def Build_Content_Graph_From_Rg(self, uid):
if 'rg' not in dir(self):
self._load_user_relation_pickle(uid)
self.cg = self.rg.copy()
self.cg.delete_edges(self.cg.es)
from ExtractContentEdge import ContentEdgeExtractorBaseRoutine
cebr = ContentEdgeExtractorBaseRoutine(self.base_dir, self.expr_dir)
edges = []
weights = []
for i, vi in enumerate(self.cg.vs):
for j in range(i + 1, len(self.cg.vs)):
vj = self.cg.vs[j]
h1, h2, h3 = cebr.compute_similarity(vi, vj)
sim = self.w1 * h1 + self.w2 * h2 + self.w3 * h3
if sim > self.criterion:
edges.append((vi.index, vj.index))
weights.append(sim)
self.cg.add_edges(edges)
self.cg.es['weight'] = weights
FSDao.write_pickle(self.base_dir + self.expr_dir,
'%s_content.pickle' % uid, self.cg)
def _write_c_adj_txt(self):
import os
directory = self.base_dir + self.expr_dir + SQLDao.ce.properties[
'matlab_dir']
if not os.path.exists(directory):
os.makedirs(directory)
import gc
gc.collect()
f = open(directory + 'adj_content_full', 'w')
from ExtractContentEdge import ContentEdgeExtractorBaseRoutine
cebr = ContentEdgeExtractorBaseRoutine(self.base_dir, self.expr_dir)
data = []
for i in range(len(self.g_sub.vs)):
l = []
for j in range(len(self.g_sub.vs)):
l.append(0.0)
data.append(l)
for i, vi in enumerate(self.g_sub.vs):
for j in range(i + 1, len(self.g_sub.vs)):
vj = self.g_sub.vs[j]
h1, h2, h3 = cebr.compute_similarity(vi, vj)
sim = self.w1 * h1 + self.w2 * h2 + self.w3 * h3
data[i][j] = sim
data[j][i] = sim
pass
for l in data:
f.write(','.join([str(j) for j in l]))
f.write('\n')
f.close()
def Build_Content_Graph_From_Rg(self,uid):
if 'rg' not in dir(self):
self._load_user_relation_pickle(uid)
self.cg=self.rg.copy()
self.cg.delete_edges(self.cg.es)
from ExtractContentEdge import ContentEdgeExtractorBaseRoutine
cebr=ContentEdgeExtractorBaseRoutine(self.base_dir,self.expr_dir)
edges=[]
weights=[]
for i,vi in enumerate(self.cg.vs):
for j in range(i+1,len(self.cg.vs)):
vj=self.cg.vs[j]
h1,h2,h3=cebr.compute_similarity(vi,vj)
sim=self.w1*h1+self.w2*h2+self.w3*h3
if sim>self.criterion:
edges.append((vi.index,vj.index))
weights.append(sim)
self.cg.add_edges(edges)
self.cg.es['weight']=weights
FSDao.write_pickle(self.base_dir+self.expr_dir,'%s_content.pickle'%uid,self.cg)
def _write_c_adj_txt(self):
import os
directory=self.base_dir+self.expr_dir+SQLDao.ce.properties['matlab_dir']
if not os.path.exists(directory):
os.makedirs(directory)
import gc
gc.collect()
f=open(directory+'adj_content_full','w')
from ExtractContentEdge import ContentEdgeExtractorBaseRoutine
cebr=ContentEdgeExtractorBaseRoutine(self.base_dir,self.expr_dir)
data=[]
for i in range(len(self.g_sub.vs)):
l=[]
for j in range(len(self.g_sub.vs)):
l.append(0.0)
data.append(l)
for i,vi in enumerate(self.g_sub.vs):
for j in range(i+1,len(self.g_sub.vs)):
vj=self.g_sub.vs[j]
h1,h2,h3=cebr.compute_similarity(vi,vj)
sim=self.w1*h1+self.w2*h2+self.w3*h3
data[i][j]=sim
data[j][i]=sim
pass
for l in data:
f.write(','.join([str(j) for j in l]))
f.write('\n')
f.close()
def _fulfill_content_graph(self, sim):
'''
since the edge of the content graph is not enough
we have to add more edge to the graph
'''
# delete all edges
self.cg.delete_edges(None)
# rebuild the edges
cebr = ContentEdgeExtractorBaseRoutine(self.base_dir, self.expr_dir)
edges = []
weights = []
for i, vi in enumerate(self.cg.vs):
for j in range(i + 1, len(self.cg.vs)):
vj = self.cg.vs[j]
h1, h2, h3 = cebr.compute_similarity(vi, vj)
similarity = self.w1 * h1 + self.w2 * h2 + self.w3 * h3
if similarity > sim:
edges.append((i, j))
weights.append(similarity)
pass
self.cg.add_edges(edges)
self.cg.es['weight'] = weights
def _fulfill_content_graph(self, sim):
"""
since the edge of the content graph is not enough
we have to add more edge to the graph
"""
# delete all edges
self.cg.delete_edges(None)
# rebuild the edges
cebr = ContentEdgeExtractorBaseRoutine(self.base_dir, self.expr_dir)
edges = []
weights = []
for i, vi in enumerate(self.cg.vs):
for j in range(i + 1, len(self.cg.vs)):
vj = self.cg.vs[j]
h1, h2, h3 = cebr.compute_similarity(vi, vj)
similarity = self.w1 * h1 + self.w2 * h2 + self.w3 * h3
if similarity > sim:
edges.append((i, j))
weights.append(similarity)
pass
self.cg.add_edges(edges)
self.cg.es["weight"] = weights
