def test_full_align_exclude(self):
m = np.array([[1, 0, .2], [0, 1, 0], [0, 0, 1]])
y = self.x * m
x0 = np.hstack((np.eye(3).flatten(), np.zeros(3)))
full = FullAlignData(self.x, size=200000, exclude=[0])
a, b, f, s, _ = full.align(y, x0=x0)
npt.assert_array_almost_equal(a[0], np.array([1, 0, 0]), decimal=1)
npt.assert_array_almost_equal(a[:, 0], np.array([1, 0, 0]), decimal=1)
def test_full_align_exclude(self):
m = np.array([[1, 0, .2], [0, 1, 0], [0, 0, 1]])
y = self.x * m
x0 = np.hstack((np.eye(3).flatten(), np.zeros(3)))
full = FullAlignData(self.x, size=200000, exclude=[0])
a, b, f, s, _ = full.align(y, x0=x0)
npt.assert_array_almost_equal(a[0], np.array([1, 0, 0]), decimal=1)
npt.assert_array_almost_equal(a[:, 0], np.array([1, 0, 0]), decimal=1)
class DiagAlignTestCase(unittest.TestCase):
def setUp(self):
self.mu1 = np.array([0, 0, 0])
self.sig = 2 * np.eye(3)
self.mu2 = np.array([5, 5, 5])
self.clust1 = DPCluster(.5, self.mu1, self.sig)
self.clust2 = DPCluster(.5, self.mu2, self.sig)
self.clusters = [self.clust1, self.clust2]
self.x = DPMixture(self.clusters, niter=1, identified=True)
self.Diag = DiagonalAlignData(self.x, size=100000)
self.Comp = CompAlignData(self.x, size=100000)
self.Full = FullAlignData(self.x, size=200000)
def testDiagAlign(self):
y = self.x + np.array([1, -1, 1])
lb = np.array([0.1, 0.1, 0.1, -np.inf, -np.inf, -np.inf])
#a, b = self.Diag.align(y, method='TNC', bounds=np.array([(0.5, 2), (None, None)]), tol=1e-8, options={'disp': False})
a, b, f, s, m = self.Diag.align(y, solver='ralg', stencil=3)
assert s, 'failed to converge'
npt.assert_array_almost_equal(a, np.eye(3), decimal=1)
npt.assert_array_almost_equal(b, np.array([-1, 1, -1]), decimal=1)
def testCompAlign(self):
m = np.array([[1, 0, .2], [0, 1, 0], [0, 0, 1]])
lb = np.ones(6) * -.2
ub = np.ones(6) * .2
y = self.x * m
a, b, f, s, _ = self.Comp.align(y,
lb=lb,
ub=ub,
fEnough=0,
ftol=1e-16,
xtol=1e-16)
assert s, 'failed to converge'
npt.assert_array_almost_equal(a, np.linalg.inv(m), decimal=1)
npt.assert_array_almost_equal(b, np.array([0, 0, 0]), decimal=1)
npt.assert_array_almost_equal((y * a).mus, self.x.mus, decimal=1)
def testFullAlign(self):
m = np.array([[1, 0, .2], [0, 1, 0], [0, 0, 1]])
y = self.x * m
a, b, f, s, _ = self.Full.align(y, ftol=1e-10, gtol=1e-10, xtol=1e-10)
assert s, 'failed to converge'
npt.assert_array_almost_equal(a, np.linalg.inv(m), decimal=1)
npt.assert_array_almost_equal(b, np.array([0, 0, 0]), decimal=1)
npt.assert_array_almost_equal(((y * a) + b).mus, self.x.mus, decimal=1)
def testFullAlignExclude(self):
m = np.array([[1, 0, .2], [0, 1, 0], [0, 0, 1]])
y = self.x * m
x0 = np.hstack((np.eye(3).flatten(), np.zeros(3)))
Full = FullAlignData(self.x, size=200000, exclude=[0])
a, b, f, s, _ = Full.align(y, x0=x0, ftol=1e-8)
npt.assert_array_almost_equal(a[0], np.array([1, 0, 0]), decimal=1)
npt.assert_array_almost_equal(a[:, 0], np.array([1, 0, 0]), decimal=1)
