Initial commit

.gitignore

@@ -0,0 +1,43 @@
+.gradle
+build/
+!gradle/wrapper/gradle-wrapper.jar
+!**/src/main/**/build/
+!**/src/test/**/build/
+.kotlin
+
+### IntelliJ IDEA ###
+.idea/modules.xml
+.idea/jarRepositories.xml
+.idea/compiler.xml
+.idea/libraries/
+*.iws
+*.iml
+*.ipr
+out/
+!**/src/main/**/out/
+!**/src/test/**/out/
+
+### Eclipse ###
+.apt_generated
+.classpath
+.factorypath
+.project
+.settings
+.springBeans
+.sts4-cache
+bin/
+!**/src/main/**/bin/
+!**/src/test/**/bin/
+
+### NetBeans ###
+/nbproject/private/
+/nbbuild/
+/dist/
+/nbdist/
+/.nb-gradle/
+
+### VS Code ###
+.vscode/
+
+### Mac OS ###
+.DS_Store

.idea/.gitignore

@@ -0,0 +1,10 @@
+# Default ignored files
+/shelf/
+/workspace.xml
+# Ignored default folder with query files
+/queries/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml
+# Editor-based HTTP Client requests
+/httpRequests/

.idea/gradle.xml

@@ -0,0 +1,16 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="GradleSettings">
+    <option name="linkedExternalProjectsSettings">
+      <GradleProjectSettings>
+        <option name="externalProjectPath" value="$PROJECT_DIR$" />
+        <option name="gradleHome" value="" />
+        <option name="modules">
+          <set>
+            <option value="$PROJECT_DIR$" />
+          </set>
+        </option>
+      </GradleProjectSettings>
+    </option>
+  </component>
+</project>

.idea/misc.xml

@@ -0,0 +1,10 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ExternalStorageConfigurationManager" enabled="true" />
+  <component name="FrameworkDetectionExcludesConfiguration">
+    <file type="web" url="file://$PROJECT_DIR$" />
+  </component>
+  <component name="ProjectRootManager" version="2" languageLevel="JDK_21" default="true" project-jdk-name="21" project-jdk-type="JavaSDK">
+    <output url="file://$PROJECT_DIR$/out" />
+  </component>
+</project>

build.gradle.kts

@@ -0,0 +1,20 @@
+plugins {
+    id("java")
+}
+
+group = "be.naaturel"
+version = "1.0-SNAPSHOT"
+
+repositories {
+    mavenCentral()
+}
+
+dependencies {
+    testImplementation(platform("org.junit:junit-bom:5.10.0"))
+    testImplementation("org.junit.jupiter:junit-jupiter")
+    testRuntimeOnly("org.junit.platform:junit-platform-launcher")
+}
+
+tasks.test {
+    useJUnitPlatform()
+}

gradle/wrapper/gradle-wrapper.properties

@@ -0,0 +1,6 @@
+#Tue Mar 17 15:36:58 CET 2026
+distributionBase=GRADLE_USER_HOME
+distributionPath=wrapper/dists
+distributionUrl=https\://services.gradle.org/distributions/gradle-9.0.0-bin.zip
+zipStoreBase=GRADLE_USER_HOME
+zipStorePath=wrapper/dists

