Refactor stack functions to use a mutable list for improved performance

src/stack.v

@@ -26,7 +26,10 @@ pub fn dstack[T](ts []&Tensor[T]) !&Tensor[T] {
 		return error('dstack was given arrays with more than two dimensions')
 	}
 	if first_tensor.rank() == 1 {
-		next_ts := ts.map(it.reshape[T]([1, it.size, 1])!)
+		mut next_ts := []&Tensor[T]{cap: ts.len}
+		for t in ts {
+			next_ts << t.reshape[T]([1, t.size, 1])!
+		}
 		return concatenate[T](next_ts, axis: 2)
 	} else {
 		mut next_ts := []&Tensor[T]{cap: ts.len}
@@ -49,7 +52,10 @@ pub fn column_stack[T](ts []&Tensor[T]) !&Tensor[T] {
 	}
 
 	if first_tensor.rank() == 1 {
-		next_ts := ts.map(it.reshape[T]([it.size, 1])!)
+		mut next_ts := []&Tensor[T]{cap: ts.len}
+		for t in ts {
+			next_ts << t.reshape[T]([t.size, 1])!
+		}
 		return concatenate[T](next_ts, axis: 1)
 	}
 
@@ -59,7 +65,10 @@ pub fn column_stack[T](ts []&Tensor[T]) !&Tensor[T] {
 // stack join a sequence of arrays along a new axis.
 pub fn stack[T](ts []&Tensor[T], data AxisData) !&Tensor[T] {
 	assert_shape[T](ts[0].shape, ts)!
-	expanded := ts.map(it.expand_dims[T](data)!)
+	mut expanded := []&Tensor[T]{cap: ts.len}
+	for t in ts {
+		expanded << t.expand_dims[T](data)!
+	}
 	return concatenate[T](expanded, data)
 }