// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2016 Igor Babuschkin <igor@babuschk.in> // // 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 <limits> #include <numeric> #include <Eigen/CXX11/Tensor> using Eigen::Tensor; template <int DataLayout, typename Type=float, bool Exclusive = false> static void test_1d_scan() { int size = 50; Tensor<Type, 1, DataLayout> tensor(size); tensor.setRandom(); Tensor<Type, 1, DataLayout> result = tensor.cumsum(0, Exclusive); VERIFY_IS_EQUAL(tensor.dimension(0), result.dimension(0)); float accum = 0; for (int i = 0; i < size; i++) { if (Exclusive) { VERIFY_IS_EQUAL(result(i), accum); accum += tensor(i); } else { accum += tensor(i); VERIFY_IS_EQUAL(result(i), accum); } } accum = 1; result = tensor.cumprod(0, Exclusive); for (int i = 0; i < size; i++) { if (Exclusive) { VERIFY_IS_EQUAL(result(i), accum); accum *= tensor(i); } else { accum *= tensor(i); VERIFY_IS_EQUAL(result(i), accum); } } } template <int DataLayout, typename Type=float> static void test_4d_scan() { int size = 5; Tensor<Type, 4, DataLayout> tensor(size, size, size, size); tensor.setRandom(); Tensor<Type, 4, DataLayout> result(size, size, size, size); result = tensor.cumsum(0); float accum = 0; for (int i = 0; i < size; i++) { accum += tensor(i, 1, 2, 3); VERIFY_IS_EQUAL(result(i, 1, 2, 3), accum); } result = tensor.cumsum(1); accum = 0; for (int i = 0; i < size; i++) { accum += tensor(1, i, 2, 3); VERIFY_IS_EQUAL(result(1, i, 2, 3), accum); } result = tensor.cumsum(2); accum = 0; for (int i = 0; i < size; i++) { accum += tensor(1, 2, i, 3); VERIFY_IS_EQUAL(result(1, 2, i, 3), accum); } result = tensor.cumsum(3); accum = 0; for (int i = 0; i < size; i++) { accum += tensor(1, 2, 3, i); VERIFY_IS_EQUAL(result(1, 2, 3, i), accum); } } template <int DataLayout> static void test_tensor_maps() { int inputs[20]; TensorMap<Tensor<int, 1, DataLayout> > tensor_map(inputs, 20); tensor_map.setRandom(); Tensor<int, 1, DataLayout> result = tensor_map.cumsum(0); int accum = 0; for (int i = 0; i < 20; ++i) { accum += tensor_map(i); VERIFY_IS_EQUAL(result(i), accum); } } void test_cxx11_tensor_scan() { CALL_SUBTEST((test_1d_scan<ColMajor, float, true>())); CALL_SUBTEST((test_1d_scan<ColMajor, float, false>())); CALL_SUBTEST((test_1d_scan<RowMajor, float, true>())); CALL_SUBTEST((test_1d_scan<RowMajor, float, false>())); CALL_SUBTEST(test_4d_scan<ColMajor>()); CALL_SUBTEST(test_4d_scan<RowMajor>()); CALL_SUBTEST(test_tensor_maps<ColMajor>()); CALL_SUBTEST(test_tensor_maps<RowMajor>()); }