private void btnin_Click(object sender, RoutedEventArgs e)
{
if (GridSearch.XGReport.ActiveRecord == null)
{
return;
}
if (txtlien.Value != null)
{
int sl_in = Convert.ToInt16(StartUp.DsTrans.Tables[0].DefaultView[0]["sl_in"]);
DataRowView drvXReport = (GridSearch.XGReport.ActiveRecord as DataRecord).DataItem as DataRowView;
string nd51 = drvXReport["nd51"].ToString();
if (nd51 == "1" && sl_in > 0)
{
if (ExMessageBox.Show(390, StartUp.SysObj, "Hóa đơn đã được in, có muốn in lại hay không?", "", MessageBoxButton.YesNo, MessageBoxImage.Question, MessageBoxResult.No) == MessageBoxResult.No)
{
return;
}
FrmLogin login = new FrmLogin();
login.ShowDialog();
if (!login.IsLogined)
{
return;
}
}
int so_lien = 1, so_lien_hd = 0, so_lien_xac_minh = 0;
int so_lien_hien_thoi = Convert.ToInt32(StartUp.GetSo_lien((DataRecord)GridSearch.XGReport.ActiveRecord, StartUp.DsTrans.Tables[0].DefaultView[0]["stt_rec"].ToString()));
int.TryParse(DsPrint.Tables["TablePH"].DefaultView[0]["so_lien_hd"].ToString(), out so_lien_hd);
int.TryParse(StartUp.DmctInfo["so_lien_xac_minh"].ToString(), out so_lien_xac_minh);
if (so_lien_hien_thoi > so_lien_hd)
{
DsPrint.Tables["TablePH"].DefaultView[0]["ban_sao"] = "BẢN SAO";
}
int so_lan_in = Convert.ToInt16(Math.Ceiling(Convert.ToDouble(txtlien.Text) / Convert.ToDouble((GridSearch.XGReport.ActiveRecord as DataRecord).Cells["so_lien"].Value)));
while (so_lien <= so_lan_in)
{
DsPrint.Tables["TablePH"].DefaultView[0]["so_lien"] = so_lien;
if (so_lien_hien_thoi <= so_lien_hd)
{
if (so_lien <= so_lien_hien_thoi || so_lien > so_lien_hd)
{
DsPrint.Tables["TablePH"].DefaultView[0]["ban_sao"] = "BẢN SAO";
}
else
{
DsPrint.Tables["TablePH"].DefaultView[0]["ban_sao"] = "";
}
}
InsertRowCT(nd51);
GridSearch.V_In(1, (so_lien_xac_minh >= so_lien && string.IsNullOrEmpty(DsPrint.Tables["TablePH"].DefaultView[0]["ban_sao"].ToString())));
so_lien++;
}
//update sl_in và so_lien in
if (nd51 == "1" && GridSearch.PrintSuccess)
{
string stt_rec = StartUp.DsTrans.Tables[0].DefaultView[0]["stt_rec"].ToString();
StartUp.UpdateSl_in(stt_rec, drvXReport["id"].ToString(), txtlien.Text);
StartUp.DsTrans.Tables[0].DefaultView[0]["sl_in"] = StartUp.GetSl_in(stt_rec);
}
ResetTableCt();
StartUp.SetPhIn(this.DataContext as DataTable);
}
this.Close();
}
private void btnin_lt_Click(object sender, RoutedEventArgs e)
{
if (GridSearch.XGReport.ActiveRecord == null)
{
return;
}
DataRowView drvXReport = (GridSearch.XGReport.ActiveRecord as DataRecord).DataItem as DataRowView;
string mau_tu_in = drvXReport["mau_tu_in"].ToString();
if (StartUp.IsQLHD && mau_tu_in == "1")
{
ExMessageBox.Show(395, StartUp.SysObj, "Có chứng từ thuộc mẫu hóa đơn tự in, không in liên tục được!", "", MessageBoxButton.OK, MessageBoxImage.Information);
return;
}
if (StartUp.SysObj.GetOption("M_IN_HOI_CK").ToString() == "1")
{
if (ExMessageBox.Show(400, StartUp.SysObj, "Có chắc chắn in tất cả các chứng từ đã được lọc?", "", MessageBoxButton.YesNo, MessageBoxImage.Question, MessageBoxResult.No) == MessageBoxResult.Yes)
{
int so_lien;
List <int> lstSo_lien = new List <int>();
if (txtlien.Value != null)
{
int iRowTmp = FrmTT_SOCTHDA_HDDT.iRow;
so_lien = 1;
bool isPrint = false;
int so_lien_xac_minh = 0;
int.TryParse(StartUp.DmctInfo["so_lien_xac_minh"].ToString(), out so_lien_xac_minh);
int so_lan_in = Convert.ToInt16(Math.Ceiling(Convert.ToDouble(txtlien.Text) / Convert.ToDouble((GridSearch.XGReport.ActiveRecord as DataRecord).Cells["so_lien"].Value)));
while (so_lien <= so_lan_in)
{
for (int i = 1; i < DsPrint.Tables[0].Rows.Count; i++)
{
string stt_rec = DsPrint.Tables[0].Rows[i]["stt_rec"].ToString();
DsPrint.Tables["TablePH"].DefaultView.RowFilter = "stt_rec= '" + stt_rec + "'";
DsPrint.Tables["TableCT"].DefaultView.RowFilter = "stt_rec= '" + stt_rec + "'";
DsPrint.Tables["TableCT"].DefaultView.Sort = "stt_rec0";
if (so_lien == 1)
{
lstSo_lien.Add(Convert.ToInt32(StartUp.GetSo_lien((DataRecord)GridSearch.XGReport.ActiveRecord, stt_rec)));
}
if (DsPrint.Tables[0].Rows[i]["status"].ToString() != "3")
{
int so_lien_hien_thoi = lstSo_lien[i - 1], so_lien_hd = 0;
int.TryParse(DsPrint.Tables["TablePH"].DefaultView[0]["so_lien_hd"].ToString(), out so_lien_hd);
if (so_lien_hien_thoi > so_lien_hd)
{
DsPrint.Tables["TablePH"].DefaultView[0]["ban_sao"] = "BẢN SAO";
}
else
{
if (so_lien <= so_lien_hien_thoi || so_lien > so_lien_hd)
{
DsPrint.Tables["TablePH"].DefaultView[0]["ban_sao"] = "BẢN SAO";
}
else
{
DsPrint.Tables["TablePH"].DefaultView[0]["ban_sao"] = "";
}
}
DsPrint.Tables["TablePH"].DefaultView[0]["so_lien"] = so_lien;
int sl_in = Convert.ToInt16(StartUp.DsTrans.Tables[0].Rows[i]["sl_in"]);
if (mau_tu_in == "1" && sl_in > 0 && !isPrint)
{
if (ExMessageBox.Show(405, StartUp.SysObj, "Hóa đơn đã được in, có muốn in lại hay không?", "", MessageBoxButton.YesNo, MessageBoxImage.Question, MessageBoxResult.No) == MessageBoxResult.No)
{
return;
}
FrmLogin login = new FrmLogin();
login.ShowDialog();
if (!login.IsLogined)
{
return;
}
// da thong bao roi, lan sau ko thong bao nua
isPrint = true;
}
InsertRowCT(mau_tu_in);
GridSearch.V_In(1, (so_lien_xac_minh >= so_lien && string.IsNullOrEmpty(DsPrint.Tables["TablePH"].DefaultView[0]["ban_sao"].ToString())));
if (mau_tu_in == "1" && so_lien == 1 && GridSearch.PrintSuccess)
{
StartUp.UpdateSl_in(stt_rec, drvXReport["id"].ToString(), txtlien.Text);
StartUp.DsTrans.Tables[0].Rows[i]["sl_in"] = StartUp.GetSl_in(stt_rec);
}
}
}
so_lien++;
}
ResetTableCt();
DsPrint.Tables["TablePH"].DefaultView.RowFilter = "stt_rec= '" + DsPrint.Tables["TablePH"].Rows[iRowTmp]["stt_rec"].ToString() + "'";
DsPrint.Tables["TableCT"].DefaultView.RowFilter = "stt_rec= '" + DsPrint.Tables["TablePH"].Rows[iRowTmp]["stt_rec"].ToString() + "'";
DsPrint.Tables["TableCT"].DefaultView.Sort = "stt_rec0";
StartUp.SetPhIn(this.DataContext as DataTable);
}
this.Close();
}
}
}
public void GridsearchConstructorTest2()
{
