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// 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);
}
}
EIGEN_DECLARE_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>());
}
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