def ctest1DistsListArray(self):
exp = numpy.array([1., 1.414213, 1.0], 'd')
desc = [
numpy.array([0.0, 0.0], 'd'),
numpy.array([1.0, 0.0], 'd'),
numpy.array([1.0, 1.0], 'd')
]
dmat = rdmmc.GetEuclideanDistMat(desc)
for i in range(numpy.shape(dmat)[0]):
assert feq(dmat[i], exp[i])
# repeat the test with a list of numpy.arrays of floats
desc = [
numpy.array([0.0, 0.0], 'f'),
numpy.array([1.0, 0.0], 'f'),
numpy.array([1.0, 1.0], 'f')
]
dmat = rdmmc.GetEuclideanDistMat(desc)
for i in range(numpy.shape(dmat)[0]):
assert feq(dmat[i], exp[i])
# repeat the test with a list of numpy.arrays of ints
desc = [
numpy.array([0, 0], 'i'),
numpy.array([1, 0], 'i'),
numpy.array([1, 1], 'i')
]
dmat = rdmmc.GetEuclideanDistMat(desc)
for i in range(numpy.shape(dmat)[0]):
assert feq(dmat[i], exp[i])
def test0DistsArray(self):
exp = numpy.array([1., 1.414213, 1.0], 'd')
# initialize a double array and check if get back the expected distances
desc = numpy.zeros((3, 2), 'd')
desc[1, 0] = 1.0
desc[2, 0] = 1.0
desc[2, 1] = 1.0
dmat = rdmmc.GetEuclideanDistMat(desc)
for i in range(numpy.shape(dmat)[0]):
assert feq(dmat[i], exp[i])
# repeat with an flaot array
desc = numpy.zeros((3, 2), 'f')
desc[1, 0] = 1.0
desc[2, 0] = 1.0
desc[2, 1] = 1.0
dmat = rdmmc.GetEuclideanDistMat(desc)
for i in range(numpy.shape(dmat)[0]):
assert feq(dmat[i], exp[i])
# finally with an interger array
desc = numpy.zeros((3, 2), 'i')
desc[1, 0] = 1
desc[2, 0] = 1
desc[2, 1] = 1
dmat = rdmmc.GetEuclideanDistMat(desc)
for i in range(numpy.shape(dmat)[0]):
assert feq(dmat[i], exp[i])
def test2DistListList(self):
exp = numpy.array([1., 1.414213, 1.0], 'd')
desc = [[0.0, 0.0], [1.0, 0.0], [1.0, 1.0]]
dmat = rdmmc.GetEuclideanDistMat(desc)
for i in range(numpy.shape(dmat)[0]):
assert feq(dmat[i], exp[i])
# test with ints
desc = [[0, 0], [1, 0], [1, 1]]
dmat = rdmmc.GetEuclideanDistMat(desc)
for i in range(numpy.shape(dmat)[0]):
assert feq(dmat[i], exp[i])
def test1HierarchPick(self):
fname = os.path.join(RDConfig.RDBaseDir, 'Code', 'SimDivPickers',
'Wrap', 'test_data', 'points.csv')
with open(fname) as infil:
lines = infil.readlines()
self.dataPts = numpy.zeros((len(lines), 2), 'd')
labels = []
i = 0
for line in lines:
tlst = line.strip().split(',')
self.dataPts[i, 0] = float(tlst[1])
self.dataPts[i, 1] = float(tlst[2])
labels.append(int(tlst[3]))
i += 1
self.dMat = rdmmc.GetEuclideanDistMat(self.dataPts)
pkr = rdSimDivPickers.HierarchicalClusterPicker(
rdSimDivPickers.ClusterMethod.WARD)
clusters = pkr.Cluster(self.dMat, i, 2)
# check that each of the clusters have the same label
for cl in clusters:
clbl = labels[cl[0]]
for id in cl:
assert clbl == labels[id]
hierarch = pkr.Pick(self.dMat, i, 2)
self.assertEqual(tuple(hierarch), (1, 30))
def setUp(self):
self.n = 1000
self.m = 80
self.d = 2
self.dataPts = numpy.zeros((self.n, self.d), 'd')
for i in range(self.n):
for j in range(self.d):
self.dataPts[i, j] = random.random()
self.dMat = rdmmc.GetEuclideanDistMat(self.dataPts)
def test3Compare(self):
n = 30
m = 5
dscArr = numpy.zeros((n, m), 'd')
for i in range(n):
for j in range(m):
dscArr[i, j] = random.random()
dmatArr = rdmmc.GetEuclideanDistMat(dscArr)
dscLL = []
for i in range(n):
row = []
for j in range(m):
row.append(dscArr[i, j])
dscLL.append(row)
dmatLL = rdmmc.GetEuclideanDistMat(dscLL)
assert numpy.shape(dmatArr) == numpy.shape(dmatLL)
for i in range(n * (n - 1) // 2):
assert feq(dmatArr[i], dmatLL[i])
def test1HierarchPick(self):
infil = open("test_data/points.csv", 'r')
lines = infil.readlines()
infil.close()
self.dataPts = numpy.zeros((len(lines), 2), 'd')
labels = []
i = 0
for line in lines:
tlst = line.strip().split(',')
self.dataPts[i, 0] = float(tlst[1])
self.dataPts[i, 1] = float(tlst[2])
labels.append(int(tlst[3]))
i += 1
self.dMat = rdmmc.GetEuclideanDistMat(self.dataPts)
pkr = rdSimDivPickers.HierarchicalClusterPicker(
rdSimDivPickers.ClusterMethod.WARD)
clusters = pkr.Cluster(self.dMat, i, 2)
# check that each of the clusters have the same label
for cl in clusters:
clbl = labels[cl[0]]
for id in cl:
assert clbl == labels[id]
hierarch = pkr.Pick(self.dMat, i, 2)
assert tuple(hierarch) == (1, 30)