gradlew

@@ -0,0 +1,234 @@
+#!/bin/sh
+
+#
+# Copyright © 2015-2021 the original authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+##############################################################################
+#
+#   Gradle start up script for POSIX generated by Gradle.
+#
+#   Important for running:
+#
+#   (1) You need a POSIX-compliant shell to run this script. If your /bin/sh is
+#       noncompliant, but you have some other compliant shell such as ksh or
+#       bash, then to run this script, type that shell name before the whole
+#       command line, like:
+#
+#           ksh Gradle
+#
+#       Busybox and similar reduced shells will NOT work, because this script
+#       requires all of these POSIX shell features:
+#         * functions;
+#         * expansions «$var», «${var}», «${var:-default}», «${var+SET}»,
+#           «${var#prefix}», «${var%suffix}», and «$( cmd )»;
+#         * compound commands having a testable exit status, especially «case»;
+#         * various built-in commands including «command», «set», and «ulimit».
+#
+#   Important for patching:
+#
+#   (2) This script targets any POSIX shell, so it avoids extensions provided
+#       by Bash, Ksh, etc; in particular arrays are avoided.
+#
+#       The "traditional" practice of packing multiple parameters into a
+#       space-separated string is a well documented source of bugs and security
+#       problems, so this is (mostly) avoided, by progressively accumulating
+#       options in "$@", and eventually passing that to Java.
+#
+#       Where the inherited environment variables (DEFAULT_JVM_OPTS, JAVA_OPTS,
+#       and GRADLE_OPTS) rely on word-splitting, this is performed explicitly;
+#       see the in-line comments for details.
+#
+#       There are tweaks for specific operating systems such as AIX, CygWin,
+#       Darwin, MinGW, and NonStop.
+#
+#   (3) This script is generated from the Groovy template
+#       https://github.com/gradle/gradle/blob/master/subprojects/plugins/src/main/resources/org/gradle/api/internal/plugins/unixStartScript.txt
+#       within the Gradle project.
+#
+#       You can find Gradle at https://github.com/gradle/gradle/.
+#
+##############################################################################
+
+# Attempt to set APP_HOME
+
+# Resolve links: $0 may be a link
+app_path=$0
+
+# Need this for daisy-chained symlinks.
+while
+    APP_HOME=${app_path%"${app_path##*/}"}  # leaves a trailing /; empty if no leading path
+    [ -h "$app_path" ]
+do
+    ls=$( ls -ld "$app_path" )
+    link=${ls#*' -> '}
+    case $link in             #(
+      /*)   app_path=$link ;; #(
+      *)    app_path=$APP_HOME$link ;;
+    esac
+done
+
+APP_HOME=$( cd "${APP_HOME:-./}" && pwd -P ) || exit
+
+APP_NAME="Gradle"
+APP_BASE_NAME=${0##*/}
+
+# Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
+DEFAULT_JVM_OPTS='"-Xmx64m" "-Xms64m"'
+
+# Use the maximum available, or set MAX_FD != -1 to use that value.
+MAX_FD=maximum
+
+warn () {
+    echo "$*"
+} >&2
+
+die () {
+    echo
+    echo "$*"
+    echo
+    exit 1
+} >&2
+
+# OS specific support (must be 'true' or 'false').
+cygwin=false
+msys=false
+darwin=false
+nonstop=false
+case "$( uname )" in                #(
+  CYGWIN* )         cygwin=true  ;; #(
+  Darwin* )         darwin=true  ;; #(
+  MSYS* | MINGW* )  msys=true    ;; #(
+  NONSTOP* )        nonstop=true ;;
+esac
+
+CLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar
+
+
+# Determine the Java command to use to start the JVM.
+if [ -n "$JAVA_HOME" ] ; then
+    if [ -x "$JAVA_HOME/jre/sh/java" ] ; then
+        # IBM's JDK on AIX uses strange locations for the executables
+        JAVACMD=$JAVA_HOME/jre/sh/java
+    else
+        JAVACMD=$JAVA_HOME/bin/java
+    fi
+    if [ ! -x "$JAVACMD" ] ; then
+        die "ERROR: JAVA_HOME is set to an invalid directory: $JAVA_HOME
