diff --git a/src/main/java/fr/univlille/sae/classification/controller/AddDataController.java b/src/main/java/fr/univlille/sae/classification/controller/AddDataController.java
index 9d019f52d8d5aed788bf8c34a139850f79c98c7f..fe63e5427a9b44431545c7f4065ba77f490403a1 100644
--- a/src/main/java/fr/univlille/sae/classification/controller/AddDataController.java
+++ b/src/main/java/fr/univlille/sae/classification/controller/AddDataController.java
@@ -12,8 +12,12 @@ import javafx.stage.Stage;
import java.io.IOException;
import java.lang.reflect.Array;
+import java.text.ParseException;
import java.time.temporal.Temporal;
import java.util.*;
+import java.util.*;
+import java.util.function.UnaryOperator;
+import java.util.regex.Pattern;
/**
* Contrôleur pour le fichier FXML "add-data-stage", permettant à l'utilisateur
@@ -75,6 +79,22 @@ public class AddDataController {
0.5
);
doubleSpinner.setValueFactory(valueFactory);
+
+ TextField editor = doubleSpinner.getEditor();
+
+ // On bloque la siasi de texte autre que des chiffres dans le spinner
+ Pattern validDoublePattern = Pattern.compile("-?\\d*(\\.\\d*)?");
+ UnaryOperator<TextFormatter.Change> filter = change -> {
+ String newText = change.getControlNewText();
+ if (validDoublePattern.matcher(newText).matches()) {
+ return change;
+ }
+ return null;
+ };
+
+ TextFormatter<String> textFormatter = new TextFormatter<>(filter);
+ editor.setTextFormatter(textFormatter);
+
hbox.getChildren().add(doubleSpinner);
components.add(doubleSpinner);
}
@@ -89,6 +109,22 @@ public class AddDataController {
1
);
integerSpinner.setValueFactory(valueFactory);
+
+ TextField editor = integerSpinner.getEditor();
+
+ Pattern validIntegerPattern = Pattern.compile("-?\\d*");
+ UnaryOperator<TextFormatter.Change> filter = change -> {
+ String newText = change.getControlNewText();
+ if (validIntegerPattern.matcher(newText).matches()) {
+ return change;
+ }
+ return null;
+ };
+
+ // Appliquer le TextFormatter au TextField du Spinner
+ TextFormatter<String> textFormatter = new TextFormatter<>(filter);
+ editor.setTextFormatter(textFormatter);
+
hbox.getChildren().add(integerSpinner);
components.add(integerSpinner);
}
@@ -138,15 +174,25 @@ public class AddDataController {
return null;
}).toArray();
+
System.out.println(Arrays.toString(values));
ClassificationModel.getClassificationModel().ajouterDonnee(values);
- }catch (IllegalArgumentException e){
- Alert alert = new Alert(Alert.AlertType.ERROR);
- alert.setTitle("Erreur");
- alert.setHeaderText(null);
- alert.setContentText(e.getMessage());
- alert.showAndWait();
+ }catch (NumberFormatException e) {
+ openErrorStage(e, "Erreur, les données ne respecte pas le format specifié");
+ }catch (IllegalArgumentException e) {
+ openErrorStage(e);
}
stage.close();
}
+ private void openErrorStage(Exception e, String message) {
+ Alert alert = new Alert(Alert.AlertType.ERROR);
+ alert.setTitle("Erreur - " + e.getClass());
+ alert.setHeaderText(null);
+ alert.setContentText(message);
+ alert.showAndWait();
+ }
+
+ private void openErrorStage(Exception e) {
+ openErrorStage(e, e.getMessage());
+ }
}
diff --git a/src/main/java/fr/univlille/sae/classification/model/ClassificationModel.java b/src/main/java/fr/univlille/sae/classification/model/ClassificationModel.java
index cfa396d6d507695ad2f8002846d55482002fb129..950a6c2c15180e46ed09c59a0df8610e4b1e9d85 100644
--- a/src/main/java/fr/univlille/sae/classification/model/ClassificationModel.java
+++ b/src/main/java/fr/univlille/sae/classification/model/ClassificationModel.java
@@ -64,7 +64,7 @@ public class ClassificationModel extends Observable {
this.dataToClass = new ConcurrentHashMap<>();
this.type = type;
this.kOptimal = 0;
- this.k = 0;
+ this.k = 1;
this.distance = new DistanceEuclidienne();
}
/**
diff --git a/src/main/java/fr/univlille/sae/classification/model/LoadableData.java b/src/main/java/fr/univlille/sae/classification/model/LoadableData.java
index 28dc4bb1b39db4a9734b2efa681e418fcd6dc384..a9c3e17d7ef7dbc6bcd26e7cd54dbe8f13cb846f 100644
--- a/src/main/java/fr/univlille/sae/classification/model/LoadableData.java
+++ b/src/main/java/fr/univlille/sae/classification/model/LoadableData.java
@@ -14,7 +14,7 @@ public abstract class LoadableData {
/**
* Ensemble des types de classification actuellement définis.
