Apply optimize stream refactoring

src/main/java/org/numenta/nupic/model/Connections.java

@@ -1553,7 +1553,7 @@ public void removeSynapseFromPresynapticMap(Synapse synapse) {
      * @return  Synapse object on the segment with the minimal permanence
      */
     private Synapse minPermanenceSynapse(DistalDendrite dd) {
-        List<Synapse> synapses = getSynapses(dd).stream().sorted().collect(Collectors.toList());
+        List<Synapse> synapses = getSynapses(dd).parallelStream().sorted().collect(Collectors.toList());
         Synapse min = null;
         double minPermanence = Double.MAX_VALUE;
 

src/main/java/org/numenta/nupic/network/sensor/FileSensor.java

@@ -157,7 +157,7 @@ public static Stream<String> getJarEntryStream(String path) {
             innerPath = innerPath.startsWith("!") ? innerPath.substring(1) : innerPath;
             InputStream inStream = jar.getInputStream(jar.getEntry(innerPath));
             BufferedReader br = new BufferedReader(new InputStreamReader(inStream));
-            retVal = br.lines().onClose(() -> {
+            retVal = br.lines().parallel().onClose(() -> {
                 try {
                     br.close();
                     jar.close();

src/main/java/org/numenta/nupic/util/GroupBy2.java

@@ -237,7 +237,7 @@ private void advanceSequences() {
      * @return  the next smallest generated key.
      */
     private boolean nextMinKey() {
-        return Arrays.stream(nextList)
+        return Arrays.stream(nextList).parallel()
             .filter(opt -> opt.isPresent())
             .map(opt -> opt.get().getSecond())
             .min((k, k2) -> k.compareTo(k2))

src/main/java/org/numenta/nupic/util/UniversalRandom.java

@@ -354,7 +354,7 @@ public static void main(String[] args) {
         int[] selectedIndices = new int[sampleSize];
         List<Integer> collectedRandoms = new ArrayList<>();
         int[] expectedSample = {1,2,3,7,8,9};
-        List<Integer> expectedRandoms = Arrays.stream(new int[] {0,0,0,5,3,3}).boxed().collect(Collectors.toList());
+        List<Integer> expectedRandoms = Arrays.stream(new int[] {0,0,0,5,3,3}).parallel().boxed().collect(Collectors.toList());
         random.sampleWithPrintout(choices, selectedIndices, collectedRandoms);
         System.out.println("samples are equal ? " + Arrays.equals(expectedSample, selectedIndices));
         System.out.println("used randoms are equal ? " + collectedRandoms.equals(expectedRandoms));

src/test/java/org/numenta/nupic/QuickDayTest.java

@@ -248,7 +248,7 @@ public void input(Double value, int recordNum, int sequenceNum) {
             ComputeCycle cc = temporalMemory.compute(memory, input, true);
             lastPredicted = predictedColumns;
             predictedColumns = getSDR(cc.predictiveCells()); //Get the predicted column indexes
-            int[] activeCellIndexes = Connections.asCellIndexes(cc.activeCells()).stream().mapToInt(i -> i).sorted().toArray();  //Get the active cells for classifier input
+            int[] activeCellIndexes = Connections.asCellIndexes(cc.activeCells()).parallelStream().mapToInt(i -> i).sorted().toArray();  //Get the active cells for classifier input
             System.out.println("TemporalMemory Input = " + Arrays.toString(input));
             System.out.println("TemporalMemory Prediction = " + Arrays.toString(predictedColumns));
 

src/test/java/org/numenta/nupic/RunLayer.java

@@ -214,7 +214,7 @@ public void networkStep(int[] sparseSPOutput, boolean learn) {
         public Tuple tmStep(int[] sparseSPOutput, boolean learn, boolean isVerbose) {
             // Input into the Temporal Memory
             ComputeCycle cc = tm.compute(connections, sparseSPOutput, learn);
-            int[] activeCellIndices = cc.activeCells().stream().mapToInt(c -> c.getIndex()).sorted().toArray();
+            int[] activeCellIndices = cc.activeCells().parallelStream().mapToInt(c -> c.getIndex()).sorted().toArray();
             int[] predColumnIndices = SDR.cellsAsColumnIndices(cc.predictiveCells(), connections.getCellsPerColumn());
             int[] activeColumns = Arrays.stream(activeCellIndices)
                 .map(cell -> cell / connections.getCellsPerColumn())
@@ -478,7 +478,7 @@ public static Tuple testTemporalMemory(TemporalMemory tm, Connections conn, int[
     }
 
     public static void loadSPOutputFile() {
-        try (Stream<String> stream = Files.lines(Paths.get(MakeshiftLayer.readFile))) {
+        try (Stream<String> stream = Files.lines(Paths.get(MakeshiftLayer.readFile)).parallel()) {
             MakeshiftLayer.input = stream.map(l -> {
                 String line = l.replace("[", "").replace("]",  "").trim();
                 int[] result = Arrays.stream(line.split("[\\s]*\\,[\\s]*")).mapToInt(i -> Integer.parseInt(i)).toArray();
@@ -490,7 +490,7 @@ public static void loadSPOutputFile() {
     }
 
     public static void loadRawInputFile() {
-        try (Stream<String> stream = Files.lines(Paths.get(MakeshiftLayer.INPUT_PATH))) {
+        try (Stream<String> stream = Files.lines(Paths.get(MakeshiftLayer.INPUT_PATH)).parallel()) {
             MakeshiftLayer.raw = stream.map(l -> l.trim()).collect(Collectors.toList());
         }catch(Exception e) {e.printStackTrace();}
     }