elif kernel == "ODDST":
print "Using ST kernel"
Vectorizer = ODDSTVectorizer(r=max_radius,
l=la,
normalization=normalization)
elif kernel == "NSPDK":
print "Using NSPDK kernel, lambda parameter interpreted as d"
Vectorizer = NSPDKVectorizer(r=max_radius,
d=int(la),
normalization=normalization)
else:
print "Unrecognized kernel"
m = 4000
d = 1
features = Vectorizer.transform(g_it.graphs) #Parallel ,njobs
featnew = []
print "examples, features", features.shape
for i in xrange(features.shape[0]):
exCMS = CountMinSketch(m, d)
ex = features[i][0]
#W=csr_matrix(ex)
rows, cols = ex.nonzero()
dot = 0.0
for row, col in zip(rows, cols):
#((row,col), ex[row,col])
#print col, ex[row,col]
#dot+=WCMS[col]*ex[row,col]
print "Using WL fast subtree kernel"
Vectorizer = WLVectorizer(r=max_radius, normalization=normalization)
elif kernel == "ODDST":
print "Using ST kernel"
Vectorizer = ODDSTVectorizer(r=max_radius,
l=la,
normalization=normalization)
elif kernel == "NSPDK":
print "Using NSPDK kernel, lambda parameter interpreted as d"
Vectorizer = NSPDKVectorizer(r=max_radius,
d=int(la),
normalization=normalization)
else:
print "Unrecognized kernel"
features = Vectorizer.transform(g_it.graphs)
target_array = np.array(g_it.target)
#features, target_array =
#print km
print "original shape", features.shape
print "features loaded, hashing..."
featuresCMS = [0] * features.shape[0]
for i in xrange(features.shape[0]):
generateCMS(featuresCMS, features[i][0], i)
#pool = multiprocessing.Pool(processes=4)
#pool.map(generateCMS, features[i][0],i)
# pool.close()
# pool.join()
# exCMS=CountMinSketch(m,d)
#
