Update ejml 32 -> 38

Author: AngledLuffa

doc/corenlp/pom-full.xml

@@ -99,19 +99,19 @@
     <dependency>
       <groupId>org.ejml</groupId>
       <artifactId>ejml-core</artifactId>
-      <version>0.32</version>
+      <version>0.38</version>
     </dependency>
 
     <dependency>
       <groupId>org.ejml</groupId>
       <artifactId>ejml-ddense</artifactId>
-      <version>0.32</version>
+      <version>0.38</version>
     </dependency>
 
     <dependency>
       <groupId>org.ejml</groupId>
       <artifactId>ejml-simple</artifactId>
-      <version>0.32</version>
+      <version>0.38</version>
     </dependency>
 
     <dependency>

doc/lexparser/pom.xml

@@ -43,19 +43,19 @@
     <dependency>
       <groupId>org.ejml</groupId>
       <artifactId>ejml-core</artifactId>
-      <version>0.32</version>
+      <version>0.38</version>
     </dependency>
 
     <dependency>
       <groupId>org.ejml</groupId>
       <artifactId>ejml-ddense</artifactId>
-      <version>0.32</version>
+      <version>0.38</version>
     </dependency>
 
     <dependency>
       <groupId>org.ejml</groupId>
       <artifactId>ejml-simple</artifactId>
-      <version>0.32</version>
+      <version>0.38</version>
     </dependency>
 
     <dependency>

lib/LIBRARY-LICENSES

@@ -5,7 +5,7 @@ A compatible JSON library is a component of Java EE (JSR 374).
 
 -----------------------------------------------------------------
 
-ejml-0.32.jar
+ejml-0.38.jar
 
 URL: http://ejml.org/
 

lib/README

@@ -286,10 +286,10 @@ LAST UPDATE: 2016-07-24
 LAST UPDATE BY: Christopher Manning
 
 -----------------------------------------------------------------------
-ejml-core-0.32.jar
-ORIGINAL JAR NAME: ejml-core-0.32.jar
-VERSION: 0.32
-RELEASE DATE: 2017-09-18
+ejml-core-0.38.jar
+ORIGINAL JAR NAME: ejml-core-0.38.jar
+VERSION: 0.38
+RELEASE DATE: 2019-03-14
 SOURCE AVAILABLE: yes
 DESCRIPTION: Another matrix library for Java, perhaps fastest for medium
     size vectors and matrices in 2012.  Otherwise, it's ojAlgo.
@@ -298,14 +298,14 @@ URL: http://ejml.org/
 USED BY: Used in deep learning, especially RNN parser and sentiment
 Needed by CoreNLP distributions.
 
-LAST UPDATE: 2017-11-30
-LAST UPDATE BY: Christopher Manning
+LAST UPDATE: 2019-07-29
+LAST UPDATE BY: John Bauer
 
 -----------------------------------------------------------------------
-ejml-ddense-0.32.jar
-ORIGINAL JAR NAME: ejml-ddense-0.32.jar
-VERSION: 0.32
-RELEASE DATE: 2017-09-18
+ejml-ddense-0.38.jar
+ORIGINAL JAR NAME: ejml-ddense-0.38.jar
+VERSION: 0.38
+RELEASE DATE: 2019-03-14
 SOURCE AVAILABLE: yes
 DESCRIPTION: Another matrix library for Java, perhaps fastest for medium
     size vectors and matrices in 2012.  Otherwise, it's ojAlgo.
@@ -314,14 +314,14 @@ URL: http://ejml.org/
 USED BY: Used in deep learning, especially RNN parser and sentiment
 Needed by CoreNLP distributions.
 
-LAST UPDATE: 2017-11-30
-LAST UPDATE BY: Christopher Manning
+LAST UPDATE: 2019-07-29
+LAST UPDATE BY: John Bauer
 
 -----------------------------------------------------------------------
-ejml-simple-0.32.jar
-ORIGINAL JAR NAME: ejml-simple-0.32.jar
-VERSION: 0.32
-RELEASE DATE: 2017-09-18
+ejml-simple-0.38.jar
+ORIGINAL JAR NAME: ejml-simple-0.38.jar
+VERSION: 0.38
+RELEASE DATE: 2019-03-14
 SOURCE AVAILABLE: yes
 DESCRIPTION: Another matrix library for Java, perhaps fastest for medium
     size vectors and matrices in 2012.  Otherwise, it's ojAlgo.
@@ -330,8 +330,8 @@ URL: http://ejml.org/
 USED BY: Used in deep learning, especially RNN parser and sentiment
 Needed by CoreNLP distributions.
 
