CLN (trapping): No need to check matrix we just created

pysindy/optimizers/trapping_sr3.py

@@ -453,8 +453,7 @@ def _set_Ptensors(
         N = self.n_features
         # If bias term is included, need to shift the tensor index
         PC_tensor = np.zeros((n_targets, n_targets, N))
-        if N > int((n_targets**2 + 3 * n_targets) / 2.0):
-            self._include_bias = True
+        if self._include_bias:
             PC_tensor[range(n_targets), range(n_targets), 0] = 1.0
 
         lib = PolynomialLibrary(2, include_bias=self._include_bias).fit(
@@ -470,109 +469,6 @@ def _set_Ptensors(
 
         return PC_tensor, PL_tensor_unsym, PL_tensor, PQ_tensor, PT_tensor, PM_tensor
 
-    def _bad_PL(self, PL):
-        """Check if PL tensor is properly defined"""
-        tol = 1e-10
-        return np.any((np.transpose(PL, [1, 0, 2, 3]) - PL) > tol)
-
-    def _bad_PQ(self, PQ):
-        """Check if PQ tensor is properly defined"""
-        tol = 1e-10
-        return np.any((np.transpose(PQ, [0, 2, 1, 3, 4]) - PQ) > tol)
-
-    def _bad_PT(self, PT):
-        """Check if PT tensor is properly defined"""
-        tol = 1e-10
-        return np.any((np.transpose(PT, [2, 1, 0, 3, 4]) - PT) > tol)
-
-    def _check_P_matrix(self, r, n_features, N):
-        """Check if P tensor is properly defined"""
-        # If these tensors are not passed, or incorrect shape, assume zeros
-        if self.PQ_ is None:
-            self.PQ_ = np.zeros((r, r, r, r, n_features))
-            warnings.warn(
-                "The PQ tensor (a requirement for the stability promotion) was"
-                " not set, so setting this tensor to all zeros. "
-            )
-        elif (self.PQ_).shape != (r, r, r, r, n_features) and (self.PQ_).shape != (
-            r,
-            r,
-            r,
-            r,
-            N,
-        ):
-            self.PQ_ = np.zeros((r, r, r, r, n_features))
-            warnings.warn(
-                "The PQ tensor (a requirement for the stability promotion) was"
-                " initialized with incorrect dimensions, "
-                "so setting this tensor to all zeros "
-                "(with the correct dimensions). "
-            )
-        if self.PT_ is None:
-            self.PT_ = np.zeros((r, r, r, r, n_features))
-            warnings.warn(
-                "The PT tensor (a requirement for the stability promotion) was"
-                " not set, so setting this tensor to all zeros. "
-            )
-        elif (self.PT_).shape != (r, r, r, r, n_features) and (self.PT_).shape != (
-            r,
-            r,
-            r,
-            r,
-            N,
-        ):
-            self.PT_ = np.zeros((r, r, r, r, n_features))
-            warnings.warn(
-                "The PT tensor (a requirement for the stability promotion) was"
-                " initialized with incorrect dimensions, "
-                "so setting this tensor to all zeros "
-                "(with the correct dimensions). "
-            )
-        if self.PL_ is None:
-            self.PL_ = np.zeros((r, r, r, n_features))
-            warnings.warn(
-                "The PL tensor (a requirement for the stability promotion) was"
-                " not set, so setting this tensor to all zeros. "
-            )
-        elif (self.PL_).shape != (r, r, r, n_features) and (self.PL_).shape != (
-            r,
-            r,
-            r,
-            N,
-        ):
-            self.PL_ = np.zeros((r, r, r, n_features))
-            warnings.warn(
-                "The PL tensor (a requirement for the stability promotion) was"
-                " initialized with incorrect dimensions, "
-                "so setting this tensor to all zeros "
-                "(with the correct dimensions). "
-            )
-
-        # Check if the tensor symmetries are properly defined
-        if self._bad_PL(self.PL_):
-            raise ValueError("PL tensor was passed but the symmetries are not correct")
-        if self._bad_PQ(self.PQ_):
-            raise ValueError("PQ tensor was passed but the symmetries are not correct")
-        if self._bad_PT(self.PT_):
-            raise ValueError("PT tensor was passed but the symmetries are not correct")
-
-        # If PL/PQ/PT finite and correct, so trapping theorem is being used,
-        # then make sure library is quadratic and correct shape
-        if (
-            np.any(self.PL_ != 0.0)
-            or np.any(self.PQ_ != 0.0)
-            or np.any(self.PT_ != 0.0)
-        ) and n_features != N:
-            print(
-                "The feature library is the wrong shape or not quadratic, "
-                "so please correct this if you are attempting to use the "
-                "trapping algorithm with the stability term included. Setting "
-                "PL and PQ tensors to zeros for now."
-            )
-            self.PL_ = np.zeros((r, r, r, n_features))
-            self.PQ_ = np.zeros((r, r, r, r, n_features))
-            self.PT_ = np.zeros((r, r, r, r, n_features))
-
     def _update_coef_constraints(self, H, x_transpose_y, P_transpose_A, coef_sparse):
         """Solves the coefficient update analytically if threshold = 0"""
         g = x_transpose_y + P_transpose_A / self.eta
@@ -786,6 +682,8 @@ def _reduce(self, x, y):
         self.n_features = n_features
         r = y.shape[1]
         N = n_features  # int((r ** 2 + 3 * r) / 2.0)
+        if N > int((r**2 + 3 * r) / 2.0):
+            self._include_bias = True
 
         if self.mod_matrix is None:
             self.mod_matrix = np.eye(r)
@@ -800,8 +698,6 @@ def _reduce(self, x, y):
             self.PT_,
             self.PM_,
         ) = self._set_Ptensors(r)
-        # make sure dimensions/symmetries are correct
-        self._check_P_matrix(r, n_features, N)
 
         # Set initial coefficients
         if self.use_constraints and self.constraint_order.lower() == "target":