Knn dev

.classpath

@@ -2,24 +2,25 @@
 <classpath>
 	<classpathentry kind="src" path="src/jmh/java"/>
 	<classpathentry kind="src" path="src/main/java"/>
+	<classpathentry kind="src" path="src/main/resources"/>
 	<classpathentry kind="src" path="src/test/java"/>
-	<classpathentry kind="con" path="org.eclipse.jdt.launching.JRE_CONTAINER"/>
-	<classpathentry kind="lib" path="libs/chutils-1.4.jar" sourcepath="/Users/cogmission/Resources/chutils/chutils-1.4-sources.jar"/>
+	<classpathentry kind="lib" path="libs/algorithmfoundry-shade-culled-1.3.jar"/>
+	<classpathentry kind="lib" path="libs/chutils-1.4.jar"/>
+	<classpathentry kind="lib" path="libs/fst-2.45.jar"/>
 	<classpathentry kind="lib" path="libs/hamcrest-core-1.3.jar"/>
 	<classpathentry kind="lib" path="libs/jackson-annotations-2.4.0.jar"/>
 	<classpathentry kind="lib" path="libs/jackson-core-2.4.4.jar"/>
 	<classpathentry kind="lib" path="libs/jackson-databind-2.4.4.jar"/>
+	<classpathentry kind="lib" path="libs/java-util-1.19.3.jar"/>
 	<classpathentry kind="lib" path="libs/jmh-core-1.5.1.jar"/>
 	<classpathentry kind="lib" path="libs/joda-time-2.5.jar"/>
 	<classpathentry kind="lib" path="libs/junit-4.11.jar"/>
 	<classpathentry kind="lib" path="libs/logback-classic-1.1.2.jar"/>
 	<classpathentry kind="lib" path="libs/logback-core-1.1.2.jar"/>
-	<classpathentry kind="lib" path="libs/rxjava-1.0.10.jar" sourcepath="/Users/cogmission/Resources/RxJava/rxjava-1.0.10-sources.jar"/>
-	<classpathentry kind="lib" path="libs/slf4j-api-1.7.10.jar"/>
-	<classpathentry kind="lib" path="libs/trove-3.1a1.jar" sourcepath="/Users/cogmission/Resources/trove/3.1a1/trove-3.1a1-src.jar"/>
-	<classpathentry kind="lib" path="libs/java-util-1.19.3.jar" sourcepath="/Users/cogmission/.m2/repository/com/cedarsoftware/java-util/1.19.3/java-util-1.19.3-sources.jar"/>
 	<classpathentry kind="lib" path="libs/objenesis-2.1.jar"/>
-	<classpathentry kind="lib" path="libs/algorithmfoundry-shade-culled-1.3.jar"/>
-	<classpathentry kind="lib" path="libs/fst-2.45.jar"/>
+	<classpathentry kind="lib" path="libs/rxjava-1.0.10.jar"/>
+	<classpathentry kind="lib" path="libs/slf4j-api-1.7.10.jar"/>
+	<classpathentry kind="lib" path="libs/trove-3.1a1.jar"/>
+	<classpathentry kind="con" path="org.eclipse.jdt.launching.JRE_CONTAINER"/>
 	<classpathentry kind="output" path="bin"/>
 </classpath>

build.gradle

@@ -4,15 +4,15 @@ apply plugin: 'eclipse'
 apply plugin: 'signing'
 
 group = 'org.numenta'
-version = '0.6.9-SNAPSHOT'
+version = '0.6.10-SNAPSHOT'
 archivesBaseName = 'htm.java'
 
 sourceCompatibility = 1.8
 targetCompatibility = 1.8
 
 jar {
     manifest {
-        attributes 'Implementation-Title': 'htm.java', 'Implementation-Version': '0.6.9-SNAPSHOT'
+        attributes 'Implementation-Title': 'htm.java', 'Implementation-Version': '0.6.10-SNAPSHOT'
     }
 }
 