-LAST UPDATE: 2017-11-30
-LAST UPDATE BY: Christopher Manning
+LAST UPDATE: 2019-07-29
+LAST UPDATE BY: John Bauer
 
 -----------------------------------------------------------------------
 javacc.jar

lib/ejml-core-0.32.jar

@@ -99,19 +99,19 @@
     <dependency>
       <groupId>org.ejml</groupId>
       <artifactId>ejml-core</artifactId>
-      <version>0.32</version>
+      <version>0.38</version>
     </dependency>
 
     <dependency>
       <groupId>org.ejml</groupId>
       <artifactId>ejml-ddense</artifactId>
-      <version>0.32</version>
+      <version>0.38</version>
     </dependency>
 
     <dependency>
       <groupId>org.ejml</groupId>
       <artifactId>ejml-simple</artifactId>
-      <version>0.32</version>
+      <version>0.38</version>
     </dependency>
 
     <dependency>

lib/ejml-core-0.38.jar

@@ -259,7 +259,7 @@ public static SimpleMatrix elementwiseApplyTanh(SimpleMatrix input) {
    */
   public static SimpleMatrix elementwiseApplyTanhDerivative(SimpleMatrix input) {
     SimpleMatrix output = new SimpleMatrix(input.numRows(), input.numCols());
-    output.set(1.0);
+    output.fill(1.0);
     output = output.minus(input.elementMult(input));
     return output;
   }

lib/ejml-ddense-0.32.jar

@@ -71,7 +71,7 @@ public SimpleTensor(SimpleTensor other) {
   public static SimpleTensor random(int numRows, int numCols, int numSlices, double minValue, double maxValue, java.util.Random rand) {
     SimpleTensor tensor = new SimpleTensor(numRows, numCols, numSlices);
     for (int i = 0; i < numSlices; ++i) {
-      tensor.slices[i] = SimpleMatrix.random64(numRows, numCols, minValue, maxValue, rand);
+      tensor.slices[i] = SimpleMatrix.random_DDRM(numRows, numCols, minValue, maxValue, rand);
     }
     return tensor;
   }
@@ -106,7 +106,7 @@ public int getNumElements() {
 
   public void set(double value) {
     for (int slice = 0; slice < numSlices; ++slice) {
-      slices[slice].set(value);
+      slices[slice].fill(value);
     }
   }
 

lib/ejml-ddense-0.38.jar

@@ -197,7 +197,7 @@ private SimpleMatrix randomContextMatrix() {
     SimpleMatrix matrix = new SimpleMatrix(numRows, numCols * 2);
     matrix.insertIntoThis(0, 0, identity.scale(op.trainOptions.scalingForInit * 0.1));
     matrix.insertIntoThis(0, numCols, identity.scale(op.trainOptions.scalingForInit * 0.1));
-    matrix = matrix.plus(SimpleMatrix.random64(numRows,numCols * 2,-1.0/Math.sqrt((double)numCols * 100.0),1.0/Math.sqrt((double)numCols * 100.0),rand));
+    matrix = matrix.plus(SimpleMatrix.random_DDRM(numRows,numCols * 2,-1.0/Math.sqrt((double)numCols * 100.0),1.0/Math.sqrt((double)numCols * 100.0),rand));
     return matrix;
   }
 
@@ -210,13 +210,13 @@ private SimpleMatrix randomTransformMatrix() {
     SimpleMatrix matrix;
     switch (op.trainOptions.transformMatrixType) {
     case DIAGONAL:
-      matrix = SimpleMatrix.random64(numRows,numCols,-1.0/Math.sqrt((double)numCols * 100.0),1.0/Math.sqrt((double)numCols * 100.0),rand).plus(identity);
+      matrix = SimpleMatrix.random_DDRM(numRows,numCols,-1.0/Math.sqrt((double)numCols * 100.0),1.0/Math.sqrt((double)numCols * 100.0),rand).plus(identity);
       break;
     case RANDOM:
-      matrix = SimpleMatrix.random64(numRows,numCols,-1.0/Math.sqrt((double)numCols),1.0/Math.sqrt((double)numCols),rand);
+      matrix = SimpleMatrix.random_DDRM(numRows,numCols,-1.0/Math.sqrt((double)numCols),1.0/Math.sqrt((double)numCols),rand);
       break;
     case OFF_DIAGONAL:
-      matrix = SimpleMatrix.random64(numRows,numCols,-1.0/Math.sqrt((double)numCols * 100.0),1.0/Math.sqrt((double)numCols * 100.0),rand).plus(identity);
+      matrix = SimpleMatrix.random_DDRM(numRows,numCols,-1.0/Math.sqrt((double)numCols * 100.0),1.0/Math.sqrt((double)numCols * 100.0),rand).plus(identity);
       for (int i = 0; i < numCols; ++i) {
         int x = rand.nextInt(numCols);
         int y = rand.nextInt(numCols);
@@ -225,7 +225,7 @@ private SimpleMatrix randomTransformMatrix() {
       }
       break;
     case RANDOM_ZEROS:
-      matrix = SimpleMatrix.random64(numRows,numCols,-1.0/Math.sqrt((double)numCols * 100.0),1.0/Math.sqrt((double)numCols * 100.0),rand).plus(identity);
+      matrix = SimpleMatrix.random_DDRM(numRows,numCols,-1.0/Math.sqrt((double)numCols * 100.0),1.0/Math.sqrt((double)numCols * 100.0),rand).plus(identity);
       for (int i = 0; i < numCols; ++i) {
         int x = rand.nextInt(numCols);
         int y = rand.nextInt(numCols);
@@ -246,7 +246,7 @@ public void addRandomUnaryMatrix(String childBasic) {
     ++numUnaryMatrices;
 