class DiagAlignTestCase(unittest.TestCase):
def setUp(self):
self.mu1 = np.array([0, 0, 0])
self.sig = 2 * np.eye(3)
self.mu2 = np.array([5, 5, 5])
self.clust1 = DPCluster(.5, self.mu1, self.sig)
self.clust2 = DPCluster(.5, self.mu2, self.sig)
self.clusters = [self.clust1, self.clust2]
self.x = DPMixture(self.clusters, niter=1, identified=True)
self.Diag = DiagonalAlignData(self.x, size=100000)
self.Comp = CompAlignData(self.x, size=100000)
self.Full = FullAlignData(self.x, size=200000)
def testDiagAlign(self):
y = self.x + np.array([1, -1, 1])
lb = np.array([0.1,0.1,0.1,-np.inf,-np.inf,-np.inf])
#a, b = self.Diag.align(y, method='TNC', bounds=np.array([(0.5, 2), (None, None)]), tol=1e-8, options={'disp': False})
a, b, f, s, m = self.Diag.align(y, solver='ralg', stencil=3)
assert s, 'failed to converge'
npt.assert_array_almost_equal(a, np.eye(3), decimal=1)
npt.assert_array_almost_equal(b, np.array([-1, 1, -1]), decimal=1)
def testCompAlign(self):
m = np.array([[1, 0, .2], [0, 1, 0], [0, 0, 1]])
lb = np.ones(6)*-.2
ub = np.ones(6)*.2
y = self.x * m
a, b, f, s, _ = self.Comp.align(y, lb=lb, ub=ub, fEnough=0, ftol=1e-16, xtol=1e-16)
assert s, 'failed to converge'
npt.assert_array_almost_equal(a, np.linalg.inv(m), decimal=1)
npt.assert_array_almost_equal(b, np.array([0, 0, 0]), decimal=1)
npt.assert_array_almost_equal((y * a).mus, self.x.mus, decimal=1)
def testFullAlign(self):
m = np.array([[1, 0, .2], [0, 1, 0], [0, 0, 1]])
y = self.x * m
a, b, f, s, _ = self.Full.align(y, ftol=1e-10, gtol=1e-10, xtol=1e-10)
assert s, 'failed to converge'
npt.assert_array_almost_equal(a, np.linalg.inv(m), decimal=1)
npt.assert_array_almost_equal(b, np.array([0, 0, 0]), decimal=1)
npt.assert_array_almost_equal(((y * a) + b).mus, self.x.mus, decimal=1)
def testFullAlignExclude(self):
m = np.array([[1, 0, .2], [0, 1, 0], [0, 0, 1]])
y = self.x * m
x0 = np.hstack((np.eye(3).flatten(), np.zeros(3)))
Full = FullAlignData(self.x, size=200000, exclude=[0])
a, b, f, s, _ = Full.align(y, x0=x0, ftol=1e-8)
npt.assert_array_almost_equal(a[0], np.array([1,0,0]), decimal=1)
npt.assert_array_almost_equal(a[:,0], np.array([1,0,0]), decimal=1)
class DiagAlignTestCase(unittest.TestCase):
def setUp(self):
self.mu1 = np.array([0, 0, 0])
self.sig = 2 * np.eye(3)
self.mu2 = np.array([5, 5, 5])
self.clust1 = DPCluster(.5, self.mu1, self.sig)
self.clust2 = DPCluster(.5, self.mu2, self.sig)
self.clusters = [self.clust1, self.clust2]
self.x = DPMixture(self.clusters, niter=1, identified=True)
self.diag = DiagonalAlignData(self.x, size=100000)
self.comp = CompAlignData(self.x, size=100000)
self.full = FullAlignData(self.x, size=200000)
def test_diag_align(self):
y = self.x + np.array([1, -1, 1])
a, b, f, s, m = self.diag.align(y, solver='ralg')
assert s, 'failed to converge'
npt.assert_array_almost_equal(a, np.eye(3), decimal=1)
npt.assert_array_almost_equal(b, np.array([-1, 1, -1]), decimal=1)
