sigma2 = 50
#Double Swiss Roll
elif dataset == 'swiss':
n_nbrs = 3
n_clusters = 48
n_labeled = 24
inner_dim = 1
gamma = 1e-2
n_data_per_anchor = 200
# heuristics: keep m = O(n/d)
algs = ["ap", "ac", "nn", "pvm", "apg"]
sigma2 = 1
data = {
"swiss" : manifold_generator.double_swiss_roll(n_samples=1000, var=.8),
"usps" : manifold_generator.usps(),
"letter" : manifold_generator.letter(),
"mnist" : manifold_generator.mnist()
}
model = {
"ap" : (
anchor_points.AnchorPoints(n_clusters, n_nbrs),
(gamma, )),
"ac" : (
anchor_clouds.AnchorClouds(inner_dim, n_clusters, n_data_per_anchor, n_nbrs),
(gamma, )),
"nn" : (
nearest_neighbors.NearestNeighbors(k=1),
()),
s2 = s**2
# initialize ppca parameters
self.sigma2 = ( np.linalg.norm(U, 'fro')**2/m - s2.sum() ) / (p - self.inner_dim)
self.a = a
self.W = Phi * np.sqrt(s2 - self.sigma2)
self.det_sqrt = np.prod(np.sqrt(s2 + self.sigma2))
try:
self.M = np.linalg.inv(self.W.T.dot(self.W) + self.sigma2*np.eye(self.inner_dim))
except np.linalg.LinAlgError:
print "singular anchor"
def log_ppca_density(self, x):
u = x - self.a
Wtu = self.W.T.dot(u)
return -0.5*(u.dot(u) - Wtu.T.dot(self.M).dot(Wtu))/self.sigma2 - np.log(self.det_sqrt)
if __name__ == '__main__':
np.random.seed(1267)
n_anchors = 32
n_data_per_anchor = 20
n_anchor_per_data = 3
inner_dim = 1
X, _, _ = manifold_generator.double_swiss_roll(n_samples=300, var=.75)
A, Z = anchor_clouds(X, 1, n_anchors, n_data_per_anchor, n_anchor_per_data)
tools.visualize_edges(X, A, Z, 1e-12)