@@ -33,10 +33,11 @@ test {
 }
 
 // UNCOMMENT TO SEE STANDARD_OUT & STANDARD_ERR DURING BUILD
-
+/*
 test {
     testLogging.showStandardStreams = true
 }
+*/
 
 dependencies {
     compile group: 'joda-time', name: 'joda-time', version: '2.5'
@@ -52,11 +53,11 @@ dependencies {
     compile group: 'com.fasterxml.jackson.core', name: 'jackson-core', version:'2.4.4'
     compile group: 'com.fasterxml.jackson.core', name: 'jackson-databind', version:'2.4.4'
     compile group: 'org.slf4j', name: 'slf4j-api', version:'1.7.10'
-	compile group: 'io.reactivex', name: 'rxjava', version: '1.0.10'
-	compile group: 'de.ruedigermoeller', name: 'fst', version: '2.45'
-	compile group: 'com.cedarsoftware', name: 'java-util', version: '1.19.3'
-	compile group: 'algorithmfoundry', name: 'algorithmfoundry-shade-culled', version:'1.3'
-	testCompile group: 'junit', name: 'junit', version:'4.11'
+    compile group: 'io.reactivex', name: 'rxjava', version: '1.0.10'
+    compile group: 'de.ruedigermoeller', name: 'fst', version: '2.45'
+    compile group: 'com.cedarsoftware', name: 'java-util', version: '1.19.3'
+    compile group: 'algorithmfoundry', name: 'algorithmfoundry-shade-culled', version:'1.3'
+    testCompile group: 'junit', name: 'junit', version:'4.11'
     testCompile group: 'ch.qos.logback', name: 'logback-classic', version:'1.1.2'
 }
 

pom.xml

@@ -4,7 +4,7 @@
 
 	<groupId>org.numenta</groupId>
 	<artifactId>htm.java</artifactId>
-	<version>0.6.9-SNAPSHOT</version>
+	<version>0.6.10-SNAPSHOT</version>
 	<name>htm.java</name>
 	<description>The Java version of Numenta's HTM technology</description>
 

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

@@ -2229,39 +2229,7 @@ public double[] getPAOverlaps() {
      * High verbose output useful for debugging
      */
     public void printParameters() {
-        System.out.println("------------ SpatialPooler Parameters ------------------");
-        System.out.println("numInputs                  = " + getNumInputs());
-        System.out.println("numColumns                 = " + getNumColumns());
-        System.out.println("cellsPerColumn             = " + getCellsPerColumn());
-        System.out.println("columnDimensions           = " + Arrays.toString(getColumnDimensions()));
-        System.out.println("numActiveColumnsPerInhArea = " + getNumActiveColumnsPerInhArea());
-        System.out.println("potentialPct               = " + getPotentialPct());
-        System.out.println("potentialRadius            = " + getPotentialRadius());
-        System.out.println("globalInhibition           = " + getGlobalInhibition());
-        System.out.println("localAreaDensity           = " + getLocalAreaDensity());
-        System.out.println("inhibitionRadius           = " + getInhibitionRadius());
-        System.out.println("stimulusThreshold          = " + getStimulusThreshold());
-        System.out.println("synPermActiveInc           = " + getSynPermActiveInc());
-        System.out.println("synPermInactiveDec         = " + getSynPermInactiveDec());
-        System.out.println("synPermConnected           = " + getSynPermConnected());
-        System.out.println("minPctOverlapDutyCycle     = " + getMinPctOverlapDutyCycles());
-        System.out.println("minPctActiveDutyCycle      = " + getMinPctActiveDutyCycles());
-        System.out.println("dutyCyclePeriod            = " + getDutyCyclePeriod());
-        System.out.println("maxBoost                   = " + getMaxBoost());
-        System.out.println("version                    = " + getVersion());
-
-        System.out.println("\n------------ TemporalMemory Parameters ------------------");
-        System.out.println("activationThreshold        = " + getActivationThreshold());
-        System.out.println("learningRadius             = " + getLearningRadius());
-        System.out.println("minThreshold               = " + getMinThreshold());
-        System.out.println("maxNewSynapseCount         = " + getMaxNewSynapseCount());
-        System.out.println("maxSynapsesPerSegment      = " + getMaxSynapsesPerSegment());
-        System.out.println("maxSegmentsPerCell         = " + getMaxSegmentsPerCell());
-        System.out.println("initialPermanence          = " + getInitialPermanence());
-        System.out.println("connectedPermanence        = " + getConnectedPermanence());
-        System.out.println("permanenceIncrement        = " + getPermanenceIncrement());
-        System.out.println("permanenceDecrement        = " + getPermanenceDecrement());
-        System.out.println("predictedSegmentDecrement  = " + getPredictedSegmentDecrement());
+        System.out.println(getPrintString());
     }
 
