12b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang#include "main.h"
22b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang
32b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang#include <Eigen/CXX11/Tensor>
42b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang
52b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wangusing Eigen::Tensor;
62b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wangusing Eigen::RowMajor;
72b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang
82b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wangstatic void test_comparison_sugar() {
92b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  // we already trust comparisons between tensors, we're simply checking that
102b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  // the sugared versions are doing the same thing
112b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  Tensor<int, 3> t(6, 7, 5);
122b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang
132b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  t.setRandom();
142b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  // make sure we have at least one value == 0
152b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  t(0,0,0) = 0;
162b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang
172b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  Tensor<bool,0> b;
182b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang
192b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang#define TEST_TENSOR_EQUAL(e1, e2) \
202b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  b = ((e1) == (e2)).all();       \
212b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  VERIFY(b())
222b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang
232b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang#define TEST_OP(op) TEST_TENSOR_EQUAL(t op 0, t op t.constant(0))
242b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang
252b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  TEST_OP(==);
262b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  TEST_OP(!=);
272b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  TEST_OP(<=);
282b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  TEST_OP(>=);
292b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  TEST_OP(<);
302b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  TEST_OP(>);
312b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang#undef TEST_OP
322b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang#undef TEST_TENSOR_EQUAL
332b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang}
342b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang
352b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang
362b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wangstatic void test_scalar_sugar_add_mul() {
372b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  Tensor<float, 3> A(6, 7, 5);
382b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  Tensor<float, 3> B(6, 7, 5);
392b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  A.setRandom();
402b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  B.setRandom();
412b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang
422b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  const float alpha = 0.43f;
432b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  const float beta = 0.21f;
442b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  const float gamma = 0.14f;
452b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang
462b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  Tensor<float, 3> R = A.constant(gamma) + A * A.constant(alpha) + B * B.constant(beta);
472b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  Tensor<float, 3> S = A * alpha + B * beta + gamma;
482b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  Tensor<float, 3> T = gamma + alpha * A + beta * B;
492b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang
502b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  for (int i = 0; i < 6*7*5; ++i) {
512b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang    VERIFY_IS_APPROX(R(i), S(i));
522b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang    VERIFY_IS_APPROX(R(i), T(i));
532b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  }
542b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang}
552b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang
562b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wangstatic void test_scalar_sugar_sub_div() {
572b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  Tensor<float, 3> A(6, 7, 5);
582b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  Tensor<float, 3> B(6, 7, 5);
592b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  A.setRandom();
602b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  B.setRandom();
612b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang
622b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  const float alpha = 0.43f;
632b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  const float beta = 0.21f;
642b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  const float gamma = 0.14f;
652b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  const float delta = 0.32f;
662b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang
672b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  Tensor<float, 3> R = A.constant(gamma) - A / A.constant(alpha)
682b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang      - B.constant(beta) / B - A.constant(delta);
692b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  Tensor<float, 3> S = gamma - A / alpha - beta / B - delta;
702b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang
712b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  for (int i = 0; i < 6*7*5; ++i) {
722b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang    VERIFY_IS_APPROX(R(i), S(i));
732b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  }
742b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang}
752b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang
762b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wangvoid test_cxx11_tensor_sugar()
772b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang{
782b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  CALL_SUBTEST(test_comparison_sugar());
792b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  CALL_SUBTEST(test_scalar_sugar_add_mul());
802b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang  CALL_SUBTEST(test_scalar_sugar_sub_div());
812b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang}
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