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test_xreducer.cpp
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/***************************************************************************
* Copyright (c) Johan Mabille, Sylvain Corlay and Wolf Vollprecht *
* Copyright (c) QuantStack *
* *
* Distributed under the terms of the BSD 3-Clause License. *
* *
* The full license is in the file LICENSE, distributed with this software. *
****************************************************************************/
#include "test_common_macros.hpp"
#include "test_common_macros.hpp"
#if (defined(__GNUC__) && !defined(__clang__))
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wmaybe-uninitialized"
#pragma GCC diagnostic ignored "-Wconversion"
#pragma GCC diagnostic ignored "-Wfloat-conversion"
#include "xtensor/xmath.hpp"
#pragma GCC diagnostic pop
#else
#include "xtensor/xmath.hpp"
#endif
#include "xtensor/xutils.hpp"
#include "xtensor/xfixed.hpp"
#include "xtensor/xbuilder.hpp"
#include "xtensor/xreducer.hpp"
#include "xtensor/xview.hpp"
#include "xtensor/xmanipulation.hpp"
#include "xtensor/xarray.hpp"
#include "xtensor/xtensor.hpp"
#include "xtensor/xrandom.hpp"
#include "xtensor/xoptional.hpp"
#include "xtensor/xoptional_assembly.hpp"
#include "xtensor/xio.hpp"
namespace xt
{
#define CHECK_RESULT_TYPE(EXPRESSION, EXPECTED_TYPE) \
{ \
using result_type = typename std::decay_t<decltype(EXPRESSION)>::value_type; \
EXPECT_TRUE((std::is_same<result_type, EXPECTED_TYPE>::value)); \
}
#define CHECK_TAG_TYPE(EXPRESSION, EXPECTED_TYPE) \
{ \
using result_type = typename std::decay_t<decltype(EXPRESSION)>::expression_tag; \
EXPECT_TRUE((std::is_same<result_type, EXPECTED_TYPE>::value)); \
}
#define CHECK_TYPE(VALUE, EXPECTED_TYPE) \
{ \
using result_type = typename std::decay_t<decltype(VALUE)>; \
EXPECT_TRUE((std::is_same<result_type, EXPECTED_TYPE>::value)); \
}
struct xreducer_features
{
using axes_type = std::array<std::size_t, 2>;
using shape_type = xarray<double>::shape_type;
using xarray_type = xarray<double>;
using func = xreducer_functors<std::plus<double>>;
axes_type m_axes;
xarray_type m_a;
xreducer<func, const xarray_type&, axes_type, xt::reducer_options<double, std::tuple<xt::evaluation_strategy::lazy_type>>> m_red;
xreducer_features();
};
xreducer_features::xreducer_features()
: m_axes({1, 3}), m_a(ones<double>({3, 2, 4, 6, 5})),
m_red(func(), m_a, m_axes, xt::evaluation_strategy::lazy)
{
for (std::size_t i = 0; i < 2; ++i)
{
for (std::size_t j = 0; j < 6; ++j)
{
m_a(1, i, 1, j, 1) = 2;
}
}
}
struct xreducer_opt_features
{
using axes_type = std::array<std::size_t, 2>;
using shape_type = typename xarray_optional<double>::shape_type;
using xarray_of_optional_type = xarray<xtl::xoptional<double>>;
using xarray_optional_type = xarray_optional<double>;
using optional_assembly_type = xoptional_assembly<xarray<double>, xarray<bool>>;
axes_type m_axes;
xarray_of_optional_type m_array_of_optional, m_simple_array_of_optional;
xarray_optional_type m_array_optional, m_simple_array_optional;
optional_assembly_type m_optional_assembly, m_simple_optional_assembly;
xreducer_opt_features():
m_axes({1, 3}),
m_array_of_optional(ones<xtl::xoptional<double>>({3, 2, 4, 6, 5})),
m_array_optional(ones<xtl::xoptional<double>>({3, 2, 4, 6, 5})),
m_optional_assembly(ones<xtl::xoptional<double>>({3, 2, 4, 6, 5}))
{
for (std::size_t i = 0; i < 2; ++i)
{
for (std::size_t j = 0; j < 6; ++j)
{
m_array_of_optional(1, i, 1, j, 1) = 2;
m_array_optional(1, i, 1, j, 1) = 2;
m_optional_assembly(1, i, 1, j, 1) = 2;
}
}
m_array_of_optional(0, 0, 0, 0, 0) = xtl::missing<double>();
m_array_optional(0, 0, 0, 0, 0) = xtl::missing<double>();
m_optional_assembly(0, 0, 0, 0, 0) = xtl::missing<double>();
m_simple_array_of_optional = xarray_of_optional_type({{1, 2, 0}, {4, 8, xtl::missing<double>()}});
m_simple_array_optional = xarray_optional_type({{1, 2, 0}, {4, 8, xtl::missing<double>()}});
m_simple_optional_assembly = optional_assembly_type({{1, 2, 0}, {4, 8, xtl::missing<double>()}});
}
};
#define TEST_EXPRESSION_TAG(INPUT, EXPECTED) \
auto res = xt::sum(INPUT, feats.m_axes); \
CHECK_TAG_TYPE(res, EXPECTED);
TEST(xreducer, expression_tag)
{
xreducer_features feats;
TEST_EXPRESSION_TAG(feats.m_a, xtensor_expression_tag);
