mvn deployed

chen0040 · chen0040 · commit 9fdffa3c8837 · 2017-06-06T16:23:37.000+08:00
diff --git a/.gitignore b/.gitignore
@@ -1,6 +1,8 @@
 .idea/
 *.iml
 
+target/
+
 # Compiled class file
 *.class
 
diff --git a/README.md b/README.md
@@ -9,7 +9,7 @@ Add the following dependency to your POM file:
 <dependency>
   <groupId>com.github.chen0040</groupId>
   <artifactId>java-ann-mlp</artifactId>
-  <version>1.0.2</version>
+  <version>1.0.3</version>
 </dependency>
 ```
 
diff --git a/pom.xml b/pom.xml
@@ -6,7 +6,7 @@
 
     <groupId>com.github.chen0040</groupId>
     <artifactId>java-ann-mlp</artifactId>
-    <version>1.0.3</version>
+    <version>1.0.4</version>
 
     <licenses>
         <license>
diff --git a/src/main/java/com/github/chen0040/mlp/ann/MLP.java b/src/main/java/com/github/chen0040/mlp/ann/MLP.java
@@ -111,7 +111,7 @@ public void train(DataFrame batch, int training_epoches)
                             double[] dE_dzj = new double[dE_dyj.length];
                             for(int j = 0; j < dE_dzj.length; ++j) {
                                 double yj = layer.neurons.get(j).output;
-                                dE_dzj[j] = layer.getTransfer().gradient(yj, yj) * dE_dyj[j];
+                                dE_dzj[j] = layer.getTransfer().gradient(yj) * dE_dyj[j];
                             }
                             int dimension = layer.neurons.get(0).dimension();
                             double[] dE_dyi = new double[dimension];
diff --git a/src/main/java/com/github/chen0040/mlp/ann/MLPLayer.java b/src/main/java/com/github/chen0040/mlp/ann/MLPLayer.java
@@ -102,7 +102,7 @@ public double[] back_propagate(double[] error)
             double[] values = neuron.values;
             double hx = neuron.getValue(values);
 
-            neuron.error = transfer.gradient(hx, y) * error[i];
+            neuron.error = transfer.gradient(hx) * error[i];
         }
 
         int k = dimension();
diff --git a/src/main/java/com/github/chen0040/mlp/functions/RectifiedLinear.java b/src/main/java/com/github/chen0040/mlp/functions/RectifiedLinear.java
@@ -5,7 +5,7 @@
  * Created by xschen on 31/5/2017.
  */
 public class RectifiedLinear extends AbstractTransferFunction {
-   @Override public double gradient(double hx, double y) {
+   @Override public double gradient(double hx) {
       if(hx > 0) return 1;
       return 0;
    }
diff --git a/src/main/java/com/github/chen0040/mlp/functions/Sigmoid.java b/src/main/java/com/github/chen0040/mlp/functions/Sigmoid.java
@@ -11,8 +11,8 @@ public double calculate(double x)
 	}
 
 
-	@Override public double gradient(double hx, double y) {
-		y = calculate(hx);
+	@Override public double gradient(double z) {
+		double y = calculate(z);
 		return y * (1-y);
 	}
 }
diff --git a/src/main/java/com/github/chen0040/mlp/functions/TransferFunction.java b/src/main/java/com/github/chen0040/mlp/functions/TransferFunction.java
@@ -6,5 +6,5 @@
 public interface TransferFunction {
     double calculate(double x);
 
-    double gradient(double hx, double y);
+    double gradient(double z);
 }

-Original file line number
+Diff line change
@@ @@ -1,6 +1,8 @@ @@
 .idea/
 *.iml
 +target/
++
 # Compiled class file
 *.class
Original file line number	Diff line number	Diff line change
`@@ -111,7 +111,7 @@ public void train(DataFrame batch, int training_epoches)`
`111`	`111`	`double[] dE_dzj = new double[dE_dyj.length];`
`112`	`112`	`for(int j = 0; j < dE_dzj.length; ++j) {`
`113`	`113`	`double yj = layer.neurons.get(j).output;`
`114`		`- dE_dzj[j] = layer.getTransfer().gradient(yj, yj) * dE_dyj[j];`
	`114`	`+ dE_dzj[j] = layer.getTransfer().gradient(yj) * dE_dyj[j];`
`115`	`115`	`}`
`116`	`116`	`int dimension = layer.neurons.get(0).dimension();`
`117`	`117`	`double[] dE_dyi = new double[dimension];`
Original file line number	Diff line number	Diff line change
`@@ -102,7 +102,7 @@ public double[] back_propagate(double[] error)`
`102`	`102`	`double[] values = neuron.values;`
`103`	`103`	`double hx = neuron.getValue(values);`
`104`	`104`
`105`		`- neuron.error = transfer.gradient(hx, y) * error[i];`
	`105`	`+ neuron.error = transfer.gradient(hx) * error[i];`
`106`	`106`	`}`
`107`	`107`
`108`	`108`	`int k = dimension();`
Original file line number	Diff line number	Diff line change
`@@ -5,7 +5,7 @@`
`5`	`5`	`* Created by xschen on 31/5/2017.`
`6`	`6`	`*/`
`7`	`7`	`public class RectifiedLinear extends AbstractTransferFunction {`
`8`		`- @Override public double gradient(double hx, double y) {`
	`8`	`+ @Override public double gradient(double hx) {`
`9`	`9`	`if(hx > 0) return 1;`
`10`	`10`	`return 0;`
`11`	`11`	`}`
Original file line number	Diff line number	Diff line change
`@@ -11,8 +11,8 @@ public double calculate(double x)`
`11`	`11`	`}`
`12`	`12`
`13`	`13`
`14`		`- @Override public double gradient(double hx, double y) {`
`15`		`- y = calculate(hx);`
	`14`	`+ @Override public double gradient(double z) {`
	`15`	`+ double y = calculate(z);`
`16`	`16`	`return y * (1-y);`
`17`	`17`	`}`
`18`	`18`	`}`
Original file line number	Diff line number	Diff line change
`@@ -6,5 +6,5 @@`
`6`	`6`	`public interface TransferFunction {`
`7`	`7`	`double calculate(double x);`
`8`	`8`
`9`		`- double gradient(double hx, double y);`
	`9`	`+ double gradient(double z);`
`10`	`10`	`}`