Accord.Math.Tools.SetupGenerator(0);
// Example binary data
double[][] inputs =
{
new double[] { -1, -1 },
new double[] { -1, 1 },
new double[] { 1, -1 },
new double[] { 1, 1 }
};
int[] xor = // xor labels
{
-1, 1, 1, -1
};
// Declare the parameters and ranges to be searched
GridSearchRange[] ranges =
{
new GridSearchRange("complexity", new double[] { 0.00000001, 5.20, 0.30, 1000000, 0.50 } ),
};
// Instantiate a new Grid Search algorithm for Kernel Support Vector Machines
var gridsearch = new GridSearch<SupportVectorMachine>(ranges);
// Set the fitting function for the algorithm
gridsearch.Fitting = delegate(GridSearchParameterCollection parameters, out double error)
{
// The parameters to be tried will be passed as a function parameter.
double complexity = parameters["complexity"].Value;
// Use the parameters to build the SVM model
SupportVectorMachine svm = new SupportVectorMachine(2);
// Create a new learning algorithm for SVMs
SequentialMinimalOptimization smo = new SequentialMinimalOptimization(svm, inputs, xor);
smo.Complexity = complexity;
// Measure the model performance to return as an out parameter
error = smo.Run();
return svm; // Return the current model
};
{
// Declare some out variables to pass to the grid search algorithm
GridSearchParameterCollection bestParameters; double minError;
// Compute the grid search to find the best Support Vector Machine
SupportVectorMachine bestModel = gridsearch.Compute(out bestParameters, out minError);
// The minimum error should be zero because the problem is well-known.
Assert.AreEqual(minError, 0.5);
Assert.IsNotNull(bestModel);
Assert.IsNotNull(bestParameters);
Assert.AreEqual(bestParameters.Count, 1);
}
{
// Compute the grid search to find the best Support Vector Machine
var result = gridsearch.Compute();
// The minimum error should be zero because the problem is well-known.
Assert.AreEqual(result.Error, 0.5);
Assert.IsNotNull(result.Model);
Assert.AreEqual(5, result.Errors.Length);
Assert.AreEqual(5, result.Models.Length);
}
}
public void BFS()
{
var path = GridSearch.BreadthFirstSearch(_grid, _startCell.GetPosition(), _endCell.GetPosition());
StartCoroutine(ShowPath(path));
}
public ModelScorerDiscrete(PhyloTree tree, GridSearch optimizer) : base(tree, optimizer)
{
}
protected ModelScorer(PhyloTree tree, GridSearch optimizer)
{
PhyloTree = tree;
GridSearch = optimizer;
}
public void create_test()
{
#region doc_create
// Ensure results are reproducible
Accord.Math.Random.Generator.Seed = 0;
// Example binary data
double[][] inputs =
{
new double[] { -1, -1 },
new double[] { -1, 1 },
new double[] { 1, -1 },
new double[] { 1, 1 }
};
int[] xor = // xor labels
{
-1, 1, 1, -1
};
// Instantiate a new Grid Search algorithm for Kernel Support Vector Machines
var gridsearch = GridSearch <double[], int> .Create(
ranges : new GridSearchRange[]
{
new GridSearchRange("complexity", new double[] { 0.00000001, 5.20, 0.30, 0.50 }),
new GridSearchRange("degree", new double[] { 1, 10, 2, 3, 4, 5 }),
new GridSearchRange("constant", new double[] { 0, 1, 2 })
},
learner : (p) => new SequentialMinimalOptimization <Polynomial>
{
Complexity = p["complexity"],
Kernel = new Polynomial((int)p["degree"].Value, p["constant"])
},
// Define how the model should be learned, if needed
fit : (teacher, x, y, w) => teacher.Learn(x, y, w),
// Define how the performance of the models should be measured
loss : (actual, expected, m) => new ZeroOneLoss(expected).Loss(actual)
);
// If needed, control the degree of CPU parallelization
gridsearch.ParallelOptions.MaxDegreeOfParallelism = 1;
// Search for the best model parameters
var result = gridsearch.Learn(inputs, xor);
// Get the best SVM generated during the search
SupportVectorMachine <Polynomial> svm = result.BestModel;
// Get an estimate for its error:
double bestError = result.BestModelError;
// Get the best values for its parameters:
double bestC = result.BestParameters["complexity"].Value;
double bestDegree = result.BestParameters["degree"].Value;
double bestConstant = result.BestParameters["constant"].Value;
#endregion
Assert.IsNotNull(svm);
Assert.AreEqual(1e-8, bestC, 1e-10);
Assert.AreEqual(0, bestError, 1e-8);
Assert.AreEqual(1, bestDegree, 1e-8);
Assert.AreEqual(1, bestConstant, 1e-8);
}
public void GridsearchConstructorTest2()
{
Accord.Math.Random.Generator.Seed = 0;
// Example binary data
double[][] inputs =
{
new double[] { -1, -1 },
new double[] { -1, 1 },
new double[] { 1, -1 },
new double[] { 1, 1 }
};
int[] xor = // xor labels
{
-1, 1, 1, -1
};
// Declare the parameters and ranges to be searched
GridSearchRange[] ranges =
{
new GridSearchRange("complexity", new double[] { 0.00000001, 5.20, 0.30, 1000000, 0.50 }),
};
// Instantiate a new Grid Search algorithm for Kernel Support Vector Machines
var gridsearch = new GridSearch <SupportVectorMachine>(ranges);
gridsearch.ParallelOptions.MaxDegreeOfParallelism = 1;
// Set the fitting function for the algorithm
gridsearch.Fitting = delegate(GridSearchParameterCollection parameters, out double error)
{
// The parameters to be tried will be passed as a function parameter.
double complexity = parameters["complexity"].Value;
// Use the parameters to build the SVM model
SupportVectorMachine svm = new SupportVectorMachine(2);
// Create a new learning algorithm for SVMs
SequentialMinimalOptimization smo = new SequentialMinimalOptimization(svm, inputs, xor);
smo.Complexity = complexity;
// Measure the model performance to return as an out parameter
error = smo.Run();
return(svm); // Return the current model
};
{
// Declare some out variables to pass to the grid search algorithm
GridSearchParameterCollection bestParameters; double minError;
// Compute the grid search to find the best Support Vector Machine
SupportVectorMachine bestModel = gridsearch.Compute(out bestParameters, out minError);
// The minimum error should be zero because the problem is well-known.