+
+Please set the JAVA_HOME variable in your environment to match the
+location of your Java installation."
+    fi
+else
+    JAVACMD=java
+    which java >/dev/null 2>&1 || die "ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
+
+Please set the JAVA_HOME variable in your environment to match the
+location of your Java installation."
+fi
+
+# Increase the maximum file descriptors if we can.
+if ! "$cygwin" && ! "$darwin" && ! "$nonstop" ; then
+    case $MAX_FD in #(
+      max*)
+        MAX_FD=$( ulimit -H -n ) ||
+            warn "Could not query maximum file descriptor limit"
+    esac
+    case $MAX_FD in  #(
+      '' | soft) :;; #(
+      *)
+        ulimit -n "$MAX_FD" ||
+            warn "Could not set maximum file descriptor limit to $MAX_FD"
+    esac
+fi
+
+# Collect all arguments for the java command, stacking in reverse order:
+#   * args from the command line
+#   * the main class name
+#   * -classpath
+#   * -D...appname settings
+#   * --module-path (only if needed)
+#   * DEFAULT_JVM_OPTS, JAVA_OPTS, and GRADLE_OPTS environment variables.
+
+# For Cygwin or MSYS, switch paths to Windows format before running java
+if "$cygwin" || "$msys" ; then
+    APP_HOME=$( cygpath --path --mixed "$APP_HOME" )
+    CLASSPATH=$( cygpath --path --mixed "$CLASSPATH" )
+
+    JAVACMD=$( cygpath --unix "$JAVACMD" )
+
+    # Now convert the arguments - kludge to limit ourselves to /bin/sh
+    for arg do
+        if
+            case $arg in                                #(
+              -*)   false ;;                            # don't mess with options #(
+              /?*)  t=${arg#/} t=/${t%%/*}              # looks like a POSIX filepath
+                    [ -e "$t" ] ;;                      #(
+              *)    false ;;
+            esac
+        then
+            arg=$( cygpath --path --ignore --mixed "$arg" )
+        fi
+        # Roll the args list around exactly as many times as the number of
+        # args, so each arg winds up back in the position where it started, but
+        # possibly modified.
+        #
+        # NB: a `for` loop captures its iteration list before it begins, so
+        # changing the positional parameters here affects neither the number of
+        # iterations, nor the values presented in `arg`.
+        shift                   # remove old arg
+        set -- "$@" "$arg"      # push replacement arg
+    done
+fi
+
+# Collect all arguments for the java command;
+#   * $DEFAULT_JVM_OPTS, $JAVA_OPTS, and $GRADLE_OPTS can contain fragments of
+#     shell script including quotes and variable substitutions, so put them in
+#     double quotes to make sure that they get re-expanded; and
+#   * put everything else in single quotes, so that it's not re-expanded.
+
+set -- \
+        "-Dorg.gradle.appname=$APP_BASE_NAME" \
+        -classpath "$CLASSPATH" \
+        org.gradle.wrapper.GradleWrapperMain \
+        "$@"
+
+# Use "xargs" to parse quoted args.
+#
+# With -n1 it outputs one arg per line, with the quotes and backslashes removed.
+#
+# In Bash we could simply go:
+#
+#   readarray ARGS < <( xargs -n1 <<<"$var" ) &&
+#   set -- "${ARGS[@]}" "$@"
+#
+# but POSIX shell has neither arrays nor command substitution, so instead we
+# post-process each arg (as a line of input to sed) to backslash-escape any
+# character that might be a shell metacharacter, then use eval to reverse
+# that process (while maintaining the separation between arguments), and wrap
+# the whole thing up as a single "set" statement.
+#
+# This will of course break if any of these variables contains a newline or
+# an unmatched quote.
+#
+
+eval "set -- $(
+        printf '%s\n' "$DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS" |
+        xargs -n1 |
+        sed ' s~[^-[:alnum:]+,./:=@_]~\\&~g; ' |
+        tr '\n' ' '
+    )" '"$@"'
+
+exec "$JAVACMD" "$@"

