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pl:miw:miw08_rbs_ml:program [2008/06/16 11:48] miw |
pl:miw:miw08_rbs_ml:program [2008/06/16 13:39] miw |
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| - | <code = "java"> | ||
| - | package MachineLearning; | ||
| - | import java.util.*; | ||
| - | |||
| - | /** | ||
| - | * Klasa z której program rozpoczyna swoje wykonywanie | ||
| - | * @author Angello | ||
| - | * | ||
| - | */ | ||
| - | public class Program { | ||
| - | |||
| - | /** | ||
| - | * Początek programu | ||
| - | * @param args | ||
| - | */ | ||
| - | public static void main(String[] args) { | ||
| - | test(); | ||
| - | } | ||
| - | |||
| - | /** | ||
| - | * Tworzy tablicę zawierająca wszyskie możliwe przypadki | ||
| - | * @return | ||
| - | */ | ||
| - | public static Object[][] create() | ||
| - | { | ||
| - | |||
| - | Object[][] learning = new Object[2016][]; | ||
| - | Integer index = -1; | ||
| - | Integer day; | ||
| - | Integer oper; | ||
| - | Integer month; | ||
| - | for (Integer i = 0; i < 7; i++) // Dni | ||
| - | { | ||
| - | day = i; | ||
| - | for (Integer j = 0; j < 24; j++) // Godziny | ||
| - | { | ||
| - | oper = j; | ||
| - | for (Integer k = 0; k < 12; k++) // Miesiące | ||
| - | { | ||
| - | month = k; | ||
| - | learning[++index] = new Object[] { day, oper, month, 0 }; | ||
| - | learning[index][3] = CountTemperature(learning[index]); | ||
| - | } | ||
| - | } | ||
| - | } | ||
| - | return learning; | ||
| - | } | ||
| - | |||
| - | /** | ||
| - | * Zbudowanie drzewa i sprawdzenie działanie drzewa dla pozostałych przykładów | ||
| - | * @param learningLength | ||
| - | * @return | ||
| - | */ | ||
| - | public static int makeOneTest(Integer learningLength, boolean c45) | ||
| - | { | ||
| - | Integer randValue; | ||
| - | Object[][] everything; | ||
| - | Object[][] testing; | ||
| - | Object[][] learning; | ||
| - | DecisionTree decisionTree; | ||
| - | Integer goodClassified = 0; | ||
| - | |||
| - | everything = create();//= Common.BinaryDeSerialize("Thermostat_2016.bin"); | ||
| - | String[] attributeNames = new String[] { "Day", "Hour", "Month" }; | ||
| - | |||
| - | Random rand = new Random(); | ||
| - | ArrayList<Integer> learningIndexes = new ArrayList<Integer>(); | ||
| - | ArrayList<Integer> testingIndexes = new ArrayList<Integer>(); | ||
| - | for (Integer i = 0; i < learningLength; i++) | ||
| - | { | ||
| - | randValue = rand.nextInt(2015); | ||
| - | if (!learningIndexes.contains(randValue)) | ||
| - | learningIndexes.add(randValue); | ||
| - | else | ||
| - | --i; | ||
| - | } | ||
| - | Collections.sort(learningIndexes); | ||
| - | |||
| - | for (Integer i = 0; i < 2016; i++) | ||
| - | { | ||
| - | if (!learningIndexes.contains(i)) | ||
| - | testingIndexes.add(i); | ||
| - | } | ||
| - | |||
| - | testing = indexesToArray(everything, testingIndexes); | ||
| - | |||
| - | learning = indexesToArray(everything, learningIndexes); | ||
| - | |||
| - | decisionTree = new DecisionTree(); | ||
| - | decisionTree.initialize(learning, attributeNames, c45); | ||
| - | |||
| - | for (Object[] row : testing) | ||
| - | { | ||
| - | if (decisionTree.findCategory(row) == (Integer)row[3]) | ||
| - | goodClassified++; | ||
| - | } | ||
| - | System.out.println(goodClassified); | ||
| - | return goodClassified; | ||
| - | } | ||
| - | |||
| - | /** | ||
| - | * Tworzy tablicę Przykładów z podanej listy indeksów oraz tablicy wszystkich przykładów | ||
| - | * @param everything | ||
| - | * @param indexesList | ||
| - | * @return | ||
| - | */ | ||
| - | private static Object[][] indexesToArray(Object[][] everything, ArrayList<Integer> indexesList) | ||