}
TEST(xreducer_array_of_optional, expression_tag)
{
xreducer_opt_features feats;
TEST_EXPRESSION_TAG(feats.m_array_of_optional, xtensor_expression_tag);
}
TEST(xreducer_array_optional, expression_tag)
{
xreducer_opt_features feats;
TEST_EXPRESSION_TAG(feats.m_array_optional, xoptional_expression_tag);
}
TEST(xreducer_optional_assembly, expression_tag)
{
xreducer_opt_features feats;
TEST_EXPRESSION_TAG(feats.m_optional_assembly, xoptional_expression_tag);
}
#undef TEST_EXPRESSION_TAG
#define TEST_VALUE_HAS_VALUE(INPUT, V_TYPE, OPTIONAL) \
using result_type = std::conditional_t<OPTIONAL, xtl::xoptional<double>, double>; \
\
auto res = xt::sum(INPUT, feats.m_axes); \
CHECK_RESULT_TYPE(res, result_type); \
CHECK_TYPE(xt::value(res)(1, 1, 1), V_TYPE); \
EXPECT_EQ(!OPTIONAL, xt::has_value(res)(0, 0, 0)); \
EXPECT_TRUE(xt::has_value(res)(1, 1, 1));
TEST(xreducer, value_has_value)
{
xreducer_features feats;
TEST_VALUE_HAS_VALUE(feats.m_a, double, false);
}
/*
TEST(xreducer_array_of_optional, value_has_value)
{
xreducer_opt_features feats;
TEST_VALUE_HAS_VALUE(feats.m_array_of_optional, xtl::xoptional<double>, true); // TODO: fail, the mask is not reflecting the missing values
}
*/
TEST(xreducer_array_optional, value_has_value)
{
xreducer_opt_features feats;
TEST_VALUE_HAS_VALUE(feats.m_array_optional, double, true);
}
TEST(xreducer_optional_assembly, value_has_value)
{
xreducer_opt_features feats;
TEST_VALUE_HAS_VALUE(feats.m_optional_assembly, double, true);
}
#undef TEST_VALUE_HAS_VALUE
TEST(xreducer, functor_type)
{
auto sum = [](auto const& left, auto const& right) { return left + right; };
auto sum_functor = xt::make_xreducer_functor(sum);
xt::xarray<int> a = {{1, 2, 3}, {4, 5, 6}};
xt::xarray<int> a_sums = xt::reduce(std::move(sum_functor), a, {1});
xt::xarray<int> a_sums2 = xt::reduce(sum_functor, a, {1});
xt::xarray<int> expect = {6, 15};
EXPECT_EQ(a_sums, expect);
EXPECT_EQ(a_sums2, expect);
xt::xarray<int> a_sums3 = xt::reduce(sum, a, {1});
EXPECT_EQ(a_sums3, expect);
}
TEST(xreducer, errors)
{
xt::xarray<int> a = {{1, 2, 3}, {4, 5, 6}};
XT_EXPECT_THROW(xt::sum(a, {1, 0}), std::runtime_error);
XT_EXPECT_THROW(xt::sum(a, {0, 2}), std::runtime_error);
XT_EXPECT_THROW(xt::sum(a, {1, 0}, evaluation_strategy::immediate), std::runtime_error);
XT_EXPECT_THROW(xt::sum(a, {0, 2}, evaluation_strategy::immediate), std::runtime_error);
}
TEST(xreducer, shape)
{
xreducer_features features;
xreducer_features::shape_type s = {3, 4, 5};
EXPECT_EQ(s, features.m_red.shape());
EXPECT_EQ(features.m_red.layout(), layout_type::dynamic);
}
TEST(xreducer, access)
{
xreducer_features features;
EXPECT_EQ(12, features.m_red(0, 0, 0));
EXPECT_EQ(24, features.m_red(1, 1, 1));
EXPECT_EQ(features.m_red(0, 1), features.m_red(0, 0, 1));
EXPECT_EQ(features.m_red(1, 2, 1, 0, 1), features.m_red(1, 0, 1));
EXPECT_EQ(12, features.m_red(2, 0, 0, 0));
EXPECT_EQ(12, features.m_red());
}
TEST(xreducer, unchecked)
{
xreducer_features features;
EXPECT_EQ(12, features.m_red.unchecked(0, 0, 0));
EXPECT_EQ(24, features.m_red.unchecked(1, 1, 1));
}
TEST(xreducer, indexed_access)
{
xreducer_features features;
EXPECT_EQ(12, (features.m_red[{0, 0, 0}]));
EXPECT_EQ(24, (features.m_red[{1, 1, 1}]));
}
TEST(xreducer, at)
{
xreducer_features features;
EXPECT_EQ(12, features.m_red.at(0, 0, 0));
EXPECT_EQ(24, features.m_red.at(1, 1, 1));
XT_EXPECT_ANY_THROW(features.m_red.at(10, 10, 10));
XT_EXPECT_ANY_THROW(features.m_red.at(0, 0, 0, 0));
}
TEST(xreducer, iterator)
{
xreducer_features features;
auto iter = features.m_red.cbegin();
auto iter_end = features.m_red.cend();
const xreducer_features::shape_type& s = features.m_red.shape();
std::size_t nb_iter = 1;
nb_iter = std::accumulate(s.cbegin(), s.cend(), nb_iter, std::multiplies<std::size_t>());
std::advance(iter, static_cast<std::ptrdiff_t>(nb_iter));
EXPECT_EQ(iter_end, iter);
}
TEST(xreducer, assign)
{
xreducer_features features;
xarray<double> res = features.m_red;
xarray<double> expected = 12 * ones<double>({3, 4, 5});
expected(1, 1, 1) = 24;
EXPECT_EQ(expected, res);
xarray_optional<double> opt_expected = 12 * ones<double>({3, 4, 5});
opt_expected(1, 1, 1) = 24;
// TODO: fix the reducer assignment issue in multithreaded env and enable
// these tests again.