gradlew.bat

@@ -0,0 +1,89 @@
+@rem
+@rem Copyright 2015 the original author or authors.
+@rem
+@rem Licensed under the Apache License, Version 2.0 (the "License");
+@rem you may not use this file except in compliance with the License.
+@rem You may obtain a copy of the License at
+@rem
+@rem      https://www.apache.org/licenses/LICENSE-2.0
+@rem
+@rem Unless required by applicable law or agreed to in writing, software
+@rem distributed under the License is distributed on an "AS IS" BASIS,
+@rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+@rem See the License for the specific language governing permissions and
+@rem limitations under the License.
+@rem
+
+@if "%DEBUG%" == "" @echo off
+@rem ##########################################################################
+@rem
+@rem  Gradle startup script for Windows
+@rem
+@rem ##########################################################################
+
+@rem Set local scope for the variables with windows NT shell
+if "%OS%"=="Windows_NT" setlocal
+
+set DIRNAME=%~dp0
+if "%DIRNAME%" == "" set DIRNAME=.
+set APP_BASE_NAME=%~n0
+set APP_HOME=%DIRNAME%
+
+@rem Resolve any "." and ".." in APP_HOME to make it shorter.
+for %%i in ("%APP_HOME%") do set APP_HOME=%%~fi
+
+@rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
+set DEFAULT_JVM_OPTS="-Xmx64m" "-Xms64m"
+
+@rem Find java.exe
+if defined JAVA_HOME goto findJavaFromJavaHome
+
+set JAVA_EXE=java.exe
+%JAVA_EXE% -version >NUL 2>&1
+if "%ERRORLEVEL%" == "0" goto execute
+
+echo.
+echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
+echo.
+echo Please set the JAVA_HOME variable in your environment to match the
+echo location of your Java installation.
+
+goto fail
+
+:findJavaFromJavaHome
+set JAVA_HOME=%JAVA_HOME:"=%
+set JAVA_EXE=%JAVA_HOME%/bin/java.exe
+
+if exist "%JAVA_EXE%" goto execute
+
+echo.
+echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME%
+echo.
+echo Please set the JAVA_HOME variable in your environment to match the
+echo location of your Java installation.
+
+goto fail
+
+:execute
+@rem Setup the command line
+
+set CLASSPATH=%APP_HOME%\gradle\wrapper\gradle-wrapper.jar
+
+
+@rem Execute Gradle
+"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% "-Dorg.gradle.appname=%APP_BASE_NAME%" -classpath "%CLASSPATH%" org.gradle.wrapper.GradleWrapperMain %*
+
+:end
+@rem End local scope for the variables with windows NT shell
+if "%ERRORLEVEL%"=="0" goto mainEnd
+
+:fail
+rem Set variable GRADLE_EXIT_CONSOLE if you need the _script_ return code instead of
+rem the _cmd.exe /c_ return code!
+if  not "" == "%GRADLE_EXIT_CONSOLE%" exit 1
+exit /b 1
+
+:mainEnd
+if "%OS%"=="Windows_NT" endlocal
+
+:omega

settings.gradle.kts

@@ -0,0 +1 @@
+rootProject.name = "ANN"

src/main/java/com/naaturel/ANN/Main.java

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+package com.naaturel.ANN;
+
+import com.naaturel.ANN.domain.abstraction.Neuron;
+import com.naaturel.ANN.domain.model.dataset.DataSet;
+import com.naaturel.ANN.domain.model.dataset.DataSetEntry;
+import com.naaturel.ANN.domain.model.dataset.Label;
+import com.naaturel.ANN.domain.model.neuron.Bias;
+import com.naaturel.ANN.domain.model.neuron.Input;
+import com.naaturel.ANN.domain.model.neuron.Synapse;
+import com.naaturel.ANN.domain.model.neuron.Weight;
+import com.naaturel.ANN.implementation.activationFunction.Heaviside;
+import com.naaturel.ANN.implementation.activationFunction.Linear;
+import com.naaturel.ANN.implementation.neuron.SimplePerceptron;
+import com.naaturel.ANN.implementation.training.GradientDescentTraining;
+
+import java.util.*;
+
+public class Main {
+
+    public static void main(String[] args){
+
+        DataSet dataSet = new DataSet(Map.ofEntries(
+                Map.entry(new DataSetEntry(List.of(1.0F, 6.0F)), new Label(1.0F)),
+                Map.entry(new DataSetEntry(List.of(7.0F, 9.0F)), new Label(-1.0F)),
+                Map.entry(new DataSetEntry(List.of(1.0F, 9.0F)), new Label(1.0F)),
+                Map.entry(new DataSetEntry(List.of(7.0F, 10.0F)), new Label(-1.0F)),
+                Map.entry(new DataSetEntry(List.of(2.0F, 5.0F)), new Label(-1.0F)),
+                Map.entry(new DataSetEntry(List.of(2.0F, 7.0F)), new Label(1.0F)),
+                Map.entry(new DataSetEntry(List.of(2.0F, 8.0F)), new Label(1.0F)),
+                Map.entry(new DataSetEntry(List.of(6.0F, 8.0F)), new Label(-1.0F)),
+                Map.entry(new DataSetEntry(List.of(6.0F, 9.0F)), new Label(-1.0F)),
+                Map.entry(new DataSetEntry(List.of(3.0F, 5.0F)), new Label(-1.0F)),
+                Map.entry(new DataSetEntry(List.of(3.0F, 6.0F)), new Label(-1.0F)),
+                Map.entry(new DataSetEntry(List.of(3.0F, 8.0F)), new Label(1.0F)),
+                Map.entry(new DataSetEntry(List.of(3.0F, 9.0F)), new Label(1.0F)),
+                Map.entry(new DataSetEntry(List.of(5.0F, 7.0F)), new Label(-1.0F)),
+                Map.entry(new DataSetEntry(List.of(5.0F, 8.0F)), new Label(-1.0F)),
+                Map.entry(new DataSetEntry(List.of(5.0F, 10.0F)), new Label(1.0F)),
+                Map.entry(new DataSetEntry(List.of(5.0F, 11.0F)), new Label(1.0F)),
+                Map.entry(new DataSetEntry(List.of(4.0F, 6.0F)), new Label(-1.0F)),
+                Map.entry(new DataSetEntry(List.of(4.0F, 7.0F)), new Label(-1.0F)),
+                Map.entry(new DataSetEntry(List.of(4.0F, 9.0F)), new Label(1.0F)),
+                Map.entry(new DataSetEntry(List.of(4.0F, 10.0F)), new Label(1.0F))
+        ));
+
+        List<Synapse> syns = new ArrayList<>();
+        syns.add(new Synapse(new Input(0), new Weight()));
+        syns.add(new Synapse(new Input(0), new Weight()));
+
+        Bias bias =  new Bias(new Weight());
+
+        Neuron n = new SimplePerceptron(syns, bias, new Linear());
+        GradientDescentTraining st = new GradientDescentTraining();
+        st.train(n, 0.0003F, dataSet);
+    }
+
+}

