def get_dis_of_face(face1, face2):
data = np.vstack((face1, face2))
l1 = np.shape(face1)[0]
l2 = np.shape(face2)[0]
index1 = range(l1)
index2 = range(l1, l1 + l2, 1)
dis = clusterdistance(data,
index1=index1,
index2=index2,
dist='e',
method='s')
return dis
def test_clusterdistance(self):
if TestCluster.module == 'Bio.Cluster':
from Bio.Cluster import clusterdistance
elif TestCluster.module == 'Pycluster':
from Pycluster import clusterdistance
# First data set
weight = numpy.array([1, 1, 1, 1, 1])
data = numpy.array([[1.1, 2.2, 3.3, 4.4, 5.5],
[3.1, 3.2, 1.3, 2.4, 1.5],
[4.1, 2.2, 0.3, 5.4, 0.5],
[12.1, 2.0, 0.0, 5.0, 0.0]])
mask = numpy.array([[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1]], int)
# Cluster assignments
c1 = [0]
c2 = [1, 2]
c3 = [3]
# TODO - Use a context manager here once we drop Python 2.6
# Method should be one letter:
self.assertRaises(ValueError, clusterdistance, data,
**{"mask": mask, "weight": weight,
"index1": c1, "index2": c2, "transpose": 0,
"method": "any", "dist": "e"})
# Distance should be one letter:
self.assertRaises(ValueError, clusterdistance, data,
**{"mask": mask, "weight": weight,
"index1": c1, "index2": c2, "transpose": 0,
"method": "a", "dist": "euclidean"})
distance = clusterdistance(data, mask=mask, weight=weight,
index1=c1, index2=c2, dist='e',
method='a', transpose=0)
self.assertAlmostEqual(distance, 6.650, places=3)
distance = clusterdistance(data, mask=mask, weight=weight,
index1=c1, index2=c3, dist='e',
method='a', transpose=0)
self.assertAlmostEqual(distance, 32.508, places=3)
distance = clusterdistance(data, mask=mask, weight=weight,
index1=c2, index2=c3, dist='e',
method='a', transpose=0)
self.assertAlmostEqual(distance, 15.118, places=3)
# Second data set
weight = numpy.array([1, 1])
data = numpy.array([[1.1, 1.2],
[1.4, 1.3],
[1.1, 1.5],
[2.0, 1.5],
[1.7, 1.9],
[1.7, 1.9],
[5.7, 5.9],
[5.7, 5.9],
[3.1, 3.3],
[5.4, 5.3],
[5.1, 5.5],
[5.0, 5.5],
[5.1, 5.2]])
mask = numpy.array([[1, 1],
[1, 1],
[1, 1],
[1, 1],
[1, 1],
[1, 1],
[1, 1],
[1, 1],
[1, 1],
[1, 1],
[1, 1],
[1, 1],
[1, 1]], int)
# Cluster assignments
c1 = [0, 1, 2, 3]
c2 = [4, 5, 6, 7]
c3 = [8]
# TODO - Use a context manager here once we drop Python 2.6
# Method should be one letter:
self.assertRaises(ValueError, clusterdistance, data,
**{"mask": mask, "weight": weight,
"index1": c1, "index2": c2,
"method": "any", "dist": "e",
"transpose": 0})
# Distance should be one letter:
self.assertRaises(ValueError, clusterdistance, data,
**{"mask": mask, "weight": weight,
"index1": c1, "index2": c2,
"method": "a", "dist": "euclidena",
"transpose": 0})
distance = clusterdistance(data, mask=mask, weight=weight,
index1=c1, index2=c2, dist='e',
method='a', transpose=0)
self.assertAlmostEqual(distance, 5.833, places=3)
distance = clusterdistance(data, mask=mask, weight=weight,
index1=c1, index2=c3, dist='e',
method='a', transpose=0)