#if !defined(XTENSOR_USE_TBB) && !defined(XTENSOR_USE_OPENMP)
xreducer_opt_features::xarray_of_optional_type opt_res1 = res;
CHECK_RESULT_TYPE(opt_res1, xtl::xoptional<double>);
CHECK_TYPE(xt::value(opt_res1)(1, 1, 1), xtl::xoptional<double>);
EXPECT_EQ(xt::has_value(opt_res1), xt::full_like(res, true));
EXPECT_EQ(opt_expected, opt_res1);
xreducer_opt_features::xarray_optional_type opt_res2 = res;
CHECK_RESULT_TYPE(opt_res2, xtl::xoptional<double>);
CHECK_TYPE(xt::value(opt_res2)(1, 1, 1), double);
EXPECT_EQ(xt::has_value(opt_res2), xt::full_like(res, true));
EXPECT_EQ(opt_expected, opt_res2);
xreducer_opt_features::optional_assembly_type opt_res3 = res;
CHECK_RESULT_TYPE(opt_res3, xtl::xoptional<double>);
CHECK_TYPE(xt::value(opt_res3)(1, 1, 1), double);
EXPECT_EQ(xt::has_value(opt_res3), xt::full_like(res, true));
EXPECT_EQ(opt_expected, opt_res3);
#endif
}
#define TEST_OPT_ASSIGNMENT(INPUT) \
auto res = xt::sum(INPUT, feats.m_axes); \
\
xreducer_opt_features::xarray_of_optional_type res1 = res; \
CHECK_RESULT_TYPE(res1, xtl::xoptional<double>); \
CHECK_TYPE(xt::value(res1)(1, 1, 1), xtl::xoptional<double>);\
EXPECT_EQ(res1(1, 1, 1), 24.); \
/* EXPECT_FALSE(xt::has_value(res1)(0, 0, 0)); */ \
EXPECT_TRUE(xt::has_value(res1)(1, 1, 1)); \
\
xreducer_opt_features::xarray_optional_type res2 = res; \
CHECK_RESULT_TYPE(res2, xtl::xoptional<double>); \
CHECK_TYPE(xt::value(res2)(1, 1, 1), double); \
EXPECT_EQ(res2(1, 1, 1), 24.); \
EXPECT_FALSE(xt::has_value(res2)(0, 0, 0)); \
EXPECT_TRUE(xt::has_value(res2)(1, 1, 1)); \
\
xreducer_opt_features::optional_assembly_type res3 = res; \
CHECK_RESULT_TYPE(res3, xtl::xoptional<double>); \
CHECK_TYPE(xt::value(res3)(1, 1, 1), double); \
EXPECT_EQ(res3(1, 1, 1), 24.); \
EXPECT_FALSE(xt::has_value(res3)(0, 0, 0)); \
EXPECT_TRUE(xt::has_value(res3)(1, 1, 1));
TEST(xreducer_array_of_optional, assign)
{
xreducer_opt_features feats;
TEST_OPT_ASSIGNMENT(feats.m_array_of_optional)
}
TEST(xreducer_array_optional, assign)
{
xreducer_opt_features feats;
TEST_OPT_ASSIGNMENT(feats.m_array_optional)
}
TEST(xreducer_optional_assembly, assign)
{
xreducer_opt_features feats;
TEST_OPT_ASSIGNMENT(feats.m_optional_assembly)
}
#undef TEST_OPT_ASSIGNMENT
TEST(xreducer, sum)
{
xreducer_features features;
xarray<double> res = sum(features.m_a, features.m_axes);
xarray<double> expected = 12 * ones<double>({3, 4, 5});
expected(1, 1, 1) = 24;
EXPECT_EQ(expected, res);
}
#define TEST_OPT_SUM(INPUT) \
auto res = xt::sum(INPUT, feats.m_axes); \
EXPECT_EQ(res.dimension(), std::size_t(3)); \
EXPECT_EQ(res(0, 0, 0), xtl::missing<double>()); \
EXPECT_EQ(res(1, 1, 1), 24.);
TEST(xreducer_array_of_optional, sum)
{
xreducer_opt_features feats;
TEST_OPT_SUM(feats.m_array_of_optional);
}
TEST(xreducer_array_optional, sum)
{
xreducer_opt_features feats;
TEST_OPT_SUM(feats.m_array_optional);
}
TEST(xreducer_optional_assembly, sum)
{
xreducer_opt_features feats;
TEST_OPT_SUM(feats.m_optional_assembly);
}
#undef TEST_OPT_SUM
TEST(xreducer, sum_tensor)
{
xtensor<double, 2> m = {{1, 2}, {3, 4}};
xarray<double> res = xt::sum(m, {0});
EXPECT_EQ(res.dimension(), std::size_t(1));
EXPECT_EQ(res(0), 4.0);
EXPECT_EQ(res(1), 6.0);
}
TEST(xreducer, single_axis_sugar)
{
xarray<double> m = {{1, 0}, {3, 4}};
xarray<std::size_t> res1 = xt::count_nonzero(m, {1});
xarray<std::size_t> res2 = xt::count_nonzero(m, 1);
EXPECT_EQ(res1, res2);
xarray<double> res3 = xt::sum(m, {1});
xarray<double> res4 = xt::sum(m, 1);
EXPECT_EQ(res3, res4);
}
