Różnice między wybraną wersją a wersją aktualną.
| Nowa wersja | Poprzednia wersja | ||
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pl:miw:miw08_rbs_ml:program [2008/06/10 14:22] miw utworzono |
pl:miw:miw08_rbs_ml:program [2008/06/16 13:39] miw |
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| Linia 1: | Linia 1: | ||
| - | <code = "csharp"> | ||
| - | using System; | ||
| - | using System.Collections.Generic; | ||
| - | using System.Windows.Forms; | ||
| - | using System.IO; | ||
| - | using System.Text; | ||
| - | using System.Xml.Serialization; | ||
| - | namespace Morcinek.Machine_Learning | ||
| - | { | ||
| - | static class Program | ||
| - | { | ||
| - | /// <summary> | ||
| - | /// The main entry point for the application. | ||
| - | /// </summary> | ||
| - | [STAThread] | ||
| - | static void Main() | ||
| - | { | ||
| - | #region Old | ||
| - | |||
| - | object[][] learning; | ||
| - | |||
| - | learning = Common.BinaryDeSerialize("Thermostat_2016.bin"); | ||
| - | /*learning = new object[2016][]; | ||
| - | int index = -1; | ||
| - | int day; | ||
| - | int oper; | ||
| - | int month; | ||
| - | for (int i = 0; i < 7; i++) // Dni | ||
| - | { | ||
| - | day = i; | ||
| - | for (int j = 0; j < 24; j++) // Godziny | ||
| - | { | ||
| - | oper = j; | ||
| - | for (int k = 0; k < 12; k++) // Miesiące | ||
| - | { | ||
| - | month = k; | ||
| - | learning[++index] = new object[] { (Days)day, oper, (Month)month, 0 }; | ||
| - | learning[index][3] = Common.CountTemperature(learning[index]); | ||
| - | } | ||
| - | } | ||
| - | }*/ | ||
| - | int testCollectionLenght = 252; | ||
| - | int randValue; | ||
| - | Random rand = new Random(); | ||
| - | List<int> learningIndexes = new List<int>(); | ||
| - | List<int> testingIndexes = new List<int>(100); | ||
| - | for (int i = 0; i < testCollectionLenght; i++) | ||
| - | { | ||
| - | randValue = rand.Next(2015); | ||
| - | if (!learningIndexes.Contains(randValue)) | ||
| - | learningIndexes.Add(randValue); | ||
| - | else | ||
| - | --i; | ||
| - | } | ||
| - | learningIndexes.Sort(); | ||
| - | for (int i = 0; i < 2016; i++) | ||
| - | { | ||
| - | if (!learningIndexes.Contains(i)) | ||
| - | testingIndexes.Add(i); | ||
| - | } | ||
| - | |||
| - | List<object[]> testingList = new List<object[]>(); | ||
| - | foreach (int index in testingIndexes) | ||
| - | { | ||
| - | testingList.Add(learning[index]); | ||
| - | } | ||
| - | object[][] testing = testingList.ToArray(); | ||
| - | |||
| - | |||
| - | List<object[]> learningList = new List<object[]>(); | ||
| - | foreach (int index in learningIndexes) | ||
| - | { | ||
| - | learningList.Add(learning[index]); | ||
| - | } | ||
| - | object[][] learn = learningList.ToArray(); | ||
| - | //*/ | ||
| - | //Common.BinarySerialize("Thermostat_2016.bin", learning); | ||
| - | string[] attributeNames = new string[] { "DayOfWeek", "Hour", "Month" }; | ||
| - | //string[] attributeNames = new string[] { "Aura", "Temperatura", "Wilgotnosc", "Wiatr" }; | ||
| - | #endregion | ||
| - | |||
| - | //DecisionTree decisionTree = new DecisionTree(); | ||
| - | //decisionTree.Initialize(learn, attributeNames); | ||
| - | int learningLength = 150; | ||
| - | |||
| - | Thermostat_252_2016(learningLength); | ||
| - | } | ||
| - | |||
| - | public static void Thermostat_252_2016(int learningLength) | ||
| - | { | ||
| - | int randValue; | ||
| - | object[][] everything; | ||
| - | object[][] testing; | ||
| - | object[][] learning; | ||
| - | DecisionTree decisionTree; | ||
| - | int goodClassified = 0; | ||
| - | |||
| - | everything = Common.BinaryDeSerialize("Thermostat_2016.bin"); | ||
| - | string[] attributeNames = new string[] { "Day", "Hour", "Month" }; | ||
| - | |||
| - | Random rand = new Random(); | ||
| - | List<int> learningIndexes = new List<int>(); | ||
| - | List<int> testingIndexes = new List<int>(); | ||
| - | for (int i = 0; i < learningLength; i++) | ||
| - | { | ||
| - | randValue = rand.Next(2015); | ||
| - | if (!learningIndexes.Contains(randValue)) | ||
| - | learningIndexes.Add(randValue); | ||
| - | else | ||
| - | --i; | ||
| - | } | ||
| - | learningIndexes.Sort(); | ||
| - | |||
| - | for (int 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); | ||
| - | |||
| - | foreach (object[] row in testing) | ||
| - | { | ||
| - | if (decisionTree.FindCategory(row) == (int)row[3]) | ||
| - | goodClassified++; | ||
| - | } | ||
| - | StringBuilder sb = new StringBuilder(); | ||
| - | sb.Append("Everything is "); | ||
| - | sb.Append(goodClassified.ToString()); | ||
| - | sb.Append("/"); | ||
| - | sb.Append(testing.Length.ToString()); | ||
| - | Console.Write(sb.ToString()); | ||
| - | string temp = sb.ToString(); | ||
| - | UpdateCorrect(goodClassified); // Funkcja zapisuje kolejne ilości dobrze sklasyfikowanych przykładów | ||
| - | } | ||
| - | |||
| - | private static object[][] IndexesToArray(object[][] everything, List<int> indexesList) | ||
| - | { | ||
| - | List<object[]> testingList = new List<object[]>(); | ||
| - | foreach (int index in indexesList) | ||
| - | { | ||
| - | testingList.Add(everything[index]); | ||
| - | } | ||
| - | return testingList.ToArray(); | ||
| - | } | ||
| - | |||
| - | private static void UpdateCorrect(int amount) | ||
| - | { | ||
| - | List<int> list; | ||
| - | XmlSerializer serializer = new XmlSerializer(typeof(List<int>)); | ||
| - | if (File.Exists("Tests.xml")) | ||
| - | { | ||
| - | TextReader reader = new StreamReader("Tests.xml"); | ||
| - | list = (List<int>)serializer.Deserialize(reader); | ||
| - | reader.Close(); | ||
| - | } | ||
| - | else | ||
| - | list = new List<int>(); | ||
| - | int sum = 0; | ||
| - | foreach (int var in list) | ||
| - | { | ||
| - | sum += var; | ||
| - | } | ||
| - | double percent = (double) sum / (double)(2016 - 150) / (double)list.Count; | ||
| - | list.Sort(); | ||
| - | |||
| - | //list.Add(amount); | ||
| - | |||
| - | TextWriter writer = new StreamWriter("Tests.xml"); | ||
| - | serializer.Serialize(writer, list); | ||
| - | writer.Close(); | ||
| - | } | ||
| - | } | ||
| - | } | ||
| - | </code> | ||
