Added HyperhemisphericalGridFilter

pyrecest/filters/hyperhemispherical_grid_filter.py

@@ -0,0 +1,83 @@
+import numpy as np
+import warnings
+from .abstract_grid_filter import AbstractGridFilter
+from .abstract_hyperhemispherical_filter import AbstractHyperhemisphericalFilter
+from pyrecest.distributions.hypersphere_subset.hyperhemispherical_grid_distribution import HyperhemisphericalGridDistribution
+from pyrecest.distributions.conditional.sd_half_cond_sd_half_grid_distribution import SdHalfCondSdHalfGridDistribution
+from pyrecest.distributions import BinghamDistribution
+from pyrecest.distributions import WatsonDistribution
+from pyrecest.distributions import VonMisesFisherDistribution
+from pyrecest.distributions import HypersphericalMixture
+from pyrecest.distributions import HyperhemisphericalUniformDistribution
+from pyrecest.distributions import AbstractHyperhemisphericalDistribution
+from pyrecest.distributions import HyperhemisphericalWatsonDistribution
+
+class HyperhemisphericalGridFilter(AbstractGridFilter, AbstractHyperhemisphericalFilter):
+    def __init__(self, no_of_coefficients, dim, grid_type='eq_point_set_symm'):
+        self.gd = HyperhemisphericalGridDistribution.from_distribution(
+            HyperhemisphericalUniformDistribution(dim), no_of_coefficients, grid_type)
+
+    def set_state(self, new_state):
+        assert self.dim == new_state.dim
+        assert isinstance(new_state, AbstractHyperhemisphericalDistribution)
+        self.gd = new_state
+
+    def predict_identity(self, d_sys):
+        assert isinstance(d_sys, AbstractHyperhemisphericalDistribution)
+        sd_half_cond_sd_half = HyperhemisphericalGridFilter.sys_noise_to_transition_density(
+            d_sys, self.gd.grid_values.shape[0])
+        self.predict_nonlinear_via_transition_density(sd_half_cond_sd_half)
+
+    def update_identity(self, meas_noise, z=None):
+        assert isinstance(meas_noise, AbstractHyperhemisphericalDistribution)
+        if not z==None:
+            measNoise = measNoise.setMode(z)
+        curr_grid = self.gd.get_grid()
+        self.gd = self.gd.multiply(HyperhemisphericalGridDistribution(curr_grid, 2 * meas_noise.pdf(curr_grid).T))
+
+    def update_nonlinear(self, likelihood, z):
+        self.gd.grid_values = self.gd.grid_values * likelihood(z, self.gd.get_grid()).T
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", category=RuntimeWarning)
+            self.gd = self.gd.normalize()
+
+    def predict_nonlinear_via_transition_density(self, f_trans):
+        assert np.array_equal(self.gd.get_grid(), f_trans.get_grid()), \
+            "fTrans is using an incompatible grid."
+        self.gd = self.gd.normalize()
+        grid_values_new = self.gd.get_manifold_size() / self.gd.grid_values.shape[0] * f_trans.grid_values.dot(
+            self.gd.grid_values)
+        self.gd = HyperhemisphericalGridDistribution(self.gd.get_grid(), grid_values_new)
+
+    def get_point_estimate(self):
+        gd_full_sphere = self.gd.to_full_sphere()
+        p = BinghamDistribution.fit(gd_full_sphere.get_grid(), gd_full_sphere.grid_values.T / np.sum(
+            gd_full_sphere.grid_values)).mode()
+        if p[-1] < 0:
+            p = -p
+        return p
+
+    @staticmethod
+    def sys_noise_to_transition_density(d_sys, no_grid_points):
+        if isinstance(d_sys, AbstractDistribution):
+            if isinstance(d_sys, (HyperhemisphericalWatsonDistribution, WatsonDistribution)):
+                def trans(xkk, xk):
+                    return np.array([2 * WatsonDistribution(xk[:, i], d_sys.kappa).pdf(xkk) for i in range(xk.shape[1])]).T
+
+            elif (isinstance(d_sys, HypersphericalMixture) and len(d_sys.dists) == 2 and
+                    np.all(d_sys.w == 0.5) and np.array_equal(d_sys.dists[0].mu, -d_sys.dists[1].mu) and
+                    d_sys.dists[0].kappa == d_sys.dists[1].kappa):
+                def trans(xkk, xk):
+                    return np.array([(VonMisesFisherDistribution(xk[:, i], d_sys.dists[0].kappa).pdf(xkk) +
+                                    VonMisesFisherDistribution(xk[:, i], d_sys.dists[0].kappa).pdf(-xkk))
+                                    for i in range(xk.shape[1])]).T
+            else:
+                raise ValueError("Distribution not supported for predict identity. Must be zonal (rotationally symmetric around last dimension)")
+
+            print("PredictIdentity:Inefficient - Using inefficient prediction. Consider precalculating the SdHalfCondSdHalfGridDistribution and using predictNonlinearViaTransitionDensity.")
+            sd_half_cond_sd_half = SdHalfCondSdHalfGridDistribution.from_function(trans, no_grid_points, True, 'eq_point_set_symm', 2 * d_sys.dim)
+            return sd_half_cond_sd_half
+
+        else:
+            raise TypeError("d_sys must be an instance of AbstractDistribution")
+