#define TEST_OPT_SINGLE_AXIS(INPUT) \
auto res = xt::sum(INPUT, feats.m_axes); \
xarray_optional<std::size_t> res1 = xt::count_nonzero(INPUT, {1}); \
xarray_optional<std::size_t> res2 = xt::count_nonzero(INPUT, 1); \
EXPECT_EQ(res1, res2); \
\
xarray_optional<double> res3 = xt::sum(INPUT, {1}); \
xarray_optional<double> res4 = xt::sum(INPUT, 1); \
EXPECT_EQ(res3, res4);
TEST(xreducer_array_of_optional, single_axis_sugar)
{
xreducer_opt_features feats;
TEST_OPT_SINGLE_AXIS(feats.m_array_of_optional);
}
TEST(xreducer_array_optional, single_axis_sugar)
{
xreducer_opt_features feats;
TEST_OPT_SINGLE_AXIS(feats.m_array_optional);
}
TEST(xreducer_optional_assembly, single_axis_sugar)
{
xreducer_opt_features feats;
TEST_OPT_SINGLE_AXIS(feats.m_optional_assembly);
}
#undef TEST_OPT_SINGLE_AXIS
TEST(xreducer, sum2)
{
xarray<double> u = ones<double>({2, 4});
xarray<double> expectedu0 = 2 * ones<double>({4});
xarray<double> resu0 = sum(u, {0});
EXPECT_EQ(expectedu0, resu0);
xarray<double> expectedu1 = 4 * ones<double>({2});
xarray<double> resu1 = sum(u, {std::size_t(1)});
xarray<double> resm1 = sum(u, {-1});
std::array<std::size_t, 1> a_us = {1};
std::array<std::ptrdiff_t, 1> a_ss = {-1};
xarray<double> res_refu1 = sum(u, a_us);
xarray<double> res_refm1 = sum(u, a_ss);
EXPECT_EQ(expectedu1, resm1);
EXPECT_EQ(expectedu1, resu1);
EXPECT_EQ(expectedu1, res_refm1);
EXPECT_EQ(expectedu1, res_refu1);
xarray<double> v = ones<double>({4, 2});
xarray<double> expectedv0 = 4 * ones<double>({2});
xarray<double> resv0 = sum(v, {0});
EXPECT_EQ(expectedv0, resv0);
xarray<double> expectedv1 = 2 * ones<double>({4});
xarray<double> resv1 = sum(v, {1});
EXPECT_EQ(expectedv1, resv1);
// check that there is no overflow
xarray<uint8_t> c = ones<uint8_t>({1000});
EXPECT_EQ(1000u, sum(c)());
}
TEST(xreducer, sum_all)
{
xreducer_features features;
auto res = sum(features.m_a);
double expected = 732;
EXPECT_EQ(res(), expected);
}
#define TEST_OPT_SUM_ALL(INPUT) \
auto res = xt::sum(INPUT); \
EXPECT_EQ(res(), xtl::missing<double>());
TEST(xreducer_array_of_optional, sum_all)
{
xreducer_opt_features feats;
TEST_OPT_SUM_ALL(feats.m_array_of_optional);
}
TEST(xreducer_array_optional, sum_all)
{
xreducer_opt_features feats;
TEST_OPT_SUM_ALL(feats.m_array_optional);
}
TEST(xreducer_optional_assembly, sum_all)
{
xreducer_opt_features feats;
TEST_OPT_SUM_ALL(feats.m_optional_assembly);
}
#undef TEST_OPT_SUM_ALL
TEST(xreducer, prod)
{
// check that there is no overflow
xarray<uint8_t> c = 2 * ones<uint8_t>({34});
EXPECT_EQ(1ULL << 34, prod<long long>(c)());
}
#define TEST_OPT_PROD(INPUT) \
auto res1 = xt::prod(INPUT); \
EXPECT_EQ(res1(), xtl::missing<double>()); \
\
auto res2 = xt::prod(INPUT, feats.m_axes); \
EXPECT_EQ(res2(), xtl::missing<double>()); \
EXPECT_EQ(res2(1, 1, 1), 4096.);
TEST(xreducer_array_of_optional, prod)
{
xreducer_opt_features feats;
TEST_OPT_PROD(feats.m_array_of_optional);
}
TEST(xreducer_array_optional, prod)
{
xreducer_opt_features feats;
TEST_OPT_PROD(feats.m_array_optional);
}
TEST(xreducer_optional_assembly, prod)
{
xreducer_opt_features feats;
TEST_OPT_PROD(feats.m_optional_assembly);
}
#undef TEST_OPT_PROD
TEST(xreducer, mean)
{
xtensor<double, 2> input
{{-1.0, 0.0}, {1.0, 0.0}};
auto mean_all = mean(input);
auto mean0 = mean(input, {0});
auto mean1 = mean(input, {1});
xtensor<double, 0> expect_all = 0.0;
xtensor<double, 1> expect0 = {0.0, 0.0};
xtensor<double, 1> expect1 = {-0.5, 0.5};
EXPECT_EQ(mean_all(), expect_all());
