diff --git a/EC.jl b/EC.jl
index 01797ed..61cadb6 100644
--- a/EC.jl
+++ b/EC.jl
@@ -23,9 +23,11 @@ end
 If a list is provided for ref_eval, they are used as reference values for picking the closest eigenvalues at each sampling point.
 If a single number is provided for ref_eval, it is used as a reference for the first point, and the previous eigenvalue is used as the reference for each successive point.
 If pseudo_inv_rtol > 0, the GEVP is avoided using Moore-Penrose psuedoinverse, using this value as the relative tolerance for dropping redundant vectors.
-If orthonormalize_threshold > 0, Gram-Schmidt orthonormalization is performed, overriding Moore-Penrose, using this value as the threshold for dropping redundant vectors.
+If orthonormalize_threshold > 0, Gram-Schmidt orthonormalization is performed, using this value as the threshold for dropping redundant vectors.
 If both are = 0, GEVP is solved instead."
-function train!(EC::affine_EC, c_vals; ref_eval=-10.0, pseudo_inv_rtol=1e-6, orthonormalize_threshold=0, CAEC=false, verbose=true, tol=1e-5)
+function train!(EC::affine_EC, c_vals; ref_eval=-10.0, pseudo_inv_rtol=1e-6, gram_schmidt_threshold=0, CAEC=false, verbose=true, tol=1e-5)
+    @assert pseudo_inv_rtol == 0 || gram_schmidt_threshold == 0 "Cannot perform both psuedoinverse and Gram-Schmidt"
+
     training_vecs = Vector{ComplexF64}[]
 
     for c in c_vals
@@ -50,23 +52,21 @@ function train!(EC::affine_EC, c_vals; ref_eval=-10.0, pseudo_inv_rtol=1e-6, ort
 
     CAEC && append!(training_vecs, conj.(training_vecs))
 
-    if orthonormalize_threshold ≠ 0
-        training_vecs = gram_schmidt!(training_vecs, EC.weights, orthonormalize_threshold; verbose=verbose)
+    if gram_schmidt_threshold ≠ 0
+        training_vecs = gram_schmidt!(training_vecs, EC.weights, gram_schmidt_threshold; verbose=verbose)
     end
 
     EC_basis = hcat(training_vecs...)
     weights_mat = spdiagm(EC.weights)
     EC.H0_EC = transpose(EC_basis) * weights_mat * EC.H0 * EC_basis
     EC.H1_EC = transpose(EC_basis) * weights_mat * EC.H1 * EC_basis
+    EC.N_EC = transpose(EC_basis) * weights_mat * EC_basis
 
-    if orthonormalize_threshold == 0
-        EC.N_EC = transpose(EC_basis) * weights_mat * EC_basis
-        if pseudo_inv_rtol > 0
-            inv_N_EC = pinv(EC.N_EC; rtol=pseudo_inv_rtol)
-            EC.H0_EC = inv_N_EC * EC.H0_EC
-            EC.H1_EC = inv_N_EC * EC.H1_EC
-            EC.N_EC = nothing
-        end
+    if pseudo_inv_rtol > 0
+        inv_N_EC = pinv(EC.N_EC; rtol=pseudo_inv_rtol)
+        EC.H0_EC = inv_N_EC * EC.H0_EC
+        EC.H1_EC = inv_N_EC * EC.H1_EC
+        EC.N_EC = nothing
     end
 
     EC.trained = true