src/main/java/com/naaturel/ANN/domain/abstraction/ActivationFunction.java

@@ -0,0 +1,7 @@
+package com.naaturel.ANN.domain.abstraction;
+
+public interface ActivationFunction {
+
+    float accept(Neuron n);
+
+}

src/main/java/com/naaturel/ANN/domain/abstraction/Neuron.java

@@ -0,0 +1,55 @@
+package com.naaturel.ANN.domain.abstraction;
+import com.naaturel.ANN.domain.model.neuron.Bias;
+import com.naaturel.ANN.domain.model.neuron.Input;
+import com.naaturel.ANN.domain.model.neuron.Synapse;
+import com.naaturel.ANN.domain.model.neuron.Weight;
+
+import java.util.ArrayList;
+import java.util.List;
+
+public abstract class Neuron {
+
+    protected List<Synapse> synapses;
+    protected Bias bias;
+    protected ActivationFunction activationFunction;
+
+    public Neuron(List<Synapse> synapses, Bias bias, ActivationFunction func){
+        this.synapses = synapses;
+        this.bias = bias;
+        this.activationFunction = func;
+    }
+
+    public abstract float predict();
+    public abstract float calculateWeightedSum();
+
+    public int getSynCount(){
+        return this.synapses.size();
+    }
+
+    public void setInput(int index, Input input){
+        Synapse syn = this.synapses.get(index);
+        syn.setInput(input.getValue());
+    }
+
+    public Bias getBias(){
+        return this.bias;
+    }
+
+    public void updateBias(Weight weight) {
+        this.bias.setWeight(weight.getValue());
+    }
+
+    public Synapse getSynapse(int index){
+        return this.synapses.get(index);
+    }
+
+    public List<Synapse> getSynapses() {
+        return new ArrayList<>(this.synapses);
+    }
+
+    public void setWeight(int index, Weight weight){
+        Synapse syn = this.synapses.get(index);
+        syn.setWeight(weight.getValue());
+    }
+
+}

src/main/java/com/naaturel/ANN/domain/abstraction/NeuronTrainer.java

@@ -0,0 +1,13 @@
+package com.naaturel.ANN.domain.abstraction;
+
+public abstract class NeuronTrainer {
+
+    private Trainable trainable;
+
+    public NeuronTrainer(Trainable trainable){
+        this.trainable = trainable;
+    }
+
+    public abstract void train();
+
+}

src/main/java/com/naaturel/ANN/domain/abstraction/Trainable.java

@@ -0,0 +1,7 @@
+package com.naaturel.ANN.domain.abstraction;
+
+public interface Trainable {
+
+
+
+}