EXPECT_TRUE(all(equal(mean0, expect0)));
EXPECT_TRUE(all(equal(mean1, expect1)));
#ifndef SKIP_ON_WERROR
// may loose precision because uint8_t is casted to long for intermediate
// computations and then divided by a double for mean
xarray<uint8_t> c = {1, 2};
EXPECT_EQ(mean(c)(), 1.5);
#endif
const auto rvalue_xarray = [] () { return xtensor<double, 1>({1, 2}); };
EXPECT_EQ(mean(rvalue_xarray(), {0})(), 1.5);
}
#define TEST_OPT_MEAN(INPUT) \
auto res1 = xt::mean(INPUT); \
EXPECT_EQ(res1(), xtl::missing<double>()); \
\
auto res2 = xt::mean(INPUT, feats.m_axes); \
EXPECT_EQ(res2(), xtl::missing<double>()); \
EXPECT_EQ(res2(1, 1, 1), 2.);
TEST(xreducer_array_of_optional, mean)
{
xreducer_opt_features feats;
TEST_OPT_MEAN(feats.m_array_of_optional);
}
TEST(xreducer_array_optional, mean)
{
xreducer_opt_features feats;
TEST_OPT_MEAN(feats.m_array_optional);
}
TEST(xreducer_optional_assembly, mean)
{
xreducer_opt_features feats;
TEST_OPT_MEAN(feats.m_optional_assembly);
}
#undef TEST_OPT_MEAN
TEST(xreducer, average)
{
xt::xtensor<float, 2> a = {{ 3, 4, 2, 1}, { 1, 1, 3, 2}};
xt::xarray<double> all_weights = {{1, 2, 3, 4}, { 5, 6, 7, 8}};
auto avg_all = xt::average(a, all_weights);
auto avg_all_2 = xt::average(a, all_weights, {0ul, 1ul});
auto avg0 = xt::average(a, xt::xarray<double>{3, 9}, {0});
auto avg1 = xt::average(a, xt::xarray<double>{1,2,3,4}, {1});
auto avg_m1 = xt::average(a, xt::xarray<double>{1,2,3,4}, {-1});
auto avg_d1 = xt::average(a, xt::xarray<double>{1,2,3,4}, {-1}, evaluation_strategy::immediate);
xtensor<double, 0> expect_all = 1.9166666666666667;
xtensor<double, 1> expect0 = {1.5, 1.75, 2.75, 1.75};
xtensor<double, 1> expect1 = {2.1, 2.0};
EXPECT_TRUE(allclose(avg_all, expect_all));
EXPECT_TRUE(allclose(avg_all_2, expect_all));
EXPECT_TRUE(all(equal(avg0, expect0)));
EXPECT_TRUE(all(equal(avg1, expect1)));
EXPECT_TRUE(all(equal(avg_m1, expect1)));
EXPECT_TRUE(all(equal(avg_d1, expect1)));
}
#define TEST_OPT_AVERAGE(INPUT) \
xt::xarray<double> all_weights = {{3, 3, 3}, \
{1, 1, 1}}; \
auto res1 = xt::average(INPUT, all_weights); \
EXPECT_EQ(res1(), xtl::missing<double>()); \
\
xtensor_optional<double, 1> expect = {1., 2., xtl::missing<double>()}; \
xt::xarray<double> weights = {2, 0}; \
auto res2 = xt::average(INPUT, weights, {0}); \
EXPECT_TRUE(all(equal(res2, expect)));
TEST(xreducer_array_of_optional, average)
{
xreducer_opt_features feats;
TEST_OPT_AVERAGE(feats.m_simple_array_of_optional);
}
TEST(xreducer_array_optional, average)
{
xreducer_opt_features feats;
TEST_OPT_AVERAGE(feats.m_simple_array_optional);
}
TEST(xreducer_optional_assembly, average)
{
xreducer_opt_features feats;
TEST_OPT_AVERAGE(feats.m_simple_optional_assembly);
}
#undef TEST_OPT_AVERAGE
TEST(xreducer, count_nonzero)
{
xarray<double> m = {{1, 0}, {3, 4}};
xarray<std::size_t> res0 = xt::count_nonzero(m, {0});
xtensor<std::size_t, 1> expect0 = {2, 1};
EXPECT_TRUE(all(equal(res0, expect0)));
xarray<std::size_t> res1 = xt::count_nonzero(m, {1});
xtensor<std::size_t, 1> expect1 = {1, 2};
EXPECT_TRUE(all(equal(res1, expect1)));
}
#define TEST_OPT_COUNT_NONZEROS(INPUT) \
auto res0 = xt::count_nonzero(INPUT, {0}); \
xtensor_optional<std::size_t, 1> expect0 = \
{2, 2, xtl::missing<std::size_t>()}; \
EXPECT_TRUE(all(equal(res0, expect0))); \
\
auto res1 = xt::count_nonzero(INPUT, {1}); \
xtensor_optional<std::size_t, 1> expect1 = \
{2, xtl::missing<std::size_t>()}; \
EXPECT_TRUE(all(equal(res1, expect1)));
/* TODO: fix policy, missing values should lead to a missing value result