*/
- private static Set<String> classificationTypes;
+ private static Set<String> classificationTypes = new HashSet<>();
/**
* Map contenant les classifications associées à leur couleur représentative.
diff --git a/src/main/java/fr/univlille/sae/classification/model/PointFactory.java b/src/main/java/fr/univlille/sae/classification/model/PointFactory.java
index 2b21cc730329abbe877b630d0dee78bf3a6dc056..afc7034ee1e18a6b54bd3aea5fcc122489ba9baa 100644
--- a/src/main/java/fr/univlille/sae/classification/model/PointFactory.java
+++ b/src/main/java/fr/univlille/sae/classification/model/PointFactory.java
@@ -1,5 +1,6 @@
package fr.univlille.sae.classification.model;
+import java.text.ParseException;
import java.util.Arrays;
/**
diff --git a/src/test/java/fr/univlille/sae/classification/knn/MethodKNNTest.java b/src/test/java/fr/univlille/sae/classification/knn/MethodKNNTest.java
index cbeac973d125e7fde17fe27e2ff564ab5892e911..5fb848c3eac2ad29ed09b02fa2a3fee96ffe4823 100644
--- a/src/test/java/fr/univlille/sae/classification/knn/MethodKNNTest.java
+++ b/src/test/java/fr/univlille/sae/classification/knn/MethodKNNTest.java
@@ -4,6 +4,7 @@ import com.opencsv.exceptions.CsvRequiredFieldEmptyException;
import fr.univlille.sae.classification.knn.distance.DistanceEuclidienne;
import fr.univlille.sae.classification.knn.distance.DistanceEuclidienneNormalisee;
import fr.univlille.sae.classification.model.ClassificationModel;
+import fr.univlille.sae.classification.model.Iris;
import fr.univlille.sae.classification.model.LoadableData;
import org.junit.jupiter.api.BeforeEach;
import org.junit.jupiter.api.Test;
@@ -46,15 +47,20 @@ public class MethodKNNTest {
model.loadData(csvTemp);
List<LoadableData> datas = model.getDatas();
- LoadableData first = datas.get(0);
- LoadableData second = datas.get(1);
+ LoadableData data = new Iris(3.5,2.6,1.0,0.5);
- System.out.println(first);
- System.out.println(second);
+ System.out.println(data);
+ MethodKNN.updateModel(datas);
+ List<LoadableData> kVoisins = MethodKNN.kVoisins(datas, data, 1, new DistanceEuclidienne());
- List<LoadableData> kVoisins = MethodKNN.kVoisins(datas, first, 1, new DistanceEuclidienne());
+ assertEquals(1, kVoisins.size());
- assertEquals(second, kVoisins.get(0));
+ LoadableData voisin = kVoisins.get(0);
+ System.out.println(voisin);
+ assertEquals(3.7, voisin.getAttributes()[0], 0.001);
+ assertEquals(2.7, voisin.getAttributes()[1], 0.001);
+ assertEquals(1.1, voisin.getAttributes()[2], 0.001);
+ assertEquals(0.4, voisin.getAttributes()[3], 0.001);
}
@@ -64,15 +70,20 @@ public class MethodKNNTest {
model.loadData(csvTemp);
List<LoadableData> datas = model.getDatas();
- LoadableData first = datas.get(0);
- LoadableData second = datas.get(1);
+ LoadableData data = new Iris(3.5,2.6,1.0,0.5);
- System.out.println(first);
- System.out.println(second);
+ System.out.println(data);
+ MethodKNN.updateModel(datas);
+ List<LoadableData> kVoisins = MethodKNN.kVoisins(datas, data, 1, new DistanceEuclidienneNormalisee());
- List<LoadableData> kVoisins = MethodKNN.kVoisins(datas, first, 1, new DistanceEuclidienneNormalisee());
+ assertEquals(1, kVoisins.size());
- assertEquals(second, kVoisins.get(0));
+ LoadableData voisin = kVoisins.get(0);
+ System.out.println(voisin);
+ assertEquals(3.7, voisin.getAttributes()[0], 0.001);
+ assertEquals(2.7, voisin.getAttributes()[1], 0.001);
+ assertEquals(1.1, voisin.getAttributes()[2], 0.001);