src/main/java/com/naaturel/ANN/domain/model/dataset/DataSet.java

@@ -0,0 +1,30 @@
+package com.naaturel.ANN.domain.model.dataset;
+
+import java.util.*;
+
+public class DataSet implements Iterable<DataSetEntry>{
+
+    private Map<DataSetEntry, Label> data;
+
+    public DataSet(){
+        this(new HashMap<>());
+    }
+
+    public DataSet(Map<DataSetEntry, Label> data){
+        this.data = data;
+    }
+
+    public List<DataSetEntry> getData(){
+        return new ArrayList<>(this.data.keySet());
+    }
+
+    public Label getLabel(DataSetEntry entry){
+        return this.data.get(entry);
+    }
+
+
+    @Override
+    public Iterator<DataSetEntry> iterator() {
+        return this.data.keySet().iterator();
+    }
+}

src/main/java/com/naaturel/ANN/domain/model/dataset/DataSetEntry.java

@@ -0,0 +1,40 @@
+package com.naaturel.ANN.domain.model.dataset;
+
+import java.util.*;
+
+public class DataSetEntry implements Iterable<Float> {
+
+    private List<Float> data;
+
+    public DataSetEntry(List<Float> data){
+        this.data = data;
+    }
+
+    public List<Float> getData() {
+        return new ArrayList<>(data);
+    }
+
+
+    @Override
+    public int hashCode() {
+        return Objects.hash(this.data);
+    }
+
+    @Override
+    public boolean equals(Object obj) {
+        if (this == obj) return true;
+        if (!(obj instanceof DataSetEntry dataSetEntry)) return false;
+        return Objects.equals(this.data, dataSetEntry.data);
+    }
+
+    @Override
+    public Iterator<Float> iterator() {
+        return this.data.iterator();
+    }
+
+
+    @Override
+    public String toString() {
+        return Arrays.toString(this.data.toArray());
+    }
+}

src/main/java/com/naaturel/ANN/domain/model/dataset/Label.java

@@ -0,0 +1,15 @@
+package com.naaturel.ANN.domain.model.dataset;
+
+public class Label {
+
+    private float value;
+
+    public Label(float value){
+        this.value = value;
+    }
+
+
+    public float getValue() {
+        return value;
+    }
+}

src/main/java/com/naaturel/ANN/domain/model/neuron/Bias.java

@@ -0,0 +1,8 @@
+package com.naaturel.ANN.domain.model.neuron;
+
+public class Bias extends Synapse {
+
+    public Bias(Weight weight) {
+        super(new Input(1), weight);
+    }
+}

src/main/java/com/naaturel/ANN/domain/model/neuron/Input.java

@@ -0,0 +1,20 @@
+package com.naaturel.ANN.domain.model.neuron;
+
+public class Input {
+
+    private float value;
+
+    public Input(float value){
+        this.value = value;
+    }
+
+    public void setValue(float value){
+        this.value = value;
+    }
+
+    public float getValue(){
+        return this.value;
+    }
+
+
+}

src/main/java/com/naaturel/ANN/domain/model/neuron/Synapse.java

@@ -0,0 +1,30 @@
+package com.naaturel.ANN.domain.model.neuron;
+
+public class Synapse {
+
+    private Input input;
+    private Weight weight;
+
+    public Synapse(Input input, Weight weight){
+        this.input = input;
+        this.weight = weight;
+    }
+
+    public float getInput(){
+        return this.input.getValue();
+    }
+
+    public void setInput(float value){
+        this.input.setValue(value);
+    }
+
+    public float getWeight() {
+        return weight.getValue();
+    }
+
+    public void setWeight(float value){
+        this.weight.setValue(value);
+    }
+
+
+}

src/main/java/com/naaturel/ANN/domain/model/neuron/Weight.java

@@ -0,0 +1,25 @@
+package com.naaturel.ANN.domain.model.neuron;
+
+import java.util.Random;
+
+public class Weight {
+
+    private float value;
+
+    public Weight(){
+        this(new Random().nextFloat() * 2 - 1);
+    }
+
+    public Weight(float value){
+        this.value = value;
+    }
+
+    public void setValue(float value){
+        this.value = value;
+    }
+
+    public float getValue(){
+        return this.value;
+    }
+
+}

