private static IImmutableList <Tuple <int, int, int, bool> > BuildInternalRowsForSudoku(Core.Sudoku s)
{
var rowsByCols =
from row in Rows
from col in Cols
let value = s.GetCell(row, col)
select BuildInternalRowsForCell(row, col, value);
return(rowsByCols.SelectMany(cols => cols).ToImmutableList());
}
// Fonction principale pour construire le CSP à partir d'un masque de Sudoku à résoudre
public static CSP GetSudokuCSP(Core.Sudoku s)
{
//initialisation à l'aide des contraintes communes
var toReturn = GetSudokuBaseCSP();
// Ajout des contraintes spécifiques au masque fourni
//var sArray = s.getInitialSudoku();
var cellVars = toReturn.getVariables();
//récupération du masque
var mask = new Dictionary <int, int>();
for (int i = 0; i < 9; i++)
{
for (int j = 0; j < 9; j++)
{
var cellVal = s.GetCell(i, j);
if (cellVal != 0)
{
mask[i * 9 + j] = cellVal;
}
}
}
//Ajout des contraintes de masque en faisant défiler les variables existantes
var maskQueue = new Queue <int>(mask.Keys);
var currentMaskIdx = maskQueue.Dequeue();
var currentVarName = GetVarName(currentMaskIdx / 9, currentMaskIdx % 9);
foreach (Variable objVar in cellVars.toArray())
{
if (objVar.getName() == currentVarName)
{
//toReturn.addConstraint(new ValueConstraint(objVar, mask[currentMaskIdx]));
var cellValue = mask[currentMaskIdx];
toReturn.setDomain(objVar, new Domain(new object[] { cellValue }));
if (maskQueue.Count == 0)
{
break;
}
currentMaskIdx = maskQueue.Dequeue();
currentVarName = GetVarName(currentMaskIdx / 9, currentMaskIdx % 9);
}
}
return(toReturn);
}
private IList <IList <IList <int> > > GetRowsPermutationsUncached(Core.Sudoku objSudoku)
{
var toReturn = new List <IList <IList <int> > >(9);
for (int i = 0; i < 9; i++)
{
var tempList = new List <IList <int> >();
foreach (var perm in AllPermutations)
{
// Permutation should match current mask row numbers, and have numbers different that other mask rows
if (!Range9.Any(rowIdx => Range9.Any(j => objSudoku.GetCell(rowIdx, j) > 0 &&
((rowIdx == i && perm[j] != objSudoku.GetCell(rowIdx, j)) || (rowIdx != i && perm[j] == objSudoku.GetCell(rowIdx, j))))))
{
tempList.Add(perm);
}
}
toReturn.Add(tempList);
}
return(toReturn);
}
public static CombinatorialSudoku Convert(Core.Sudoku sudoku)
{
var problem = new[, ]
{
{ 0, 0, 7, 0, 0, 2, 9, 3, 0 },
{ 0, 8, 1, 0, 0, 0, 0, 0, 5 },
{ 9, 0, 4, 7, 0, 0, 1, 6, 0 },
{ 0, 1, 0, 8, 0, 0, 0, 0, 6 },
{ 8, 4, 6, 0, 0, 0, 5, 9, 2 },
{ 5, 0, 0, 0, 0, 6, 0, 1, 0 },
{ 0, 9, 2, 0, 0, 8, 3, 0, 1 },
{ 4, 0, 0, 0, 0, 0, 6, 5, 0 },
{ 0, 6, 5, 4, 0, 0, 2, 0, 0 }
};
for (int row = 1; row <= 9; row++)
{
for (int column = 1; column <= 9; column++)
{
problem[row - 1, column - 1] = sudoku.GetCell(row - 1, column - 1);
}
}
return(new CombinatorialSudoku(problem));
}
public static (BaseModel model, double accuracy) TrainAndTest(BaseModel model)
{
// Global parameters
string datasetPath = @"Sudoku.NeuralNetwork\Dataset\sudoku.csv.gz";
int numSudokus = 1000;
// ML parameters
double testPercent = 0.2;
float learningRate = .001F;
int batchSize = 32;
int epochs = 2;
Console.WriteLine("Initialize dataset");
var(sPuzzles, sSols) = DataSetHelper.ParseCSV(datasetPath, numSudokus);
var(_sPuzzzlesTrain, _sPuzzlesTest) = DataSetHelper.SplitDataSet(sPuzzles, testPercent);
var(_sSolsTrain, _sSolsTest) = DataSetHelper.SplitDataSet(sSols, testPercent);
Console.WriteLine("Preprocess data");
var sPuzzzlesTrain = DataSetHelper.PreprocessSudokus(_sPuzzzlesTrain);
var sSolsTrain = DataSetHelper.PreprocessSudokus(_sSolsTrain);
var sPuzzlesTest = DataSetHelper.PreprocessSudokus(_sPuzzlesTest);
var sSolsTest = DataSetHelper.PreprocessSudokus(_sSolsTest);
// Add optimizer
var adam = new Keras.Optimizers.Adam(learningRate);
model.Compile(loss: "sparse_categorical_crossentropy", optimizer: adam);
Console.WriteLine("Train model");
model.Fit(sPuzzzlesTrain, sSolsTrain, batch_size: batchSize, epochs: epochs);
// Test model
int correct = 0;
for (int i = 0; i < 1; i++)
{
Console.WriteLine("Testing " + i);
// Predict result
var prediction = Solve(sPuzzlesTest[i], model);
// Convert to sudoku
var sPredict = new Core.Sudoku()
{
Cells = prediction.flatten().astype(np.int32).GetData <int>().ToList()
};
var sSol = new Core.Sudoku()
{
Cells = ((sSolsTest[i] + 0.5) * 9).flatten().astype(np.int32).GetData <int>().ToList()
};
// Compare sudoku
var same = true;
for (int j = 0; j < 9; j++)
{
for (int k = 0; k < 9; k++)
{
if (sPredict.GetCell(j, k) != sSol.GetCell(j, k))
{
same = false;
}
}
}
Console.WriteLine("Prediction : \n" + sPredict);
Console.WriteLine("Solution : \n" + sSol);
if (same)
{
correct += 1;
}
}
// Calculate accuracy
var accuracy = (correct / sPuzzlesTest.size) * 100;
// Return
return(model, accuracy);
}
