diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/reduce3/Dot.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/reduce3/Dot.java
index 7c00c7933..acfe9a0a3 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/reduce3/Dot.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/reduce3/Dot.java
@@ -28,7 +28,8 @@ import java.util.Arrays;
import java.util.List;
/**
- * Dot product
+ * Dot product.
+ *
* @author Adam Gibson
*/
public class Dot extends BaseReduce3Op {
@@ -37,21 +38,42 @@ public class Dot extends BaseReduce3Op {
super(sameDiff, i_v, i_v2, dimensions);
}
- public Dot() {}
+ public Dot() {
+ }
+ /**
+ * Full array dot product reduction, optionally along specified dimensions.
+ * See wikipedia for details.
+ *
+ * @param x input variable.
+ * @param y input variable.
+ * @param z (optional) place holder for the result. Must have the expected shape.
+ * @param dimensions (optional) Dimensions to reduce over. If dimensions are not specified, full array reduction is performed.
+ * @see org.nd4j.linalg.ops.transforms.Transforms#dot Transforms.dot(...) for a wrapper around the common use case of 2 INDArrays.
+ */
public Dot(INDArray x, INDArray y, INDArray z, int... dimensions) {
this(x, y, z, true, false, dimensions);
}
- public Dot(INDArray x, INDArray y, int... dimensions) {
+
+ /**
+ * @see #Dot(INDArray x, INDArray y, INDArray z, int...)
+ */
+ public Dot(INDArray x, INDArray y, int... dimensions) {
this(x, y, null, dimensions);
}
+ /**
+ * @see #Dot(INDArray x, INDArray y, INDArray z, int...)
+ */
public Dot(INDArray x, INDArray y, INDArray z) {
this(x, y, z, null);
}
- public Dot(INDArray x, INDArray y, INDArray z, boolean newFormat, boolean keepDims, int... dimensions){
+ /**
+ * @see #Dot(INDArray x, INDArray y, INDArray z, int...)
+ */
+ public Dot(INDArray x, INDArray y, INDArray z, boolean newFormat, boolean keepDims, int... dimensions) {
super(x, y, z, keepDims, false, dimensions);
}
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/ops/transforms/Transforms.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/ops/transforms/Transforms.java
index 421376f94..43df4a430 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/ops/transforms/Transforms.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/ops/transforms/Transforms.java
@@ -56,7 +56,8 @@ import java.util.List;
public class Transforms {
- private Transforms() {}
+ private Transforms() {
+ }
/**
* Cosine similarity
@@ -64,7 +65,6 @@ public class Transforms {
* @param d1 the first vector
* @param d2 the second vector
* @return the cosine similarities between the 2 arrays
- *
*/
public static double cosineSim(@NonNull INDArray d1, @NonNull INDArray d2) {
return Nd4j.getExecutioner().exec(new CosineSimilarity(d1, d2)).getDouble(0);
@@ -103,12 +103,21 @@ public class Transforms {
return Nd4j.getExecutioner().exec(new OldReverse(x, dup ? x.ulike() : x));
}
- public static INDArray dot(INDArray x, INDArray y){
- return Nd4j.getExecutioner().exec(new Dot(x,y));
+ /**
+ * Dot product, new INDArray instance will be returned.
+ * Note that the Nd4J design is different from Numpy. Numpy dot on 2d arrays is matrix multiplication. Nd4J is
+ * full array dot product reduction.
