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C++ Rect And Point Search Algorithm

news 来源：原创 2024/9/20 12:07:23

测试

//
// Created by www on 2024/8/8.
//
#include "include/cxstructs.h"
#include "include/cxml/k-NN.h"// 可扩展Rect内搜索子Rect或Point
void testRectSearch() {using namespace cxstructs;std::random_device rd;std::mt19937 gen(rd());std::uniform_real_distribution<float> distr(0, 200);std::cout << "TESTING QUAD TREE" << std::endl;std::cout << "   Testing insert..." << std::endl;QuadTree<Point> tree({0, 0, 200, 200});for (uint_fast32_t i = 0; i < 1000; i++) {tree.insert({distr(gen), distr(gen)});}CX_ASSERT(tree.depth() == 3, "");CX_ASSERT(tree.size() == 1000, "");std::cout << "   Testing count subrect..." << std::endl;CX_ASSERT(tree.count_subrect({0, 0, 200, 200}) == 1000, "");CX_ASSERT(tree.count_subrect({10, 10, 50, 50}) > 10, "");tree.insert({2, 2});CX_ASSERT(tree.size() == 1001, "");tree.erase({2, 2});CX_ASSERT(tree.size() == 1000, "");std::cout << "   Testing max capacity..." << std::endl;QuadTree<Point> tree1({0, 0, 200, 200}, 2, 10);for (uint_fast32_t i = 0; i < 100000; i++) {tree.insert({distr(gen), distr(gen)});}std::cout << "   Testing object retrieval..." << std::endl;tree1.clear();tree1.insert({2, 2});for (auto ptr : tree1.get_subrect({0, 0, 2, 2})) {CX_ASSERT(*ptr == Point(2, 2), "");}
}// 点集处理，可分类
void testKNN() {using namespace cxstructs;enum Category { A, B, C };struct DataPoint : public DataPoint_<Category> {Category category;float x_;float y_;float weight = 1;DataPoint() {}DataPoint(float x, float y, Category category) : x_(x), y_(y), category(category) {}float x() const final { return x_; }float y() const final { return y_; }Category getCategory() final { return category; }float getWeight() const final { return weight; }};std::cout << "TESTING k-NN" << std::endl;std::vector<DataPoint> data{};data.emplace_back(1, 2, Category::A);data.emplace_back(2, 3, Category::A);data.emplace_back(3, 4, Category::A);data.emplace_back(4, 5, Category::B);data.emplace_back(5, 6, Category::B);data.emplace_back(6, 7, Category::B);data.emplace_back(7, 8, Category::C);data.emplace_back(8, 9, Category::C);data.emplace_back(9, 10, Category::C);Category cat1;kNN_2D<DataPoint> knn(data, DISTANCE_FUNCTION_2D::EUCLIDEAN);std::cout << "   Testing absolute classification" << std::endl;cat1 = knn.classify_by_category_count(0, 0, 4);CX_ASSERT((int)cat1 == 0, "");cat1 = knn.classify_by_category_count(5, 5, 4);CX_ASSERT((int)cat1 == 1, "");std::cout << "   Testing distance weighted classification" << std::endl;cat1 = knn.classify_by_sum_distance(0, 0, 4);CX_ASSERT((int)cat1 == 0, "");cat1 = knn.classify_by_sum_distance(5, 5, 4);CX_ASSERT((int)cat1 == 1, "");std::cout << "   Testing weight classification" << std::endl;cat1 = knn.classify_by_sum_weight(0, 0, 4);CX_ASSERT((int)cat1 == 0, "");cat1 = knn.classify_by_sum_weight(5, 5, 4);CX_ASSERT((int)cat1 == 1, "");std::cout << "   Testing weighted * dist classification" << std::endl;cat1 = knn.classify_by_sum_weighted_distance(0, 0, 4);CX_ASSERT((int)cat1 == 0, "");cat1 = knn.classify_by_sum_weighted_distance(5, 5, 4);CX_ASSERT((int)cat1 == 1, "");
}// 字典
void testTrie() {using namespace cxstructs;std::cout << "TESTING TRIE" << std::endl;std::cout << "   Testing insertion..." << std::endl;Trie trie;trie.insert("hello");std::cout << "   Testing contained..." << std::endl;CX_ASSERT(trie.contains("hello") == true, "");CX_ASSERT(trie.contains("helloh") == false, "");std::cout << "   Testing startsWith..." << std::endl;CX_ASSERT(trie.startsWith("he")[0] == "hello", "");
}int main() {
//  testKNN();testRectSearch();testTrie();return 0;
}

输出

TESTING QUAD TREE
Testing insert...
Testing count subrect...
Testing max capacity...
Testing object retrieval...
TESTING TRIE
Testing insertion...
Testing contained...
Testing startsWith...