Assert.AreEqual(minError, 0.5);
Assert.IsNotNull(bestModel);
Assert.IsNotNull(bestParameters);
Assert.AreEqual(bestParameters.Count, 1);
}
{
// Compute the grid search to find the best Support Vector Machine
var result = gridsearch.Compute();
// The minimum error should be zero because the problem is well-known.
Assert.AreEqual(result.Error, 0.5);
Assert.IsNotNull(result.Model);
Assert.AreEqual(5, result.Errors.Length);
Assert.AreEqual(5, result.Models.Length);
}
}
internal static void Search(GridGraph graph, GridSearch search, int x, int y, float[,] weights, IComparer <GridEdge> comparer)
{
int width = graph.Width;
int height = graph.Height;
bool[,] isVisited = new bool[width, height];
isVisited[x, y] = true;
search.Order.Add(new Vector2i(x, y));
if (search.Parent != null)
{
search.Parent[x, y] = new Vector2i(x, y);
}
PriorityQueue <GridEdge> queue = new PriorityQueue <GridEdge>(comparer);
List <GridEdge> edges = new List <GridEdge>(8);
graph.GetEdges(x, y, edges, weights);
if (edges.Count != 0)
{
foreach (GridEdge edge in edges)
{
queue.Push(edge);
}
edges.Clear();
}
while (queue.Count != 0)
{
GridEdge edge = queue.Pop();
Vector2i v = edge.To;
if (isVisited[v.x, v.y])
{
continue;
}
search.Order.Add(v);
isVisited[v.x, v.y] = true;
if (search.Parent != null)
{
search.Parent[v.x, v.y] = edge.From;
}
if (graph.Edges[v.x, v.y] == 0)
{
continue;
}
graph.GetEdges(v.x, v.y, edges, weights);
foreach (GridEdge e in edges)
{
if (isVisited[e.To.x, e.To.y])
{
continue;
}
queue.Push(e);
}
edges.Clear();
}
}
public void learn_test()
{
#region doc_learn
// Ensure results are reproducible
Accord.Math.Random.Generator.Seed = 0;
// Example binary data
double[][] inputs =
{
new double[] { -1, -1 },
new double[] { -1, 1 },
new double[] { 1, -1 },
new double[] { 1, 1 }
};
int[] xor = // xor labels
{
-1, 1, 1, -1
};
// Instantiate a new Grid Search algorithm for Kernel Support Vector Machines
var gridsearch = new GridSearch <SupportVectorMachine <Polynomial>, double[], int>()
{
// Here we can specify the range of the parameters to be included in the search
ParameterRanges = new GridSearchRangeCollection()
{
new GridSearchRange("complexity", new double[] { 0.00000001, 5.20, 0.30, 0.50 }),
new GridSearchRange("degree", new double[] { 1, 10, 2, 3, 4, 5 }),
new GridSearchRange("constant", new double[] { 0, 1, 2 })
},
// Indicate how learning algorithms for the models should be created
Learner = (p) => new SequentialMinimalOptimization <Polynomial>
{
Complexity = p["complexity"],
Kernel = new Polynomial((int)p["degree"], p["constant"])
},
// Define how the performance of the models should be measured
Loss = (actual, expected, m) => new ZeroOneLoss(expected).Loss(actual)
};
// If needed, control the degree of CPU parallelization
gridsearch.ParallelOptions.MaxDegreeOfParallelism = 1;
// Search for the best model parameters
var result = gridsearch.Learn(inputs, xor);
// Get the best SVM found during the parameter search
SupportVectorMachine <Polynomial> svm = result.BestModel;
// Get an estimate for its error:
double bestError = result.BestModelError;
// Get the best values found for the model parameters:
double bestC = result.BestParameters["complexity"].Value;
double bestDegree = result.BestParameters["degree"].Value;
double bestConstant = result.BestParameters["constant"].Value;
#endregion
Assert.IsNotNull(svm);
Assert.AreEqual(1e-8, bestC, 1e-10);
Assert.AreEqual(0, bestError, 1e-8);
Assert.AreEqual(1, bestDegree, 1e-8);
Assert.AreEqual(1, bestConstant, 1e-8);
Assert.AreEqual(1, svm.Kernel.Degree);
Assert.AreEqual(1, svm.Kernel.Constant);
}
public void cross_validation_decision_tree()
{
#region doc_learn_tree_cv
// Ensure results are reproducible
Accord.Math.Random.Generator.Seed = 0;
// This is a sample code showing how to use Grid-Search in combination with
// Cross-Validation to assess the performance of Decision Trees with C4.5.