| - | { | ||
| - | ArrayList<Object[]> testingList = new ArrayList<Object[]>(); | ||
| - | for (Integer index : indexesList) | ||
| - | { | ||
| - | testingList.add(everything[index]); | ||
| - | } | ||
| - | Object[][] returnTable = new Object[indexesList.size()][everything[0].length]; | ||
| - | testingList.toArray(returnTable); | ||
| - | return returnTable; | ||
| - | } | ||
| - | |||
| - | /** | ||
| - | * Oblicza jaka powinna być nastawa termostatu przy przedstawionych atrybutach (example) | ||
| - | * @param example Zbiór atrybutów | ||
| - | * @return | ||
| - | */ | ||
| - | public static int CountTemperature(Object[] example) | ||
| - | { | ||
| - | boolean workday = true; | ||
| - | if (Days.saturday.ordinal() == (Integer)example[0] || Days.sunday.ordinal() == (Integer)example[0]) | ||
| - | workday = false; | ||
| - | |||
| - | boolean oper = false; | ||
| - | if ((Integer)example[1] >= 9 && (Integer)example[1] < 17) | ||
| - | oper = true; | ||
| - | boolean business = false; | ||
| - | if (workday && oper) | ||
| - | business = true; | ||
| - | |||
| - | Thermostat temperature = Thermostat.t14; | ||
| - | switch ((Integer)example[2]) | ||
| - | { | ||
| - | case 11: | ||
| - | case 0: | ||
| - | case 1: | ||
| - | if (business) | ||
| - | temperature = Thermostat.t18; | ||
| - | else | ||
| - | temperature = Thermostat.t14; | ||
| - | break; | ||
| - | case 2: | ||
| - | case 3: | ||
| - | case 4: | ||
| - | if (business) | ||
| - | temperature = Thermostat.t20; | ||
| - | else | ||
| - | temperature = Thermostat.t15; | ||
| - | |||
| - | break; | ||
| - | case 5: | ||
| - | case 6: | ||
| - | case 7: | ||
| - | if (business) | ||
| - | temperature = Thermostat.t24; | ||
| - | else | ||
| - | temperature = Thermostat.t27; | ||
| - | break; | ||
| - | case 8: | ||
| - | case 9: | ||
| - | case 10: | ||
| - | if (business) | ||
| - | temperature = Thermostat.t20; | ||
| - | else | ||
| - | temperature = Thermostat.t16; | ||
| - | break; | ||
| - | } | ||
| - | |||
| - | return temperature.ordinal(); | ||
| - | } | ||
| - | |||
| - | /** | ||
| - | * Wykonuje 20 testów i wypisuje statystyki | ||
| - | */ | ||
| - | private static void test() | ||
| - | { | ||
| - | int learningLength = 150; | ||
| - | ArrayList<Integer> list = new ArrayList<Integer>(); | ||
| - | Integer sum = 0; | ||
| - | for (int i = 0; i < 20; i++) { | ||
| - | list.add(makeOneTest(learningLength, true)); | ||
| - | } | ||
| - | for (Integer var : list) | ||
| - | { | ||
| - | sum += var; | ||
| - | } | ||
| - | double percent = (double) sum / (double)(2016 - 150) / (double)list.size(); | ||
| - | Collections.sort(list); | ||
| - | |||
| - | ArrayList<Integer> listc45 = new ArrayList<Integer>(); | ||
| - | Integer sumc45 = 0; | ||
| - | for (int i = 0; i < 20; i++) { | ||
| - | listc45.add(makeOneTest(learningLength, false)); | ||
| - | } | ||
| - | for (Integer var : listc45) | ||
| - | { | ||
| - | sumc45 += var; | ||
| - | } | ||
| - | double percentc45 = (double) sumc45 / (double)(2016 - 150) / (double)listc45.size(); | ||
| - | Collections.sort(listc45); | ||
| - | |||
| - | System.out.println("Średnia skuteczność dla ID3: "+percent); | ||
| - | System.out.println("Najgorsza: "+list.get(0)); | ||
| - | System.out.println("Najlepsza: "+list.get(listc45.size()-1)); | ||
| - | |||
| - | System.out.println("Średnia skuteczność dla C4.5: "+percentc45); | ||
| - | System.out.println("Najgorsza: "+listc45.get(0)); | ||
| - | System.out.println("Najlepsza: "+listc45.get(listc45.size()-1)); | ||
| - | |||
| - | } | ||
| - | } | ||
| - | |||
| - | </code> | ||
