xtensor/test/test_xfunction.cpp at master · xtensor-stack/xtensor · GitHub

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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 "xtensor/containers/xarray.hpp"

#include "xtensor/containers/xfixed.hpp"

#include "xtensor/containers/xtensor.hpp"

#include "xtensor/core/xvectorize.hpp"

#include "xtensor/generators/xrandom.hpp"

#include "xtensor/views/xview.hpp"

#include "test_common.hpp"

#include "test_common_macros.hpp"

namespace xt

{

using std::size_t;

struct xfunction_features

{

xarray<int, layout_type::dynamic> m_a; // shape = { 3, 2, 4 }

xarray<int, layout_type::dynamic> m_b; // shape = { 3, 1, 4 }

xarray<int, layout_type::dynamic> m_c; // shape = { 4, 3, 2, 4 }

xfunction_features();

};

xfunction_features::xfunction_features()

{

row_major_result<> rm;

m_a.resize(rm.shape(), rm.strides());

std::copy(rm.storage().cbegin(), rm.storage().cend(), m_a.begin());

unit_shape_result<> us;

m_b.resize(us.shape(), us.strides());

std::copy(us.storage().cbegin(), us.storage().cend(), m_b.begin());

using shape_type = layout_result<>::shape_type;

shape_type sh = {4, 3, 2, 4};

m_c.resize(sh);

for (size_t i = 0; i < sh[0]; ++i)

{

for (size_t j = 0; j < sh[1]; ++j)

{

for (size_t k = 0; k < sh[2]; ++k)

{

for (size_t l = 0; l < sh[3]; ++l)

{

m_c(i, j, k, l) = m_a(j, k, l) + static_cast<int>(i);

}

template <typename E>

auto normalize(E&& expr)

{

return E{expr} / xt::view(xt::sum(E{expr}, {1}), xt::all(), xt::newaxis());

}

TEST(xfunction, copy_constructor)

{

xt::xarray<double> freqs({10, 5}, 1);

xt::xarray<double> other_freqs({10, 5}, 2);

// Compilation test

xarray<double> probs = normalize(freqs + other_freqs);

}

TEST(xfunction, broadcast_shape)

{

using shape_type = layout_result<>::shape_type;

xfunction_features f;

SUBCASE("same shape")

{

shape_type sh = uninitialized_shape<shape_type>(3);

bool trivial = (f.m_a + f.m_a).broadcast_shape(sh);

EXPECT_EQ(sh, f.m_a.shape());

ASSERT_TRUE(trivial);

}

SUBCASE("different shape")

{

shape_type sh = uninitialized_shape<shape_type>(3);

bool trivial = (f.m_a + f.m_b).broadcast_shape(sh);

EXPECT_EQ(sh, f.m_a.shape());

ASSERT_FALSE(trivial);

}

SUBCASE("different dimensions")

{

shape_type sh = uninitialized_shape<shape_type>(4);

bool trivial = (f.m_a + f.m_c).broadcast_shape(sh);

EXPECT_EQ(sh, f.m_c.shape());

ASSERT_FALSE(trivial);

}

TEST(xfunction, broadcast_shape_exception)

{

xt::xarray<double> arr1{{1.0, 2.0, 3.0}};

xt::xarray<double> arr2{5.0, 6.0, 7.0, 99.0};

XT_EXPECT_ANY_THROW(xt::xarray<double> res = arr1 * arr2);

}

TEST(xfunction, shape)

{

xfunction_features f;

auto func = f.m_a + f.m_c;

const auto& sh = func.shape();

for (std::size_t i = 0; i < sh.size(); ++i)

{

EXPECT_EQ(sh[i], func.shape(i));

}

TEST(xfunction, layout_type)

{

xarray<int, layout_type::dynamic> m_d;

xarray<int, layout_type::row_major> m_r;

xarray<int, layout_type::column_major> m_c;

xscalar<int> m_a(2);

auto res_dd = m_d + m_d;

EXPECT_EQ(res_dd.layout(), layout_type::dynamic);

auto res_dr = m_d + m_r;

EXPECT_EQ(res_dr.layout(), layout_type::dynamic);

auto res_dc = m_d + m_c;

EXPECT_EQ(res_dc.layout(), layout_type::dynamic);

auto res_da = m_d + m_a;

