def Voronoi_diagram(self,seeds,samples):
"""
Defines the graph as the Voronoi diagram (VD)
that links the seeds.
The VD is defined using the sample points.
Parameters
----------
seeds: array of shape (self.V,dim)
samples: array of shape (nsamples,dim)
Note
----
by default, the weights are a Gaussian function of the distance
The implementation is not optimal
"""
# checks
if seeds.shape[0]!=self.V:
raise ValueError,"The numberof seeds is not as expected"
if np.size(seeds) == self.V:
seeds = np.reshape(seeds,(np.size(seeds),1))
if np.size(samples) == samples.shape[0]:
samples = np.reshape(samples,(np.size(samples),1))
if seeds.shape[1]!=samples.shape[1]:
raise ValueError,"The seeds and samples do not belong \
to the same space"
#1. define the graph knn(samples,seeds,2)
i,j,d = graph_cross_knn(samples,seeds,2)
#2. put all the pairs i the target graph
Ns = np.shape(samples)[0]
self.E = Ns
self.edges = np.array([j[2*np.arange(Ns)],j[2*np.arange(Ns)+1]]).T
self.weights = np.ones(self.E)
#3. eliminate the redundancies and set the weights
self.cut_redundancies()
self.symmeterize()
self.set_gaussian(seeds)
def cross_knn(self,X,Y,k=1):
"""
set the graph to be the k-nearest-neighbours graph of from X to Y
Parameters
----------
X,Y arrays of shape (self.V) or (self.V,p)
and (self.W) or (self.W,p) respectively
where p = dimension of the features
k=1, int is the number of neighbours considered
Returns
-------
self.E, int the number of edges of the resulting graph
Note
----
It is assumed that the features are embedded in a
(locally) Euclidian space
for the sake of speed it is advisable to give
PCA-preprocessed matrices X and Y.
"""
self.check_feature_matrices(X,Y)
try:
k=int(k)
except :
"k cannot be cast to an int"
if np.isnan(k):
raise ValueError, 'k is nan'
if np.isinf(k):
raise ValueError, 'k is inf'
i,j,d = graph_cross_knn(X,Y,k)
self.E = np.size(i)
self.edges = np.zeros((self.E,2),np.int)
self.edges[:,0] = i
self.edges[:,1] = j
self.weights = np.array(d)
return self.E