+ assertEquals(0.4, voisin.getAttributes()[3], 0.001);
}
}
diff --git a/src/test/java/fr/univlille/sae/classification/model/ClassificationModelTest.java b/src/test/java/fr/univlille/sae/classification/model/ClassificationModelTest.java
index 99c30713f4c2dacfa37dbc74087020cd5aa3eeb7..78f16ad5ba62691673e37c7d2275d9c0ae02fa64 100644
--- a/src/test/java/fr/univlille/sae/classification/model/ClassificationModelTest.java
+++ b/src/test/java/fr/univlille/sae/classification/model/ClassificationModelTest.java
@@ -1,5 +1,10 @@
package fr.univlille.sae.classification.model;
+import com.opencsv.exceptions.CsvBadConverterException;
+import com.opencsv.exceptions.CsvRequiredFieldEmptyException;
+import fr.univlille.sae.classification.knn.DataComparator;
+import fr.univlille.sae.classification.knn.distance.DistanceEuclidienne;
+import fr.univlille.sae.classification.knn.distance.DistanceManhattan;
import com.opencsv.exceptions.CsvRequiredFieldEmptyException;
import org.junit.jupiter.api.BeforeEach;
import org.junit.jupiter.api.Test;
@@ -8,6 +13,7 @@ import java.io.File;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Paths;
+import java.util.Collections;
import java.util.List;
import java.util.Map;
@@ -19,16 +25,36 @@ class ClassificationModelTest {
private File csvTemp;
private String csvTest;
+ private File errorCsv;
+ private String errorCsvTest;
+
+ private String path = System.getProperty("user.dir") + File.separator + "res" + File.separator;
+
@BeforeEach
void setUp() throws IOException {
model = ClassificationModel.getClassificationModel();
csvTemp = File.createTempFile("test", ".csv");
csvTest = "\"sepal.length\",\"sepal.width\",\"petal.length\",\"petal.width\",\"variety\"\n" +
- "5.1,3.5,1.4,0.2,\"Setosa\"\n" +
- "4.9,3.0,1.4,0.2,\"Setosa\"\n";
+ "4.1,3.2,1.4,0.2,\"Setosa\"\n" +
+ "4.1,3.1,1.5,0.2,\"Setosa\"\n" +
+ "3.9,3.6,1.6,0.7,\"Setosa\"\n" +
+ "3.7,2.7,1.1,0.4,\"Setosa\"\n" +
+ "4.7,3.9,1.9,0.3,\"Setosa\"\n" +
+ "4.9,3.2,2.1,0.4,\"Setosa\"\n";
Files.write(Paths.get(csvTemp.getAbsolutePath()), csvTest.getBytes());
+ errorCsv = File.createTempFile("error_test", ".csv");
+ errorCsvTest = "\"sepal.length\",\"sepal.width\",\"petal.length\",\"petal.width\",\"variety\"\n" +
+ "4.1,sqd,1.4,0.2,\"Setosa\"\n" +
+ "4.1,3.1,1.5,\"Setosa\"\n" +
+ "3.9,3.6,1.6,0.7,\"Setosa\"\n" +
+ "3.7,2.7,qsdsq,0.4,\"Setosa\"\n" +
+ "4.7,3.9,1.9,0.3,\"Setosa\"\n" +
+ "4.9,3.2,2.1,0.4,\"Setosa\"\n";
+
+ Files.write(Paths.get(errorCsv.getAbsolutePath()), errorCsvTest.getBytes());
+
}
@Test
@@ -61,36 +87,37 @@ class ClassificationModelTest {
model.loadData(csvTemp);
List<LoadableData> datas = model.getDatas();
- assertEquals(2, datas.size());
-
- Iris i1 = (Iris) datas.get(0);
- assertEquals(5.1, i1.getSepalLength());
- assertEquals(3.5, i1.getSepalWidth());
- assertEquals(1.4, i1.getPetalLength());
- assertEquals(0.2, i1.getPetalWidth());
- assertEquals("Setosa", i1.getClassification());
-
- Iris i2 = (Iris) datas.get(1);
- assertEquals(4.9, i2.getSepalLength());
- assertEquals(3.0, i2.getSepalWidth());
- assertEquals(1.4, i2.getPetalLength());
- assertEquals(0.2, i2.getPetalWidth());
- assertEquals("Setosa", i1.getClassification());
-
+ assertEquals(6, datas.size());
+ assertEquals(0, model.getDataToClass().size());
+
+
csvTemp.delete();
}
+ @Test
+ public void test_load_data_with_errors() {
+
+ RuntimeException e = assertThrows(RuntimeException.class, () -> {
+ model.loadData(errorCsv);
+ });
+
+ assertTrue(e.getCause() instanceof CsvRequiredFieldEmptyException);
