def getWeightPrimeMatrix(self):
# compute D_x^(-1/2)
D_x = getDiagonalMatrix(self.weightMatrix)
D_x_powered = powDiagonalMatrix(D_x, -0.5)
# compute D_y^(-1/2)
D_y = getDiagonalMatrix(np.transpose(self.weightMatrix))
D_y_powered = powDiagonalMatrix(D_y, -0.5)
# multiply matrices
w_prime = np.dot(np.dot(D_x_powered, self.weightMatrix), D_y_powered)
return w_prime
def partition(self):
print len(self.matrix.keys())
print self.weightMatrix
# get singular vectors
x,y = self.getLeftRightSingularVectors()
# compute D_x^(-1/2)
D_x = getDiagonalMatrix(self.weightMatrix)
D_x_powered = powDiagonalMatrix(D_x, -0.5)
# compute D_y^(-1/2)
D_y = getDiagonalMatrix(np.transpose(self.weightMatrix))
D_y_powered = powDiagonalMatrix(D_y, -0.5)
#print "weight_matrix", self.weightMatrix
#print "D_x", D_x
x_new = np.dot(D_x_powered, x)
y_new = np.dot(D_y_powered, y)
print "x'", x_new
print "y'", y_new
A = []
A_c = []
B = []
B_c = []
c_x = 0.0
# Partition advertisements
index = 0
print "c_x", c_x
for adv in sorted(self.matrix.keys(), key = lambda adv : adv.advertiser_id):
if x_new.item(index) >= c_x:
A.append(adv)
else:
A_c.append(adv)
if adv.advertiser_id == '27961':
print "c_x specific", x_new.item(index)
index += 1
c_y = 0.0
# Partition ads
index = 0
print "c_y", c_y
for ad in sorted(self.dict_of_keywords.keys(), key = lambda ad : ad.keyword_id):
if y_new.item(index) >= c_y:
B.append(ad)
else:
B_c.append(ad)
if ad.keyword_id == '394':
print "c_y_specific", y_new.item(index)
index += 1
# Build new partitioned bipartite graphs
# where G = (A,B) and G_c = (A_c,B_c)
partition = SpectralBipartiteGraph()
partition_c = SpectralBipartiteGraph()
for adv in sorted(A, key = lambda adv : adv.advertiser_id):
for ad in sorted(B, key = lambda ad : ad.keyword_id):
if self.matrix[adv][ad] >= 1:
partition.add(adv,ad, self.matrix[adv][ad])
for adv in sorted(A_c, key = lambda adv : adv.advertiser_id):
for ad in sorted(B_c, key = lambda ad : ad.keyword_id):
if self.matrix[adv][ad] >= 1:
partition_c.add(adv, ad, self.matrix[adv][ad])
# Do check for partitions to ensure no keyword is left out
for kw in sorted(self.dict_of_keywords.keys(), key = lambda ad : ad.keyword_id):
if partition.getKeyword(kw) == None and partition_c.getKeyword(kw) == None:
for ad in self.dict_of_keywords[kw]:
if partition.getAdv(ad) != None:
partition.add(ad, kw, self.matrix[ad][kw])
else:
partition_c.add(ad, kw, self.matrix[ad][kw])
partition.setWeightMatrix()
partition_c.setWeightMatrix()
return (partition, partition_c)