     /**
@@ -2275,7 +2243,6 @@ public String getPrintString() {
         pw.println("columnDimensions           = " + Arrays.toString(getColumnDimensions()));
         pw.println("inputDimensions            = " + Arrays.toString(getInputDimensions()));
         pw.println("cellsPerColumn             = " + getCellsPerColumn());
-
         pw.println("random                     = " + getRandom());
         pw.println("seed                       = " + getSeed());
 
@@ -2294,8 +2261,8 @@ public String getPrintString() {
         pw.println("synPermConnected           = " + getSynPermConnected());
         pw.println("synPermBelowStimulusInc    = " + getSynPermBelowStimulusInc());
         pw.println("synPermTrimThreshold       = " + getSynPermTrimThreshold());
-        pw.println("minPctOverlapDutyCycles     = " + getMinPctOverlapDutyCycles());
-        pw.println("minPctActiveDutyCycles      = " + getMinPctActiveDutyCycles());
+        pw.println("minPctOverlapDutyCycles    = " + getMinPctOverlapDutyCycles());
+        pw.println("minPctActiveDutyCycles     = " + getMinPctActiveDutyCycles());
         pw.println("dutyCyclePeriod            = " + getDutyCyclePeriod());
         pw.println("maxBoost                   = " + getMaxBoost());
         pw.println("version                    = " + getVersion());

src/main/java/org/numenta/nupic/Constants.java

@@ -0,0 +1,16 @@
+package org.numenta.nupic;
+
+
+public class Constants {
+    public static final String ADAPTIVE = "adaptive";
+
+    public enum KNN { 
+        ADAPTIVE("adaptive");
+
+        private String description;
+        private KNN(String desc) { this.description = desc; }
+        /** {@inheritDoc} */
+        public String toString() { return description; }
+    }
+
+}

src/main/java/org/numenta/nupic/DistanceMethod.java

@@ -0,0 +1,58 @@
+package org.numenta.nupic;
+
+/**
+ * <p>
+ * The method used to compute distance between input patterns and prototype patterns. 
+ * </p><p>
+ * The possible options are:
+ * <ul>
+ *  <li>NORM:                   When distanceNorm is 2, this is the euclidean distance,
+ *                              When distanceNorm is 1, this is the manhattan distance
+ *                              In general: sum(abs(x-proto) ^ distanceNorm) ^ (1/distanceNorm)
+ *                              The distances are normalized such that farthest prototype from
+ *                              a given input is 1.0.</li>
+ *            
+ *  <li>RAW_OVERLAP:            Only appropriate when inputs are binary. This computes:
+ *                              (width of the input) - (# bits of overlap between input
+ *                              and prototype).</li>
+ *  
+ *  <li>PCT_INPUT_OVERLAP:      Only appropriate for binary inputs. This computes
+ *                              1.0 - (# bits overlap between input and prototype) /
+ *                              (# ON bits in input)
+ *                              
+ *  <li>PCT_PROTO_OVERLAP:      Only appropriate for binary inputs. This computes
+ *                              1.0 - (# bits overlap between input and prototype) /
+ *                              (# ON bits in prototype)
+ *                               
+ *  <li>PCT_LARGER_OVERLAP:     Only appropriate for binary inputs. This computes
+ *                              1.0 - (# bits overlap between input and prototype) /
+ *                              max(# ON bits in input, # ON bits in prototype)                   
+ *                       
+ *
+ * @author Numenta
+ * @author cogmission
+ */
+public enum DistanceMethod {
+    NORM("norm"), 
+    RAW_OVERLAP("rawOverlap"), 
+    PCT_INPUT_OVERLAP("pctOverlapOfInput"),
+    PCT_PROTO_OVERLAP("pctOverlapOfProto"),
+    PCT_LARGER_OVERLAP("pctOverlapOfLarger");
+
+    private String key;
+
+    /**
+     * Constructor passing the value used for KNN Distance Method key.
+     * @param key   the string value identifying the distance method type.
+     */
+    private DistanceMethod(String key) {
+        this.key = key;
+    }
+
+    /**
+     * {@inheritDoc}
+     */
+    public String toString() {
+        return key;
+    }
+}

src/main/java/org/numenta/nupic/Parameters.java

@@ -32,6 +32,7 @@
 import java.util.Random;
 import java.util.Set;
 
