Some more Gram matrix emptyness safety

sparse_dot_mkl/_gram_matrix.py

@@ -283,6 +283,22 @@ def _gram_matrix(
     :rtype: scipy.sparse.csr_matrix, np.ndarray
     """
 
+    if _sps.issparse(matrix) and not (is_csr(matrix) or is_csc(matrix)):
+        raise ValueError(
+            "gram_matrix requires sparse matrix to be CSR or CSC format"
+        )
+    elif is_csc(matrix) and not cast:
+        raise ValueError(
+            "gram_matrix cannot use a CSC matrix unless cast=True"
+        )
+    elif out is not None and not dense:
+        raise ValueError(
+            "out argument cannot be used with sparse (dot) sparse "
+            "matrix multiplication"
+        )
+    elif out is not None and not isinstance(out, np.ndarray):
+        raise ValueError("out argument must be dense")
+
     # Check for edge condition inputs which result in empty outputs
     if _empty_output_check(matrix, matrix):
         debug_print(
@@ -294,8 +310,14 @@ def _gram_matrix(
             if transpose
             else (matrix.shape[0], matrix.shape[0])
         )
-        output_func = _sps.csr_matrix if _sps.isspmatrix(matrix) else np.zeros
-        return output_func(output_shape, dtype=matrix.dtype)
+        if out is None:
+            output_func = np.zeros if dense else _sps.csr_matrix
+            return output_func(output_shape, dtype=matrix.dtype)
+        elif out_scalar is not None and not out_scalar:
+            out.fill(0)
+        elif out_scalar is not None:
+            out *= out_scalar
+        return out
 
     if np.iscomplexobj(matrix):
         raise ValueError(
@@ -304,15 +326,7 @@ def _gram_matrix(
 
     matrix = _type_check(matrix, cast=cast)
 
-    if _sps.issparse(matrix) and not (is_csr(matrix) or is_csc(matrix)):
-        raise ValueError(
-            "gram_matrix requires sparse matrix to be CSR or CSC format"
-        )
-    elif is_csc(matrix) and not cast:
-        raise ValueError(
-            "gram_matrix cannot use a CSC matrix unless cast=True"
-        )
-    elif not _sps.issparse(matrix):
+    if not _sps.issparse(matrix):
         return _gram_matrix_dense_to_dense(
             matrix,
             aat=transpose,
@@ -326,11 +340,6 @@ def _gram_matrix(
             out=out,
             out_scalar=out_scalar
         )
-    elif out is not None:
-        raise ValueError(
-            "out argument cannot be used with sparse (dot) sparse "
-            "matrix multiplication"
-        )
     else:
         return _gram_matrix_sparse(
             matrix,