Why I get random result from seemingly non-random code with python sklearn?

I have a list of strings named "str_tuple". I want to compute some similarity measures between the first element in the list and the rest of the elements. I run the following six-line code snippet.

What completely baffles me is that the outcome seems to be completely random every time I run the code. However, I cannot see any randomness introduced in my six-liner.

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import TruncatedSVD
from sklearn.preprocessing import Normalizer

str_tuple = [u'l bracket', u'simpson strong tie 12 gaug angl', u'angl make joint stronger provid consist straight corner simpson strong tie offer wide varieti angl various size thick handl light duti job project structur connect need bent skew match project outdoor project moistur present use zmax zinc coat connector provid extra resist corros look "z" end model number .versatil connector various 90 connect home repair projectsstrong angl nail screw fasten alonehelp ensur joint consist straight strongdimensions: 3 in. xbi 3 in. xbi 1 0.5 in. made 12 gaug steelgalvan extra corros resistanceinstal 10 d common nail 9 xbi 1 0.5 in. strong drive sd screw', u'simpson strong-tie', u'', u'versatile connector for various 90\xe2\xb0 connections and home repair projects stronger than angled nailing or screw fastening alone help ensure joints are consistently straight and strong dimensions: 3 in. x 3 in. x 1-1/2 in. made from 12-gauge steel galvanized for extra corrosion resistance install with 10d common nails or #9 x 1-1/2 in. strong-drive sd screws']

vectorizer = CountVectorizer(token_pattern=r"\d+\.\d+|\d+\/\d+|\b\w+\b")
cmat = vectorizer.fit_transform(str_tuple).astype(float)    # sparse matrix
cmat = TruncatedSVD(2).fit_transform(cmat)    # dense numpy array
cmat = Normalizer().fit_transform(cmat)    # dense numpy array
sim = np.dot(cmat, cmat.T)
sim[0,1:].tolist()