+import org.joda.time.format.DateTimeFormatter;
 import org.numenta.nupic.algorithms.SpatialPooler;
 import org.numenta.nupic.algorithms.TemporalMemory;
 import org.numenta.nupic.model.Cell;
@@ -65,6 +66,7 @@ public class Parameters implements Persistable {
     private static final Map<KEY, Object> DEFAULTS_TEMPORAL;
     private static final Map<KEY, Object> DEFAULTS_SPATIAL;
     private static final Map<KEY, Object> DEFAULTS_ENCODER;
+    private static final Map<KEY, Object> DEFAULTS_KNN;
 
 
     static {
@@ -135,6 +137,28 @@ public class Parameters implements Persistable {
         defaultEncoderParams.put(KEY.AUTO_CLASSIFY, Boolean.FALSE);
         DEFAULTS_ENCODER = Collections.unmodifiableMap(defaultEncoderParams);
         defaultParams.putAll(DEFAULTS_ENCODER);
+
+        ////////////////// KNNClassifier Defaults ///////////////////
+        Map<KEY, Object> defaultKNNParams = new ParametersMap();
+        defaultKNNParams.put(KEY.K, 1);
+        defaultKNNParams.put(KEY.EXACT, false);
+        defaultKNNParams.put(KEY.DISTANCE_NORM, 2.0);
+        defaultKNNParams.put(KEY.DISTANCE_METHOD, DistanceMethod.NORM);
+        defaultKNNParams.put(KEY.DISTANCE_THRESHOLD, .0);
+        defaultKNNParams.put(KEY.DO_BINARIZATION, false);
+        defaultKNNParams.put(KEY.BINARIZATION_THRESHOLD, 0.5);
+        defaultKNNParams.put(KEY.USE_SPARSE_MEMORY, true);
+        defaultKNNParams.put(KEY.SPARSE_THRESHOLD, 0.1);
+        defaultKNNParams.put(KEY.RELATIVE_THRESHOLD, false);
+        defaultKNNParams.put(KEY.NUM_WINNERS, 0);
+        defaultKNNParams.put(KEY.NUM_SVD_SAMPLES, -1);
+        defaultKNNParams.put(KEY.NUM_SVD_DIMS, Constants.KNN.ADAPTIVE);
+        defaultKNNParams.put(KEY.FRACTION_OF_MAX, -1.0);
+        defaultKNNParams.put(KEY.MAX_STORED_PATTERNS, -1);
+        defaultKNNParams.put(KEY.REPLACE_DUPLICATES, false);
+        defaultKNNParams.put(KEY.KNN_CELLS_PER_COL, 0);
+        DEFAULTS_KNN = Collections.unmodifiableMap(defaultKNNParams);
+        defaultParams.putAll(DEFAULTS_KNN);
 
         DEFAULTS_ALL = Collections.unmodifiableMap(defaultParams);
     }
@@ -299,7 +323,6 @@ public static enum KEY {
         // Network Layer indicator for auto classifier generation
         AUTO_CLASSIFY("hasClassifiers", Boolean.class),
 
