def test_zigzag1d(self):
a = fred.Curve(np.array([0.0, 1.0, 0.0, 1.0]))
b = fred.Curve(np.array([0.0, 0.75, 0.25, 1.0]))
c = fred.Curve(np.array([0.0, 1.0]))
self.assertEqual(fred.continuous_frechet(a, b).value, 0.25)
self.assertEqual(fred.continuous_frechet(a, c).value, 0.5)
def test_longsegment(self):
a = fred.Curve(np.array([0.0, 500.0e3, 1.0e6]))
b = fred.Curve(np.array([0.0, 1.0e6]))
self.assertEqual(fred.discrete_frechet(a, b).value, 500000.0)
import Fred.backend as fred
import Fred
import numpy as np
sep = 1000
p1 = np.array([[0, 0], [sep / 2, 0], [0, 0.1], [sep / 4, 0.1], [sep / 2, 0.1]])
p2 = np.array([[0, -0.1], [sep, -0.1], [sep / 2, 0], [sep, 0], [sep / 2, 0.1]])
curves = fred.Curves()
curves.add(fred.Curve(p1, "Input Curve {}".format(1)))
curves.add(fred.Curve(p2, "Input Curve {}".format(2)))
clustering = fred.two_two_dtw_one_two_median(curves)
clustering_e = fred.two_two_dtw_one_two_median_exact(curves)
print(clustering.value / clustering_e.value)
Fred.plot_curve(curves, clustering, clustering_e)
import Fred.backend as fred
import Fred
import numpy as np
c1 = fred.Curve(
np.array([[0.0, -1.0], [-1.0, 3.0], [1.0, 0.1], [6.1, 0.1], [6.2, 0.1],
[6.3, 0.1], [6.4, 0.1], [6.5, 0.1], [7.0, 0.1], [8.0, 0.1],
[8.3, 0.1], [8.4, 0.1], [8.5, 0.1], [8.6, 0.1], [8.7, 0.1],
[8.8, 0.1], [8.9, 0.1], [10, -1.0]]), "Eingabe 1")
c2 = fred.Curve(
np.array([[0.0, -1.0], [1.0, -0.1], [1.1, -0.1], [1.2, -0.1], [1.3, -0.1],
[1.4, -0.1], [1.5, -0.1], [1.6, -0.1], [2.0, -0.1], [3.0, -0.1],
[4.0, -0.1], [5.0, -0.1], [6.0, -0.1], [7.0, -0.1], [8.0, -0.1],
[11.0, 3.0], [10.0, -1.0]]), "Eingabe 2")
curves = fred.Curves()
curves.add(c1)
curves.add(c2)
clustering = fred.two_two_dtw_one_two_median(curves)
clustering_e = fred.two_two_dtw_one_two_median_exact(curves)
print(clustering.value / clustering_e.value)
Fred.plot_curve(curves, clustering, clustering_e)
for j in range(clusteroutliers):
kk = int(np.random.rand() * clusternumber)
while kk == k:
kk = int(np.random.rand() * clusternumber)
k = kk
randomdirection = np.random.normal(0, 1, (1, 2)).flatten()
randomdirection /= np.linalg.norm(randomdirection)
c1[k] += outlierradius * randomdirection
k = None
for j in range(clusteroutliers):
kk = int(np.random.rand() * clusternumber)
while kk == k:
kk = int(np.random.rand() * clusternumber)
k = kk
randomdirection = np.random.normal(0, 1, (1, 2)).flatten()
randomdirection /= np.linalg.norm(randomdirection)
c2[k] += outlierradius * randomdirection
curves.add(
fred.Curve(np.concatenate([c1, inb, c2]),
"Input Curve {}".format(i + 1)))
clustering = fred.two_two_dtw_one_two_median(curves)
clustering_e = fred.two_two_dtw_one_two_median_exact(curves)
print(clustering.value / clustering_e.value)
Fred.plot_curve(curves, clustering, clustering_e)