def __init__(self):
self.gauss = Gaussian2D([0, 0], (0.7, 1, 0.3), label='Gaussian')
self.bending = Mixture2D([[0, 0], [2, 1.8]], [(np.sqrt(0.5), 1, 0.9), (1, 1, 0.8)], [0.6, 0.4], xmin=-1,
label='bending')
self.hammer = Mixture2D([[0, 0], [1, 1.8]], [(np.sqrt(0.5), 1, 0.9), (0.3, 1, -0.7)], [0.5, 0.5],
label='hammer')
cov = make_2D_Cov(np.sqrt(0.5), 1, 0.1)
self.skew = Mixture2D([[0, 0], [0, 1.2]], [cov, cov / 4], [0.5, 0.5], label='skew')
cov = make_2D_Cov(np.sqrt(0.5), 1, 0.1)
self.broadtail = Mixture2D([[0, 0], [0, 0.2]], [cov, cov * 8], [0.9, 0.1], label='broad tail')
self.tensorlike = Mixture2D([[0, 0.03], [0, 0.03]], [(0.03, 0.03, 0.1), (0.03, 0.06, 0.1)], [0.85, 0.15],
ymin=0, label='tensor like')
self.rotating = Mixture2D([[0, 0], [0, 0.2]], [(1, 1, 0.5), (2, 2, -0.5)], [0.6, 0.4], label='rotating')
self.tight = Mixture2D([[0, 0], [2.5, 3.5]], [(1, 1, 0.99), (1, 1.5, 0.98)], [0.6, 0.4], label='tight')
self.cut_correlated = Gaussian2D([0, 0], (0.7, 1, 0.95), ymin=0.3, xmax=1.2, label='cut correlated')
self.shape_set = [self.gauss, self.bending, self.hammer, self.skew, self.broadtail, self.rotating, self.tight,
self.cut_correlated, self.tensorlike]
self.cut_gaussians = self.cutGaussians((0.7, 1, 0.3))
# these examples are from Wand and Jones 93
self.bimodal = []
self.bimodal.append(
Mixture2D([[-1, 0], [1, 0]], [(2. / 3, 2. / 3, 0), (2. / 3, 2. / 3, 0)], label='bimodal WJ1'))
self.bimodal.append(
Mixture2D([[-3. / 2, 0], [3. / 2, 0]], [(1. / 4, 1, 0), (1. / 4, 1, 0)], label='bimodal WJ2'))
self.bimodal.append(
Mixture2D([[-1, 1], [1, -1]], [(2. / 3, 2. / 3, 3. / 5), (2. / 3, 2. / 3, 3. / 5)], label='bimodal WJ3'))
self.bimodal.append(
Mixture2D([[1, -1], [-1, 1]], [(2. / 3, 2. / 3, 7. / 10), (2. / 3, 2. / 3, 0)], label='bimodal WJ4'))
self.trimodal = []
self.trimodal.append(Mixture2D([[-6. / 5, 6. / 5], [6. / 5, -6. / 5], [0, 0]],
[(3. / 5, 3. / 5, 3. / 10), (3. / 5, 3. / 5, -3. / 5), (0.25, 0.25, 0.2)],
weights=[9, 9, 2], label='trimodal WJ1'))
self.trimodal.append(Mixture2D([[-6. / 5, 0], [6. / 5, 0], [0, 0]],
[(3. / 5, 3. / 5, 0.7), (3. / 5, 3. / 5, 0.7), (0.25, 0.25, -0.7)],
label='trimodal WJ2'))
self.trimodal.append(Mixture2D([[-1, 0], [1, 2 * np.sqrt(3) / 3], [1, -2 * np.sqrt(3) / 3]],
[(0.6, 0.7, 0.6), (0.6, 0.7, 0), (0.4, 0.7, 0)], weights=[3, 3, 1],
label='trimodal WJ3'))
self.quadrimodal = []
self.quadrimodal.append(Mixture2D([[-1, 1], [-1, -1], [1, -1], [1, 1]],
[(2. / 3, 2. / 3, 2. / 5), (2. / 3, 2. / 3, 3. / 5), (2. / 3, 2. / 3, -0.7),
(2. / 3, 2. / 3, -0.5)],
weights=[1, 3, 1, 3], label='quadrimodal'))
self.all = self.shape_set + self.bimodal + self.trimodal + self.quadrimodal + self.cut_gaussians
