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hungarian_test.cc
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hungarian_test.cc
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// Copyright 2010-2024 Google LLC
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// Test file for hungarian.h
#include "ortools/algorithms/hungarian.h"
#include <cmath>
#include <cstdint>
#include <random>
#include <vector>
#include "absl/container/flat_hash_map.h"
#include "absl/random/distributions.h"
#include "absl/types/span.h"
#include "gtest/gtest.h"
#include "ortools/base/macros.h"
#include "ortools/base/map_util.h"
#include "ortools/base/types.h"
namespace operations_research {
// Generic check function that checks consistency of a linear assignment
// result as well as whether the result is the expected one.
void GenericCheck(const int expected_assignment_size,
const absl::flat_hash_map<int, int>& direct_assignment,
const absl::flat_hash_map<int, int>& reverse_assignment,
const int expected_agents[], const int expected_tasks[]) {
EXPECT_EQ(expected_assignment_size, direct_assignment.size());
EXPECT_EQ(expected_assignment_size, reverse_assignment.size());
for (int i = 0; i < expected_assignment_size; ++i) {
EXPECT_EQ(gtl::FindOrDie(direct_assignment, expected_agents[i]),
expected_tasks[i]);
EXPECT_EQ(gtl::FindOrDie(reverse_assignment, expected_tasks[i]),
expected_agents[i]);
}
for (const auto& direct_iter : direct_assignment) {
EXPECT_EQ(gtl::FindOrDie(reverse_assignment, direct_iter.second),
direct_iter.first)
<< direct_iter.first << " -> " << direct_iter.second;
}
}
void TestMinimization(absl::Span<const std::vector<double>> cost,
const int expected_assignment_size,
const int expected_agents[], const int expected_tasks[]) {
absl::flat_hash_map<int, int> direct_assignment;
absl::flat_hash_map<int, int> reverse_assignment;
MinimizeLinearAssignment(cost, &direct_assignment, &reverse_assignment);
SCOPED_TRACE("Minimization");
GenericCheck(expected_assignment_size, direct_assignment, reverse_assignment,
expected_agents, expected_tasks);
}
void TestMaximization(absl::Span<const std::vector<double>> cost,
const int expected_assignment_size,
const int expected_agents[], const int expected_tasks[]) {
absl::flat_hash_map<int, int> direct_assignment;
absl::flat_hash_map<int, int> reverse_assignment;
MaximizeLinearAssignment(cost, &direct_assignment, &reverse_assignment);
SCOPED_TRACE("Maximization");
GenericCheck(expected_assignment_size, direct_assignment, reverse_assignment,
expected_agents, expected_tasks);
}
// Test on an empty matrix
TEST(LinearAssignmentTest, NullMatrix) {
std::vector<std::vector<double>> cost;
const int* expected_agents = nullptr;
const int* expected_tasks = nullptr;
TestMinimization(cost, 0, expected_agents, expected_tasks);
TestMaximization(cost, 0, expected_agents, expected_tasks);
}
// Testing with NaN value in the input.
TEST(LinearAssignmentTest, InvalidMatrix) {
const std::vector<std::vector<double>> cost_nan = {{1, 2},
{-std::sqrt(-1), 3}};
const int* expected_agents = nullptr;
const int* expected_tasks = nullptr;
TestMaximization(cost_nan, 0, expected_agents, expected_tasks);
TestMinimization(cost_nan, 0, expected_agents, expected_tasks);
}
#define MATRIX_TEST \
{ \
std::vector<std::vector<double>> cost(kMatrixHeight); \
for (int row = 0; row < kMatrixHeight; ++row) { \
cost[row].resize(kMatrixWidth); \
for (int col = 0; col < kMatrixWidth; ++col) { \
cost[row][col] = kCost[row][col]; \
} \
} \
EXPECT_EQ(arraysize(expected_agents_for_min), \
arraysize(expected_tasks_for_min)); \
EXPECT_EQ(arraysize(expected_agents_for_max), \
arraysize(expected_tasks_for_max)); \
const int assignment_size = arraysize(expected_agents_for_max); \
TestMinimization(cost, assignment_size, expected_agents_for_min, \
expected_tasks_for_min); \
TestMaximization(cost, assignment_size, expected_agents_for_max, \
expected_tasks_for_max); \
}
// Test on a 1x1 matrix
TEST(LinearAssignmentTest, SizeOneMatrix) {
const int kMatrixHeight = 1;
const int kMatrixWidth = 1;
const double kCost[kMatrixHeight][kMatrixWidth] = {{4}};
const int expected_agents_for_min[] = {0};
const int expected_tasks_for_min[] = {0};
const int expected_agents_for_max[] = {0};
const int expected_tasks_for_max[] = {0};
MATRIX_TEST;
}
// Test on a 4x4 matrix. Example taken at
// http://www.ee.oulu.fi/~mpa/matreng/eem1_2-1.htm
TEST(LinearAssignmentTest, Small4x4Matrix) {
const int kMatrixHeight = 4;
const int kMatrixWidth = 4;
const double kCost[kMatrixHeight][kMatrixWidth] = {{90, 75, 75, 80},
{35, 85, 55, 65},
{125, 95, 90, 105},
{45, 110, 95, 115}};
const int expected_agents_for_min[] = {0, 1, 2, 3};
const int expected_tasks_for_min[] = {3, 2, 1, 0};
const int expected_agents_for_max[] = {0, 1, 2, 3};
const int expected_tasks_for_max[] = {2, 1, 0, 3};
MATRIX_TEST;
}
// Test on a 3x4 matrix. Sub-problem of Small4x4Matrix
TEST(LinearAssignmentTest, Small3x4Matrix) {
const int kMatrixHeight = 3;
const int kMatrixWidth = 4;
const double kCost[kMatrixHeight][kMatrixWidth] = {
{90, 75, 75, 80}, {35, 85, 55, 65}, {125, 95, 90, 105}};
const int expected_agents_for_min[] = {0, 1, 2};
const int expected_tasks_for_min[] = {1, 0, 2};
const int expected_agents_for_max[] = {0, 1, 2};
const int expected_tasks_for_max[] = {3, 1, 0};
MATRIX_TEST;
}
// Test on a 4x3 matrix. Sub-problem of Small4x4Matrix
TEST(LinearAssignmentTest, Small4x3Matrix) {
const int kMatrixHeight = 4;
const int kMatrixWidth = 3;
const double kCost[kMatrixHeight][kMatrixWidth] = {
{90, 75, 75}, {35, 85, 55}, {125, 95, 90}, {45, 110, 95}};
const int expected_agents_for_min[] = {0, 1, 3};
const int expected_tasks_for_min[] = {1, 2, 0};
const int expected_agents_for_max[] = {0, 2, 3};
const int expected_tasks_for_max[] = {2, 0, 1};
MATRIX_TEST;
}
#undef MATRIX_TEST
} // namespace operations_research