Stable cov

pyest/gm/gm.py

@@ -6,6 +6,7 @@
 import numpy as np
 import pandas as pd
 from numpy import pi, sqrt
+from copy import copy
 from numpy.random import rand, randn
 from scipy.stats._multivariate import _LOG_2PI, _PSD, _squeeze_output
 from scipy.stats import Covariance, _covariance
@@ -871,18 +872,30 @@ def mean(self):
         return np.sum(self.w[:, np.newaxis] * self.m, axis=0)
 
     def cov(self):
-        """ return covariance of the distribution
+        """Compute the covariance matrix of the distribution.
 
         Returns
         -------
         ndarray
-          (nx,nx) full covariance matrix of distribution
+            (nx, nx) full covariance matrix of the distribution
         """
-        wsum = np.sum(self.w)
+        # Compute the mean of the distribution
         mean = self.mean()
-        return np.sum([
-            w/wsum*(P + np.outer(m, m)) for w, m, P in self
-        ], axis=0) - np.outer(mean, mean)
+
+        w = copy(self.w)
+        w = w / np.sum(w)
+
+        # Extract individual covariances
+        covs = self.P
+
+        # Compute the difference between each mixand mean and the distribution mean
+        diffs = self.m - mean  # shape (nC, nx)
+
+        # Compute the outer product of diffs for each sample
+        outer_diffs = diffs[:, :, None] * diffs[:, None, :]  # shape (nC, nx, nx)
+
+        # Weighted sum of covariances and outer products
+        return np.sum(w[:, None, None] * (covs + outer_diffs), axis=0)
 
     def cdf(self, x, allow_singular=False):
         """ evaluate GM CDF at points x"""
@@ -1024,3 +1037,4 @@ def __init__(
         self.state_idxs = np.array(state_idxs)
         self.spectral_direction_only = spectral_direction_only
         self.variance_preserving = variance_preserving
+

tests/test_gm.py

@@ -8,6 +8,7 @@
 from pyest.sensors import ConvexPolyhedralFieldOfView
 from pyest.utils import fail
 import pytest
+from pyest.metrics import max_covariance_ratio
 
 
 def test_gm_equal_weights():
@@ -555,6 +556,57 @@ def test_init_mismatch_fail():
 
     fail(gm.GaussianMixture, ValueError, w, m, P)
 
+def test_cov():
+    # Initial covariance matrix P0 (6×6)
+    P0 = np.array([
+        [2.25e-06, 0, 0, 0, 0, 0],
+        [0, 2.25e-06, 0, 0, 0, 0],
+        [0, 0, 2.25e-06, 0, 0, 0],
+        [0, 0, 0, 1.77777778e-14, 0, 0],
+        [0, 0, 0, 0, 1.77777778e-14, 0],
+        [0, 0, 0, 0, 0, 1.77777778e-14]
+    ])
+
+    # Initial large mean vector m0
+    m0 = np.array([
+        -3.95911227e+05, -1.80422801e+05, -8.03243134e+04,
+         5.48813474e-01, -1.02480997e+00, -5.93612009e-01
+    ])
+
+    # Single-component GMM (weight 1)
+    weights = np.array([1])
+    p0 = gm.GaussianMixture(weights, m0, P0)
+
+    # Options for Gaussian splitting
+    split_opts = gm.GaussSplitOptions(
+        L=3,              # number of components created per split
+        lam=1e-3,         # scaling parameter
+        recurse_depth=3,  # maximum recursion depth
+        min_weight=-np.inf
+    )
+
+    # No weight threshold
+    split_tol = -np.inf
+
+    # Arguments passed to recursive_split: splitting method + tolerance
+    recursive_split_args = (gm.split.id_variance, split_tol)
+
+    # Apply recursive splitting to the initial GMM
+    p0 = gm.split.recursive_split(p0, split_opts, *recursive_split_args)
+
+    # Cholesky decomposition of the resulting GMM covariance
+    Schol_gmm = np.atleast_2d(np.linalg.cholesky(p0.cov()))
+
+    # Cholesky decomposition of the original covariance
+    Schol_P0 = np.atleast_2d(np.linalg.cholesky(P0))
+
+    # Compute the maximum covariance ratio between the two matrices
+    max_cov_ratio = abs(max_covariance_ratio(Schol_gmm, Schol_P0))
+
+    # Test: the covariance should not increase by more than 0.01%
+    assert max_cov_ratio <= 1.0001, f"max_cov_ratio is too large: {max_cov_ratio}"
 
 if __name__ == '__main__':
     pytest.main([__file__])
+
+