def __init__(self):
self.gauss = Gaussian2D([0, 0], (0.7, 1, 0.3), label='Gaussian')
self.bending = Mixture2D([[0, 0], [2, 1.8]], [(np.sqrt(0.5), 1, 0.9), (1, 1, 0.8)], [0.6, 0.4], xmin=-1, label='bending')
self.hammer = Mixture2D([[0, 0], [1, 1.8]], [(np.sqrt(0.5), 1, 0.9), (0.3, 1, -0.7)], [0.5, 0.5], label='hammer')
cov = make_2D_Cov(np.sqrt(0.5), 1, 0.1)
self.skew = Mixture2D([[0, 0], [0, 1.2]], [cov, cov / 4], [0.5, 0.5], label='skew')
cov = make_2D_Cov(np.sqrt(0.5), 1, 0.1)
self.broadtail = Mixture2D([[0, 0], [0, 0.2]], [cov, cov * 8], [0.9, 0.1], label='broad tail')
self.tensorlike = Mixture2D([[0, 0.03], [0, 0.03]], [(0.03, 0.03, 0.1), (0.03, 0.06, 0.1)], [0.85, 0.15], ymin=0, label='tensor like')
self.rotating = Mixture2D([[0, 0], [0, 0.2]], [(1, 1, 0.5), (2, 2, -0.5)], [0.6, 0.4], label='rotating')
self.tight = Mixture2D([[0, 0], [2.5, 3.5]], [(1, 1, 0.99), (1, 1.5, 0.98)], [0.6, 0.4], label='tight')
self.cut_correlated = Gaussian2D([0, 0], (0.7, 1, 0.95), ymin=0.3, xmax=1.2, label='cut correlated')
self.shape_set = [self.gauss, self.bending, self.hammer, self.skew, self.broadtail, self.rotating, self.tight,
self.cut_correlated, self.tensorlike]
self.cut_gaussians = self.cutGaussians((0.7, 1, 0.3))
# these examples are from Wand and Jones 93
self.bimodal = []
self.bimodal.append(Mixture2D([[-1, 0], [1, 0]], [(2. / 3, 2. / 3, 0), (2. / 3, 2. / 3, 0)], label='bimodal WJ1'))
self.bimodal.append(Mixture2D([[-3. / 2, 0], [3. / 2, 0]], [(1. / 4, 1, 0), (1. / 4, 1, 0)], label='bimodal WJ2'))
self.bimodal.append(Mixture2D([[-1, 1], [1, -1]], [(2. / 3, 2. / 3, 3. / 5), (2. / 3, 2. / 3, 3. / 5)], label='bimodal WJ3'))
self.bimodal.append(Mixture2D([[1, -1], [-1, 1]], [(2. / 3, 2. / 3, 7. / 10), (2. / 3, 2. / 3, 0)], label='bimodal WJ4'))
self.trimodal = []
self.trimodal.append(Mixture2D([[-6. / 5, 6. / 5], [6. / 5, -6. / 5], [0, 0]],
[(3. / 5, 3. / 5, 3. / 10), (3. / 5, 3. / 5, -3. / 5), (0.25, 0.25, 0.2)], weights=[9, 9, 2], label='trimodal WJ1'))
self.trimodal.append(Mixture2D([[-6. / 5, 0], [6. / 5, 0], [0, 0]],
[(3. / 5, 3. / 5, 0.7), (3. / 5, 3. / 5, 0.7), (0.25, 0.25, -0.7)], label='trimodal WJ2'))
self.trimodal.append(Mixture2D([[-1, 0], [1, 2 * np.sqrt(3) / 3], [1, -2 * np.sqrt(3) / 3]],
[(0.6, 0.7, 0.6), (0.6, 0.7, 0), (0.4, 0.7, 0)], weights=[3, 3, 1], label='trimodal WJ3'))
self.quadrimodal = []
self.quadrimodal.append(Mixture2D([[-1, 1], [-1, -1], [1, -1], [1, 1]],
[(2. / 3, 2. / 3, 2. / 5), (2. / 3, 2. / 3, 3. / 5), (2. / 3, 2. / 3, -0.7), (2. / 3, 2. / 3, -0.5)],
weights=[1, 3, 1, 3], label='quadrimodal'))
self.all = self.shape_set + self.bimodal + self.trimodal + self.quadrimodal + self.cut_gaussians