pyrecest/tests/filters/test_hyperhemispherical_grid_filter.py

@@ -0,0 +1,96 @@
+import unittest
+import numpy as np
+from pyrecest.filters.hyperhemispherical_grid_filter import HyperhemisphericalGridFilter
+from pyrecest.distributions.hypersphere_subset.hyperhemispherical_grid_distribution import HyperhemisphericalGridDistribution
+from pyrecest.distributions.conditional.sd_half_cond_sd_half_grid_distribution import SdHalfCondSdHalfGridDistribution
+from pyrecest.distributions import BinghamDistribution, HypersphericalMixture, VonMisesFisherDistribution
+
+class HyperhemisphericalGridFilterTest(unittest.TestCase):
+    def test_set_state_s2(self):
+        np.random.seed(0)
+        no_grid_points = 1001
+        sgf = HyperhemisphericalGridFilter(no_grid_points, 3)
+
+        self.assertEqual(sgf.get_estimate().grid_values.shape, (no_grid_points, 1))
+
+        # Test if it is uniform at the beginning
+        self.assertAlmostEqual(np.sum(np.abs(sgf.get_estimate().grid_values - (1 / sgf.get_estimate().get_manifold_size() * np.ones((no_grid_points, 1))))), 0, delta=1e-13)
+
+        M = np.eye(3)
+        Z = np.array([-2, -1, 0]).reshape(-1, 1)
+        bd = BinghamDistribution(Z, M)
+        bd.F = bd.F * bd.integrate_numerically()
+
+        sgd_state = HyperhemisphericalGridDistribution.from_distribution(bd, no_grid_points)
+        self.assertIsInstance(sgf.gd, HyperhemisphericalGridDistribution)
+        sgf.set_state(sgd_state)
+        self.assertIsInstance(sgf.gd, HyperhemisphericalGridDistribution)
+
+        # Verify that it is no longer a uniform distribution
+        self.assertGreater(np.sum(np.abs(sgf.get_estimate().grid_values - (1 / sgf.get_estimate().get_manifold_size()))), 60)
+
+        # Verify estimate matches a mode of the Bingham
+        self.assertAlmostEqual(np.min(np.linalg.norm(sgf.get_point_estimate() - np.hstack((bd.mode(), -bd.mode())), axis=0)), 0, delta=1e-11)
+
+        # Verify errors
+        full_grid = sgd_state.get_grid()
+        sgd_state.grid = full_grid[:, -1]
+        sgd_state.grid_values = sgd_state.grid_values[:-1]
+        self.assertIsInstance(sgf.gd, HyperhemisphericalGridDistribution)
+        sgf_tmp = sgf.copy()
+
+        with self.assertRaises(ValueError):
+            sgf_tmp.set_state(sgd_state)
+
+        with self.assertRaises(ValueError):
+            sgf.set_state(bd)
+
+    def test_set_state_s3(self):
+        no_grid_points = 1001
+        sgf = HyperhemisphericalGridFilter(no_grid_points, 4)
+        self.assertEqual(sgf.get_estimate().grid_values.shape, (no_grid_points, 1))
+
+        # Test if it is uniform at the beginning
+        self.assertAlmostEqual(np.sum(np.abs(np.diff(sgf.get_estimate().grid_values.T))), 0)
+
+        M = np.eye(4)
+        Z = np.array([-2, -1, -0.5, 0]).reshape(-1, 1)
+        bd = BinghamDistribution(Z, M)
+        bd.F = bd.F * bd.integrate_numerically()
+
+        sgd_state = HyperhemisphericalGridDistribution.from_distribution(bd, no_grid_points)
+        sgf.set_state(sgd_state)
+
+        # Verify that it is no longer a uniform distribution
+        self.assertGreater(np.sum(np.abs(np.diff(sgf.get_estimate().grid_values.T))), 5)
+    def test_predict_converges_to_uniform_S2_S3(self):
+        test_predict_converges_to_uniform(3, 501, [-2, -1, 0], 3, 5e-5, 'eq_point_set_symm', 6)
+        test_predict_converges_to_uniform(4, 1001, [-2, -1, -0.5, 0], 5, 1e-3, 'eq_point_set', 8)
+        def test_predict_converges_to_uniform(dim, no_grid_points, z_values, tol_verify_greater, tol_verify_equal, eq_point_set_type, eq_point_set_arg):
+            sgf = HyperhemisphericalGridFilter(no_grid_points, dim)
+            M = np.eye(dim)
+            Z = np.array(z_values).reshape(-1, 1)
+            bd = BinghamDistribution(Z, M)
+            bd.F = bd.F * bd.integrate_numerically()
+            sgf.set_state(HyperhemisphericalGridDistribution.from_distribution(bd, no_grid_points))
+
+            # Verify that it is not a uniform distribution
+            assert sum(abs(np.diff(sgf.get_estimate().grid_values.T))) > tol_verify_greater
+
+            # Predict 10 times with VM-distributed noise
+            def trans(xkk, xk):
+                return 2 * np.hstack([HypersphericalMixture([VonMisesFisherDistribution(xk[:, i], 1), VonMisesFisherDistribution(-xk[:, i], 1)], [0.5, 0.5]).pdf(xkk) for i in range(xk.shape[1])])
+
+            sdsd = SdHalfCondSdHalfGridDistribution.from_function(trans, no_grid_points, True, eq_point_set_type, eq_point_set_arg)
+            manifold_size = sgf.get_estimate().get_manifold_size()
+
+            for i in range(10):
+                values_alternative_formula = (manifold_size / sgf.get_estimate().get_grid().shape[1]) * np.sum(sgf.get_estimate().grid_values.T * sdsd.grid_values, axis=1)
+                sgf.predict_nonlinear_via_transition_density(sdsd)
+                assert np.allclose(sgf.get_estimate().grid_values, values_alternative_formula, atol=1e-12)
+
+            # Verify that it is approximately uniform now
+            assert np.isclose(sum(abs(np.diff(sgf.get_estimate().grid_values.T))), 0, atol=tol_verify_equal)
+
+if __name__ == '__main__':
+    unittest.main()