BMTI: combine standard and reg into a single function

Author: AldoGl

dadapy/density_advanced.py

@@ -273,47 +273,17 @@ def compute_density_BMTI(
         delta_F_inv_cov="uncorr",
         comp_log_den_err=False,
         mem_efficient=False,
-    ):
-        """Compute the log-density for each point using BMTI
-
-        Args:
-            delta_F_inv_cov (str): see compute_density_BMTI_reg docs.
-            comp_log_den_err (bool): see compute_density_BMTI_reg docs.
-            mem_efficient (bool): see compute_density_BMTI_reg docs.
-
-        """
-
-        # call compute_density_BMTI_reg with alpha=1 and log_den and log_den_err as arrays of ones
-        self.compute_density_BMTI_reg(
-            alpha=1.0,
-            log_den=np.ones(self.N),
-            log_den_err=np.ones(self.N),
-            delta_F_inv_cov=delta_F_inv_cov,
-            comp_log_den_err=comp_log_den_err,
-            mem_efficient=mem_efficient,
-        )
-
-    # ----------------------------------------------------------------------------------------------
-
-    def compute_density_BMTI_reg(
-        self,
-        alpha=0.1,
+        alpha=1,
         log_den=None,
         log_den_err=None,
-        delta_F_inv_cov="uncorr",
-        comp_log_den_err=False,
-        mem_efficient=False,
     ):
-        """Compute the log-density for each point using BMTI plus kstarNN estimator as a regulariser.
+        """Compute the log-density for each point using BMTI.
 
-        The regulariser log-density and its errors can be passed as arguments: log_den and log_den_err. If any of these
-        two is not specified, use kstarNN estimator as a regulariser.
+        If alpha<1, the algorithm also includes a regularisatin. The regulariser log-density and its errors can be
+        passed as arguments: log_den and log_den_err. If any of these two is not specified, use kstarNN estimator
+        as a regulariser.
 
         Args:
-            alpha (float): can take values from 0.0 to 1.0. Indicates the portion of BMTI in the sum of the likelihoods
-                alpha*L_BMTI + (1-alpha)*L_kstarNN. Setting alpha=1.0 corresponds to not reguarising BMTI.
-            log_den (np.ndarray(float)): size N. The array of the log-densities of the regulariser.
-            log_den_err (np.ndarray(float)): size N. The array of the log-density errors of the regulariser.
             delta_F_inv_cov (str): specify the method used to invert the cross-covariance matrix C of the log-density
                 deviations cov[deltaF_ij,deltaF_kl]. Currently implemented methods:
                     "uncorr" (default): all the deltaFs are assumed uncorrelated, i.e. C is assumed to be diagonal with
@@ -326,6 +296,10 @@ def compute_density_BMTI_reg(
             comp_log_den_err (bool): if True, compute the error on the BMTI estimates. Can be highly time consuming
             mem_efficient (bool): if True, use a sparse matrice to solve BMTI linear system (slower). If False, use a
                 dense NxN matrix; this is faster, but can require a great amount of memory if the system is large.
+            alpha (float): can take values from 0.0 to 1.0. Indicates the portion of BMTI in the sum of the likelihoods
+                alpha*L_BMTI + (1-alpha)*L_kstarNN. Setting alpha=1.0 corresponds to not reguarising BMTI.
+            log_den (np.ndarray(float)): size N. The array of the log-densities of the regulariser.
+            log_den_err (np.ndarray(float)): size N. The array of the log-density errors of the regulariser.
 
         """
 

tests/test_density_advanced/test_density_advanced.py

@@ -75,39 +75,7 @@ def test_compute_deltaFs():
     assert np.allclose(da.Fij_var_array, expected_Fij_var_array)
 
 
-# define the expected density
 expected_density_BMTI = np.array(
-    [
-        0.012505854084320398,
-        -1.4989243919120265,
-        -0.8325855985351576,
-        -1.8954811470419732,
-        -0.08608518234399808,
-        -1.377358160570784,
-        -2.2853275320451556,
-        -0.08077062180341209,
-        0.03151493142829422,
-        -2.295060446120319,
-        -0.485534023025263,
-        -1.5291208381769597,
-        -2.0291222925304333,
-        -2.507439558393103,
-        0.05236125958005627,
-        -0.6844157822716908,
-        -0.205568978673708,
-        -1.3777138853748458,
-        -1.2926910126536086,
-        -1.1630749466695476,
-        -1.9641366761139865,
-        -1.421685853814561,
-        -0.4840608241935639,
-        -0.9553572813490178,
-        -0.8380943495955488,
-    ]
-)
-
-
-expected_density_BMTI_reg = np.array(
     [
         -1.698780556695925,
         -3.189031310462691,
@@ -138,7 +106,7 @@ def test_compute_deltaFs():
 )
 
 
-def test_density_BMTI_reg():
+def test_density_BMTI():
     """Test the density_BMTI method."""
     filename = os.path.join(os.path.split(__file__)[0], "../2gaussians_in_2d.npy")
 
@@ -147,6 +115,6 @@ def test_density_BMTI_reg():
     da = DensityAdvanced(coordinates=X, maxk=10, verbose=True)
     da.compute_distances()
     da.set_id(2)
-    da.compute_density_BMTI_reg(alpha=0.99)
+    da.compute_density_BMTI(alpha=0.99)
 
-    assert np.allclose(da.log_den, expected_density_BMTI_reg)
+    assert np.allclose(da.log_den, expected_density_BMTI)