/// <summary>
/// Read a an object.
/// </summary>
public Object Read(Stream mask0)
{
var result = new HopfieldNetwork();
var ins0 = new EncogReadHelper(mask0);
EncogFileSection section;
while ((section = ins0.ReadNextSection()) != null)
{
if (section.SectionName.Equals("HOPFIELD")
&& section.SubSectionName.Equals("PARAMS"))
{
IDictionary<String, String> paras = section.ParseParams();
EngineArray.PutAll(paras, result.Properties);
}
if (section.SectionName.Equals("HOPFIELD")
&& section.SubSectionName.Equals("NETWORK"))
{
IDictionary<String, String> p = section.ParseParams();
result.Weights = NumberList.FromList(CSVFormat.EgFormat,
(p[PersistConst.Weights]));
result.SetCurrentState(NumberList.FromList(CSVFormat.EgFormat,
(p[PersistConst.Output])));
result.NeuronCount = EncogFileSection.ParseInt(p,
PersistConst.NeuronCount);
}
}
return result;
}
private void ValidateHopfield(HopfieldNetwork network)
{
Assert.AreEqual(4, network.NeuronCount);
Assert.AreEqual(4, network.CurrentState.Count);
Assert.AreEqual(16, network.Weights.Length);
Assert.AreEqual(1.0, network.GetWeight(1, 1));
}
/// <summary>
/// Read a an object.
/// </summary>
public Object Read(Stream mask0)
{
var result = new HopfieldNetwork();
var ins0 = new EncogReadHelper(mask0);
EncogFileSection section;
while ((section = ins0.ReadNextSection()) != null)
{
if (section.SectionName.Equals("HOPFIELD") &&
section.SubSectionName.Equals("PARAMS"))
{
IDictionary <String, String> paras = section.ParseParams();
EngineArray.PutAll(paras, result.Properties);
}
if (section.SectionName.Equals("HOPFIELD") &&
section.SubSectionName.Equals("NETWORK"))
{
IDictionary <String, String> p = section.ParseParams();
result.Weights = NumberList.FromList(CSVFormat.EgFormat,
(p[PersistConst.Weights]));
result.SetCurrentState(NumberList.FromList(CSVFormat.EgFormat,
(p[PersistConst.Output])));
result.NeuronCount = EncogFileSection.ParseInt(p,
PersistConst.NeuronCount);
}
}
return(result);
}
public void Execute(IExampleInterface app)
{
this.app = app;
// Create the neural network.
BasicLayer hopfield;
var network = new HopfieldNetwork(4);
// This pattern will be trained
bool[] pattern1 = {true, true, false, false};
// This pattern will be presented
bool[] pattern2 = {true, false, false, false};
IMLData result;
var data1 = new BiPolarMLData(pattern1);
var data2 = new BiPolarMLData(pattern2);
var set = new BasicMLDataSet();
set.Add(data1);
// train the neural network with pattern1
app.WriteLine("Training Hopfield network with: "
+ FormatBoolean(data1));
network.AddPattern(data1);
// present pattern1 and see it recognized
result = network.Compute(data1);
app.WriteLine("Presenting pattern:" + FormatBoolean(data1)
+ ", and got " + FormatBoolean(result));
// Present pattern2, which is similar to pattern 1. Pattern 1
// should be recalled.
result = network.Compute(data2);
app.WriteLine("Presenting pattern:" + FormatBoolean(data2)
+ ", and got " + FormatBoolean(result));
}
public void TestPersistSerial()
{
HopfieldNetwork network = new HopfieldNetwork(4);
network.SetWeight(1, 1, 1);
SerializeObject.Save(SERIAL_FILENAME.ToString(), network);
HopfieldNetwork network2 = (HopfieldNetwork)SerializeObject.Load(SERIAL_FILENAME.ToString());
ValidateHopfield(network2);
}
public void TestPersistEG()
{
HopfieldNetwork network = new HopfieldNetwork(4);
network.SetWeight(1, 1, 1);
network.SetProperty("x", 10);
EncogDirectoryPersistence.SaveObject(EG_FILENAME, network);
HopfieldNetwork network2 = (HopfieldNetwork)EncogDirectoryPersistence.LoadObject((EG_FILENAME));
ValidateHopfield(network2);
}
/// <summary>
/// Generate the Hopfield neural network.
/// </summary>
///
/// <returns>The generated network.</returns>
public IMLMethod Generate()
{
var logic = new HopfieldNetwork(_neuronCount);
return logic;
}
public void Evaluate(HopfieldNetwork hopfield, String[][] pattern)
{
for (int i = 0; i < pattern.Length; i++)
{
BiPolarMLData pattern1 = ConvertPattern(pattern, i);
hopfield.CurrentState = pattern1;
int cycles = hopfield.RunUntilStable(100);
BiPolarMLData pattern2 = hopfield.CurrentState;
Console.WriteLine("Cycles until stable(max 100): " + cycles + ", result=");
Display(pattern1, pattern2);
Console.WriteLine(@"----------------------");
}
}
public object Read(Stream mask0)
{
EncogReadHelper helper;
EncogFileSection section;
IDictionary<string, string> dictionary2;
HopfieldNetwork network = new HopfieldNetwork();
goto Label_00F0;
Label_000D:
if (section.SectionName.Equals("HOPFIELD") || (0 != 0))
{
goto Label_0031;
}
Label_0022:
if ((section = helper.ReadNextSection()) != null)
{
if (section.SectionName.Equals("HOPFIELD"))
{
if (0 != 0)
{
goto Label_0031;
}
if (3 != 0)
{
if (0xff == 0)
{
goto Label_0022;
}
if (section.SubSectionName.Equals("PARAMS"))
{
IDictionary<string, string> source = section.ParseParams();
if (0 == 0)
{
EngineArray.PutAll<string, string>(source, network.Properties);
}
}
}
}
goto Label_000D;
}
return network;
Label_0031:
if (section.SubSectionName.Equals("NETWORK"))
{
dictionary2 = section.ParseParams();
}
else
{
goto Label_0022;
}
network.Weights = NumberList.FromList(CSVFormat.EgFormat, dictionary2["weights"]);
if (0 == 0)
{
network.SetCurrentState(NumberList.FromList(CSVFormat.EgFormat, dictionary2["output"]));
network.NeuronCount = EncogFileSection.ParseInt(dictionary2, "neurons");
goto Label_0022;
}
if (3 != 0)
{
goto Label_000D;
}
Label_00F0:
helper = new EncogReadHelper(mask0);
goto Label_0022;
}