def test_mds():
from milk.unsupervised import pdist
np.random.seed(232)
for _ in range(12):
features = np.random.random_sample((12, 4))
X = milk.unsupervised.mds(features, 4)
D = pdist(features)
D2 = pdist(X)
assert np.mean((D - D2)**2) < 10e-4
def test_mds():
from milk.unsupervised import pdist
np.random.seed(232)
for _ in range(12):
features = np.random.random_sample((12,4))
X = milk.unsupervised.mds(features,4)
D = pdist(features)
D2 = pdist(X)
assert np.mean( (D - D2) ** 2) < 10e-4
def test_pdist():
np.random.seed(222)
X = np.random.randn(100,23)
Y = np.random.randn(80,23)
Dxx = pdist(X)
for i in xrange(X.shape[0]):
for j in xrange(X.shape[1]):
assert np.allclose(Dxx[i,j], np.sum((X[i]-X[j])**2))
Dxy = pdist(X,Y)
for i in xrange(X.shape[0]):
for j in xrange(Y.shape[1]):
assert np.allclose(Dxy[i,j], np.sum((X[i]-Y[j])**2))
def call_many(self, qs):
from milk.unsupervised import pdist
dists = pdist(self.X, qs, 'euclidean2')
dists /= -self.sigma
np.exp(dists, dists)
dists *= self.beta
return dists.T
def call_many(self, qs):
from milk.unsupervised import pdist
dists = pdist(self.X, qs, 'euclidean2')
dists /= -self.sigma
np.exp(dists, dists)
dists *= self.beta
return dists.T
def test_mds_dists():
from milk.unsupervised import pdist
np.random.seed(232)
for _ in xrange(12):
features = np.random.random_sample((12,4))
D = pdist(features)
X = milk.unsupervised.mds(features,4)
X2 = milk.unsupervised.mds_dists(D, 4)
assert np.mean( (X - X2) ** 2) < 10e-4
def test_mds_dists():
from milk.unsupervised import pdist
np.random.seed(232)
for _ in xrange(12):
features = np.random.random_sample((12, 4))
D = pdist(features)
X = milk.unsupervised.mds(features, 4)
X2 = milk.unsupervised.mds_dists(D, 4)
assert np.mean((X - X2)**2) < 10e-4
def prune_similar(features, threshold=None, frac=None):
from milk.unsupervised import pdist
dists = pdist(features.T.astype(np.float32))
if frac is not None:
Ds = dists.ravel().copy()
Ds.sort()
threshold = Ds[int(frac*len(Ds))]
elif threshold is None:
threshold = 0
X,Y = np.where(dists <= threshold)
select = np.ones(len(features.T), bool)
for x,y in zip(X,Y):
if x != y and select[x] and select[y]:
select[x] = 0
return features[:,select]
np.seterr(all="ignore")
print __doc__ + "\n"
if not len(sys.argv) == 2:
print misc.USAGE % __file__
sys.exit(-1)
else:
dataset = sys.argv[1]
print "Loading data ..."
data = misc.load_data(dataset)
# set sigma to something useful
from milk.unsupervised import pdist
sigma = np.median(pdist(data[0]))
print "Done, %s samples with %s features loaded into " "memory" % data[0].shape
score, res_shogun = misc.bench(bench_shogun, data)
print "Shogun: mean %.2f, std %.2f" % (np.mean(res_shogun), np.std(res_shogun))
print "Score: %.2f\n" % score
score, res_mdp = misc.bench(bench_mdp, data)
print "MDP: mean %.2f, std %.2f" % (np.mean(res_mdp), np.std(res_mdp))
print "Score: %.2f\n" % score
score, res_skl = misc.bench(bench_skl, data)
print "scikits.learn: mean %.2f, std %.2f" % (np.mean(res_skl), np.std(res_skl))
print "Score: %.2f\n" % score
warnings.simplefilter('ignore')
np.seterr(all='ignore')
print __doc__ + '\n'
if not len(sys.argv) == 2:
print misc.USAGE % __file__
sys.exit(-1)
else:
dataset = sys.argv[1]
print 'Loading data ...'
data = misc.load_data(dataset)
# set sigma to something useful
from milk.unsupervised import pdist
sigma = np.median(pdist(data[0]))
print 'Done, %s samples with %s features loaded into ' \
'memory' % data[0].shape
score, res_shogun = misc.bench(bench_shogun, data)
print 'Shogun: mean %.2f, std %.2f' % (
np.mean(res_shogun), np.std(res_shogun))
print 'Score: %.2f\n' % score
score, res_mdp = misc.bench(bench_mdp, data)
print 'MDP: mean %.2f, std %.2f' % (
np.mean(res_mdp), np.std(res_mdp))
print 'Score: %.2f\n' % score
score, res_skl = misc.bench(bench_skl, data)