def calc(self):
from numpy import append
from scipy.spatial.distance import cosine
if self.sim_type == 'lda_cosine':
for i in range(len(self.key_list)-1):
for j in range(i+1, len(self.key_list)):
d = cosine(self.corpus[self.key_list[i]], self.corpus[self.key_list[j]])
out_tag = self.key_list[i] + '_' + self.key_list[j]
self.calculated[out_tag] = d
self.raw[out_tag] = append(self.corpus[self.key_list[i]],self.corpus[self.key_list[j]])
elif self.sim_type == 'word2vec_cosine':
elif sim_type == 'kernel':
from PyML import sequenceData
docs = [self.corpus[key] for key in sorted(self.key_list)]
kernel = sequenceData.spectrum_data(docs, k)
mat = kernel.getKernelMatrix()
for i in range(len(docs)):
for j in range(i+1, len(docs)):
tag = self.key_list[i] + '_' + self.key_list[j]
self.calculated[tag] = mat[i][j]
else:
raise KeyError('Please check your similarity type!')
def string_kernel_sim(corpus, vals, k):
docs = [corpus[i][1] for i in vals]
kernel = sequenceData.spectrum_data(docs, k)
mat = kernel.getKernelMatrix()
sims = list(chain(*[[mat[i][j] for j in range(i+1, len(docs))] for i in range(len(docs))]))
return sims