src/main/java/com/naaturel/ANN/implementation/activationFunction/Heaviside.java

@@ -0,0 +1,17 @@
+package com.naaturel.ANN.implementation.activationFunction;
+
+import com.naaturel.ANN.domain.abstraction.ActivationFunction;
+import com.naaturel.ANN.domain.abstraction.Neuron;
+
+public class Heaviside implements ActivationFunction {
+
+    public Heaviside(){
+
+    }
+
+    @Override
+    public float accept(Neuron n) {
+        float weightedSum = n.calculateWeightedSum();
+        return weightedSum <= 0 ? 0:1;
+    }
+}

src/main/java/com/naaturel/ANN/implementation/activationFunction/Linear.java

@@ -0,0 +1,13 @@
+package com.naaturel.ANN.implementation.activationFunction;
+
+import com.naaturel.ANN.domain.abstraction.ActivationFunction;
+import com.naaturel.ANN.domain.abstraction.Neuron;
+
+public class Linear implements ActivationFunction {
+
+    @Override
+    public float accept(Neuron n) {
+        return n.calculateWeightedSum();
+    }
+
+}

src/main/java/com/naaturel/ANN/implementation/neuron/SimplePerceptron.java

@@ -0,0 +1,32 @@
+package com.naaturel.ANN.implementation.neuron;
+
+import com.naaturel.ANN.domain.abstraction.ActivationFunction;
+import com.naaturel.ANN.domain.abstraction.Neuron;
+import com.naaturel.ANN.domain.abstraction.Trainable;
+import com.naaturel.ANN.domain.model.neuron.Bias;
+import com.naaturel.ANN.domain.model.neuron.Synapse;
+
+import java.util.List;
+
+public class SimplePerceptron extends Neuron implements Trainable {
+
+    public SimplePerceptron(List<Synapse> synapses, Bias b, ActivationFunction func) {
+        super(synapses, b, func);
+    }
+
+    @Override
+    public float predict() {
+        return activationFunction.accept(this);
+    }
+
+    @Override
+    public float calculateWeightedSum() {
+        float res = 0;
+        for(Synapse syn : super.synapses){
+            res += syn.getWeight() * syn.getInput();
+        }
+
+        return res;
+    }
+
+}

src/main/java/com/naaturel/ANN/implementation/training/AdalineTraining.java

@@ -0,0 +1,4 @@
+package com.naaturel.ANN.implementation.training;
+
+public class AdalineTraining {
+}

src/main/java/com/naaturel/ANN/implementation/training/GradientDescentTraining.java