+ *
+ * @param x the first vector
+ * @param y the second vector
+ * @return the dot product between the 2 arrays
+ */
+ public static INDArray dot(INDArray x, INDArray y) {
+ return Nd4j.getExecutioner().exec(new Dot(x, y));
}
- public static INDArray cross(INDArray x, INDArray y){
- Cross c = new Cross(x,y,null);
+ public static INDArray cross(INDArray x, INDArray y) {
+ Cross c = new Cross(x, y, null);
List shape = c.calculateOutputShape();
INDArray out = Nd4j.create(shape.get(0));
c.addOutputArgument(out);
@@ -117,7 +126,6 @@ public class Transforms {
}
/**
- *
* @param d1
* @param d2
* @return
@@ -139,7 +147,6 @@ public class Transforms {
}
/**
- *
* @param d1
* @param d2
* @return
@@ -188,7 +195,6 @@ public class Transforms {
}
-
/**
* Returns the negative of an ndarray
*
@@ -224,6 +230,7 @@ public class Transforms {
/**
* Ceiling function
+ *
* @param ndArray
* @param copyOnOps
* @return
@@ -244,7 +251,6 @@ public class Transforms {
/**
- *
* @param ndArray
* @param k
* @return
@@ -255,6 +261,7 @@ public class Transforms {
/**
* Sin function
+ *
* @param in
* @return
*/
@@ -264,6 +271,7 @@ public class Transforms {
/**
* Sin function
+ *
* @param in
* @param copy
* @return
@@ -275,6 +283,7 @@ public class Transforms {
/**
* Sin function
+ *
* @param in
* @return
*/
@@ -284,6 +293,7 @@ public class Transforms {
/**
* Sin function
+ *
* @param in
* @param copy
* @return
@@ -294,6 +304,7 @@ public class Transforms {
/**
* Sinh function
+ *
* @param in
* @return
*/
@@ -303,6 +314,7 @@ public class Transforms {
/**
* Sinh function
+ *
* @param in
* @param copy
* @return
@@ -312,7 +324,6 @@ public class Transforms {
}
/**
- *
* @param in
* @return
*/
@@ -321,7 +332,6 @@ public class Transforms {
}
/**
- *
* @param in
* @param copy
* @return
@@ -331,7 +341,6 @@ public class Transforms {
}
/**
- *
* @param in
* @return
*/
@@ -340,7 +349,6 @@ public class Transforms {
}
/**
- *
* @param in
* @param copy
* @return
@@ -435,7 +443,6 @@ public class Transforms {
}
-
public static INDArray leakyRelu(INDArray arr, double cutoff) {
return leakyRelu(arr, cutoff, true);
}
@@ -455,7 +462,6 @@ public class Transforms {
}
-
public static INDArray softPlus(INDArray arr) {
return softPlus(arr, true);
}
@@ -495,14 +501,12 @@ public class Transforms {
}
-
public static INDArray softmax(INDArray arr) {
return softmax(arr, true);
}
/**
- *
* @param in
* @param copy
* @return
@@ -513,11 +517,12 @@ public class Transforms {
/**
* out = in * (1-in)
+ *
* @param in Input array
* @param copy If true: copy. False: apply in-place
* @return
*/
- public static INDArray timesOneMinus(INDArray in, boolean copy){
+ public static INDArray timesOneMinus(INDArray in, boolean copy) {
return Nd4j.getExecutioner().exec(new TimesOneMinus(in, (copy ? in.ulike() : in)));
}
@@ -534,6 +539,7 @@ public class Transforms {
/**
* Run the exp operation
+ *
* @param ndArray
* @return
*/
@@ -558,7 +564,7 @@ public class Transforms {
return exec(dup ? new HardTanh(ndArray, ndArray.ulike()) : new HardTanh(ndArray));
}
- public static INDArray hardSigmoid(INDArray arr, boolean dup){
+ public static INDArray hardSigmoid(INDArray arr, boolean dup) {
return Nd4j.getExecutioner().exec(new HardSigmoid(arr, (dup ? arr.ulike() : arr)));
}
@@ -580,9 +586,7 @@ public class Transforms {
}
-
/**
- *
* @param ndArray
* @return
*/
@@ -1018,7 +1022,6 @@ public class Transforms {
}
-
/**
* Sqrt function
*
@@ -1104,7 +1107,6 @@ public class Transforms {
}
-
/**
* Apply the given elementwise op
*
@@ -1131,8 +1133,9 @@ public class Transforms {
* repeated squarings to minimize the number of mmul() operations needed
* If n is zero, the identity matrix is returned.
* If n is negative, the matrix is inverted and raised to the abs(n) power.
- * @param in A square matrix to raise to an integer power, which will be changed if dup is false.
- * @param n The integer power to raise the matrix to.
+ *
+ * @param in A square matrix to raise to an integer power, which will be changed if dup is false.
+ * @param n The integer power to raise the matrix to.
* @param dup If dup is true, the original input is unchanged.
* @return The result of raising in to the nth power.
*/