def __init__(self, N=64, seed=None, **kwargs):
self.seed = seed
if N == 64:
data = utils.loadmat('pointclouds/david64')
assert data['N'][0, 0] == N
W = data['W']
coords = data['coords']
elif N == 500:
data = utils.loadmat('pointclouds/david500')
assert data['N'][0, 0] == N
W = data['W']
coords = data['coords']
else:
coords = np.random.RandomState(seed).rand(N, 2)
target_dist_cutoff = -0.125 * N / 436.075 + 0.2183
T = 0.6
s = np.sqrt(-target_dist_cutoff**2/(2*np.log(T)))
d = utils.distanz(coords.T)
W = np.exp(-np.power(d, 2)/(2.*s**2))
W[W < T] = 0
W[np.diag_indices(N)] = 0
plotting = {"limits": [0, 1, 0, 1]}
super(DavidSensorNet, self).__init__(W, coords=coords,
plotting=plotting, **kwargs)
def __init__(self, N=64, seed=None, **kwargs):
self.seed = seed
if N == 64:
data = utils.loadmat('pointclouds/david64')
assert data['N'][0, 0] == N
W = data['W']
coords = data['coords']
elif N == 500:
data = utils.loadmat('pointclouds/david500')
assert data['N'][0, 0] == N
W = data['W']
coords = data['coords']
else:
coords = np.random.RandomState(seed).rand(N, 2)
target_dist_cutoff = -0.125 * N / 436.075 + 0.2183
T = 0.6
s = np.sqrt(-target_dist_cutoff**2 / (2 * np.log(T)))
d = utils.distanz(coords.T)
W = np.exp(-np.power(d, 2) / (2. * s**2))
W[W < T] = 0
W[np.diag_indices(N)] = 0
plotting = {"limits": [0, 1, 0, 1]}
super(DavidSensorNet, self).__init__(W,
coords=coords,
plotting=plotting,
**kwargs)
def __init__(self, connected=True, **kwargs):
self.connected = connected
data = utils.loadmat('pointclouds/minnesota')
self.labels = data['labels']
A = data['A']
plotting = {"limits": np.array([-98, -89, 43, 50]), "vertex_size": 40}
if connected:
# Missing edges needed to connect the graph.
A = sparse.lil_matrix(A)
A[348, 354] = 1
A[354, 348] = 1
A = sparse.csc_matrix(A)
# Binarize: 8 entries are equal to 2 instead of 1.
A = (A > 0).astype(bool)
super(Minnesota, self).__init__(A,
coords=data['xy'],
plotting=plotting,
**kwargs)
def __init__(self, moontype='standard', dim=2, sigmag=0.05,
N=400, sigmad=0.07, distance=0.5, seed=None, **kwargs):
self.moontype = moontype
self.dim = dim
self.sigmag = sigmag
self.sigmad = sigmad
self.distance = distance
self.seed = seed
if moontype == 'standard':
N1, N2 = 1000, 1000
data = utils.loadmat('pointclouds/two_moons')
Xin = data['features'][:dim].T
elif moontype == 'synthesized':
N1 = N // 2
N2 = N - N1
coords1 = self._create_arc_moon(N1, sigmad, distance, 1, seed)
coords2 = self._create_arc_moon(N2, sigmad, distance, 2, seed)
Xin = np.concatenate((coords1, coords2))
else:
raise ValueError('Unknown moontype {}'.format(moontype))
self.labels = np.concatenate((np.zeros(N1), np.ones(N2)))
plotting = {
'vertex_size': 30,
}
super(TwoMoons, self).__init__(Xin=Xin, sigma=sigmag, k=5,
plotting=plotting, **kwargs)
def __init__(self, **kwargs):
data = utils.loadmat('pointclouds/logogsp')
self.info = {"idx_g": data["idx_g"],
"idx_s": data["idx_s"],
"idx_p": data["idx_p"]}
plotting = {"limits": np.array([0, 640, -400, 0])}
super(Logo, self).__init__(W=data['W'], coords=data['coords'],
plotting=plotting, **kwargs)
def __init__(self, **kwargs):
data = utils.loadmat('pointclouds/logogsp')
# Remove 1 because the index in python start at 0 and not at 1
self.info = {"idx_g": data["idx_g"]-1,
"idx_s": data["idx_s"]-1,
"idx_p": data["idx_p"]-1}
plotting = {"limits": np.array([0, 640, -400, 0])}
super(Logo, self).__init__(W=data['W'], coords=data['coords'],