EXPECT_EQ(res_da.layout(), layout_type::dynamic);

auto res_rd = m_r + m_d;

EXPECT_EQ(res_rd.layout(), layout_type::dynamic);

auto res_rr = m_r + m_r;

EXPECT_EQ(res_rr.layout(), layout_type::row_major);

auto res_rc = m_r + m_c;

EXPECT_EQ(res_rc.layout(), layout_type::dynamic);

auto res_ra = m_r + m_a;

EXPECT_EQ(res_ra.layout(), layout_type::row_major);

auto res_cd = m_c + m_d;

EXPECT_EQ(res_cd.layout(), layout_type::dynamic);

auto res_cr = m_c + m_r;

EXPECT_EQ(res_cr.layout(), layout_type::dynamic);

auto res_cc = m_c + m_c;

EXPECT_EQ(res_cc.layout(), layout_type::column_major);

auto res_ca = m_c + m_a;

EXPECT_EQ(res_ca.layout(), layout_type::column_major);

auto res_ad = m_a + m_d;

EXPECT_EQ(res_ad.layout(), layout_type::dynamic);

auto res_ar = m_a + m_r;

EXPECT_EQ(res_ar.layout(), layout_type::row_major);

auto res_ac = m_a + m_c;

EXPECT_EQ(res_ac.layout(), layout_type::column_major);

auto res_aa = m_a + m_a;

EXPECT_EQ(res_aa.layout(), layout_type::any);

}

TEST(xfunction, access)

{

xfunction_features f;

size_t i = f.m_a.shape()[0] - 1;

size_t j = f.m_a.shape()[1] - 1;

size_t k = f.m_a.shape()[2] - 1;

SUBCASE("same shape")

{

int a = (f.m_a + f.m_a)(i, j, k);

int b = f.m_a(i, j, k) + f.m_a(i, j, k);

EXPECT_EQ(a, b);

}

SUBCASE("different shape")

{

int a = (f.m_a + f.m_b)(i, j, k);

int b = f.m_a(i, j, k) + f.m_b(i, 0, k);

EXPECT_EQ(a, b);

}

SUBCASE("different dimensions")

{

int a = (f.m_a + f.m_c)(1, i, j, k);

int b = f.m_a(i, j, k) + f.m_c(1, i, j, k);

EXPECT_EQ(a, b);

}

TEST(xfunction, access_dangling_reference)

{

auto values_original = xt::xtensor<double, 1>{1., 2., 3.};

auto indexes = xt::arange<size_t>(values_original.size());

auto map = [&](const size_t& index) -> auto&

{

return values_original[index];

};

auto values_mapped = xt::vectorize(map)(indexes);

for (size_t i = 0; i < values_original.size(); ++i)

{

EXPECT_TRUE(&(values_mapped[i]) == &(values_original[i]));

}

TEST(xfunction, unchecked)

{

xfunction_features f;

size_t i = f.m_a.shape()[0] - 1;

size_t j = f.m_a.shape()[1] - 1;

size_t k = f.m_a.shape()[2] - 1;

SUBCASE("same shape")

{

int a = (f.m_a + f.m_a)(i, j, k);

int b = (f.m_a + f.m_a).unchecked(i, j, k);

EXPECT_EQ(a, b);

}

SUBCASE("different shape")

{

int a = (f.m_a + f.m_b)(i, j, k);

int b = (f.m_a + f.m_b).unchecked(i, j, k);

EXPECT_EQ(a, b);

}

TEST(xfunction, at)

{

xfunction_features f;

size_t i = f.m_a.shape()[0] - 1;

size_t j = f.m_a.shape()[1] - 1;

size_t k = f.m_a.shape()[2] - 1;

SUBCASE("same shape")

{

int a = (f.m_a + f.m_a).at(i, j, k);

int b = f.m_a.at(i, j, k) + f.m_a.at(i, j, k);

EXPECT_EQ(a, b);

XT_EXPECT_ANY_THROW((f.m_a + f.m_a).at(0, 0, 0, 0));

XT_EXPECT_ANY_THROW((f.m_a + f.m_a).at(10, 10, 10));

}

SUBCASE("different shape")

{

int a = (f.m_a + f.m_b).at(i, j, k);

int b = f.m_a.at(i, j, k) + f.m_b.at(i, 0, k);

EXPECT_EQ(a, b);

