def make(N, Nseed, D):
# generate random data in latent space
Nboth = N + Nseed
Z = randn((Nboth, D))
# generate X and Y from Z.
# they should be random transformations of Z with added noise.
X = words.Words()
Y = words.Words()
# create a set of words with which it will be easy to run edit distance
#X.words = ['a' + str(i) for i in xrange(Ns)] + ['b' + str(i) for i in xrange(Nr)]
#Y.words = ['a' + str(i) for i in xrange(Ns)] + ['c' + str(i*2) for i in xrange(Nr)]
X.words = np.array([str(i) for i in xrange(Nboth)])
Y.words = np.array([str(i) for i in xrange(Nboth)])
X.freq = np.array([i for i in xrange(Nboth)]) # mock frequencies
Y.freq = np.array([i for i in xrange(Nboth)])
X.features = Z
Y.features = Z
# create random permutation, but keep last (1-q) in place
#X.G = np.zeros((N, N), dtype=np.float)
#Y.G = np.zeros((N, N), dtype=np.float)
X.features = random_proj_shift(X.features)
Y.features = random_proj_shift(Y.features)
# permute Y randomly
pi = perm.ID(N)
topX = Nboth # int(0.92*N)
pi = np.append(perm.randperm(pi[:topX]), pi[topX:])
Y.permuteFirstWords(pi)
return X, Y, pi
# G[i][j] = self.K[word_i][word_j]
#
# return np.mat(G)
if __name__ == '__main__': # test
np.random.seed(1)
N = 400
D = 800
DD = dict()
rangeD = range(D)
rangeN = range(N)
for i in xrange(N):
DD[i] = dict()
S = perm.randperm(rangeD)
S = S[:D/4]
for j in S:
DD[i][j] = randn((1, 1))[0, 0]
print "finished constructing."
t = time.time()
# K = DictDictKernel(DD)
# #import cProfile
# #cProfile.runct("K.compute(R, R)", globals(), locals())
# K.compute(rangeN, rangeN)
# G = K.materialize(rangeN, rangeN)
# print 'elapsed', time.time() - t
# print G