plotting=plotting, **kwargs)
def __init__(self, **kwargs):
data = utils.loadmat('pointclouds/logogsp')
# Remove 1 because the index in python start at 0 and not at 1
self.info = {"idx_g": data["idx_g"]-1,
"idx_s": data["idx_s"]-1,
"idx_p": data["idx_p"]-1}
plotting = {"limits": np.array([0, 640, -400, 0])}
super(Logo, self).__init__(data['W'], coords=data['coords'],
plotting=plotting, **kwargs)
def __init__(self, **kwargs):
data = utils.loadmat('pointclouds/bunny')
plotting = {
'vertex_size': 10,
'elevation': -90,
'azimuth': 90,
'distance': 7,
}
super(Bunny, self).__init__(Xin=data['bunny'], epsilon=0.2,
NNtype='radius', plotting=plotting,
**kwargs)
def __init__(self, **kwargs):
data = utils.loadmat('pointclouds/bunny')
plotting = {
'vertex_size': 10,
'elevation': -90,
'azimuth': 90,
'distance': 8,
}
super(Bunny, self).__init__(Xin=data['bunny'],
epsilon=0.02, NNtype='radius',
center=False, rescale=False,
plotting=plotting, **kwargs)
def __init__(self, **kwargs):
data = utils.loadmat('pointclouds/airfoil')
coords = np.concatenate((data['x'], data['y']), axis=1)
i_inds = np.reshape(data['i_inds'] - 1, 12289)
j_inds = np.reshape(data['j_inds'] - 1, 12289)
A = sparse.coo_matrix((np.ones(12289), (i_inds, j_inds)), shape=(4253, 4253))
W = (A + A.T) / 2.
plotting = {"vertex_size": 30,
"limits": np.array([-1e-4, 1.01*data['x'].max(),
-1e-4, 1.01*data['y'].max()])}
super(Airfoil, self).__init__(W, coords=coords, plotting=plotting,
**kwargs)
def __init__(self, **kwargs):
data = utils.loadmat('pointclouds/airfoil')
coords = np.concatenate((data['x'], data['y']), axis=1)
i_inds = np.reshape(data['i_inds'] - 1, 12289)
j_inds = np.reshape(data['j_inds'] - 1, 12289)
A = sparse.coo_matrix((np.ones(12289), (i_inds, j_inds)), shape=(4253, 4253))
W = (A + A.T) / 2.
plotting = {"vertex_size": 30,
"limits": np.array([-1e-4, 1.01*data['x'].max(),
-1e-4, 1.01*data['y'].max()])}
super(Airfoil, self).__init__(W=W, coords=coords, plotting=plotting,
**kwargs)
def __init__(self, connected=True, **kwargs):
self.connected = connected
data = utils.loadmat('pointclouds/minnesota')
self.labels = data['labels']
A = data['A']
plotting = {"limits": np.array([-98, -89, 43, 50]),
"vertex_size": 40}
if connected:
# Missing edges needed to connect the graph.
A = sparse.lil_matrix(A)
A[348, 354] = 1
A[354, 348] = 1
A = sparse.csc_matrix(A)
# Binarize: 8 entries are equal to 2 instead of 1.
A = (A > 0).astype(bool)
super(Minnesota, self).__init__(A, coords=data['xy'],
plotting=plotting, **kwargs)
def __init__(self, moontype='standard', dim=2, sigmag=0.05,
N=400, sigmad=0.07, distance=0.5, seed=None, **kwargs):
self.moontype = moontype
self.dim = dim
self.sigmag = sigmag
self.sigmad = sigmad
self.distance = distance
self.seed = seed
if moontype == 'standard':
N1, N2 = 1000, 1000
data = utils.loadmat('pointclouds/two_moons')
Xin = data['features'][:dim].T
elif moontype == 'synthesized':
N1 = N // 2
N2 = N - N1
coords1 = self._create_arc_moon(N1, sigmad, distance, 1, seed)
coords2 = self._create_arc_moon(N2, sigmad, distance, 2, seed)
Xin = np.concatenate((coords1, coords2))
else:
raise ValueError('Unknown moontype {}'.format(moontype))
self.labels = np.concatenate((np.zeros(N1), np.ones(N2)))
plotting = {
'vertex_size': 30,
}
super(TwoMoons, self).__init__(Xin=Xin, sigma=sigmag, k=5,
center=False, rescale=False,
plotting=plotting, **kwargs)