var parkinsons = new Parkinsons();
double[][] input = parkinsons.Features;
int[] output = parkinsons.ClassLabels;
// Create a new Grid-Search with Cross-Validation algorithm. Even though the
// generic, strongly-typed approach used accross the framework is most of the
// time easier to handle, combining those both methods in a single call can be
// difficult. For this reason. the framework offers a specialized method for
// combining those two algorirthms:
var gscv = GridSearch.CrossValidate(
// Here we can specify the range of the parameters to be included in the search
ranges: new
{
Join = GridSearch.Range(fromInclusive: 1, toExclusive: 20),
MaxHeight = GridSearch.Range(fromInclusive: 1, toExclusive: 20),
},
// Indicate how learning algorithms for the models should be created
learner: (p, ss) => new C45Learning
{
// Here, we can use the parameters we have specified above:
Join = p.Join,
MaxHeight = p.MaxHeight,
},
// Define how the model should be learned, if needed
fit: (teacher, x, y, w) => teacher.Learn(x, y, w),
// Define how the performance of the models should be measured
loss: (actual, expected, r) => new ZeroOneLoss(expected).Loss(actual),
folds: 3, // use k = 3 in k-fold cross validation
x: input, y: output // so the compiler can infer generic types
);
// If needed, control the parallelization degree
gscv.ParallelOptions.MaxDegreeOfParallelism = 1;
// Search for the best decision tree
var result = gscv.Learn(input, output);
// Get the best cross-validation result:
var crossValidation = result.BestModel;
// Get an estimate of its error:
double bestAverageError = result.BestModelError;
double trainError = result.BestModel.Training.Mean;
double trainErrorVar = result.BestModel.Training.Variance;
double valError = result.BestModel.Validation.Mean;
double valErrorVar = result.BestModel.Validation.Variance;
// Get the best values for the parameters:
int bestJoin = result.BestParameters.Join;
int bestHeight = result.BestParameters.MaxHeight;
// Use the best parameter values to create the final
// model using all the training and validation data:
var bestTeacher = new C45Learning
{
Join = bestJoin,
MaxHeight = bestHeight,
};
// Use the best parameters to create the final tree model:
DecisionTree finalTree = bestTeacher.Learn(input, output);
#endregion
int height = finalTree.GetHeight();
Assert.AreEqual(5, height);
Assert.AreEqual(22, result.BestModel.NumberOfInputs);
Assert.AreEqual(2, result.BestModel.NumberOfOutputs);
Assert.AreEqual(195, result.BestModel.NumberOfSamples);
Assert.AreEqual(65, result.BestModel.AverageNumberOfSamples);
Assert.AreEqual(bestAverageError, valError);
Assert.AreEqual(5, bestJoin, 1e-10);
Assert.AreEqual(0.1076923076923077, bestAverageError, 1e-8);
Assert.AreEqual(5, bestHeight, 1e-8);
}
protected virtual void Filter(object sender, EventArgs e)
{
AutoCompleteWheretermFormater = null;
BeforeFilter();
if (AutoCompleteWheretermFormater != null)
{
if (tbxCode.Text != "")
{
var param = AutoCompleteWheretermFormater;
Array.Resize(ref param, param.Length + 1);
param[param.Length - 1] = WhereTerm.Default(tbxCode.Text, CodeColumn, EnumSqlOperator.Like);
AutoCompleteWheretermFormater = param;
}
}
else
{
if (tbxCode.Text != "")
{
AutoCompleteWheretermFormater = new IListParameter[] { WhereTerm.Default(tbxCode.Text, CodeColumn, EnumSqlOperator.Like) };
}
}
var paramEmpty = true;
foreach (Control o in MainContainer.Panel1.Controls)
{
if (o is TextBox)
{
if (o.Text != "")
{
paramEmpty = false;
break;
}
}
if (o is dTextBox)
{
if (o.Text != "")
{
paramEmpty = false;
break;
}
}
if (o is dTextBoxNumber)
{
if (o.Text != "")
{
paramEmpty = false;
break;
}
}
if (o is dCalendar)
{
if (((dCalendar)o).EditValue != null)
{
paramEmpty = false;
o.Focus();
break;
}
}
if (o is dLookupC)
{
if (((dLookupC)o).Value != null)
{
paramEmpty = false;
break;
}
}
if (o is ComboBox)
{
if (((ComboBox)o).SelectedValue != null)
{
paramEmpty = false;
break;
}
}
}
if (paramEmpty)
{
MessageBox.Show(@"Masukkan parameter pencarian", Resources.title_information, MessageBoxButtons.OK);
return;
}
if (ByPaging)
{
PageLimit = 10;
PagingForm = new Paging
{
Direction = SortDirection,
SortColumn = SortColumn
};
CurrentFilter = GotoFirstPage <TModel>(sender, e);
}
else
{
CurrentFilter = DataManager.Get <TModel>(AutoCompleteWheretermFormater);
}
GridSearch.DataSource = CurrentFilter;
SearchView.RefreshData();
NavigatorPagingState = PagingState;
GridSearch.Focus();
}
private Tuple <MulticlassSupportVectorMachine <Gaussian>, double, double, double> TrainingPaper(List <double[]> inputsList, List <int> outputsList)
{
var gridsearch = GridSearch <double[], int> .CrossValidate(
// Here we can specify the range of the parameters to be included in the search
ranges : new
{
Complexity = GridSearch.Values(Math.Pow(2, -12), Math.Pow(2, -11), Math.Pow(2, -10), Math.Pow(2, -8),
Math.Pow(2, -6), Math.Pow(2, -4), Math.Pow(2, -2), Math.Pow(2, 0), Math.Pow(2, 2),
Math.Pow(2, 4), Math.Pow(2, 6), Math.Pow(2, 8), Math.Pow(2, 10), Math.Pow(2, 11), Math.Pow(2, 12)),
Gamma = GridSearch.Values(Math.Pow(2, -12), Math.Pow(2, -11), Math.Pow(2, -10), Math.Pow(2, -8),
Math.Pow(2, -6), Math.Pow(2, -4), Math.Pow(2, -2), Math.Pow(2, 0), Math.Pow(2, 2),
Math.Pow(2, 4), Math.Pow(2, 6), Math.Pow(2, 8), Math.Pow(2, 10), Math.Pow(2, 11), Math.Pow(2, 12))
},
// Indicate how learning algorithms for the models should be created
learner : (p, ss) => new MulticlassSupportVectorLearning <Gaussian>()
{
// Configure the learning algorithm to use SMO to train the
// underlying SVMs in each of the binary class subproblems.
Learner = (param) => new SequentialMinimalOptimization <Gaussian>()
{
// Estimate a suitable guess for the Gaussian kernel's parameters.
// This estimate can serve as a starting point for a grid search.