TEST(xreducer_array_of_optional, count_nonzero)
{
xreducer_opt_features feats;
TEST_OPT_COUNT_NONZEROS(feats.m_simple_array_of_optional);
}
TEST(xreducer_array_optional, count_nonzero)
{
xreducer_opt_features feats;
TEST_OPT_COUNT_NONZEROS(feats.m_simple_array_optional);
}
TEST(xreducer_optional_assembly, count_nonzero)
{
xreducer_opt_features feats;
TEST_OPT_COUNT_NONZEROS(feats.m_simple_optional_assembly);
}
*/
#undef TEST_OPT_COUNT_NONZEROS
TEST(xreducer, minmax)
{
using A = std::array<double, 2>;
xtensor<double, 2> input
{{-1.0, 0.0}, {1.0, 0.0}};
EXPECT_EQ(minmax(input)(), (A{-1.0, 1.0}));
}
TEST(xreducer, immediate)
{
xarray<double> a = xt::arange(27);
a.resize({3, 3, 3});
xarray<double> a_lz = sum(a);
auto a_gd = sum(a, evaluation_strategy::immediate);
EXPECT_EQ(a_lz, a_gd);
a_lz = sum(a, {1});
a_gd = sum(a, {1}, evaluation_strategy::immediate);
EXPECT_EQ(a_lz, a_gd);
a_lz = sum(a, {0, 2});
a_gd = sum(a, {0, 2}, evaluation_strategy::immediate);
EXPECT_EQ(a_lz, a_gd);
a_lz = sum(a, {1, 2});
a_gd = sum(a, {1, 2}, evaluation_strategy::immediate);
EXPECT_EQ(a_lz, a_gd);
a = xt::arange(4 * 3 * 6 * 2 * 7);
a.resize({4, 3, 6, 2, 7});
a_lz = sum(a);
a_gd = sum(a, evaluation_strategy::immediate);
EXPECT_EQ(a_lz, a_gd);
a_lz = sum(a, {1});
a_gd = sum(a, {1}, evaluation_strategy::immediate);
EXPECT_EQ(a_lz, a_gd);
a_lz = sum(a, {0, 2});
a_gd = sum(a, {0, 2}, evaluation_strategy::immediate);
EXPECT_EQ(a_lz, a_gd);
a_lz = sum(a, {1, 2});
a_gd = sum(a, {1, 2}, evaluation_strategy::immediate);
EXPECT_EQ(a_lz, a_gd);
a_lz = sum(a, {1, 3, 4});
a_gd = sum(a, {1, 3, 4}, evaluation_strategy::immediate);
EXPECT_EQ(a_lz, a_gd);
a_lz = sum(a, {0, 1, 4});
a_gd = sum(a, {0, 1, 4}, evaluation_strategy::immediate);
EXPECT_EQ(a_lz, a_gd);
a_lz = sum(a, {0, 1, 3});
a_gd = sum(a, {0, 1, 3}, evaluation_strategy::immediate);
EXPECT_EQ(a_lz, a_gd);
a_lz = sum(a, {0, 2, 3});
a_gd = sum(a, {0, 2, 3}, evaluation_strategy::immediate);
EXPECT_EQ(a_lz, a_gd);
a_lz = sum(a, {1, 2, 3});
a_gd = sum(a, {1, 2, 3}, evaluation_strategy::immediate);
EXPECT_EQ(a_lz, a_gd);
xtensor<short, 3, layout_type::column_major> ct = xt::random::randint<short>({1, 5, 3});
EXPECT_EQ(sum(ct, {0, 2}), sum(ct, {0, 2}, evaluation_strategy::immediate));
xtensor<short, 5, layout_type::column_major> ct2 = xt::random::randint<short>({1, 5, 1, 2, 3});
EXPECT_EQ(sum(ct2, {0, 1, 2}), sum(ct2, {0, 1, 2}, evaluation_strategy::immediate));
EXPECT_EQ(sum(ct2, {2, 3}), sum(ct2, {2, 3}, evaluation_strategy::immediate));
EXPECT_EQ(sum(ct2, {1, 3}), sum(ct2, {1, 3}, evaluation_strategy::immediate));
}
TEST(xreducer, chaining_reducers)
{
xt::xarray<double> a = {{ 1., 2. },
{ 3., 4. }};
auto b = a - xt::sum(a, { 0 });
auto c = xt::sum(b, { 0 });
EXPECT_EQ(c(0), -4.);
EXPECT_EQ(c(1), -6.);
}
TEST(xreducer, immediate_shape)
{
xtensor<double, 2> c = {{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 10, 11, 12}};
auto xa = xt::sum(c, {0}, evaluation_strategy::immediate);
auto is_arr = [](const auto& c)
{
bool istrue = detail::is_array<std::decay_t<decltype(c)>>::value;
return istrue;
};
EXPECT_TRUE(is_arr(xa.shape()));
xtensor<double, 3> a;
a.resize({3, 3, 3});
std::iota(a.storage().begin(), a.storage().end(), 0);
xarray<double> a_lz = sum(a);
auto a_gd = sum(a, evaluation_strategy::immediate);
EXPECT_EQ(a_lz, a_gd);
EXPECT_TRUE(is_arr(a_gd.shape()));
a_lz = sum(a, {1});
auto a_gd_1 = sum(a, {1}, evaluation_strategy::immediate);
EXPECT_EQ(a_lz, a_gd_1);
