fixed all the examples, after last refactoring

Author: deepnetts

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

@@ -3,17 +3,19 @@
 import java.io.File;
 import java.io.IOException;
 import java.net.URL;
+import java.nio.file.Paths;
 
 import javax.visrec.ml.classification.BinaryClassifier;
 import javax.visrec.ml.classification.Classifiable;
 import javax.visrec.ml.classification.NeuralNetBinaryClassifier;
+import javax.visrec.ml.model.ModelCreationException;
 
 /**
  * @author Nishant
  *
  */
 public class CreditCardFraudDetection {
-	public static void main(String[] args) throws IOException {
+	public static void main(String[] args) throws IOException, ModelCreationException {
 		// create data set from specified file
 		URL fraudCsvResource = CreditCardFraudDetection.class.getClassLoader().getResource("creditcard.csv");
 		if (fraudCsvResource == null) {
@@ -23,7 +25,7 @@ public static void main(String[] args) throws IOException {
 		// 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)
-				.trainingPath((new File(fraudCsvResource.getFile()))).build();
+				.trainingPath(Paths.get(fraudCsvResource.getFile())).build();
 		Float result = fraudClassifier.classify(new CreditCardFraud().getClassifierInput());
 		System.out.println(result);
 	}

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

@@ -2,8 +2,6 @@
 
 
 import javax.imageio.ImageIO;
-import javax.visrec.ml.ClassificationException;
-import javax.visrec.ml.ClassifierCreationException;
 import javax.visrec.ml.classification.ImageClassifier;
 import javax.visrec.ml.classification.NeuralNetImageClassifier;
 import java.awt.image.BufferedImage;

src/main/java/jsr381/example/IrisFlowersClassificationExample.java

@@ -3,15 +3,14 @@
 import deepnetts.data.DataSets;
 import jsr381.example.util.DataSetExamples;
 
-import javax.visrec.ml.ClassificationException;
 import javax.visrec.ml.classification.MultiClassClassifier;
 import javax.visrec.ml.data.DataSet;
 import javax.visrec.ri.ml.classification.MultiClassClassifierNetwork;
 import java.io.IOException;
 import java.util.Map;
 
 public class IrisFlowersClassificationExample {
-    public static void main(String[] args) throws IOException, ClassificationException {
+    public static void main(String[] args) throws IOException {
 
         // Load iris data set
         DataSet dataSet = DataSetExamples.getIrisClassificationDataSet();

src/main/java/jsr381/example/LogisticRegressionExample.java

@@ -4,11 +4,11 @@
 import jsr381.example.util.DataSetExamples;
 import javax.visrec.ri.ml.regression.LogisticRegressionNetwork;
 
-import javax.visrec.ml.ClassificationException;
+
 import javax.visrec.ml.data.DataSet;
-import javax.visrec.ml.regression.LogisticRegression;
 import java.io.IOException;
 import java.util.Map;
+import javax.visrec.ml.classification.LogisticRegression;
 
 /**
  * This example shows how to instantiate, train, evaluate and use Logistic Regression using Machine Learning Layer from VisRec API.
@@ -17,7 +17,7 @@
  */
 public class LogisticRegressionExample {
 
-    public static void main(String[] args) throws IOException, ClassificationException {
+    public static void main(String[] args) throws IOException{
 
         // Get the DataSet from file location
         DataSet dataSet = DataSetExamples.getSonarDataSet();

src/main/java/jsr381/example/MnistExample.java

@@ -2,8 +2,6 @@
 
 import jsr381.example.util.DataSetExamples;
 
-import javax.visrec.ml.ClassificationException;
-import javax.visrec.ml.ClassifierCreationException;
 import javax.visrec.ml.classification.ImageClassifier;
 import javax.visrec.ml.classification.NeuralNetImageClassifier;
 import java.awt.image.BufferedImage;
@@ -12,6 +10,8 @@
 import java.net.URL;
 import java.util.HashMap;
 import java.util.Map;
+import javax.imageio.ImageIO;
+import javax.visrec.ml.model.ModelCreationException;
 