//UseComplexityHeuristic = true,
//UseKernelEstimation = true
Complexity = p.Complexity,
Kernel = Gaussian.FromGamma(p.Gamma)
}
},
// Define how the model should be learned, if needed
fit : (teacher, x, y, w) => teacher.Learn(x, y, w),
// Define how the performance of the models should be measured
/*loss: (actual, expected, m) =>
* {
* double totalError = 0;
* foreach (var input in _originalInputsList)
* {
* if (!m.Decide(input.Item1).Equals(input.Item2))
* {
* totalError++;
* }
* }
* return totalError / _originalInputsList.Count;
* },*/
loss : (actual, expected, m) => new HammingLoss(expected).Loss(actual),
folds : 10
);
gridsearch.ParallelOptions.MaxDegreeOfParallelism = _paralelism;
Console.WriteLine("y nos ponemos a aprender");
// Search for the best model parameters
var result = gridsearch.Learn(inputsList.ToArray(), outputsList.ToArray());
Console.WriteLine("Error modelo: " + result.BestModelError);
var model = CreateModel(inputsList, outputsList, result.BestParameters.Complexity, result.BestParameters.Gamma);
double error = 0;
Console.WriteLine("Largo: " + _originalInputsList.Count);
foreach (var input in _originalInputsList)
{
if (!model.Decide(input.Item1).Equals(input.Item2))
{
error++;
}
}
error = error / (_originalInputsList.Count);
Console.WriteLine("Error real: " + error);
return(new Tuple <MulticlassSupportVectorMachine <Gaussian>, double, double, double>(model, error, result.BestParameters.Gamma.Value, result.BestParameters.Complexity.Value));
}
protected void DropDownTickets_SelectedIndexChanged(object sender, EventArgs e)
{
if (DropDownTickets.SelectedValue == "$1.00-$10.00")
{
string connectionstring2 = WebConfigurationManager.ConnectionStrings["CapstoneSQLConn"].ConnectionString;
string selectsql2 = "select e.name as 'Event', c.name as 'Artist', e.startDate as 'Date', e.startTime as 'Time', v.name as 'Venue', a.price as 'Ticket Price', s.name as 'Seat Section' from [Event] as e, [Venue] as v, [Section] as s, [AvailableTickets] as a, [Artist] as c where e.venueID = v.venueID and e.eventID = a.eventID and a.sectionID = s.sectionID and e.artistID = c.artistID and a.price between 1 and 10 order by a.price ASC";
SqlConnection connect2 = new SqlConnection(connectionstring2);
SqlCommand cmd = new SqlCommand(selectsql2, connect2);
SqlDataAdapter adapter = new SqlDataAdapter(cmd);
DataSet ds2 = new DataSet();
adapter.Fill(ds2);
GridSearch.DataSource = ds2;
GridSearch.DataBind();
if (ds2.Tables[0].Rows.Count == 0)
{
lblerror.Text = "*Your Search Returned Zero Result!!, Please Try Searching Again!!!";
}
else
{
lblerror.Text = "";
}
}
if (DropDownTickets.SelectedValue == "$11.00-$20.00")
{
string connectionstring2 = WebConfigurationManager.ConnectionStrings["CapstoneSQLConn"].ConnectionString;
string selectsql2 = "select e.name as 'Event', c.name as 'Artist', e.startDate as 'Date', e.startTime as 'Time', v.name as 'Venue', a.price as 'Ticket Price', s.name as 'Seat Section' from [Event] as e, [Venue] as v, [Section] as s, [AvailableTickets] as a, [Artist] as c where e.venueID = v.venueID and e.eventID = a.eventID and a.sectionID = s.sectionID and e.artistID = c.artistID and a.price between 11 and 20 order by a.price ASC";
SqlConnection connect2 = new SqlConnection(connectionstring2);
SqlCommand cmd = new SqlCommand(selectsql2, connect2);
SqlDataAdapter adapter = new SqlDataAdapter(cmd);
DataSet ds2 = new DataSet();
adapter.Fill(ds2);
GridSearch.DataSource = ds2;
GridSearch.DataBind();
if (ds2.Tables[0].Rows.Count == 0)
{
lblerror.Text = "*Your Search Returned Zero Result!!, Please Try Searching Again!!!";
}
else
{
lblerror.Text = "";
}
}
if (DropDownTickets.SelectedValue == "$21.00-$30.00")
{
string connectionstring2 = WebConfigurationManager.ConnectionStrings["CapstoneSQLConn"].ConnectionString;
string selectsql2 = "select e.name as 'Event', c.name as 'Artist', e.startDate as 'Date', e.startTime as 'Time', v.name as 'Venue', a.price as 'Ticket Price', s.name as 'Seat Section' from [Event] as e, [Venue] as v, [Section] as s, [AvailableTickets] as a, [Artist] as c where e.venueID = v.venueID and e.eventID = a.eventID and a.sectionID = s.sectionID and e.artistID = c.artistID and a.price between 21 and 30 order by a.price ASC";
SqlConnection connect2 = new SqlConnection(connectionstring2);
SqlCommand cmd = new SqlCommand(selectsql2, connect2);
SqlDataAdapter adapter = new SqlDataAdapter(cmd);
DataSet ds2 = new DataSet();
adapter.Fill(ds2);
GridSearch.DataSource = ds2;
GridSearch.DataBind();
if (ds2.Tables[0].Rows.Count == 0)
{
lblerror.Text = "*Your Search Returned Zero Result!!, Please Try Searching Again!!!";
}
else
{
lblerror.Text = "";
}
}
if (DropDownTickets.SelectedValue == "$31.00-$40.00")
{
string connectionstring2 = WebConfigurationManager.ConnectionStrings["CapstoneSQLConn"].ConnectionString;
string selectsql2 = "select e.name as 'Event', c.name as 'Artist', e.startDate as 'Date', e.startTime as 'Time', v.name as 'Venue', a.price as 'Ticket Price', s.name as 'Seat Section' from [Event] as e, [Venue] as v, [Section] as s, [AvailableTickets] as a, [Artist] as c where e.venueID = v.venueID and e.eventID = a.eventID and a.sectionID = s.sectionID and e.artistID = c.artistID and a.price between 31 and 40 order by a.price ASC";
SqlConnection connect2 = new SqlConnection(connectionstring2);
SqlCommand cmd = new SqlCommand(selectsql2, connect2);
SqlDataAdapter adapter = new SqlDataAdapter(cmd);
DataSet ds2 = new DataSet();
adapter.Fill(ds2);
GridSearch.DataSource = ds2;
GridSearch.DataBind();
if (ds2.Tables[0].Rows.Count == 0)
{
lblerror.Text = "*Your Search Returned Zero Result!!, Please Try Searching Again!!!";
}
else
{
lblerror.Text = "";
}
}
if (DropDownTickets.SelectedValue == "$41.00-$50.00")
{
string connectionstring2 = WebConfigurationManager.ConnectionStrings["CapstoneSQLConn"].ConnectionString;
string selectsql2 = "select e.name as 'Event', c.name as 'Artist', e.startDate as 'Date', e.startTime as 'Time', v.name as 'Venue', a.price as 'Ticket Price', s.name as 'Seat Section' from [Event] as e, [Venue] as v, [Section] as s, [AvailableTickets] as a, [Artist] as c where e.venueID = v.venueID and e.eventID = a.eventID and a.sectionID = s.sectionID and e.artistID = c.artistID and a.price between 41 and 50 order by a.price ASC";
SqlConnection connect2 = new SqlConnection(connectionstring2);
SqlCommand cmd = new SqlCommand(selectsql2, connect2);
SqlDataAdapter adapter = new SqlDataAdapter(cmd);
DataSet ds2 = new DataSet();
adapter.Fill(ds2);
GridSearch.DataSource = ds2;
GridSearch.DataBind();
if (ds2.Tables[0].Rows.Count == 0)
{
lblerror.Text = "*Your Search Returned Zero Result!!, Please Try Searching Again!!!";
}
else
{
lblerror.Text = "";
}
}
if (DropDownTickets.SelectedValue == "$50.00 or Above")
{
string connectionstring2 = WebConfigurationManager.ConnectionStrings["CapstoneSQLConn"].ConnectionString;
string selectsql2 = "select e.name as 'Event', c.name as 'Artist', e.startDate as 'Date', e.startTime as 'Time', v.name as 'Venue', a.price as 'Ticket Price', s.name as 'Seat Section' from [Event] as e, [Venue] as v, [Section] as s, [AvailableTickets] as a, [Artist] as c where e.venueID = v.venueID and e.eventID = a.eventID and a.sectionID = s.sectionID and e.artistID = c.artistID and a.price > 50 order by a.price ASC";
SqlConnection connect2 = new SqlConnection(connectionstring2);
SqlCommand cmd = new SqlCommand(selectsql2, connect2);
SqlDataAdapter adapter = new SqlDataAdapter(cmd);
DataSet ds2 = new DataSet();
adapter.Fill(ds2);
GridSearch.DataSource = ds2;
GridSearch.DataBind();
if (ds2.Tables[0].Rows.Count == 0)
{
lblerror.Text = "*Your Search Returned Zero Result!!, Please Try Searching Again!!!";
}
else
{
lblerror.Text = "";
}
}
}
public void learn_test_strongly_typed()
{
#region doc_learn_strongly_typed
// Ensure results are reproducible
Accord.Math.Random.Generator.Seed = 0;
// This is a sample code showing how to use Grid-Search in combination with
// Cross-Validation to assess the performance of Support Vector Machines.