     // scoring matrix
-    SimpleMatrix score = SimpleMatrix.random64(1, numCols, -1.0/Math.sqrt((double)numCols),1.0/Math.sqrt((double)numCols),rand);
+    SimpleMatrix score = SimpleMatrix.random_DDRM(1, numCols, -1.0/Math.sqrt((double)numCols),1.0/Math.sqrt((double)numCols),rand);
     unaryScore.put(childBasic, score.scale(op.trainOptions.scalingForInit));
 
     SimpleMatrix transform;
@@ -270,7 +270,7 @@ public void addRandomBinaryMatrix(String leftBasic, String rightBasic) {
     ++numBinaryMatrices;
 
     // scoring matrix
-    SimpleMatrix score = SimpleMatrix.random64(1, numCols, -1.0/Math.sqrt((double)numCols),1.0/Math.sqrt((double)numCols),rand);
+    SimpleMatrix score = SimpleMatrix.random_DDRM(1, numCols, -1.0/Math.sqrt((double)numCols),1.0/Math.sqrt((double)numCols),rand);
     binaryScore.put(leftBasic, rightBasic, score.scale(op.trainOptions.scalingForInit));
 
     SimpleMatrix binary;
@@ -583,8 +583,8 @@ public void readWordVectors() {
     }
 
     if (op.trainOptions.useContextWords) {
-      SimpleMatrix start = SimpleMatrix.random64(op.lexOptions.numHid, 1, -0.5, 0.5, rand);
-      SimpleMatrix end = SimpleMatrix.random64(op.lexOptions.numHid, 1, -0.5, 0.5, rand);
+      SimpleMatrix start = SimpleMatrix.random_DDRM(op.lexOptions.numHid, 1, -0.5, 0.5, rand);
+      SimpleMatrix end = SimpleMatrix.random_DDRM(op.lexOptions.numHid, 1, -0.5, 0.5, rand);
       wordVectors.put(START_WORD, start);
       wordVectors.put(END_WORD, end);
     }

lib/ejml-simple-0.32.jar

@@ -154,7 +154,7 @@ public static void main(String[] args) throws IOException {
     }
 
     if (!wordVectors.containsKey(SentimentModel.UNKNOWN_WORD)) {
-      wordVectors.put(SentimentModel.UNKNOWN_WORD, SimpleMatrix.random64(numSlices, 1, -0.00001, 0.00001, new Random()));
+      wordVectors.put(SentimentModel.UNKNOWN_WORD, SimpleMatrix.random_DDRM(numSlices, 1, -0.00001, 0.00001, new Random()));
     }
 
     SentimentModel model = SentimentModel.modelFromMatrices(W, Wcat, tensor, wordVectors, op);

lib/ejml-simple-0.38.jar

@@ -452,7 +452,7 @@ SimpleMatrix randomTransformMatrix() {
 
   SimpleMatrix randomTransformBlock() {
     double range = 1.0 / (Math.sqrt((double) numHid) * 2.0);
-    return SimpleMatrix.random64(numHid,numHid,-range,range,rand).plus(identity);
+    return SimpleMatrix.random_DDRM(numHid,numHid,-range,range,rand).plus(identity);
   }
 
   /**
@@ -461,9 +461,9 @@ SimpleMatrix randomTransformBlock() {
   SimpleMatrix randomClassificationMatrix() {
     SimpleMatrix score = new SimpleMatrix(numClasses, numHid + 1);
     double range = 1.0 / (Math.sqrt((double) numHid));
-    score.insertIntoThis(0, 0, SimpleMatrix.random64(numClasses, numHid, -range, range, rand));
+    score.insertIntoThis(0, 0, SimpleMatrix.random_DDRM(numClasses, numHid, -range, range, rand));
     // bias column goes from 0 to 1 initially
-    score.insertIntoThis(0, numHid, SimpleMatrix.random64(numClasses, 1, 0.0, 1.0, rand));
+    score.insertIntoThis(0, numHid, SimpleMatrix.random_DDRM(numClasses, 1, 0.0, 1.0, rand));
     return score.scale(op.trainOptions.scalingForInit);
   }