a_lz = sum(a, {0, 2});
auto a_gd_2 = sum(a, {0, 2}, evaluation_strategy::immediate);
EXPECT_EQ(a_lz, a_gd_2);
EXPECT_TRUE(is_arr(a_gd_1.shape()));
EXPECT_TRUE(is_arr(a_gd_2.shape()));
a_lz = sum(a, {1, 2});
a_gd_2 = sum(a, {1, 2}, evaluation_strategy::immediate);
EXPECT_EQ(a_lz, a_gd_2);
}
TEST(xreducer, xfixed_reduction)
{
xtensor_fixed<double, xshape<3, 3, 3>> a;
std::iota(a.storage().begin(), a.storage().end(), 0);
xtensor<double, 3> b;
b.resize({3, 3, 3});
std::iota(b.storage().begin(), b.storage().end(), 0);
auto is_arr = [](const auto& c)
{
bool istrue = detail::is_array<std::decay_t<decltype(c)>>::value;
return istrue;
};
xarray<double> a_lz = sum(a);
auto a_gd = sum(a, evaluation_strategy::immediate);
EXPECT_EQ(a_lz, a_gd);
// EXPECT_TRUE(is_fixed(a_gd.shape())); // this actually evaluates to const_array
a_lz = sum(a, xt::dynamic_shape<std::size_t>{1});
auto a_gd_1 = sum(a, xt::dynamic_shape<std::size_t>{1}, evaluation_strategy::immediate);
auto b_gd_1 = sum(b, {1}, evaluation_strategy::immediate);
EXPECT_EQ(a_lz, a_gd_1);
EXPECT_EQ(a_lz, b_gd_1);
a_lz = sum(a, {0, 2});
auto a_gd_2 = sum(a, {0, 2}, evaluation_strategy::immediate);
auto b_gd_2 = sum(b, {0, 2}, evaluation_strategy::immediate);
EXPECT_EQ(a_lz, a_gd_2);
EXPECT_EQ(b_gd_2, a_gd_2);
EXPECT_EQ(a_gd_2.dimension(), std::size_t(1));
// EXPECT_TRUE(is_arr(a_gd_1.shape()));
EXPECT_TRUE(is_arr(a_gd_2.shape()));
a_lz = sum(a, {1, 2});
a_gd_2 = sum(a, {1, 2}, evaluation_strategy::immediate);
EXPECT_EQ(a_lz, a_gd_2);
auto a_lx_3 = sum(a, {1, 2});
auto a_lz_3 = sum(a, xshape<1, 2>());
auto a_gd_3 = sum(a, xshape<1, 2>(), evaluation_strategy::immediate);
xarray<double> xevd = a_lz_3;
EXPECT_EQ(a_lz_3, a_gd_2);
EXPECT_TRUE(a_gd_3 == a_gd_2);
bool truth = std::is_same<decltype(a_gd_3), xtensor_fixed<double, xshape<3>>>::value;
EXPECT_TRUE(truth);
xtensor<short, 3> ct = xt::random::randint<short>({1, 5, 3});
xtensor_fixed<short, xshape<1, 5, 3>> c = ct;
auto b_fx_1 = sum(c, xshape<0, 2>(), evaluation_strategy::immediate);
auto b_fx_2 = sum(c, xshape<0, 1>(), evaluation_strategy::immediate);
auto b_fx_3 = sum(c, xshape<0, 1, 2>(), evaluation_strategy::immediate);
EXPECT_EQ(sum(ct, {0, 2}, evaluation_strategy::immediate), sum(c, {0, 2}));
EXPECT_TRUE(b_fx_1 == sum(c, {0, 2}));
EXPECT_TRUE(b_fx_2 == sum(c, {0, 1}));
EXPECT_EQ(b_fx_3, sum(c, {0, 1, 2}));
truth = std::is_same<std::decay_t<decltype(b_fx_1)>, xtensor_fixed<int, xshape<5>>>::value;
EXPECT_TRUE(truth);
truth = std::is_same<std::decay_t<decltype(b_fx_3)>, xtensor_fixed<int, xshape<>>>::value;
EXPECT_TRUE(truth);
truth = std::is_same<xshape<1, 3>, typename fixed_xreducer_shape_type<xshape<1, 5, 3>, xshape<1>>::type>();
EXPECT_TRUE(truth);
truth = std::is_same<xshape<5>, typename fixed_xreducer_shape_type<xshape<1, 5, 3>, xshape<0, 2>>::type>();
EXPECT_TRUE(truth);
truth = std::is_same<xshape<>, typename fixed_xreducer_shape_type<xshape<1, 5, 3>, xshape<0, 1, 2>>::type>();
EXPECT_TRUE(truth);
truth = std::is_same<xshape<1, 5>, typename fixed_xreducer_shape_type<xshape<1, 5>, xshape<2>>::type>();
EXPECT_TRUE(truth);
}
TEST(xreducer, view_steppers)
{
xt::xtensor<double, 2> X({10, 20});
xt::xtensor<double, 2> Y(X.shape());
X = xt::random::randn<double>(X.shape());
xt::xtensor<double, 2> vx0 = xt::view(xt::sum(X, {1}), xt::all(), xt::newaxis());
xt::xtensor<double, 2> vx1 = xt::expand_dims(xt::sum(X, {1}), 1);
EXPECT_EQ(vx0, vx1);
}
TEST(xreducer, wrong_number_of_indices)
{
xt::xtensor<double, 4> a = xt::random::rand<double>({5, 5, 5, 5});