// Consider the example binary data. We will be trying to learn a XOR
// problem and see how well does SVMs perform on this data.
double[][] inputs =
{
new double[] { -1, -1 }, new double[] { 1, -1 },
new double[] { -1, 1 }, new double[] { 1, 1 },
new double[] { -1, -1 }, new double[] { 1, -1 },
new double[] { -1, 1 }, new double[] { 1, 1 },
new double[] { -1, -1 }, new double[] { 1, -1 },
new double[] { -1, 1 }, new double[] { 1, 1 },
new double[] { -1, -1 }, new double[] { 1, -1 },
new double[] { -1, 1 }, new double[] { 1, 1 },
};
int[] xor = // result of xor for the sample input data
{
-1, 1,
1, -1,
-1, 1,
1, -1,
-1, 1,
1, -1,
-1, 1,
1, -1,
};
// Create a new Grid-Search with Cross-Validation algorithm. Even though the
// generic, strongly-typed approach used accross the framework is most of the
// time easier to handle, meta-algorithms such as grid-search can be a bit hard
// to setup. For this reason. the framework offers a specialized method for it:
var gridsearch = GridSearch <double[], int> .Create(
// Here we can specify the range of the parameters to be included in the search
ranges : new
{
Kernel = GridSearch.Range(new IKernel[] { new Linear(), new ChiSquare(), new Gaussian(), new Sigmoid() }),
Complexity = GridSearch.Range(new[] { 0.00000001, 5.20, 0.30, 0.50 }),
Tolerance = GridSearch.Range(Vector.Range(1e-10, 1.0, stepSize: 0.05))
},
// Indicate how learning algorithms for the models should be created
learner : (p) => new SequentialMinimalOptimization <IKernel>
{
Complexity = p.Complexity,
Kernel = p.Kernel.Value,
Tolerance = p.Tolerance
},
// Define how the model should be learned, if needed
fit : (teacher, x, y, w) => teacher.Learn(x, y, w),
// Define how the performance of the models should be measured
loss : (actual, expected, m) => new ZeroOneLoss(expected).Loss(actual)
);
// If needed, control the degree of CPU parallelization
gridsearch.ParallelOptions.MaxDegreeOfParallelism = 1;
// Search for the best model parameters
var result = gridsearch.Learn(inputs, xor);
// Get the best SVM:
SupportVectorMachine <IKernel> svm = result.BestModel;
// Estimate its error:
double bestError = result.BestModelError;
// Get the best values for the parameters:
double bestC = result.BestParameters.Complexity;
double bestTolerance = result.BestParameters.Tolerance;
IKernel bestKernel = result.BestParameters.Kernel.Value;
#endregion
Assert.IsNotNull(svm);
Assert.AreEqual(1e-8, bestC, 1e-10);
Assert.AreEqual(0, bestError, 1e-8);
Assert.AreEqual(0, bestTolerance, 1e-8);
Assert.AreEqual(typeof(Gaussian), bestKernel.GetType());
}
protected void GridSearch_PageIndexChanging(object sender, GridViewPageEventArgs e)
{
GridSearch.PageIndex = e.NewPageIndex;
GridSearch.DataBind();
}
public void cross_validation_test()
{
#region doc_learn_cv
// Ensure results are reproducible
Accord.Math.Random.Generator.Seed = 0;
// This is a sample code showing how to use Grid-Search in combination with
// Cross-Validation to assess the performance of Support Vector Machines.
// Consider the example binary data. We will be trying to learn a XOR
// problem and see how well does SVMs perform on this data.
double[][] inputs =
{
new double[] { -1, -1 }, new double[] { 1, -1 },
new double[] { -1, 1 }, new double[] { 1, 1 },
new double[] { -1, -1 }, new double[] { 1, -1 },
new double[] { -1, 1 }, new double[] { 1, 1 },
new double[] { -1, -1 }, new double[] { 1, -1 },
new double[] { -1, 1 }, new double[] { 1, 1 },
new double[] { -1, -1 }, new double[] { 1, -1 },
new double[] { -1, 1 }, new double[] { 1, 1 },
};
int[] xor = // result of xor for the sample input data
{
-1, 1,
1, -1,
-1, 1,
1, -1,
-1, 1,
1, -1,
-1, 1,
1, -1,
};
// Create a new Grid-Search with Cross-Validation algorithm. Even though the
// generic, strongly-typed approach used accross the framework is most of the
// time easier to handle, combining those both methods in a single call can be
// difficult. For this reason. the framework offers a specialized method for
// combining those two algorirthms:
var gscv = GridSearch <double[], int> .CrossValidate(
// Here we can specify the range of the parameters to be included in the search
ranges : new
{
Complexity = GridSearch.Range(new double[] { 0.00000001, 5.20, 0.30, 0.50 }),
Degree = GridSearch.Range(new int[] { 1, 10, 2, 3, 4, 5 }),
Constant = GridSearch.Range(new double[] { 0, 1, 2 }),
},
// Indicate how learning algorithms for the models should be created
learner : (p, ss) => new SequentialMinimalOptimization <Polynomial>
{
// Here, we can use the parameters we have specified above:
Complexity = p.Complexity,
Kernel = new Polynomial(p.Degree, p.Constant)
},
// Define how the model should be learned, if needed
fit : (teacher, x, y, w) => teacher.Learn(x, y, w),
// Define how the performance of the models should be measured
loss : (actual, expected, r) => new ZeroOneLoss(expected).Loss(actual),
folds : 3 // use k = 3 in k-fold cross validation
);
// If needed, control the parallelization degree
gscv.ParallelOptions.MaxDegreeOfParallelism = 1;
// Search for the best vector machine
var result = gscv.Learn(inputs, xor);
// Get the best cross-validation result:
var crossValidation = result.BestModel;
// Estimate its error:
double bestError = result.BestModelError;
double trainError = result.BestModel.Training.Mean;
double trainErrorVar = result.BestModel.Training.Variance;
double valError = result.BestModel.Validation.Mean;
double valErrorVar = result.BestModel.Validation.Variance;
// Get the best values for the parameters:
double bestC = result.BestParameters.Complexity;
double bestDegree = result.BestParameters.Degree;
double bestConstant = result.BestParameters.Constant;
#endregion
Assert.AreEqual(2, result.BestModel.NumberOfInputs);
Assert.AreEqual(1, result.BestModel.NumberOfOutputs);
Assert.AreEqual(16, result.BestModel.NumberOfSamples);
Assert.AreEqual(5.333333333333333, result.BestModel.AverageNumberOfSamples);
Assert.AreEqual(1e-8, bestC, 1e-10);
Assert.AreEqual(0, bestError, 1e-8);
Assert.AreEqual(10, bestDegree, 1e-8);
Assert.AreEqual(0, bestConstant, 1e-8);
}
public void GridsearchConstructorTest()
{
Accord.Math.Random.Generator.Seed = 0;
// Example binary data
double[][] inputs =
{
new double[] { -1, -1 },
new double[] { -1, 1 },
new double[] { 1, -1 },
new double[] { 1, 1 }
};
int[] xor = // xor labels
{
-1, 1, 1, -1
};
// Declare the parameters and ranges to be searched
GridSearchRange[] ranges =
{
new GridSearchRange("complexity", new double[] { 0.00000001, 5.20, 0.30, 0.50 }),
new GridSearchRange("degree", new double[] { 1, 10, 2,3, 4, 5 }),
new GridSearchRange("constant", new double[] { 0, 1, 2 })
};
// Instantiate a new Grid Search algorithm for Kernel Support Vector Machines
var gridsearch = new GridSearch <KernelSupportVectorMachine>(ranges);
#if DEBUG
gridsearch.ParallelOptions.MaxDegreeOfParallelism = 1;
#endif
// Set the fitting function for the algorithm
gridsearch.Fitting = delegate(GridSearchParameterCollection parameters, out double error)
{
// The parameters to be tried will be passed as a function parameter.
