fixed few examples, many of themneeds to get fixed after last changes...

Author: neuroph

pom.xml

@@ -16,13 +16,19 @@
     <dependencies>
         <dependency>
             <groupId>javax.visrec</groupId>
+            <artifactId>jsr381-examples</artifactId>
+            <version>1</version>
+            <type>jar</type>
+        </dependency>
+        <dependency>
+            <groupId>${project.groupId}</groupId>
             <artifactId>visrec-api</artifactId>
-            <version>1.0.1</version>
+            <version>1.0.1-SNAPSHOT</version>
         </dependency>
         <dependency>
-            <groupId>javax.visrec</groupId>
+            <groupId>${project.groupId}</groupId>
             <artifactId>visrec-ri</artifactId>
-            <version>1.0.1</version>
+            <version>1.0.2-SNAPSHOT</version>
         </dependency>
     </dependencies>
 

src/main/java/jsr381/example/CatDogRecognition.java

@@ -8,9 +8,10 @@
 import java.util.Map;
 
 import javax.imageio.ImageIO;
-import javax.visrec.ml.ClassifierCreationException;
+
 import javax.visrec.ml.classification.ImageClassifier;
 import javax.visrec.ml.classification.NeuralNetImageClassifier;
+import javax.visrec.ml.model.ModelCreationException;
 
 import jsr381.example.util.DataSetExamples;
 
@@ -19,7 +20,7 @@
  *
  */
 public class CatDogRecognition {
-	public static void main(String[] args) throws ClassifierCreationException, IOException {
+	public static void main(String[] args) throws IOException, ModelCreationException {
 		// Download the dataset and calculate how much time it took
 		long start = System.currentTimeMillis();
 		DataSetExamples.ExampleDataSet dataSet = DataSetExamples.getCatDogDataSet();
@@ -28,8 +29,8 @@ public static void main(String[] args) throws ClassifierCreationException, IOExc
 
 		// Configuration to train the model
 		ImageClassifier<BufferedImage> classifier = NeuralNetImageClassifier.builder().inputClass(BufferedImage.class)
-				.imageHeight(128).imageWidth(128).labelsFile(dataSet.getLabelsFile())
-				.trainingFile(dataSet.getTrainingFile())
+				.imageHeight(128).imageWidth(128).labelsFile(dataSet.getLabelsFile().toPath())
+				.trainingFile(dataSet.getTrainingFile().toPath())
 				//.trainingFile(new File("catdog_arch.json"))
 				.exportModel(Paths.get("catdog.dnet")).maxError(0.03f).maxEpochs(1000).learningRate(0.01f).build();
 

src/main/java/jsr381/example/CreditCardFraudDetection.java

@@ -4,18 +4,16 @@
 import java.io.IOException;
 import java.net.URL;
 
-import javax.visrec.ml.ClassifierCreationException;
 import javax.visrec.ml.classification.BinaryClassifier;
+import javax.visrec.ml.classification.Classifiable;
 import javax.visrec.ml.classification.NeuralNetBinaryClassifier;
 
-import jsr381.example.spam.Classifiable;
-
 /**
  * @author Nishant
  *
  */
 public class CreditCardFraudDetection {
-	public static void main(String[] args) throws IOException, ClassifierCreationException {
+	public static void main(String[] args) throws IOException {
 		// create data set from specified file
 		URL fraudCsvResource = CreditCardFraudDetection.class.getClassLoader().getResource("creditcard.csv");
 		if (fraudCsvResource == null) {
@@ -25,7 +23,7 @@ public static void main(String[] args) throws IOException, ClassifierCreationExc
 		// Build binary classifer based on neural network
 		BinaryClassifier<float[]> fraudClassifier = NeuralNetBinaryClassifier.builder().inputClass(float[].class)
 				.inputsNum(29).hiddenLayers(29, 15).maxError(0.03f).maxEpochs(15000).learningRate(0.001f)
-				.trainingFile(new File(fraudCsvResource.getFile())).build();
+				.trainingPath((new File(fraudCsvResource.getFile()))).build();
 		Float result = fraudClassifier.classify(new CreditCardFraud().getClassifierInput());
 		System.out.println(result);
 	}

src/main/java/jsr381/example/Duke.java

@@ -11,6 +11,7 @@
 import java.io.IOException;
 import java.nio.file.Paths;
 import java.util.Map;
+import javax.visrec.ml.model.ModelCreationException;
 
 /**
  * Hand written digit recognition using MNIST data set - image classification hello world.
@@ -19,16 +20,16 @@
  */
 public class Duke {
 
-    public static void main(String[] args) throws IOException, ClassifierCreationException, ClassificationException {
+    public static void main(String[] args) throws IOException, ModelCreationException {
 
         // Configure and train ML model to classify images
         ImageClassifier<BufferedImage> classifier = NeuralNetImageClassifier.builder()
                 .inputClass(BufferedImage.class)
                 .imageHeight(64)
                 .imageWidth(64)
-                .labelsFile(new File("datasets/duke_and_nonduke/labels.txt")) // category labels
-                .trainingFile(new File("datasets/duke_and_nonduke/index.txt")) // list of images
-                .networkArchitecture(new File("src/main/resources/duke_net.json"))
+                .labelsFile(Paths.get("datasets/duke_and_nonduke/labels.txt")) // category labels
+                .trainingFile(Paths.get("datasets/duke_and_nonduke/index.txt")) // list of images
+                .networkArchitecture(Paths.get("src/main/resources/duke_net.json"))
                 .exportModel(Paths.get("duke.dnet"))
                 .maxError(0.05f)
                 .maxEpochs(1000)

src/main/java/jsr381/example/spam/Classifiable.java

@@ -1,5 +1,7 @@
 package jsr381.example.spam;
 
+import javax.visrec.ml.classification.Classifiable;
+
 /**
  * Example Email class that implements Classifiable interface in order to enable classification of its instances.
  * It should wrap instance of some email, and provide interface that will allow building classifiers.

src/main/java/jsr381/example/spam/Email.java

@@ -40,9 +40,9 @@ public static void main(String[] args) throws IOException, ClassificationExcepti
         Float result = spamClassifier.classify(testEmail);
         System.out.println(result);
 
-        Email email2=null;
-        BinaryClassifier<Email> spamClassifier2 =null;
-        float spamProbability = spamClassifier2.classify(email2);
+        Email email2=new Email("Some email string ...");
+        //BinaryClassifier<Email> spamClassifier2 =null;
+        float spamProbability = spamClassifier.classify(email2.getClassifierInput());
     }
 
     static float[] getExampleEmailToClassify() {