-
         // How many bits to use if encoding the respective date fields.
         // e.g. Tuple(bits to use:int, radius:double)
         DATEFIELD_SEASON("season", Tuple.class), 
@@ -308,7 +331,81 @@ public static enum KEY {
         DATEFIELD_HOLIDAY("holiday", Tuple.class),
         DATEFIELD_TOFD("timeOfDay", Tuple.class),
         DATEFIELD_CUSTOM("customDays", Tuple.class), // e.g. Tuple(bits:int, List<String>:"mon,tue,fri")
-        DATEFIELD_PATTERN("formatPattern", String.class);
+        DATEFIELD_PATTERN("formatPattern", String.class),
+        DATEFIELD_FORMATTER("dateFormatter", DateTimeFormatter.class),
+
+
+        ///////////// KNNClassifier Parameters //////////////
+        /** The number of nearest neighbors used in the classification of patterns. <b>Must be odd</b> */
+        K("k", Integer.class),
+        /** If true, patterns must match exactly when assigning class labels */
+        EXACT("exact", Boolean.class),
+        /** When distance method is "norm", this specifies the p value of the Lp-norm */
+        DISTANCE_NORM("distanceNorm", Double.class),
+        /** 
+         * The method used to compute distance between input patterns and prototype patterns.
+         * see({@link DistanceMethod}) 
+         */
+        DISTANCE_METHOD("distanceMethod", DistanceMethod.class),
+        /** 
+         * A threshold on the distance between learned
+         * patterns and a new pattern proposed to be learned. The distance must be
+         * greater than this threshold in order for the new pattern to be added to
+         * the classifier's memory
+         */
+        DISTANCE_THRESHOLD("distanceThreshold", Double.class),
+        /** If True, then scalar inputs will be binarized. */
+        DO_BINARIZATION("doBinarization", Boolean.class),
+        /** If doBinarization is True, this specifies the threshold for the binarization of inputs */
+        BINARIZATION_THRESHOLD("binarizationThreshold", Double.class),
+        /** If True, classifier will use a sparse memory matrix */
+        USE_SPARSE_MEMORY("useSparseMemory", Boolean.class),
+        /** 
+         * If useSparseMemory is True, input variables whose absolute values are 
+         * less than this threshold will be stored as zero
+         */
+        SPARSE_THRESHOLD("sparseThreshold", Double.class),
+        /** Flag specifying whether to multiply sparseThreshold by max value in input */
+        RELATIVE_THRESHOLD("relativeThreshold", Boolean.class),
+        /** Number of elements of the input that are stored. If 0, all elements are stored */
+        NUM_WINNERS("numWinners", Integer.class),
+        /** 
+         * Number of samples the must occur before a SVD
+         * (Singular Value Decomposition) transformation will be performed. If 0,
+         * the transformation will never be performed
+         */
+        NUM_SVD_SAMPLES("numSVDSamples", Integer.class),
+        /** 
+         * Controls dimensions kept after SVD transformation. If "adaptive", 
+         * the number is chosen automatically
+         */
+        NUM_SVD_DIMS("numSVDDims", Constants.KNN.class),
+        /**
+         * If numSVDDims is "adaptive", this controls the
+         * smallest singular value that is retained as a fraction of the largest
+         * singular value
+         */
+        FRACTION_OF_MAX("fractionOfMax", Double.class),
+        /**
+         * Limits the maximum number of the training
+         * patterns stored. When KNN learns in a fixed capacity mode, the unused
+         * patterns are deleted once the number of stored patterns is greater than
+         * maxStoredPatterns. A value of -1 is no limit
+         */
+        MAX_STORED_PATTERNS("maxStoredPatterns", Integer.class),
+        /**
+         * A boolean flag that determines whether,
+         * during learning, the classifier replaces duplicates that match exactly,
+         * even if distThreshold is 0. Should be TRUE for online learning
+         */
+        REPLACE_DUPLICATES("replaceDuplicates", Boolean.class),
+        /**
+         * If >= 1, input is assumed to be organized into
+         * columns, in the same manner as the temporal pooler AND whenever a new
+         * prototype is stored, only the start cell (first cell) is stored in any
+         * bursting column
+         */
+        KNN_CELLS_PER_COL("cellsPerCol", Integer.class);     
 
 
         private static final Map<String, KEY> fieldMap = new HashMap<>();
@@ -459,6 +556,15 @@ public static Parameters getSpatialDefaultParameters() {
     public static Parameters getEncoderDefaultParameters() {
         return getParameters(DEFAULTS_ENCODER);
     }
+
+    /**
+     * Factory method. Return KNNClassifier {@link Parameters} object with default values
+     * @return
+     */
+    public static Parameters getKNNDefaultParameters() {
+        return getParameters(DEFAULTS_KNN);
+    }
+
     /**
      * Called internally to populate a {@link Parameters} object with the keys
      * and values specified in the passed in map.