int degree = (int)parameters["degree"].Value;
double constant = parameters["constant"].Value;
double complexity = parameters["complexity"].Value;
// Use the parameters to build the SVM model
Polynomial kernel = new Polynomial(degree, constant);
KernelSupportVectorMachine ksvm = new KernelSupportVectorMachine(kernel, 2);
// Create a new learning algorithm for SVMs
SequentialMinimalOptimization smo = new SequentialMinimalOptimization(ksvm, inputs, xor);
smo.Complexity = complexity;
// Measure the model performance to return as an out parameter
error = smo.Run();
return(ksvm); // Return the current model
};
// Declare some out variables to pass to the grid search algorithm
GridSearchParameterCollection bestParameters; double minError;
// Compute the grid search to find the best Support Vector Machine
KernelSupportVectorMachine bestModel = gridsearch.Compute(out bestParameters, out minError);
// A linear kernel can't solve the xor problem.
Assert.AreEqual(1, bestParameters["degree"].Value);
Assert.AreEqual(1, bestParameters["constant"].Value);
Assert.AreEqual(1e-8, bestParameters["complexity"].Value);
// The minimum error should be zero because the problem is well-known.
Assert.AreEqual(minError, 0.0);
Assert.IsNotNull(bestModel);
Assert.IsNotNull(bestParameters);
Assert.AreEqual(bestParameters.Count, 3);
}
public void internals_test()
{
Accord.Math.Random.Generator.Seed = 0;
string[] inputs =
{
"input 1",
"input 2",
"input 3",
"input 4",
};
string[] outputs =
{
"output 1",
"output 2",
"output 3",
};
double[] weights =
{
1.0,
2.0,
3.0
};
var lossModels = new List <Mapper>();
var ranges = new
{
Parameter1 = GridSearch.Range("parameter 11", "parameter 12"),
Parameter2 = GridSearch.Range("parameter 21", "parameter 22", "parameter 23", "parameter 24"),
Parameter3 = GridSearch.Range("parameter 31")
};
var result = GridSearch.Create(
ranges: ranges,
learner: (p) => new MapperLearning
{
Parameter1 = p.Parameter1,
Parameter2 = p.Parameter2,
Parameter3 = p.Parameter3,
},
fit: (teacher, x, y, w) => teacher.Learn(x, y, w),
loss: (actual, expected, m) =>
{
if (m.Parameter1 == "parameter 12" && m.Parameter2 == "parameter 21" && m.Parameter3 == "parameter 31")
{
return(-42);
}
lock (lossModels)
{
lossModels.Add(m);
}
return(Math.Abs(int.Parse(m.Parameter1.Replace("parameter ", ""))
+ 100 * int.Parse(m.Parameter2.Replace("parameter ", ""))
+ 10000 * int.Parse(m.Parameter3.Replace("parameter ", ""))));
},
x: inputs,
y: outputs,
weights: weights
);
Mapper bestModel = result.BestModel;
Assert.AreEqual("parameter 12", bestModel.Parameter1);
Assert.AreEqual("parameter 21", bestModel.Parameter2);
Assert.AreEqual("parameter 31", bestModel.Parameter3);
Assert.AreEqual(inputs, bestModel.Inputs);
Assert.AreEqual(outputs, bestModel.Outputs);
Assert.AreEqual(weights, bestModel.Weights);
Assert.AreEqual(-42, result.BestModelError);
Assert.AreEqual(4, result.BestModelIndex);
var bestParameters = result.BestParameters;
Assert.AreNotSame(ranges, bestParameters);
Assert.AreEqual(1, bestParameters.Parameter1.Index);
Assert.AreEqual(0, bestParameters.Parameter2.Index);
Assert.AreEqual(0, bestParameters.Parameter3.Index);
Assert.AreEqual("parameter 12", bestParameters.Parameter1.Value);
Assert.AreEqual("parameter 21", bestParameters.Parameter2.Value);
Assert.AreEqual("parameter 31", bestParameters.Parameter3.Value);
Assert.AreEqual(8, result.Count);
Assert.AreEqual(result.Errors, new double[] {
312111, 312211, 312311, 312411,
-42, Double.PositiveInfinity, 312312, 312412
});
Exception[] exceptions = result.Exceptions;
for (int i = 0; i < exceptions.Length; i++)
{
if (i != 5)
{
Assert.IsNull(exceptions[i]);
}
else
{
Assert.AreEqual("Exception test", exceptions[i].Message);
}
}
Mapper[] models = result.Models;
Assert.AreEqual(8, models.Length);
Assert.AreEqual(6, lossModels.Count);
int a = ranges.Parameter1.Length;
int b = ranges.Parameter2.Length;
int c = ranges.Parameter3.Length;
Assert.AreEqual(2, a);
Assert.AreEqual(4, b);
Assert.AreEqual(1, c);
for (int i = 0; i < models.Length; i++)
{
if (i == 5)
{
Assert.IsNull(models[i]);
}
else
{
Assert.AreEqual(inputs, models[i].Inputs);
Assert.AreEqual(outputs, models[i].Outputs);
Assert.AreEqual(weights, models[i].Weights);
Assert.AreEqual(4, models[i].NumberOfInputs);
Assert.AreEqual(2, models[i].NumberOfOutputs);
Assert.AreEqual(ranges.Parameter1.Values[((i / c) / b) % a], models[i].Parameter1);
Assert.AreEqual(ranges.Parameter2.Values[(i / c) % b], models[i].Parameter2);
Assert.AreEqual(ranges.Parameter3.Values[i % c], models[i].Parameter3);
if (i != 4)
{
Assert.IsTrue(lossModels.Contains(models[i]));
}
}
}
Assert.AreEqual(4, result.NumberOfInputs);