self.assertAlmostEqual(distance, 3.298, places=3)
distance = clusterdistance(data, mask=mask, weight=weight,
index1=c2, index2=c3, dist='e',
method='a', transpose=0)
self.assertAlmostEqual(distance, 0.360, places=3)
def test_clusterdistance(module):
if module == 'Bio.Cluster':
from Bio.Cluster import clusterdistance
elif module == 'Pycluster':
from Pycluster import clusterdistance
else:
raise 'Unknown module name', module
print "test_clusterdistance:"
# First data set
weight1 = array([1, 1, 1, 1, 1])
data1 = array([[
1.1,
2.2,
3.3,
4.4,
5.5,
], [
3.1,
3.2,
1.3,
2.4,
1.5,
], [
4.1,
2.2,
0.3,
5.4,
0.5,
], [
12.1,
2.0,
0.0,
5.0,
0.0,
]])
mask1 = array([[1, 1, 1, 1, 1], [1, 1, 1, 1, 1], [1, 1, 1, 1, 1],
[1, 1, 1, 1, 1]])
# Second data set
weight2 = array([1, 1])
data2 = array([[1.1, 1.2], [1.4, 1.3], [1.1, 1.5], [2.0, 1.5], [1.7, 1.9],
[1.7, 1.9], [5.7, 5.9], [5.7, 5.9], [3.1, 3.3], [5.4, 5.3],
[5.1, 5.5], [5.0, 5.5], [5.1, 5.2]])
mask2 = array([[1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 1],
[1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 1]])
# Cluster assignments
c1 = [0]
c2 = [1, 2]
c3 = [3]
print "First data set:"
print_matrix(data1, mask1)
print "Clusters are", c1, c2, c3
distance = clusterdistance(data1,
mask=mask1,
weight=weight1,
index1=c1,
index2=c2,
dist='e',
method='a',
transpose=0)
print "Distance between cluster", c1, "and", c2, "is %7.3f." % distance
distance = clusterdistance(data1,
mask=mask1,
weight=weight1,
index1=c1,
index2=c3,
dist='e',
method='a',
transpose=0)
print "Distance between cluster", c1, "and", c3, "is %7.3f." % distance
distance = clusterdistance(data1,
mask=mask1,
weight=weight1,
index1=c2,
index2=c3,
dist='e',
method='a',
transpose=0)
print "Distance between cluster", c2, "and", c3, "is %7.3f." % distance
# Cluster assignments
c1 = [0, 1, 2, 3]
c2 = [4, 5, 6, 7]
c3 = [8]
print "Second data set:"
print_matrix(data2, mask2)
print "Clusters are", c1, c2, c3
distance = clusterdistance(data2,
mask=mask2,
weight=weight2,
index1=c1,
index2=c2,
dist='e',
method='a',
transpose=0)
print "Distance between cluster", c1, "and", c2, "is %7.3f." % distance
distance = clusterdistance(data2,
mask=mask2,
weight=weight2,
index1=c1,
index2=c3,
dist='e',
method='a',
transpose=0)
print "Distance between cluster", c1, "and", c3, "is %7.3f." % distance
distance = clusterdistance(data2,
mask=mask2,
weight=weight2,
index1=c2,
index2=c3,
dist='e',
method='a',
transpose=0)
print "Distance between cluster", c2, "and", c3, "is %7.3f." % distance
print
def test_clusterdistance(self):
if TestCluster.module == "Bio.Cluster":
from Bio.Cluster import clusterdistance
elif TestCluster.module == "Pycluster":
from Pycluster import clusterdistance
# First data set
weight = numpy.array([1, 1, 1, 1, 1])
data = numpy.array(
[
[1.1, 2.2, 3.3, 4.4, 5.5],
[3.1, 3.2, 1.3, 2.4, 1.5],
[4.1, 2.2, 0.3, 5.4, 0.5],
[12.1, 2.0, 0.0, 5.0, 0.0],
]
)
