spectral decomposition of the dual solution (X)
u, s, v = la.svd(X)
results = []
for i in range(dim):
results.append(np.sqrt(s[i]) * u[:, i])
# returns the neighborhood graph for proper plotting
return results, pts, res
def MVU_slack(datafile, dim = 3):
# takes in a pickled matrix of points - outputs a MVU embedding
fp = open(datafile)
pts = pickle.load(fp)
ans = pickle.load(fp) # latent space coordinates
size = len(pts)
k = len(ans[0]) # the number of latent dimensions
# mean center coordinates
m = np.mean(pts, axis=0)
pts = pts - m
# TODO: move graph cluster algorithm to own file - write in C?
# compute the distance matrix and cluster
Y = hc.squareform(hc.pdist(pts,'euclidean'))
res = cluster_graph(Y, fnc = 'k', size = 8)
x,y = np.nonzero(res & (Y != 0)) # indices of nearest neighbors
# generate data to write problem in SPDA format
# TODO: add slack variable block
indx = []
for (i,j) in zip(x,y):
if i <= j:
indx.append((i,j))
m = len(indx) + 1
nblocks = 2
c = [0.0]
for (i,j) in indx:
c.append(Y[i,j]**2)
write_spda_file_slack("../ds/sdp.dat", m, nblocks, size, c, indx, .01)
# TODO: add some error checking
os.system("csdp ../ds/sdp.dat ../ds/sdp.sol")
y,Z,X = read_sol_file_slack("../ds/sdp.sol", size)
# spectral decomposition of the dual solution (X)
u,s,v = la.svd(X)
results = []
for i in range(dim):
results.append(np.sqrt(s[i]) * u[:,i])
# returns the neighborhood graph for proper plotting
return results, pts, res
def expand(N, k=None):
X = np.zeros(N.shape)
X[:, :] = np.inf
X[N.nonzero()] = N[N.nonzero()]
for i in range(X.shape[0]):
X[i, i] = 0
X = shortest_paths(X)
if k:
I = cluster_graph(X, fnc='k', size=k)
J = np.zeros(N.shape)
J[I] = X[I]
return J
else:
return X
def expand(N, k=None):
X = np.zeros(N.shape)
X[:, :] = np.inf
X[N.nonzero()] = N[N.nonzero()]
for i in range(X.shape[0]):
X[i, i] = 0
X = shortest_paths(X)
if k:
I = cluster_graph(X, fnc='k', size=k)
J = np.zeros(N.shape)
J[I] = X[I]
return J
else:
return X