XT_EXPECT_ANY_THROW((f.m_a + f.m_a).at(0, 0, 0, 0));

XT_EXPECT_ANY_THROW((f.m_a + f.m_a).at(10, 10, 10));

}

SUBCASE("different dimensions")

{

int a = (f.m_a + f.m_c).at(1, i, j, k);

int b = f.m_a.at(i, j, k) + f.m_c.at(1, i, j, k);

EXPECT_EQ(a, b);

XT_EXPECT_ANY_THROW((f.m_a + f.m_a).at(0, 0, 0, 0, 0));

XT_EXPECT_ANY_THROW((f.m_a + f.m_a).at(10, 10, 10, 10));

}

TEST(xfunction, periodic)

{

xfunction_features f;

size_t i = f.m_a.shape()[0];

size_t j = f.m_a.shape()[1];

size_t k = f.m_a.shape()[2];

SUBCASE("same shape")

{

int a = (f.m_a + f.m_a)(0, 0, 0);

int b = (f.m_a + f.m_a).periodic(i, j, k);

EXPECT_EQ(a, b);

}

SUBCASE("different shape")

{

int a = (f.m_a + f.m_b)(0, 0, 0);

int b = (f.m_a + f.m_b).periodic(i, j, k);

EXPECT_EQ(a, b);

}

TEST(xfunction, front)

{

xfunction_features f;

int a = (f.m_a + f.m_a).front();

int b = f.m_a.front() + f.m_a.front();

EXPECT_EQ(a, b);

}

TEST(xfunction, back)

{

xfunction_features f;

int a = (f.m_a + f.m_a).back();

int b = f.m_a.back() + f.m_a.back();

EXPECT_EQ(a, b);

}

TEST(xfunction, flat)

{

xfunction_features f;

int a = (f.m_a + f.m_a).flat(0);

int b = f.m_a.flat(0) + f.m_a.flat(0);

EXPECT_EQ(a, b);

}

TEST(xfunction, in_bounds)

{

xfunction_features f;

size_t i = f.m_a.shape()[0];

size_t j = f.m_a.shape()[1];

size_t k = f.m_a.shape()[2];

SUBCASE("same shape")

{

EXPECT_TRUE((f.m_a + f.m_a).in_bounds(0, 0, 0) == true);

EXPECT_TRUE((f.m_a + f.m_a).in_bounds(i, j, k) == false);

}

SUBCASE("different shape")

{

EXPECT_TRUE((f.m_a + f.m_b).in_bounds(0, 0, 0) == true);

EXPECT_TRUE((f.m_a + f.m_b).in_bounds(i, j, k) == false);

}

TEST(xfunction, indexed_access)

{

xfunction_features f;

xindex index(f.m_a.dimension());

index[0] = f.m_a.shape()[0] - 1;

index[1] = f.m_a.shape()[1] - 1;

index[2] = f.m_a.shape()[2] - 1;

SUBCASE("same shape")

{

int a = (f.m_a + f.m_a)[index];

int b = f.m_a[index] + f.m_a[index];

EXPECT_EQ(a, b);

EXPECT_EQ(((f.m_a + f.m_a)[{0, 0, 0}]), (f.m_a[{0, 0, 0}] + f.m_a[{0, 0, 0}]));

}

SUBCASE("different shape")

{

int a = (f.m_a + f.m_b)[index];

xindex index2 = index;

index2[1] = 0;

int b = f.m_a[index] + f.m_b[index2];

EXPECT_EQ(a, b);

EXPECT_EQ(((f.m_a + f.m_b)[{0, 0, 0}]), (f.m_a[{0, 0, 0}] + f.m_b[{0, 0, 0}]));

}

SUBCASE("different dimensions")

{

xindex index2(f.m_c.dimension());

index2[0] = 1;

index2[1] = index[0];

index2[2] = index[1];

index2[3] = index[2];

int a = (f.m_a + f.m_c)[index2];

int b = f.m_a[index] + f.m_c[index2];

EXPECT_EQ(a, b);

EXPECT_EQ(((f.m_a + f.m_c)[{0, 0, 0, 0}]), (f.m_a[{0, 0, 0, 0}] + f.m_c[{0, 0, 0, 0}]));

}

void test_xfunction_iterator(const xarray<int>& a, const xarray<int>& b)