mask = numpy.array([[1, 1, 1, 1, 1], [1, 1, 1, 1, 1], [1, 1, 1, 1, 1], [1, 1, 1, 1, 1]], int)
# Cluster assignments
c1 = [0]
c2 = [1, 2]
c3 = [3]
distance = clusterdistance(
data, mask=mask, weight=weight, index1=c1, index2=c2, dist="e", method="a", transpose=0
)
self.assertAlmostEqual(distance, 6.650, 3)
distance = clusterdistance(
data, mask=mask, weight=weight, index1=c1, index2=c3, dist="e", method="a", transpose=0
)
self.assertAlmostEqual(distance, 32.508, 3)
distance = clusterdistance(
data, mask=mask, weight=weight, index1=c2, index2=c3, dist="e", method="a", transpose=0
)
self.assertAlmostEqual(distance, 15.118, 3)
# Second data set
weight = numpy.array([1, 1])
data = numpy.array(
[
[1.1, 1.2],
[1.4, 1.3],
[1.1, 1.5],
[2.0, 1.5],
[1.7, 1.9],
[1.7, 1.9],
[5.7, 5.9],
[5.7, 5.9],
[3.1, 3.3],
[5.4, 5.3],
[5.1, 5.5],
[5.0, 5.5],
[5.1, 5.2],
]
)
mask = numpy.array(
[[1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 1]],
int,
)
# Cluster assignments
c1 = [0, 1, 2, 3]
c2 = [4, 5, 6, 7]
c3 = [8]
distance = clusterdistance(
data, mask=mask, weight=weight, index1=c1, index2=c2, dist="e", method="a", transpose=0
)
self.assertAlmostEqual(distance, 5.833, 3)
distance = clusterdistance(
data, mask=mask, weight=weight, index1=c1, index2=c3, dist="e", method="a", transpose=0
)
self.assertAlmostEqual(distance, 3.298, 3)
distance = clusterdistance(
data, mask=mask, weight=weight, index1=c2, index2=c3, dist="e", method="a", transpose=0
)
self.assertAlmostEqual(distance, 0.360, 3)
def test_clusterdistance(self):
if TestCluster.module == 'Bio.Cluster':
from Bio.Cluster import clusterdistance
elif TestCluster.module == 'Pycluster':
from Pycluster import clusterdistance
# First data set
weight = numpy.array([1, 1, 1, 1, 1])
data = numpy.array([[1.1, 2.2, 3.3, 4.4,
5.5], [3.1, 3.2, 1.3, 2.4, 1.5],
[4.1, 2.2, 0.3, 5.4, 0.5],
[9.9, 2.0, 0.0, 5.0, 0.0]])
mask = numpy.array([[1, 1, 1, 1, 1], [1, 1, 1, 1, 1], [1, 1, 1, 1, 1],
[1, 1, 1, 1, 1]], int)
# Cluster assignments
c1 = [0]
c2 = [1, 2]
c3 = [3]
distance = clusterdistance(data,
mask=mask,
weight=weight,
index1=c1,
index2=c2,
dist='e',
method='a',
transpose=0)
self.assertAlmostEqual(distance, 6.650, places=3)
distance = clusterdistance(data,
mask=mask,
weight=weight,
index1=c1,
index2=c3,
dist='e',
method='a',
transpose=0)
self.assertAlmostEqual(distance, 23.796, places=3)
distance = clusterdistance(data,
mask=mask,
weight=weight,
index1=c2,
index2=c3,
dist='e',
method='a',
transpose=0)
self.assertAlmostEqual(distance, 8.606, places=3)
# Second data set
weight = numpy.array([1, 1])
data = numpy.array([[1.1, 1.2], [1.4, 1.3], [1.1, 1.5], [2.0, 1.5],
[1.7, 1.9], [1.7, 1.9], [5.7, 5.9], [5.7, 5.9],
[3.1, 3.3], [5.4, 5.3], [5.1, 5.5], [5.0, 5.5],
[5.1, 5.2]])
mask = numpy.array(
[[1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 1],
[1, 1], [1, 1], [1, 1], [1, 1], [1, 1]], int)
# Cluster assignments
c1 = [0, 1, 2, 3]
