rmontanana/BayesNet
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Remove using namespace from Library

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This commit is contained in:
Ricardo Montañana Gómez
2023-11-08 18:45:35 +01:00
parent 92820555da
commit f9258e43b9
96 changed files with 1316 additions and 1340 deletions
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26
tests/TestBayesNetwork.cc
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@@ -1,13 +1,13 @@
#include <catch2/catch_test_macros.hpp>
#include <catch2/catch_approx.hpp>
#include <catch2/generators/catch_generators.hpp>
#include <string>
#include <std::string>
#include "TestUtils.h"
#include "Network.h"
void buildModel(bayesnet::Network& net, const vector<string>& features, const string& className)
void buildModel(bayesnet::Network& net, const std::vector<std::string>& features, const std::std::string& className)
{
vector<pair<int, int>> network = { {0, 1}, {0, 2}, {1, 3} };
std::vector<pair<int, int>> network = { {0, 1}, {0, 2}, {1, 3} };
for (const auto& feature : features) {
net.addNode(feature);
}
@@ -30,9 +30,9 @@ TEST_CASE("Test Bayesian Network", "[BayesNet]")
{
net.addNode("A");
net.addNode("B");
REQUIRE(net.getFeatures() == vector<string>{"A", "B"});
REQUIRE(net.getFeatures() == std::vector<std::string>{"A", "B"});
net.addNode("C");
REQUIRE(net.getFeatures() == vector<string>{"A", "B", "C"});
REQUIRE(net.getFeatures() == std::vector<std::string>{"A", "B", "C"});
}
SECTION("Test get edges")
{
@@ -41,10 +41,10 @@ TEST_CASE("Test Bayesian Network", "[BayesNet]")
net.addNode("C");
net.addEdge("A", "B");
net.addEdge("B", "C");
REQUIRE(net.getEdges() == vector<pair<string, string>>{ {"A", "B"}, { "B", "C" } });
REQUIRE(net.getEdges() == std::vector<pair<std::string, std::string>>{ {"A", "B"}, { "B", "C" } });
REQUIRE(net.getNumEdges() == 2);
net.addEdge("A", "C");
REQUIRE(net.getEdges() == vector<pair<string, string>>{ {"A", "B"}, { "A", "C" }, { "B", "C" } });
REQUIRE(net.getEdges() == std::vector<pair<std::string, std::string>>{ {"A", "B"}, { "A", "C" }, { "B", "C" } });
REQUIRE(net.getNumEdges() == 3);
}
SECTION("Test getNodes")
@@ -66,7 +66,7 @@ TEST_CASE("Test Bayesian Network", "[BayesNet]")
buildModel(net, raw.featuresv, raw.classNamev);
buildModel(net2, raw.featurest, raw.classNamet);
buildModel(net3, raw.featurest, raw.classNamet);
vector<pair<string, string>> edges = {
std::vector<pair<std::string, std::string>> edges = {
{"class", "sepallength"}, {"class", "sepalwidth"}, {"class", "petallength"},
{"class", "petalwidth" }, {"sepallength", "sepalwidth"}, {"sepallength", "petallength"},
{"sepalwidth", "petalwidth"}
@@ -74,7 +74,7 @@ TEST_CASE("Test Bayesian Network", "[BayesNet]")
REQUIRE(net.getEdges() == edges);
REQUIRE(net2.getEdges() == edges);
REQUIRE(net3.getEdges() == edges);
vector<string> features = { "sepallength", "sepalwidth", "petallength", "petalwidth", "class" };
std::vector<std::string> features = { "sepallength", "sepalwidth", "petallength", "petalwidth", "class" };
REQUIRE(net.getFeatures() == features);
REQUIRE(net2.getFeatures() == features);
REQUIRE(net3.getFeatures() == features);
@@ -84,7 +84,7 @@ TEST_CASE("Test Bayesian Network", "[BayesNet]")
// Check Nodes parents & children
for (const auto& feature : features) {
// Parents
vector<string> parents, parents2, parents3, children, children2, children3;
std::vector<std::string> parents, parents2, parents3, children, children2, children3;
auto nodeParents = nodes[feature]->getParents();
auto nodeParents2 = nodes2[feature]->getParents();
auto nodeParents3 = nodes3[feature]->getParents();
@@ -173,8 +173,8 @@ TEST_CASE("Test Bayesian Network", "[BayesNet]")
// {
// auto net = bayesnet::Network();
// net.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, raw.classNamev, raw.statesv);
// vector<vector<int>> test = { {1, 2, 0, 1}, {0, 1, 2, 0}, {1, 1, 1, 1}, {0, 0, 0, 0}, {2, 2, 2, 2} };
// vector<int> y_test = { 0, 1, 1, 0, 2 };
// std::vector<std::vector<int>> test = { {1, 2, 0, 1}, {0, 1, 2, 0}, {1, 1, 1, 1}, {0, 0, 0, 0}, {2, 2, 2, 2} };
// std::vector<int> y_test = { 0, 1, 1, 0, 2 };
// auto y_pred = net.predict(test);
// REQUIRE(y_pred == y_test);
// }
@@ -183,7 +183,7 @@ TEST_CASE("Test Bayesian Network", "[BayesNet]")
// {
// auto net = bayesnet::Network();
// net.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, raw.classNamev, raw.statesv);
// vector<vector<int>> test = { {1, 2, 0, 1}, {0, 1, 2, 0}, {1, 1, 1, 1}, {0, 0, 0, 0}, {2, 2, 2, 2} };
// std::vector<std::vector<int>> test = { {1, 2, 0, 1}, {0, 1, 2, 0}, {1, 1, 1, 1}, {0, 0, 0, 0}, {2, 2, 2, 2} };
// auto y_test = { 0, 1, 1, 0, 2 };
// auto y_pred = net.predict(test);
// REQUIRE(y_pred == y_test);