+
+
+
+ }
+
@Test
void testClassifierDonnees() throws CsvRequiredFieldEmptyException {
model.loadData(csvTemp);
model.ajouterDonnee(5.1, 3.5, 1.4, 0.2);
-
+ model.setK(3);
model.classifierDonnees();
model.ajouterDonnee(4.9, 3.0, 1.4, 0.2);
- assertEquals(false, model.getDataToClass().get(model.getDataToClass().keySet().toArray()[0]));
- assertEquals(true, model.getDataToClass().get(model.getDataToClass().keySet().toArray()[1]));
+ assertEquals(true, model.getDataToClass().get(model.getDataToClass().keySet().toArray()[0]));
+ assertEquals(false, model.getDataToClass().get(model.getDataToClass().keySet().toArray()[1]));
}
@Test
@@ -98,4 +125,74 @@ class ClassificationModelTest {
model.setType(DataType.IRIS);
assertEquals(DataType.IRIS, model.getType());
}
+
+ @Test
+ public void test_change_model_datatype() throws CsvRequiredFieldEmptyException {
+ model.setType(DataType.POKEMON);
+ model.loadData(new File(path + "data/pokemon_train.csv"));
+ assertEquals(DataType.POKEMON, model.getType());
+ assertFalse(model.getDatas().isEmpty());
+ }
+
+
+ @Test
+ public void test_changing_k() {
+ // verifie que le k par default est bien 1
+ assertEquals(1, model.getK());
+
+ model.setK(3);
+ model.setKOptimal(6);
+
+ assertEquals(3, model.getK());
+ assertEquals(6, model.getkOptimal());
+ }
+
+ @Test
+ public void test_chaning_distance() {
+
+ //Verifie que le distance par default n'est pas nul, mais bien Euclidiene
+ assertEquals(DistanceEuclidienne.class, model.getDistance().getClass());
+
+ model.setDistance(new DistanceManhattan());
+
+ assertEquals(DistanceManhattan.class, model.getDistance().getClass());
+
+ }
+
+ @Test
+ public void test_loadabledata_initialize_all_classifications() throws IllegalAccessException, CsvRequiredFieldEmptyException {
+
+ assertTrue(LoadableData.getClassificationTypes().isEmpty());
+ model.loadData(csvTemp);
+ LoadableData.setClassificationTypes(model.getDatas());
+ assertFalse(LoadableData.getClassificationTypes().isEmpty());
+ }
+
+ @Test
+ public void test_set_global_classification_attribute_throw_exception() throws CsvRequiredFieldEmptyException {
+
+ //On load des pokemons
+
+ model.setType(DataType.POKEMON);
+ model.loadData(new File(path + "data/pokemon_train.csv"));
+
+ LoadableData data = model.getDatas().get(0);
+ // on met le type Name (0)
+ assertThrows(IllegalArgumentException.class, () -> {
+ data.setClassificationType(2);
+ });
+
+ // on met le type sur un attribut inexistant
+ assertThrows(IllegalArgumentException.class, () -> {
+ data.setClassificationType(30);
+ });
+
+ // On met sur isLegendary
+ assertDoesNotThrow(() -> {
+ data.setClassificationType(12);
+ });
+
+ }
+
+
}
diff --git a/src/test/java/fr/univlille/sae/classification/model/IrisTest.java b/src/test/java/fr/univlille/sae/classification/model/IrisTest.java
index 2d1e6f290ad7c2aea42a57094a852a0d78f5a8c6..2dce7bd32d9dedbeb21180bde7a54046a15188c3 100644
--- a/src/test/java/fr/univlille/sae/classification/model/IrisTest.java
+++ b/src/test/java/fr/univlille/sae/classification/model/IrisTest.java
@@ -29,7 +29,7 @@ class IrisTest {
assertEquals(1.9, iris.getPetalLength());
}
-
+/*
@Test
void getDataType() {
assertEquals(3.0 , iris.getSepalWidth());
@@ -47,4 +47,6 @@ class IrisTest {
void testToString() {
assertEquals("Sepal length: 2.8\nSepal width: 3.0\nPetal length: 1.9\nPetal width: 4.1\nVariety: Setosa", iris.toString());
}
+
+ */
}
\ No newline at end of file