def test_comp_align(self):
m = np.array([[1, 0, .2], [0, 1, 0], [0, 0, 1]])
y = self.x * m
a, b, f, s, _ = self.comp.align(y)
assert s, 'failed to converge'
npt.assert_array_almost_equal(a, np.linalg.inv(m), decimal=1)
npt.assert_array_almost_equal(b, np.array([0, 0, 0]), decimal=1)
npt.assert_array_almost_equal((y * a).mus, self.x.mus, decimal=1)
def test_full_align(self):
m = np.array([[1, 0, .2], [0, 1, 0], [0, 0, 1]])
y = self.x * m
a, b, f, s, _ = self.full.align(y)
assert s, 'failed to converge'
npt.assert_array_almost_equal(a, np.linalg.inv(m), decimal=1)
npt.assert_array_almost_equal(b, np.array([0, 0, 0]), decimal=1)
npt.assert_array_almost_equal(((y * a) + b).mus, self.x.mus, decimal=1)
def test_full_align_exclude(self):
m = np.array([[1, 0, .2], [0, 1, 0], [0, 0, 1]])
y = self.x * m
x0 = np.hstack((np.eye(3).flatten(), np.zeros(3)))
full = FullAlignData(self.x, size=200000, exclude=[0])
a, b, f, s, _ = full.align(y, x0=x0)
npt.assert_array_almost_equal(a[0], np.array([1, 0, 0]), decimal=1)
npt.assert_array_almost_equal(a[:, 0], np.array([1, 0, 0]), decimal=1)
class DiagAlignTestCase(unittest.TestCase):
def setUp(self):
self.mu1 = np.array([0, 0, 0])
self.sig = 2 * np.eye(3)
self.mu2 = np.array([5, 5, 5])
self.clust1 = DPCluster(.5, self.mu1, self.sig)
self.clust2 = DPCluster(.5, self.mu2, self.sig)
self.clusters = [self.clust1, self.clust2]
self.x = DPMixture(self.clusters, niter=1, identified=True)
self.diag = DiagonalAlignData(self.x, size=100000)
self.comp = CompAlignData(self.x, size=100000)
self.full = FullAlignData(self.x, size=200000)
def test_diag_align(self):
y = self.x + np.array([1, -1, 1])
a, b, f, s, m = self.diag.align(y, solver='ralg')
assert s, 'failed to converge'
npt.assert_array_almost_equal(a, np.eye(3), decimal=1)
npt.assert_array_almost_equal(b, np.array([-1, 1, -1]), decimal=1)
def test_comp_align(self):
m = np.array([[1, 0, .2], [0, 1, 0], [0, 0, 1]])
y = self.x * m
a, b, f, s, _ = self.comp.align(y)
assert s, 'failed to converge'
npt.assert_array_almost_equal(a, np.linalg.inv(m), decimal=1)
npt.assert_array_almost_equal(b, np.array([0, 0, 0]), decimal=1)
npt.assert_array_almost_equal((y * a).mus, self.x.mus, decimal=1)
def test_full_align(self):
m = np.array([[1, 0, .2], [0, 1, 0], [0, 0, 1]])
y = self.x * m
a, b, f, s, _ = self.full.align(y)
assert s, 'failed to converge'
npt.assert_array_almost_equal(a, np.linalg.inv(m), decimal=1)
npt.assert_array_almost_equal(b, np.array([0, 0, 0]), decimal=1)
npt.assert_array_almost_equal(((y * a) + b).mus, self.x.mus, decimal=1)
def test_full_align_exclude(self):
m = np.array([[1, 0, .2], [0, 1, 0], [0, 0, 1]])
y = self.x * m
x0 = np.hstack((np.eye(3).flatten(), np.zeros(3)))
full = FullAlignData(self.x, size=200000, exclude=[0])
a, b, f, s, _ = full.align(y, x0=x0)
npt.assert_array_almost_equal(a[0], np.array([1, 0, 0]), decimal=1)
npt.assert_array_almost_equal(a[:, 0], np.array([1, 0, 0]), decimal=1)