@@ -0,0 +1,99 @@
+package com.naaturel.ANN.implementation.training;
+
+import com.naaturel.ANN.domain.abstraction.Neuron;
+import com.naaturel.ANN.domain.model.dataset.DataSet;
+import com.naaturel.ANN.domain.model.dataset.DataSetEntry;
+import com.naaturel.ANN.domain.model.neuron.Bias;
+import com.naaturel.ANN.domain.model.neuron.Input;
+import com.naaturel.ANN.domain.model.neuron.Synapse;
+import com.naaturel.ANN.domain.model.neuron.Weight;
+
+import java.util.ArrayList;
+import java.util.List;
+
+public class GradientDescentTraining {
+
+    public GradientDescentTraining(){
+
+    }
+
+    public void train(Neuron n, float learningRate, DataSet dataSet) {
+        int epoch = 1;
+        int maxEpoch = 10000;
+        float errorThreshold = 0.125F;
+        float currentError;
+
+        do {
+            if(epoch > maxEpoch) break;
+
+            float biasCorrector = 0;
+            currentError = 0;
+            List<Float> correctorTerms = this.initCorrectorTerms(n.getSynCount());
+
+            for(DataSetEntry entry : dataSet) {
+                this.updateInputs(n, entry);
+                float prediction = n.predict();
+                float expectation = dataSet.getLabel(entry).getValue();
+                float delta = this.calculateDelta(expectation, prediction);
+                float loss = this.calculateLoss(delta);
+
+                currentError += loss;
+                Bias b = n.getBias();
+                biasCorrector += this.calculateWeightCorrection(learningRate, b.getInput(), delta);
+
+                for(int i = 0; i < correctorTerms.size(); i++){
+                    Synapse syn = n.getSynapse(i);
+                    float c = correctorTerms.get(i);
+                    c += this.calculateWeightCorrection(learningRate, syn.getInput(), delta);
+                    correctorTerms.set(i, c);
+                }
+                System.out.printf("Epoch : %d ", epoch);
+                System.out.printf("predicted : %.2f, ", prediction);
+                System.out.printf("expected : %.2f, ", expectation);
+                System.out.printf("delta : %.2f, ", delta);
+                System.out.printf("loss : %.2f\n", loss);
+
+            }
+            System.out.printf("[Total error : %.2f]\n", currentError);
+            n.updateBias(new Weight(biasCorrector));
+
+            for(int i = 0; i < correctorTerms.size(); i++){
+                Synapse syn = n.getSynapse(i);
+                float c = correctorTerms.get(i);
+                syn.setWeight(syn.getWeight() + c);
+            }
+
+            epoch++;
+        } while(currentError > errorThreshold);
+
+    }
+
+    private List<Float> initCorrectorTerms(int number){
+        List<Float> res = new ArrayList<>();
+        for(int i = 0; i < number; i++){
+            res.add(0F);
+        }
+        return res;
+    }
+
+    private void updateInputs(Neuron n, DataSetEntry entry){
+        int index = 0;
+        for(float value : entry){
+            n.setInput(index, new Input(value));
+            index++;
+        }
+    }
+
+    private float calculateDelta(float expected, float predicted){
+        return expected - predicted;
+    }
+
+    private float calculateLoss(float delta){
+        return ((float) Math.pow(delta, 2))/2;
+    }
+
+    private float calculateWeightCorrection(float lr, float value, float delta){
+        return lr * value * delta;
+    }
+
+}

src/main/java/com/naaturel/ANN/implementation/training/SimpleTraining.java

@@ -0,0 +1,68 @@
+package com.naaturel.ANN.implementation.training;
+
+import com.naaturel.ANN.domain.abstraction.Neuron;
+import com.naaturel.ANN.domain.model.dataset.DataSet;
+import com.naaturel.ANN.domain.model.dataset.DataSetEntry;
+import com.naaturel.ANN.domain.model.neuron.Input;
+import com.naaturel.ANN.domain.model.neuron.Synapse;
+import com.naaturel.ANN.domain.model.neuron.Weight;
+
+public class SimpleTraining {
+
+    public SimpleTraining() {
+
+    }
+
+    public void train(Neuron n, float learningRate, DataSet dataSet) {
+        int epoch = 1;
+        int errorCount;
+
+        do {
+            errorCount = 0;
+            System.out.printf("Epoch : %d\n", epoch);
+            for(DataSetEntry entry : dataSet) {
+                this.updateInputs(n, entry);
+                float prediction = n.predict();
+                float expectation = dataSet.getLabel(entry).getValue();
+                float delta = this.calculateDelta(expectation, prediction);
+                float loss = this.calculateLoss(delta);
+                if(delta > 1e-6f) {
+                    this.updateWeights(n, learningRate, delta);
+                    errorCount += 1;
+                }
+                System.out.printf("predicted : %.2f, ", prediction);
+                System.out.printf("expected : %.2f, ", expectation);
+                System.out.printf("delta : %.2f\n", this.calculateDelta(expectation, prediction));
+            }
+            System.out.print("====================================\n");
+            epoch++;
+        } while (errorCount != 0);
+    }
+
+    private void updateInputs(Neuron n, DataSetEntry entry){
+        int index = 0;
+        for(float value : entry){
+            n.setInput(index, new Input(value));
+            index++;
+        }
+    }
+
+    private void updateWeights(Neuron n, float rate, float delta){
+
+        Weight biasCorrection = new Weight(n.getBias().getWeight() + (rate * delta * n.getBias().getInput()));
+        n.updateBias(biasCorrection);
+
+        for(Synapse syn : n.getSynapses()){
+            syn.setWeight(syn.getWeight() + (rate * delta * syn.getInput()));
+        }
+    }
+
+    private float calculateDelta(float expected, float predicted){
+        return expected - predicted;
+    }
+
+    private float calculateLoss(float delta){
+        return Math.abs(delta);
+    }
+
+}