TEST(NaiveBayesTest, test2) {
enum Bird {
SMALL,
MIDDLE,
BIG
};
TrainingSet<Bird, 2> trainingSet;
// weight, height
std::array<double, 2> s1 = {2, 10};
std::array<double, 2> s2 = {2.3, 12};
trainingSet.add(SMALL, s1);
trainingSet.add(SMALL, s2);
std::array<double, 2> m1 = {4, 15};
std::array<double, 2> m2 = {4.7, 17.2};
trainingSet.add(MIDDLE, m1);
trainingSet.add(MIDDLE, m2);
std::array<double, 2> b1 = {7, 23};
std::array<double, 2> b2 = {8.5, 22.5};
trainingSet.add(BIG, b1);
trainingSet.add(BIG, b2);
NaiveBayesClasifier<Bird, 2> clasifier;
EXPECT_TRUE(clasifier.train(trainingSet));
std::array<double, 2> sample1 = {1.5, 9};
std::array<double, 2> sample2 = {4.9, 16};
std::array<double, 2> sample3 = {9, 20};
EXPECT_EQ(clasifier.clasify(sample1), SMALL);
EXPECT_EQ(clasifier.clasify(sample2), MIDDLE);
EXPECT_EQ(clasifier.clasify(sample3), BIG);
}
TEST(TrainingSetTest, test1) {
enum Bike {
MOUNTAIN_BIKE,
FAST_BIKE
};
TrainingSet<Bike, 2> trainingSet;
std::array<double, 2> t1 = {1, 2};
std::array<double, 2> t2 = {3, 4};
std::array<double, 2> t3 = {5, 6};
std::array<double, 2> t4 = {7, 8};
trainingSet.add(MOUNTAIN_BIKE, t1);
trainingSet.add(MOUNTAIN_BIKE, t2);
trainingSet.add(FAST_BIKE, t3);
trainingSet.add(FAST_BIKE, t4);
std::vector<double> expected1 = {1, 3};
std::vector<double> expected2 = {2, 4};
EXPECT_TRUE(trainingSet.hasMoreTypes());
EXPECT_EQ(trainingSet.getNextType(), MOUNTAIN_BIKE);
EXPECT_TRUE(trainingSet.getNextFeature() == expected1);
EXPECT_TRUE(trainingSet.getNextFeature() == expected2);
EXPECT_TRUE(trainingSet.hasMoreTypes());
EXPECT_EQ(trainingSet.getNextType(), FAST_BIKE);
EXPECT_FALSE(trainingSet.hasMoreFeatures());
}
TEST(NaiveBayesTest, test1) {
enum Type {
MALE,
FEMALE
};
TrainingSet<Type, 3> trainingSet;
// feature contains height, weight and foot size
std::array<double, 3> m1 = {6, 180, 12};
std::array<double, 3> m2 = {5.92, 190, 11};
std::array<double, 3> m3 = {5.58, 170, 12};
std::array<double, 3> m4 = {5.92, 165, 10};
trainingSet.add(MALE, m1);
trainingSet.add(MALE, m2);
trainingSet.add(MALE, m3);
trainingSet.add(MALE, m4);
std::array<double, 3> f1 = {5, 100, 6};
std::array<double, 3> f2 = {5.5, 150, 8};
std::array<double, 3> f3 = {5.42, 130, 7};
std::array<double, 3> f4 = {5.75, 150, 9};
trainingSet.add(FEMALE, f1);
trainingSet.add(FEMALE, f2);
trainingSet.add(FEMALE, f3);
trainingSet.add(FEMALE, f4);
std::array<double, 3> sample1 = {5, 120, 7.7};
std::array<double, 3> sample2 = {6.3, 172, 11.5};
NaiveBayesClasifier<Type, 3> clasifier;
EXPECT_TRUE(clasifier.train(trainingSet));
EXPECT_EQ(clasifier.clasify(sample1), FEMALE);
EXPECT_EQ(clasifier.clasify(sample2), MALE);
}
