Adding Clojure examples 2, 3 and 4, and changing example 1 to use the let formalism.

demos/clojure/examples/example1TeBinaryData.clj

@@ -23,13 +23,16 @@
 ; Import relevant classes:
 (import infodynamics.measures.discrete.TransferEntropyCalculatorDiscrete)
 
-; Generate some random binary data.
-(def sourceArray (int-array (take 100 (repeatedly #(rand-int 2)))))
-(def destArray (int-array (cons 0 (butlast sourceArray)))) ; shifts sourceArray by 1
-(def sourceArray2 (int-array (take 100 (repeatedly #(rand-int 2)))))
+(let
+    ; Generate some random binary data.
+    [sourceArray (int-array (take 100 (repeatedly #(rand-int 2))))
+     destArray (int-array (cons 0 (butlast sourceArray))) ; shifts sourceArray by 1
+     sourceArray2 (int-array (take 100 (repeatedly #(rand-int 2))))
+     ; Create TE calculator
+     teCalc (TransferEntropyCalculatorDiscrete. 2 1)
+    ]
 
-; Create a TE calculator and run it:
-(def teCalc (TransferEntropyCalculatorDiscrete. 2 1))
+; Initialise the TE calculator and run it:
 (.initialise teCalc)
 (.addObservations teCalc sourceArray destArray)
 (println "For copied source, result should be close to 1 bit : "
@@ -40,3 +43,4 @@
 (println "For random source, result should be close to 0 bits : "
 	(.computeAverageLocalOfObservations teCalc))
 
+)

demos/clojure/examples/example2TeMultidimBinaryData.clj

@@ -0,0 +1,45 @@
+;
+;  Java Information Dynamics Toolkit (JIDT)
+;  Copyright (C) 2012, Joseph T. Lizier
+;  
+;  This program is free software: you can redistribute it and/or modify
+;  it under the terms of the GNU General Public License as published by
+;  the Free Software Foundation, either version 3 of the License, or
+;  (at your option) any later version.
+;  
+;  This program is distributed in the hope that it will be useful,
+;  but WITHOUT ANY WARRANTY; without even the implied warranty of
+;  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+;  GNU General Public License for more details.
+;  
+;  You should have received a copy of the GNU General Public License
+;  along with this program.  If not, see <http://www.gnu.org/licenses/>.
+;
+
+; = Example 2 - Transfer entropy on multidimensional binary data =
+
+; Simple transfer entropy (TE) calculation on multidimensional binary data using the discrete TE calculator.
+
+; Import relevant classes:
+(import infodynamics.measures.discrete.TransferEntropyCalculatorDiscrete)
+
+(let
+    ; Create many columns in a multidimensional array (2 rows by 100 columns),
+    ;  where the next time step (row 2) copies the value of the column on the left
+    ;  from the previous time step (row 1):
+    [row1 (int-array (take 100 (repeatedly #(rand-int 2))))
+     row2 (int-array (cons (aget row1 99) (butlast row1))) ; shifts row1 by 1
+     twoDTimeSeriesClojure (into-array (map int-array [row1 row2]))
+     ; Create TE calculator
+     teCalc (TransferEntropyCalculatorDiscrete. 2 1)
+    ]
+
+
+; Initialise the TE calculator and run it:
+(.initialise teCalc)
+; Add observations of transfer across one cell to the right per time step:
+(.addObservations teCalc twoDTimeSeriesClojure 1)
+(println "The result should be close to 1 bit here, since we are executing copy operations of what is effectively a random bit to each cell here:"
+	(.computeAverageLocalOfObservations teCalc))
+
+)

demos/clojure/examples/example3TeContinuousDataKernel.clj

@@ -0,0 +1,65 @@
+;
+;  Java Information Dynamics Toolkit (JIDT)
+;  Copyright (C) 2012, Joseph T. Lizier
+;  
+;  This program is free software: you can redistribute it and/or modify
+;  it under the terms of the GNU General Public License as published by
+;  the Free Software Foundation, either version 3 of the License, or
+;  (at your option) any later version.
+;  
+;  This program is distributed in the hope that it will be useful,
+;  but WITHOUT ANY WARRANTY; without even the implied warranty of
+;  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+;  GNU General Public License for more details.
+;  
+;  You should have received a copy of the GNU General Public License
+;  along with this program.  If not, see <http://www.gnu.org/licenses/>.
+;
+
+; = Example 3 - Transfer entropy on continuous data using kernel estimators =
+
+; Simple transfer entropy (TE) calculation on continuous-valued data using the (box) kernel-estimator TE calculator.
+
+; Import relevant classes:
+(import infodynamics.measures.continuous.kernel.TransferEntropyCalculatorKernel)
+(import java.util.Random)
+(def rg (Random.))
+
+(let 
+    [numObservations 1000
+     covariance 0.4
+     ; Generate some random normalised data.
+     sourceArray (double-array (take numObservations (repeatedly #(.nextGaussian rg))))
+     destArray (double-array 
+	(cons 0 
+	    (map + 
+		(map (partial * covariance) (butlast sourceArray)) 
+		(map (partial * (- covariance 1)) (double-array (take (- numObservations 1) (repeatedly #(.nextGaussian rg))))) )))
+     sourceArray2 (double-array (take numObservations (repeatedly #(.nextGaussian rg))))
+     teCalc (TransferEntropyCalculatorKernel. )
+    ]
+
+; Set up the calculator
+(.setProperty teCalc "NORMALISE" "true")
+(.initialise teCalc 1 0.5) ; Use history length 1 (Schreiber k=1), kernel width of 0.5 normalised units
+
+(.setObservations teCalc sourceArray destArray)
+; For copied source, should give something close to expected value for correlated Gaussians:
+(println "TE result " (.computeAverageLocalOfObservations teCalc)
+	" expected to be close to " (/ (Math/log (/ 1 (- 1 (* covariance covariance)))) (Math/log 2))
+	" for these correlated Gaussians but biased upward")
+
+(.initialise teCalc ) ; Initialise leaving the parameters the same
+(.setObservations teCalc sourceArray2 destArray)
+; For random source, it should give something close to 0 bits
+(println "TE result " (.computeAverageLocalOfObservations teCalc)
+	" expected to be close to 0 bits for these uncorrelated Gaussians but will be biased upward")
+
+; We can get insight into the bias by examining the null distribution:
+(def nullDist (.computeSignificance teCalc 100))
+(println "Null distribution for unrelated source and destination "
+	"(i.e. the bias) has mean " (.getMeanOfDistribution nullDist)
+	" and standard deviation " (.getStdOfDistribution nullDist))
+
+)
+

