def c_class_neighbors(SIDE_N, class_connectivity,
JITTER = 0.0, default_param=0.01, obsmodel=None):
"""
for each of c classes, we create a grid of SIDE_N x SIDE_N
and then use the class_connectivity dictionary to set up
threshold connectivity
class_connectivity = {(c1, c2) : (threshold, param),
}
PROX: proximitiy of neighbors
Planar
returns nodes, connectivity -- note that connectivity is really observations
"""
CLASS_N = np.max(np.array([class_connectivity.keys()]).flatten()) + 1
nodes = create_nodes_grid(SIDE_N, CLASS_N, JITTER)
def node_pred(c1, pos1, c2, pos2):
for (c1_t, c2_t), (thold, params) in class_connectivity.iteritems():
if (c1 == c1_t) and (c2 == c2_t):
d = dist(pos1, pos2)
if d < thold:
return params
return default_param
observations = synth.connect(nodes, node_pred, obsmodel)
return nodes, observations
def c_mixed_dist_block(SIDE_N, class_connectivity,
JITTER = 0.0, rand_conn_prob = 0.01,
obsmodel = None):
"""
Mixed membership
class_connectivity = {(c1, c2) : ('p', prob),
(c2, c3) : ('d', dist, prob)
}
"""
CLASS_N = np.max(np.array([class_connectivity.keys()]).flatten()) + 1
nodes = create_nodes_grid(SIDE_N, CLASS_N, JITTER)
def node_pred(c1, pos1, c2, pos2):
for (c1_t, c2_t), v in class_connectivity.iteritems():
if (c1 == c1_t) and (c2 == c2_t):
if v[0] == 'p' :
return v[1]
else :
thold = v[1]
prob = v[2]
d = dist(pos1, pos2)
if d < thold:
return prob
return rand_conn_prob
connectivity = synth.connect(nodes, node_pred, obsmodel)
return nodes, connectivity
def c_bump_dist_block(SIDE_N, class_connectivity,
JITTER = 0.0, rand_conn_prob = 0.01,
p_min = 0.01, obsmodel=None):
"""
Mixed membership
class_connectivity = {(c1, c2) : (dist, prob, width),
}
"""
CLASS_N = np.max(np.array([class_connectivity.keys()]).flatten()) + 1
nodes = create_nodes_grid(SIDE_N, CLASS_N, JITTER)
def node_pred(c1, pos1, c2, pos2):
for (c1_t, c2_t), v in class_connectivity.iteritems():
if (c1 == c1_t) and (c2 == c2_t):
mu = v[0]
prob = v[1]
width = v[2]
d = dist(pos1, pos2)
return util.norm_dist_bump(d, mu, width, prob, p_min)
return rand_conn_prob
connectivity = synth.connect(nodes, node_pred, obsmodel)
return nodes, connectivity