c2 = [4, 5, 6, 7]
c3 = [8]
distance = clusterdistance(data,
mask=mask,
weight=weight,
index1=c1,
index2=c2,
dist='e',
method='a',
transpose=0)
self.assertAlmostEqual(distance, 5.833, places=3)
distance = clusterdistance(data,
mask=mask,
weight=weight,
index1=c1,
index2=c3,
dist='e',
method='a',
transpose=0)
self.assertAlmostEqual(distance, 3.298, places=3)
distance = clusterdistance(data,
mask=mask,
weight=weight,
index1=c2,
index2=c3,
dist='e',
method='a',
transpose=0)
self.assertAlmostEqual(distance, 0.360, places=3)
def test_clusterdistance(module):
if module=='Bio.Cluster':
from Bio.Cluster import clusterdistance
elif module=='Pycluster':
from Pycluster import clusterdistance
else:
raise 'Unknown module name', module
print "test_clusterdistance:"
# First data set
weight1 = array([ 1,1,1,1,1 ])
data1 = array([[ 1.1, 2.2, 3.3, 4.4, 5.5, ],
[ 3.1, 3.2, 1.3, 2.4, 1.5, ],
[ 4.1, 2.2, 0.3, 5.4, 0.5, ],
[ 12.1, 2.0, 0.0, 5.0, 0.0, ]])
mask1 = array([[ 1, 1, 1, 1, 1],
[ 1, 1, 1, 1, 1],
[ 1, 1, 1, 1, 1],
[ 1, 1, 1, 1, 1]])
# Second data set
weight2 = array([ 1,1 ])
data2 = array([[ 1.1, 1.2 ],
[ 1.4, 1.3 ],
[ 1.1, 1.5 ],
[ 2.0, 1.5 ],
[ 1.7, 1.9 ],
[ 1.7, 1.9 ],
[ 5.7, 5.9 ],
[ 5.7, 5.9 ],
[ 3.1, 3.3 ],
[ 5.4, 5.3 ],
[ 5.1, 5.5 ],
[ 5.0, 5.5 ],
[ 5.1, 5.2 ]])
mask2 = array([[ 1, 1 ],
[ 1, 1 ],
[ 1, 1 ],
[ 1, 1 ],
[ 1, 1 ],
[ 1, 1 ],
[ 1, 1 ],
[ 1, 1 ],
[ 1, 1 ],
[ 1, 1 ],
[ 1, 1 ],
[ 1, 1 ],
[ 1, 1 ]])
# Cluster assignments
c1 = [0]
c2 = [1,2]
c3 = [3]
print "First data set:"
print_matrix(data1, mask1)
print "Clusters are", c1, c2, c3
distance = clusterdistance(data1, mask=mask1, weight=weight1, index1=c1, index2=c2, dist='e', method='a', transpose=0);
print "Distance between cluster", c1, "and", c2, "is %7.3f." % distance
distance = clusterdistance(data1, mask=mask1, weight=weight1, index1=c1, index2=c3, dist='e', method='a', transpose=0);
print "Distance between cluster", c1, "and", c3, "is %7.3f." % distance
distance = clusterdistance(data1, mask=mask1, weight=weight1, index1=c2, index2=c3, dist='e', method='a', transpose=0);
print "Distance between cluster", c2, "and", c3, "is %7.3f." % distance
# Cluster assignments
c1 = [ 0, 1, 2, 3 ]
c2 = [ 4, 5, 6, 7 ]
c3 = [ 8 ]
print "Second data set:"
print_matrix(data2, mask2)
print "Clusters are", c1, c2, c3
distance = clusterdistance(data2, mask=mask2, weight=weight2, index1=c1, index2=c2, dist='e', method='a', transpose=0);
print "Distance between cluster", c1, "and", c2, "is %7.3f." % distance
distance = clusterdistance(data2, mask=mask2, weight=weight2, index1=c1, index2=c3, dist='e', method='a', transpose=0);
print "Distance between cluster", c1, "and", c3, "is %7.3f." % distance
distance = clusterdistance(data2, mask=mask2, weight=weight2, index1=c2, index2=c3, dist='e', method='a', transpose=0);
print "Distance between cluster", c2, "and", c3, "is %7.3f." % distance
print