colors.append(c)
movieName = movieName.decode('unicode-escape')
genre = genre.decode('unicode-escape')
labels.append(movieName + "\n" + genre)
# get feature vector
feats = g.readline()
elems = feats.split(';')
v = []
for elem in elems:
e = elem.strip().strip("{}")
#print e
#print str(len(vectors)) + " " + str(len(v)) + " string is " + e
v.append(float(e))
vectors.append(v)
# Now use these vectors for tSNE
if int(numTopics) < 50 :
X_reduced = vectors
else:
X_reduced = TruncatedSVD(n_components=50, random_state=0).fit_transform(vectors)
X_embedded = TSNE(n_components=2, perplexity=30, verbose=2).fit_transform(X_reduced)
fig = figure(figsize=(10, 10))
ax = axes(frameon=False)
setp(ax, xticks=(), yticks=())
subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=0.9,
wspace=0.0, hspace=0.0)