 /**
  * Handwritten digit recognition using MNIST data set - image classification hello world.
@@ -21,7 +21,7 @@
 @Deprecated
 public class MnistExample {
 
-    public static void main(String[] args) throws IOException, ClassificationException, ClassifierCreationException {
+    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.getMnistDataSet();
@@ -57,7 +57,9 @@ public static void main(String[] args) throws IOException, ClassificationExcepti
         if (input == null) {
             throw new IOException("Input file not found in resources");
         }
-        Map<String, Float> results = imageClassifier.classify(new File(input.getFile()));
+        
+        BufferedImage testImg = ImageIO.read(new File(input.getFile()));        
+        Map<String, Float> results = imageClassifier.classify(testImg);
 
         // Print the outcome
         System.out.println(results);

src/main/java/jsr381/example/MnistWithBuildingBlocksExample.java

@@ -3,8 +3,6 @@
 import jsr381.example.util.DataSetExamples;
 
 import javax.imageio.ImageIO;
-import javax.visrec.ml.ClassificationException;
-import javax.visrec.ml.ClassifierCreationException;
 import javax.visrec.ml.classification.ImageClassifier;
 import javax.visrec.ml.classification.NeuralNetImageClassifier;
 import java.awt.image.BufferedImage;
@@ -13,6 +11,7 @@
 import java.net.URL;
 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.
@@ -21,7 +20,7 @@
  */
 public class MnistWithBuildingBlocksExample {
 
-    public static void main(String[] args) throws IOException, ClassifierCreationException, ClassificationException {
+    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.getMnistDataSet();
@@ -32,9 +31,9 @@ public static void main(String[] args) throws IOException, ClassifierCreationExc
                 .inputClass(BufferedImage.class)
                 .imageHeight(28)
                 .imageWidth(28)
-                .labelsFile(dataSet.getLabelsFile())
-                .trainingFile(dataSet.getTrainingFile())
-                .networkArchitecture(new File("src/main/resources/mnist_arch.json")) //change this not in data set but from jsin file in resources folder?
+                .labelsFile(dataSet.getLabelsFile().toPath())
+                .trainingFile(dataSet.getTrainingFile().toPath())
+                .networkArchitecture(Paths.get("src/main/resources/mnist_arch.json")) //change this not in data set but from jsin file in resources folder?
                 .exportModel(Paths.get("mnist.dnet"))
                 .maxError(0.05f)
                 .maxEpochs(3)

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

@@ -1,12 +1,12 @@
 package jsr381.example.spam;
 
-import javax.visrec.ml.ClassificationException;
-import javax.visrec.ml.ClassifierCreationException;
 import javax.visrec.ml.classification.BinaryClassifier;
 import javax.visrec.ml.classification.NeuralNetBinaryClassifier;
 import java.io.File;
 import java.io.IOException;
 import java.net.URL;
+import java.nio.file.Paths;
+import javax.visrec.ml.model.ModelCreationException;
 
 /**
  * Minimum example for creating binary classifier from CSV file.
@@ -16,7 +16,7 @@
  */
 public class SpamClassificationExample {
 
-    public static void main(String[] args) throws IOException, ClassificationException, ClassifierCreationException {
+    public static void main(String[] args) throws IOException, ModelCreationException {
 
         // create data set from specified file
         URL spamCsvResource = SpamClassificationExample.class.getClassLoader().getResource("spam.csv");
@@ -32,7 +32,7 @@ public static void main(String[] args) throws IOException, ClassificationExcepti
                                                         .maxError(0.03f)
                                                         .maxEpochs(15000)
                                                         .learningRate(0.01f)                     
-                                                        .trainingFile(new File(spamCsvResource.getFile()))
+                                                        .trainingPath(Paths.get(spamCsvResource.getFile()))
                                                         .build();
 
         // using trained classifier