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authorGravatar Benoit Steiner <benoit.steiner.goog@gmail.com>2014-08-20 17:00:50 -0700
committerGravatar Benoit Steiner <benoit.steiner.goog@gmail.com>2014-08-20 17:00:50 -0700
commit3d298da2696ac956a430f6fbef93bf65ada0d304 (patch)
treea4ccaec1aab6f8af017dddccc14cff594cc3c850 /unsupported/test/cxx11_tensor_broadcasting.cpp
parent9ac3c821ea3b956634116bcdf80bfab7d9a00d91 (diff)
Added support for broadcasting
Diffstat (limited to 'unsupported/test/cxx11_tensor_broadcasting.cpp')
-rw-r--r--unsupported/test/cxx11_tensor_broadcasting.cpp114
1 files changed, 114 insertions, 0 deletions
diff --git a/unsupported/test/cxx11_tensor_broadcasting.cpp b/unsupported/test/cxx11_tensor_broadcasting.cpp
new file mode 100644
index 000000000..9663912a4
--- /dev/null
+++ b/unsupported/test/cxx11_tensor_broadcasting.cpp
@@ -0,0 +1,114 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+static void test_simple_broadcasting()
+{
+ Tensor<float, 4> tensor(2,3,5,7);
+ tensor.setRandom();
+ array<ptrdiff_t, 4> broadcasts;
+ broadcasts[0] = 1;
+ broadcasts[1] = 1;
+ broadcasts[2] = 1;
+ broadcasts[3] = 1;
+
+ Tensor<float, 4> no_broadcast;
+ no_broadcast = tensor.broadcast(broadcasts);
+
+ VERIFY_IS_EQUAL(no_broadcast.dimension(0), 2);
+ VERIFY_IS_EQUAL(no_broadcast.dimension(1), 3);
+ VERIFY_IS_EQUAL(no_broadcast.dimension(2), 5);
+ VERIFY_IS_EQUAL(no_broadcast.dimension(3), 7);
+
+ for (int i = 0; i < 2; ++i) {
+ for (int j = 0; j < 3; ++j) {
+ for (int k = 0; k < 5; ++k) {
+ for (int l = 0; l < 7; ++l) {
+ VERIFY_IS_EQUAL(tensor(i,j,k,l), no_broadcast(i,j,k,l));
+ }
+ }
+ }
+ }
+
+ broadcasts[0] = 2;
+ broadcasts[1] = 3;
+ broadcasts[2] = 1;
+ broadcasts[3] = 4;
+ Tensor<float, 4> broadcast;
+ broadcast = tensor.broadcast(broadcasts);
+
+ VERIFY_IS_EQUAL(broadcast.dimension(0), 4);
+ VERIFY_IS_EQUAL(broadcast.dimension(1), 9);
+ VERIFY_IS_EQUAL(broadcast.dimension(2), 5);
+ VERIFY_IS_EQUAL(broadcast.dimension(3), 28);
+
+ for (int i = 0; i < 4; ++i) {
+ for (int j = 0; j < 9; ++j) {
+ for (int k = 0; k < 5; ++k) {
+ for (int l = 0; l < 28; ++l) {
+ VERIFY_IS_EQUAL(tensor(i%2,j%3,k%5,l%7), broadcast(i,j,k,l));
+ }
+ }
+ }
+ }
+}
+
+
+static void test_vectorized_broadcasting()
+{
+ Tensor<float, 3> tensor(8,3,5);
+ tensor.setRandom();
+ array<ptrdiff_t, 3> broadcasts;
+ broadcasts[0] = 2;
+ broadcasts[1] = 3;
+ broadcasts[2] = 4;
+
+ Tensor<float, 3> broadcast;
+ broadcast = tensor.broadcast(broadcasts);
+
+ VERIFY_IS_EQUAL(broadcast.dimension(0), 16);
+ VERIFY_IS_EQUAL(broadcast.dimension(1), 9);
+ VERIFY_IS_EQUAL(broadcast.dimension(2), 20);
+
+ for (int i = 0; i < 16; ++i) {
+ for (int j = 0; j < 9; ++j) {
+ for (int k = 0; k < 20; ++k) {
+ VERIFY_IS_EQUAL(tensor(i%8,j%3,k%5), broadcast(i,j,k));
+ }
+ }
+ }
+
+ tensor.resize(11,3,5);
+ tensor.setRandom();
+ broadcast = tensor.broadcast(broadcasts);
+
+ VERIFY_IS_EQUAL(broadcast.dimension(0), 22);
+ VERIFY_IS_EQUAL(broadcast.dimension(1), 9);
+ VERIFY_IS_EQUAL(broadcast.dimension(2), 20);
+
+ for (int i = 0; i < 22; ++i) {
+ for (int j = 0; j < 9; ++j) {
+ for (int k = 0; k < 20; ++k) {
+ VERIFY_IS_EQUAL(tensor(i%11,j%3,k%5), broadcast(i,j,k));
+ }
+ }
+ }
+}
+
+
+void test_cxx11_tensor_broadcasting()
+{
+ CALL_SUBTEST(test_simple_broadcasting());
+ CALL_SUBTEST(test_vectorized_broadcasting());
+}