static void Main(string[] args)
{
Random seed = new Random();
CrossValidate(seed);
return;
Console.WriteLine("Start time: " + DateTime.Now.ToString());
DatasetParser datasetParser = new DatasetParser();
Example[] examples = datasetParser.parseDatasetFile(EXAMPLES_FILENAME);
Shuffle(examples, seed);
CharacterRecognition cr = new CharacterRecognition(NUM_NETWORKS,
NETWORK_NUM_INPUTS, NETWORK_NUM_OUTPUTS,
NETWORK_NUM_HIDDEN_LAYERS, NETWORK_NUM_NEURONS_PER_HIDDEN_LAYER, seed);
cr.Initialize(examples);
/*while (true)
* {
* double[] input = null;
* double[] output = cr.RecognizeCharacter(input);
*
* foreach(double d in output){Console.Write(d+", ");}Console.WriteLine();
* }*/
/*Console.WriteLine("Guessing: ");
* cr.RecognizeCharacter(new double[] {
* 0, 1, 0, 0,
* 0, 1, 0, 0,
* 0, 1, 1, 0,
* 0, 0, 1, 0
* });
*
* cr.RecognizeCharacter(new double[] {
* 0, 1, 1, 0,
* 1, 1, 0, 1,
* 0, 0, 1, 1,
* 0, 0, 1, 0
* });*/
Console.WriteLine("Guessing: ");
cr.RecognizeCharacter(new double[] {
0, 0, 0, 0, 0, 1, 1, 1, 1, 0,
0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
0, 0, 1, 1, 0, 0, 0, 0, 0, 1,
0, 1, 1, 0, 0, 0, 0, 0, 0, 0,
1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 1, 1, 0, 0, 0, 0, 0, 1,
0, 0, 1, 1, 1, 0, 0, 0, 1, 1,
0, 0, 0, 0, 1, 1, 1, 1, 1, 0,
0, 0, 0, 0, 0, 1, 1, 0, 0, 0,
});
}
public void GenerateFromFiles(Random seed, string[] filenames)
{
//DatasetParser datasetParser = new DatasetParser();
//Example[] examples = datasetParser.parseDatasetFile(EXAMPLES_FILENAME);
//Shuffle(examples, seed);
this.characterRecognizer = new CharacterRecognition(NUM_NETWORKS,
NETWORK_NUM_INPUTS, NETWORK_NUM_OUTPUTS,
NETWORK_NUM_HIDDEN_LAYERS, NETWORK_NUM_NEURONS_PER_HIDDEN_LAYER, seed);
this.characterRecognizer.InitializeFromFiles(filenames);
}
public CrossValidation(Example[] _dataset, int _numTests, ref CharacterRecognition _cr, Random seed)
{
this.seed = seed;
this.numTests = _numTests;
this.datasetPartitions = new List <Example> [this.numTests];
for (int n = 0; n < this.numTests; n++)
{
this.datasetPartitions[n] = new List <Example>();
}
for (int i = 0; i < _dataset.Length; i++)
{
this.datasetPartitions[i % this.numTests].Add(_dataset[i]);
}
this.cr = _cr;
}
public CrossValidation(Example[] _dataset, int _numTests, ref CharacterRecognition _cr, Random seed)
{
this.seed = seed;
this.numTests = _numTests;
this.datasetPartitions = new List<Example>[this.numTests];
for (int n = 0; n < this.numTests; n++)
{
this.datasetPartitions[n] = new List<Example>();
}
for (int i = 0; i < _dataset.Length; i++)
{
this.datasetPartitions[i % this.numTests].Add(_dataset[i]);
}
this.cr = _cr;
}
public double CrossValidate(Random seed)
{
//Console.WriteLine("Start time: " + DateTime.Now.ToString());
DatasetParser datasetParser = new DatasetParser();
Example[] examples = datasetParser.parseDatasetFile(this.EXAMPLES_FILENAME);
CharacterRecognition cr = new CharacterRecognition(this.NUM_NETWORKS,
NETWORK_NUM_INPUTS, NETWORK_NUM_OUTPUTS,
NETWORK_NUM_HIDDEN_LAYERS, NETWORK_NUM_NEURONS_PER_HIDDEN_LAYER
, seed);
CrossValidation cv = new CrossValidation(examples, 5, ref cr, seed);
double precision = cv.CalculatePrecision(ref examples);
//Console.WriteLine(1.0 - precision);
//Console.WriteLine("End time: " + DateTime.Now.ToString());
return(1.0 - precision);
}
public static void CrossValidate(Random seed)
{
DatasetParser datasetParser;
Example[] examples;
CharacterRecognition cr;
CrossValidation cv;
NetworkEvolution.GENERATIONS_NUMBER = 20000;
NetworkEvolution.STAGNANT_NUMBER = 50;
Console.WriteLine("-------stag50||its20000_mut0,003_pop20_30_L1_70-------------------------");
Console.WriteLine("Start time: " + DateTime.Now.ToString());
datasetParser = new DatasetParser();
examples = datasetParser.parseDatasetFile(EXAMPLES_FILENAME);
cr = new CharacterRecognition(NUM_NETWORKS,
NETWORK_NUM_INPUTS, NETWORK_NUM_OUTPUTS,
1, new int[] { 70 }, seed);
cv = new CrossValidation(examples, 5, ref cr, seed);
Console.WriteLine(cv.CalculatePrecision(ref examples));
Console.WriteLine("End time: " + DateTime.Now.ToString());
}
