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mark-watsonmark-watson
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added a Clojure wrapper exampel for Java neural network code
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‎clojure_examples/README

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‎clojure_examples/neural_network/.gitignore

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/target
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/lib
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/classes
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/checkouts
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pom.xml
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pom.xml.asc
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*.jar
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*.class
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.lein-deps-sum
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.lein-plugins
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‎clojure_examples/neural_network/README.md

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# neural_network example backpropagation netowrk
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This will compile the Java source files in the directory ../../src/neuralnetworks and add them to the project.
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Run with:
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~~~~~~~~
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lein test
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~~~~~~~~

‎clojure_examples/neural_network/project.clj

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(defproject neural_network "0.1.0-SNAPSHOT"
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:description "FIXME: write description"
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:url "http://example.com/FIXME"
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:license {:name "Eclipse Public License"
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:url "http://www.eclipse.org/legal/epl-v10.html"}
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:dependencies [[org.clojure/clojure "1.5.1"]]
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;;:java-source [["../../src/neuralnetworks/Hopfield.java"]]
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:java-source-paths ["../../src/neuralnetworks"]
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)

‎clojure_examples/neural_network/src/neural_network/core.clj

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(ns neural-network.core)
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(defn foo
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"I don't do a whole lot."
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[x]
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(println x "Hello, World!"))

‎clojure_examples/neural_network/test/neural_network/core_test.clj

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(ns neural-network.core-test
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(:use clojure.test
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neural-network.core))
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(import '(neuralnetworks.Neural_2H_momentum))
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(def nn (neuralnetworks.Neural_2H_momentum. 3 3 3 3 0.2))
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(println nn)
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(def in1 (float-array [0.1 0.1 0.9]))
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(println in1)
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(def in2 (float-array [0.1 0.9 0.1]))
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(def in3 (float-array [0.9 0.1 0.1]))
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(def out1 (float-array [0.9 0.1 0.1]))
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(def out2 (float-array [0.1 0.1 0.9]))
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(def out3 (float-array [0.1 0.9 0.1]))
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(def test1 (float-array [0.1 0.1 0.9]))
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(def test2 (float-array [0.1 0.9 0.1]))
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(def test3 (float-array [0.9 0.1 0.1]))
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(.addTrainingExample nn in1 out1)
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(.addTrainingExample nn in2 out2)
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(.addTrainingExample nn in3 out3)
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(doseq [i (range 300)] (println (.train nn))) ;; train net 300 cycles
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;; test to make sure we have learned the input patterns:
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(println (seq (.recall nn test1)))
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(println (seq (.recall nn test2)))
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(println (seq (.recall nn test3)))
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‎clojure_examples/project.clj

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‎clojure_examples/src/clojure_examples/core.clj

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‎clojure_examples/test/clojure_examples/test/core.clj

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‎jruby_examples/README

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‎jruby_examples/lib/wrapper.rb

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‎jruby_examples/test.rb

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‎src/neuralnetworks/Neural_2H_momentum.java

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public float TRAINING_RATE = 0.5f;
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private float alpha = 0f; // momentum scaling term that is applied to last delta weight
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// use a reasonable default momentum term (alpha):
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public Neural_2H_momentum(int num_in, int num_hidden1, int num_hidden2, int num_output) {
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this(num_in, num_hidden1, num_hidden1, num_output, 0.6f);
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}
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public Neural_2H_momentum(int num_in, int num_hidden1, int num_hidden2, int num_output,
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float alpha) {
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this.alpha = alpha;
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System.out.println("addTrainingExample(): array size is wrong");
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return;
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}
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//System.out.println("addTrainingExample(): inputs: " + Arrays.toString(inputs));
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//System.out.println("addTrainingExample(): outputs: " + Arrays.toString(outputs));
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inputTraining.add(inputs);
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outputTraining.add(outputs);
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}
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}
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public float[] recall(float[] in) {
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//System.out.println("recall(" + Arrays.toString(inputs) + ")");
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for (int i = 0; i < numInputs; i++) inputs[i] = in[i];
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forwardPass();
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float[] ret = new float[numOutputs];

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