{

auto func = (a + b);

auto iter = func.begin();

auto itera = a.begin();

auto iterb = b.begin(a.shape());

auto nb_iter = a.shape().back() * 2 + 1;

for (size_t i = 0; i < nb_iter; ++i)

{

++iter, ++itera, ++iterb;

EXPECT_EQ(*iter, *itera + *iterb);

}

EXPECT_EQ(*iter, *itera + *iterb);

}

TEST(xfunction, iterator)

{

xfunction_features f;

SUBCASE("same shape")

{

test_xfunction_iterator(f.m_a, f.m_a);

}

SUBCASE("different shape")

{

test_xfunction_iterator(f.m_a, f.m_b);

}

SUBCASE("different dimensions")

{

test_xfunction_iterator(f.m_c, f.m_a);

}

void test_xfunction_iterator_end(const xarray<int>& a, const xarray<int>& b)

{

auto func = (a + b);

auto iter = func.begin();

auto iter_end = func.end();

auto size = a.size();

for (size_t i = 0; i < size; ++i)

{

++iter;

}

EXPECT_EQ(iter, iter_end);

}

TEST(xfunction, iterator_end)

{

xfunction_features f;

SUBCASE("same shape")

{

test_xfunction_iterator_end(f.m_a, f.m_a);

}

SUBCASE("different shape")

{

test_xfunction_iterator_end(f.m_a, f.m_b);

}

SUBCASE("different dimensions")

{

test_xfunction_iterator_end(f.m_c, f.m_a);

}

template <class F>

void iterator_tester(const F& func)

{

auto res1 = empty_like(func);

auto res2 = empty_like(func);

auto res3 = empty_like(func);

auto res4 = empty_like(func);

auto res5 = func;

auto resit1 = res1.template begin<XTENSOR_DEFAULT_LAYOUT>();

for (auto it = func.template begin<XTENSOR_DEFAULT_LAYOUT>();

it != func.template end<XTENSOR_DEFAULT_LAYOUT>();

++it)

{

*(resit1++) = *it;

}

auto resit3 = res3.template rbegin<XTENSOR_DEFAULT_LAYOUT>();

for (auto it = func.template rbegin<XTENSOR_DEFAULT_LAYOUT>();

it != func.template rend<XTENSOR_DEFAULT_LAYOUT>();

++it)

{

*(resit3++) = *it;

}

if (func.has_linear_assign(res2.strides()))

{

auto resit2 = res2.template begin<XTENSOR_DEFAULT_LAYOUT>();

for (auto it = func.linear_begin(); it != func.linear_end(); ++it)

{

*(resit2++) = *it;

}

auto resit4 = res4.template rbegin<XTENSOR_DEFAULT_LAYOUT>();

for (auto it = func.linear_rbegin(); it != func.linear_rend(); ++it)

{

*(resit4++) = *it;

}

EXPECT_TENSOR_EQ(res2, res5);

EXPECT_TENSOR_EQ(res4, res5);

}

EXPECT_TENSOR_EQ(res1, res5);

EXPECT_TENSOR_EQ(res3, res5);

EXPECT_TENSOR_EQ(func, res5);

}

TEST(xfunction, all_iterators)

{

xt::xarray<double> x = xt::random::rand<double>({5, 5, 5});

xt::xarray<double> y = xt::random::rand<double>({5});

xt::xarray<double> z = xt::random::rand<double>({5, 5});

auto f1 = 2.0 * x;

auto f2 = x * 2.0 * x;

iterator_tester(f1);

iterator_tester(f2);

iterator_tester(x * y * 3.0);

iterator_tester(5.0 + x * y * 3.0);

iterator_tester(x * y * z);

iterator_tester(x / y / z);

}

TEST(xfunction, xfunction_in_xfunction)

{

using Point3 = xt::xtensor_fixed<double, xshape<3>>;

Point3 a1({1., 2., 3.});

Point3 a2({3., 1., 3.});

Point3 a3({54., 5., 5.});

xtensor<Point3, 1> a({a1, a2, a3});

xtensor<Point3, 1> c = a + a;

Point3 r1({2., 4., 6.}), r2({6., 2., 6.}), r3({108., 10., 10.});

xtensor<Point3, 1> res{r1, r2, r3};

EXPECT_EQ(c, res);

}

xtensor/test/test_xfunction.cpp at master · xtensor-stack/xtensor · GitHub

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