still refining regress with multiple outputs

pycse/PYCSE.py

@@ -136,14 +136,16 @@ def regress(A, y, alpha=0.05, *args, **kwargs):
         CI = sT * se
 
         # bint is a little tricky, and depends on the shape of the output.
-        if len(y.shape) == 1:
-            bint = np.array([(b - CI, b + CI)])
-        else:
-            nrows, ncols = y.shape
-            bint = []
-            for i in range(ncols):
-                bint += [[b - CI, b + CI]]
-            bint = np.array(bint)
+        bint = np.array([(b - CI, b + CI)])
+        # if len(y.shape) == 1:
+        #    bint = np.array([(b - CI, b + CI)])
+        # else:
+        #     nrows, ncols = y.shape
+        #     bint = []
+        #     print(f'bint: {b.shape}, {CI.shape}')
+        #     for i in range(ncols):
+        #         bint += [[b[:, i] - CI[:, i], b[:, i] + CI[:, i]]]
+        #     bint = np.array(bint)
 
     return (b, bint.squeeze(), se)
 
@@ -193,7 +195,12 @@ def predict(X, y, pars, XX, alpha=0.05, ub=1e-5, ef=1.05):
         pred_se = np.sqrt([_mse * np.diag(gprime @ I_fisher @ gprime.T) for _mse in mse]).T
         # This happens if mse is a single number
     except TypeError:
-        pred_se = np.sqrt(mse * np.diag(gprime @ I_fisher @ gprime.T)).T
+        # you need at least 1d to get a diagonal. This line is needed because
+        # there is a case where there is one prediction where this product leads
+        # to a scalar quantity and we need to upgrade it to 1d to avoid an
+        # error.
+        gig = np.atleast_1d(gprime @ I_fisher @ gprime.T)
+        pred_se = np.sqrt(mse * np.diag(gig)).T
 
     tval = t.ppf(1.0 - alpha / 2.0, dof)