|  | // 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::RowMajor; | 
|  | using Eigen::Tensor; | 
|  |  | 
|  | using Scalar = float; | 
|  |  | 
|  | using TypedLTOp = internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LT, true>; | 
|  | using TypedLEOp = internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LE, true>; | 
|  | using TypedGTOp = internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GT, true>; | 
|  | using TypedGEOp = internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GE, true>; | 
|  | using TypedEQOp = internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_EQ, true>; | 
|  | using TypedNEOp = internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_NEQ, true>; | 
|  |  | 
|  | static void test_orderings() { | 
|  | Tensor<Scalar, 3> mat1(2, 3, 7); | 
|  | Tensor<Scalar, 3> mat2(2, 3, 7); | 
|  |  | 
|  | mat1.setRandom(); | 
|  | mat2.setRandom(); | 
|  |  | 
|  | Tensor<bool, 3> lt(2, 3, 7); | 
|  | Tensor<bool, 3> le(2, 3, 7); | 
|  | Tensor<bool, 3> gt(2, 3, 7); | 
|  | Tensor<bool, 3> ge(2, 3, 7); | 
|  |  | 
|  | Tensor<Scalar, 3> typed_lt(2, 3, 7); | 
|  | Tensor<Scalar, 3> typed_le(2, 3, 7); | 
|  | Tensor<Scalar, 3> typed_gt(2, 3, 7); | 
|  | Tensor<Scalar, 3> typed_ge(2, 3, 7); | 
|  |  | 
|  | lt = mat1 < mat2; | 
|  | le = mat1 <= mat2; | 
|  | gt = mat1 > mat2; | 
|  | ge = mat1 >= mat2; | 
|  |  | 
|  | typed_lt = mat1.binaryExpr(mat2, TypedLTOp()); | 
|  | typed_le = mat1.binaryExpr(mat2, TypedLEOp()); | 
|  | typed_gt = mat1.binaryExpr(mat2, TypedGTOp()); | 
|  | typed_ge = mat1.binaryExpr(mat2, TypedGEOp()); | 
|  |  | 
|  | for (int i = 0; i < 2; ++i) { | 
|  | for (int j = 0; j < 3; ++j) { | 
|  | for (int k = 0; k < 7; ++k) { | 
|  | VERIFY_IS_EQUAL(lt(i, j, k), mat1(i, j, k) < mat2(i, j, k)); | 
|  | VERIFY_IS_EQUAL(le(i, j, k), mat1(i, j, k) <= mat2(i, j, k)); | 
|  | VERIFY_IS_EQUAL(gt(i, j, k), mat1(i, j, k) > mat2(i, j, k)); | 
|  | VERIFY_IS_EQUAL(ge(i, j, k), mat1(i, j, k) >= mat2(i, j, k)); | 
|  |  | 
|  | VERIFY_IS_EQUAL(lt(i, j, k), (bool)typed_lt(i, j, k)); | 
|  | VERIFY_IS_EQUAL(le(i, j, k), (bool)typed_le(i, j, k)); | 
|  | VERIFY_IS_EQUAL(gt(i, j, k), (bool)typed_gt(i, j, k)); | 
|  | VERIFY_IS_EQUAL(ge(i, j, k), (bool)typed_ge(i, j, k)); | 
|  | } | 
|  | } | 
|  | } | 
|  | } | 
|  |  | 
|  | static void test_equality() { | 
|  | Tensor<Scalar, 3> mat1(2, 3, 7); | 
|  | Tensor<Scalar, 3> mat2(2, 3, 7); | 
|  |  | 
|  | mat1.setRandom(); | 
|  | mat2.setRandom(); | 
|  | for (int i = 0; i < 2; ++i) { | 
|  | for (int j = 0; j < 3; ++j) { | 
|  | for (int k = 0; k < 7; ++k) { | 
|  | if (internal::random<bool>()) { | 
|  | mat2(i, j, k) = mat1(i, j, k); | 
|  | } | 
|  | } | 
|  | } | 
|  | } | 
|  |  | 
|  | Tensor<bool, 3> eq(2, 3, 7); | 
|  | Tensor<bool, 3> ne(2, 3, 7); | 
|  |  | 
|  | Tensor<Scalar, 3> typed_eq(2, 3, 7); | 
|  | Tensor<Scalar, 3> typed_ne(2, 3, 7); | 
|  |  | 
|  | eq = (mat1 == mat2); | 
|  | ne = (mat1 != mat2); | 
|  |  | 
|  | typed_eq = mat1.binaryExpr(mat2, TypedEQOp()); | 
|  | typed_ne = mat1.binaryExpr(mat2, TypedNEOp()); | 
|  |  | 
|  | for (int i = 0; i < 2; ++i) { | 
|  | for (int j = 0; j < 3; ++j) { | 
|  | for (int k = 0; k < 7; ++k) { | 
|  | VERIFY_IS_EQUAL(eq(i, j, k), mat1(i, j, k) == mat2(i, j, k)); | 
|  | VERIFY_IS_EQUAL(ne(i, j, k), mat1(i, j, k) != mat2(i, j, k)); | 
|  |  | 
|  | VERIFY_IS_EQUAL(eq(i, j, k), (bool)typed_eq(i, j, k)); | 
|  | VERIFY_IS_EQUAL(ne(i, j, k), (bool)typed_ne(i, j, k)); | 
|  | } | 
|  | } | 
|  | } | 
|  | } | 
|  |  | 
|  | static void test_isnan() { | 
|  | Tensor<Scalar, 3> mat(2, 3, 7); | 
|  |  | 
|  | mat.setRandom(); | 
|  | for (int i = 0; i < 2; ++i) { | 
|  | for (int j = 0; j < 3; ++j) { | 
|  | for (int k = 0; k < 7; ++k) { | 
|  | if (internal::random<bool>()) { | 
|  | mat(i, j, k) = std::numeric_limits<Scalar>::quiet_NaN(); | 
|  | } | 
|  | } | 
|  | } | 
|  | } | 
|  | Tensor<bool, 3> nan(2, 3, 7); | 
|  | nan = (mat.isnan)(); | 
|  | for (int i = 0; i < 2; ++i) { | 
|  | for (int j = 0; j < 3; ++j) { | 
|  | for (int k = 0; k < 7; ++k) { | 
|  | VERIFY_IS_EQUAL(nan(i, j, k), (std::isnan)(mat(i, j, k))); | 
|  | } | 
|  | } | 
|  | } | 
|  | } | 
|  |  | 
|  | EIGEN_DECLARE_TEST(cxx11_tensor_comparisons) { | 
|  | CALL_SUBTEST(test_orderings()); | 
|  | CALL_SUBTEST(test_equality()); | 
|  | CALL_SUBTEST(test_isnan()); | 
|  | } |