demos/clojure/examples/example4TeContinuousDataKraskov.clj

@@ -0,0 +1,71 @@
+;
+;  Java Information Dynamics Toolkit (JIDT)
+;  Copyright (C) 2012, Joseph T. Lizier
+;  
+;  This program is free software: you can redistribute it and/or modify
+;  it under the terms of the GNU General Public License as published by
+;  the Free Software Foundation, either version 3 of the License, or
+;  (at your option) any later version.
+;  
+;  This program is distributed in the hope that it will be useful,
+;  but WITHOUT ANY WARRANTY; without even the implied warranty of
+;  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+;  GNU General Public License for more details.
+;  
+;  You should have received a copy of the GNU General Public License
+;  along with this program.  If not, see <http://www.gnu.org/licenses/>.
+;
+
+; = Example 4 - Transfer entropy on continuous data using Kraskov estimators =
+
+; Simple transfer entropy (TE) calculation on continuous-valued data using the Kraskov-estimator TE calculator.
+
+; Import relevant classes:
+(import infodynamics.measures.continuous.kraskov.TransferEntropyCalculatorKraskov)
+(import java.util.Random)
+(def rg (Random.))
+
+(let 
+    [numObservations 1000
+     covariance 0.4
+     ; Generate some random normalised data.
+     sourceArray (double-array (take numObservations (repeatedly #(.nextGaussian rg))))
+     destArray (double-array 
+	(cons 0 
+	    (map + 
+		(map (partial * covariance) (butlast sourceArray)) 
+		(map (partial * (- covariance 1)) (double-array (take (- numObservations 1) (repeatedly #(.nextGaussian rg))))) )))
+     sourceArray2 (double-array (take numObservations (repeatedly #(.nextGaussian rg))))
+     teCalc (TransferEntropyCalculatorKraskov. )
+    ]
+
+; Set up the calculator
+(.setProperty teCalc "k" "4") ; Use Kraskov parameter K=4 for 4 nearest points
+(.initialise teCalc 1) ; Use history length 1 (Schreiber k=1)
+
+; Perform calculation with correlated source:
+(.setObservations teCalc sourceArray destArray)
+; Note that the calculation is a random variable (because the generated
+;  data is a set of random variables) - the result will be of the order
+;  of what we expect, but not exactly equal to it; in fact, there will
+;  be a large variance around it.
+(println "TE result " (.computeAverageLocalOfObservations teCalc)
+	" nats expected to be close to " (Math/log (/ 1 (- 1 (* covariance covariance))))
+	" nats for these correlated Gaussians")
+
+; Perform calculation with uncorrelated source:
+(.initialise teCalc ) ; Initialise leaving the parameters the same
+(.setObservations teCalc sourceArray2 destArray)
+; For random source, it should give something close to 0 bits
+(println "TE result " (.computeAverageLocalOfObservations teCalc)
+	" nats expected to be close to 0 nats for these uncorrelated Gaussians")
+
+; We can also compute the local TE values for the time-series samples here:
+;  (See more about utility of local TE in the CA demos)
+(def localTE (.computeLocalOfPreviousObservations teCalc))
+
+(println "Notice that the mean of locals, "
+	(/ (reduce + localTE) (- numObservations 1))
+	" nats, equals the previous result")
+
+)

demos/clojure/examples/project.clj

@@ -7,5 +7,6 @@
       :url "http://www.gnu.org/licenses/gpl.html"
       :distribution :repo
     }
-  :dependencies [[org.clojure/clojure "1.6.0"] [me.lizier/jidt "LATEST"] ])
+  :dependencies [[org.clojure/clojure "1.6.0"]
+  		 [me.lizier/jidt "LATEST"] ])