def test_2d_1cluster(self):
#create simple data:
a = range(0, 11)
print a
d = []
for i in a:
for j in a:
d.append([i * 1.0, j * 1.0])
dbscanner = Dbscan(np.array(d), 3, 2.0)
dbscanner.run()
plotting.plotting(dbscanner.getClusterList(), dbscanner.getNoise())
def test_2d_1cluster(self):
#create simple data:
a = range(0, 11)
print a
d = []
for i in a:
for j in a:
d.append([i*1.0, j*1.0])
dbscanner = Dbscan(np.array(d), 3, 2.0)
dbscanner.run()
plotting.plotting(dbscanner.getClusterList(),dbscanner.getNoise())
def test_plotting(self):
#some dummy data
number, x_coordinate, y_coordinate = loadtxt('testdata/eps3-minpts5-cluster5-noise20.dat', unpack = True)
D=[None]*len(x_coordinate)
for ii in range(len(x_coordinate)):
D[ii]=[x_coordinate[ii],y_coordinate[ii]]
#put in the data we want to use
minNeighbors = 5
epsilon = 3.
data = np.array(D, dtype=np.float64)
# use dbscan
dbscanner = Dbscan(data, minNeighbors, epsilon)
dbscanner.run()
# use plotting
plotting.plotting(dbscanner.getClusterList(),dbscanner.getNoise())
def test_plotting(self):
#some dummy data
number, x_coordinate, y_coordinate = loadtxt(
'testdata/eps3-minpts5-cluster5-noise20.dat', unpack=True)
D = [None] * len(x_coordinate)
for ii in range(len(x_coordinate)):
D[ii] = [x_coordinate[ii], y_coordinate[ii]]
#put in the data we want to use
minNeighbors = 5
epsilon = 3.
data = np.array(D, dtype=np.float64)
# use dbscan
dbscanner = Dbscan(data, minNeighbors, epsilon)
dbscanner.run()
# use plotting
plotting.plotting(dbscanner.getClusterList(), dbscanner.getNoise())
def test_dbscan2(self):
testdata2='testdata/eps0p01-minpts1-cluster0-noise100.dat'
number, x_coordinate, y_coordinate = np.loadtxt(testdata2, unpack = True)
D=[None]*len(x_coordinate)
for ii in range(len(x_coordinate)):
D[ii]=[x_coordinate[ii],y_coordinate[ii]]
D=np.array(D)
epsilon=0.01
minNeighbors=1
dbscanner = Dbscan(D, minNeighbors, epsilon)
dbscanner.run()
datennoise=dbscanner.getNoise()
datennoise=np.array(datennoise)
b=[]
cluster1=dbscanner.getClusterList()
np.testing.assert_array_equal(cluster1,b)
np.testing.assert_array_equal(datennoise,D)
def test_dbscan2(self):
testdata2 = 'testdata/eps0p01-minpts1-cluster0-noise100.dat'
number, x_coordinate, y_coordinate = np.loadtxt(testdata2, unpack=True)
D = [None] * len(x_coordinate)
for ii in range(len(x_coordinate)):
D[ii] = [x_coordinate[ii], y_coordinate[ii]]
D = np.array(D)
epsilon = 0.01
minNeighbors = 1
dbscanner = Dbscan(D, minNeighbors, epsilon)
dbscanner.run()
datennoise = dbscanner.getNoise()
datennoise = np.array(datennoise)
b = []
cluster1 = dbscanner.getClusterList()
np.testing.assert_array_equal(cluster1, b)
np.testing.assert_array_equal(datennoise, D)
def test_dbscan1(self):
testdata='testdata/eps2-minpts3-cluster1-noise0.dat'
number, x_coordinate, y_coordinate = np.loadtxt(testdata, unpack = True)
D=[None]*len(x_coordinate)
for ii in range(len(x_coordinate)):
D[ii]=[x_coordinate[ii],y_coordinate[ii]]
D=np.array(D)
epsilon=4.0
minNeighbors=3
dbscanner = Dbscan(D, minNeighbors, epsilon)
dbscanner.run()
datennoise=dbscanner.getNoise()
Dresultall=[]
for daten in dbscanner.getClusterList():
Dresult=[]
for item in daten:
Dresult.append(item)
Dresultall.append(Dresult) #
#assert D==a
Dr=np.array(Dresultall)
a=np.array([])
np.testing.assert_array_equal(a,datennoise)
def test_dbscan1(self):
testdata = 'testdata/eps2-minpts3-cluster1-noise0.dat'
number, x_coordinate, y_coordinate = np.loadtxt(testdata, unpack=True)
D = [None] * len(x_coordinate)
for ii in range(len(x_coordinate)):
D[ii] = [x_coordinate[ii], y_coordinate[ii]]
D = np.array(D)
epsilon = 4.0
minNeighbors = 3
dbscanner = Dbscan(D, minNeighbors, epsilon)
dbscanner.run()
datennoise = dbscanner.getNoise()
Dresultall = []
for daten in dbscanner.getClusterList():
Dresult = []
for item in daten:
Dresult.append(item)
Dresultall.append(Dresult) #
#assert D==a
Dr = np.array(Dresultall)
a = np.array([])
np.testing.assert_array_equal(a, datennoise)