double e = xt::sum(a)();
double s1 = xt::sum(a)(0);
EXPECT_EQ(s1, e);
double s2 = xt::sum(a)(0, 1, 2, 3, 4, 5, 0);
EXPECT_EQ(s2, e);
auto red = xt::sum(a, {0});
EXPECT_EQ(red(2), red(0, 0, 2));
EXPECT_EQ(red(1, 2), red(0, 1, 2));
EXPECT_EQ(red(1, 2), red(1, 1, 1, 1, 1, 0, 1, 2));
}
TEST(xreducer, normalize_axes)
{
xt::xtensor<double, 4> x{};
std::vector<std::size_t> sva {0, 1, 2, 3};
std::vector<std::ptrdiff_t> svb = {-4, 1, -2, -1};
std::initializer_list<std::ptrdiff_t> initlist = {-4, 1, -2, -1};
std::array<std::size_t, 4> saa = {0, 1, 2, 3};
std::array<std::ptrdiff_t, 4> sab = {-4, 1, -2, -1};
auto resa = forward_normalize<std::vector<std::size_t>>(x, svb);
EXPECT_EQ(forward_normalize<std::vector<std::size_t>>(x, svb), sva);
EXPECT_EQ(forward_normalize<std::vector<std::size_t>>(x, initlist), sva);
auto resaa = forward_normalize<std::array<std::size_t, 4>>(x, saa);
EXPECT_EQ(resaa, saa);
auto resab = forward_normalize<std::array<std::size_t, 4>>(x, sab);
EXPECT_EQ(resab, saa);
}
TEST(xreducer, input_0d)
{
xt::xarray<double> a;
EXPECT_EQ(0., xt::amin(a)[0]);
using A = std::array<double, 2>;
xt::xarray<double> b(1.2);
EXPECT_EQ(b.dimension(), 0u);
EXPECT_EQ(minmax(b)(), (A{1.2, 1.2}));
}
template <std::size_t... I, std::size_t... J>
bool operator==(fixed_shape<I...>, fixed_shape<J...>)
{
std::array<std::size_t, sizeof...(I)> ix = {I...};
std::array<std::size_t, sizeof...(J)> jx = {J...};
return sizeof...(J) == sizeof...(I) && std::equal(ix.begin(), ix.end(), jx.begin());
}
TEST(xreducer, keep_dims)
{
xt::xtensor<double, 4> a = xt::reshape_view(xt::arange<double>(5 * 5 * 5 * 5), {5, 5, 5, 5});
auto res = xt::sum(a, {0, 1}, xt::keep_dims | xt::evaluation_strategy::immediate);
EXPECT_EQ(res.shape(), (std::array<std::size_t, 4>{1, 1, 5, 5}));
auto res2 = xt::sum(a, {0, 1}, xt::keep_dims);
EXPECT_EQ(res2.shape(), (std::array<std::size_t, 4>{1, 1, 5, 5}));
xt::xarray<double> b = a;
auto res3 = xt::sum(b, {0, 1}, xt::keep_dims | xt::evaluation_strategy::immediate);
EXPECT_EQ(res3.shape(), (xt::dynamic_shape<std::size_t>{1, 1, 5, 5}));
auto res4 = xt::sum(b, {0, 1}, xt::keep_dims | xt::evaluation_strategy::lazy);
EXPECT_EQ(res4.shape(), (xt::dynamic_shape<std::size_t>{1, 1, 5, 5}));
xt::xarray<double> resx3 = xt::sum(a, {0, 1});
auto exp1 = xt::sum(a, {0, 1});
EXPECT_EQ(res, res2);
EXPECT_EQ(res, res3);
EXPECT_EQ(res, res4);
EXPECT_TRUE(xt::allclose(res, res2));
EXPECT_TRUE(xt::allclose(res, res3));
EXPECT_TRUE(xt::allclose(res, res4));
auto res5 = xt::sum(a, xt::keep_dims);
EXPECT_EQ(res5.shape(), (xt::static_shape<size_t, 4>{1, 1, 1, 1}));
auto res6 = xt::sum(a);
EXPECT_EQ(res6.shape(), (xt::static_shape<size_t, 0>{}));
xt::xtensor_fixed<double, xshape<5, 5, 5, 5>> c = a;
auto res7 = xt::sum(c);
EXPECT_EQ(res7.shape(), (xt::xshape<>{}));
auto res8 = xt::sum(c, xt::keep_dims);
EXPECT_EQ(res8.shape(), (xt::xshape<1, 1, 1, 1>{}));
}
TEST(xreducer, initial_value)
{
xt::xtensor<double, 4> a = xt::reshape_view(xt::arange<double>(5 * 5 * 5 * 5), {5, 5, 5, 5});
auto res = xt::sum(a, {0, 2}, xt::keep_dims | xt::evaluation_strategy::immediate | initial(5));
auto reso = xt::sum(a, {0, 2}, xt::keep_dims | xt::evaluation_strategy::immediate);
EXPECT_EQ(res, reso + 5);
xt::xarray<double> res2 = xt::sum(a, {0, 2}, xt::keep_dims | initial(5));
auto reso2 = xt::sum(a, {0, 2}, xt::keep_dims);
EXPECT_EQ(res2, reso2 + 5);
auto re0 = xt::prod(a, {1, 2}, xt::keep_dims | xt::evaluation_strategy::immediate | initial(0));