Assert.AreEqual(2, result.NumberOfOutputs);
var parameters = result.Parameters;
for (int i = 0; i < parameters.Length; i++)
{
for (int j = 0; j < parameters.Length; j++)
{
if (i != j)
{
Assert.AreNotSame(parameters[i], parameters[j]);
Assert.AreNotEqual(parameters[i], parameters[j]);
Assert.AreNotEqual(parameters[i].Parameter1, parameters[j].Parameter1);
Assert.AreNotEqual(parameters[i].Parameter2, parameters[j].Parameter2);
Assert.AreNotEqual(parameters[i].Parameter3, parameters[j].Parameter3);
}
Assert.AreEqual(parameters[i].Parameter1.Values, parameters[j].Parameter1.Values);
Assert.AreEqual(parameters[i].Parameter2.Values, parameters[j].Parameter2.Values);
Assert.AreEqual(parameters[i].Parameter3.Values, parameters[j].Parameter3.Values);
}
}
}
public ModelScorerGaussian(PhyloTree tree, GridSearch optimizer) : base(tree, optimizer)
{
}
private void grdConfirm_OnOk(object sender, RoutedEventArgs e)
{
try
{
if (Keyboard.FocusedElement.GetType().Equals(typeof(TextBoxAutoComplete)))
{
AutoCompleteTextBox txt = (Keyboard.FocusedElement as TextBoxAutoComplete).ParentControl;
if (!txt.CheckLostFocus())
{
return;
}
}
if (CheckValid())
{
SysObj.SetSysvar("M_ngay_ct1", txtNgay_ct1.dValue);
SysObj.SetSysvar("M_ngay_ct2", txtNgay_ct2.dValue);
bool IsshowView = false;
GridSearch._GenerateSQLString();
GridSearch.GrdSearch.ExecuteCommand(DataPresenterCommands.EndEditModeAndAcceptChanges);
StartUp.TransFilterCmd.Parameters["@PhFilter"].Value = GetPhFilterExpr(); // "ngay_ct between '20100101' and '20100131'";
StartUp.TransFilterCmd.Parameters["@CtFilter"].Value = GetCtFilterExpr();
StartUp.TransFilterCmd.Parameters["@Sl_ct"].Value = 0;
DataSet newDs = DataProvider.FillCommand(StartUp.SysObj, StartUp.TransFilterCmd);
// xuất thông báo tìm kiếm
int n = 0;
Decimal a = (from p
in newDs.Tables[0].AsEnumerable()
select p.Field <Decimal?>("t_tt")).Sum().Value;
string tongPsVND = a.ToString(SysObj.GetOption("M_IP_TIEN").ToString());
Decimal tongPsNT = (from p
in newDs.Tables[0].AsEnumerable()
select p.Field <Decimal?>("t_tt_nt")).Sum().Value;
string _tongPsNT = tongPsNT.ToString(SysObj.GetOption("M_IP_TIEN_NT").ToString());
n = newDs.Tables[0].Rows.Count;
//a = StartUp.DsTrans.Tables[0].AsEnumerable().Sum("t_tt").Value.ToString();
if (n > 0)
{
IsshowView = true;
Sm.Windows.Controls.ExMessageBox.Show(410, StartUp.SysObj, "Có " + "[" + n + "]" + " chứng từ. Tổng phát sinh " + "[" + _tongPsNT + "]" + " / " + "[" + tongPsVND + "]", "", MessageBoxButton.OK, MessageBoxImage.Information);
}
else
{
Sm.Windows.Controls.ExMessageBox.Show(415, StartUp.SysObj, "Không có chứng từ nào như vậy!", "", MessageBoxButton.OK, MessageBoxImage.Information);
}
if (IsshowView == true)
{
// show form View
//string stringBrowse1 = "ngay_ct:100:h=Ngày c.từ:FL:D;so_ct:70:h=Số c.từ:FL;so_seri:70:h=Số seri;" +
// "ma_kh:100:h=Mã khách;ten_kh:180:h=Tên khách;dien_giai:225:h=Diễn giải;" +
// "ma_bp:100:h=NVBH;t_tien_nt2:130:h=Tiền hàng nt:N1:S;t_thue_nt:130:h=Tiền thuế nt:N1:S;" +
// "t_tt_nt:130:h=Tổng tt nt:N1:S;ma_nx:80:h=Mã nx;thue_suat:80:h=Thuế suất:F=2;tk_thue_co:80:h=Tk thuế;" +
// "t_tien2:130:h=Tiền hàng:N0:S;t_thue:130:h=Tiền thuế:N0:S;t_tt:130:h=Tổng tt:N0:S;ma_nt:100:h=Mã nt;" +
// "ty_gia:130:h=Tỷ giá:R;date:105:h=Ngày cập nhật:D;time:100:h=Giờ cập nhật;" +
// "user_id:100:h=Số hiệu NSD:N;user_name:100:h=Tên NSD";
//string stringBrowse2 = "ma_vt:100:h=Mã vật tư:FL;ten_vt:270:h=Tên vật tư:FL;dvt1:50:h=Ðvt;" +
// "ma_kho_i:70:h=Mã kho;so_luong:130:h=Số lượng:Q:S;gia_nt2:130:h=Giá bán nt:P1;tien_nt2:130:h=Thành tiền nt:N1:S;" +
// "tk_dt:80:h=Tk dt;gia_nt:130:h=Giá vốn nt:P1;tien_nt:130:h=Tiền vốn nt:N1:S;tk_vt:80:h=Tk kho;" +
// "tk_gv:80:h=Tk gv;gia2:130:h=Giá bán:P0;tien2:130:h=Thành tiền:N0:S;" +
// "gia:130:h=Giá vốn:N1;tien:130:h=Tiền vốn:N0:S";
SmVoucherLib.FormView _frmView = new SmVoucherLib.FormView(SysObj, newDs.Tables[0].DefaultView, newDs.Tables[1].DefaultView, StartUp.stringBrowse1, StartUp.stringBrowse2, "stt_rec");
_frmView.ListFieldSum = "t_tt_nt;t_tt";
_frmView.TongCongLabel = "Tổng thanh toán";
if (StartUp.M_LAN.Equals("V"))
{
_frmView.frmBrw.Title = StartUp.M_Tilte + ". Ky " + txtNgay_ct1.Text + " - " + txtNgay_ct2.Text;
}
else
{
_frmView.frmBrw.Title = StartUp.M_Tilte + ". Period " + txtNgay_ct1.Text + " - " + txtNgay_ct2.Text;
}
//Them cac truong tu do
SmVoucherLib.FreeCodeFieldLib.InitFreeCodeField(StartUp.SysObj, _frmView.frmBrw.oBrowseCt, StartUp.Ma_ct, 1);
_frmView.frmBrw.LanguageID = "TT_SOCTHDA_HDDT_8";
_frmView.ShowDialog();
StartUp.DataFilter(StartUp.DsTrans.Tables[0].Rows[0]["stt_rec"].ToString());
int Count1 = StartUp.DsTrans.Tables[0].Rows.Count;
int Count2 = StartUp.DsTrans.Tables[1].Rows.Count;
for (int i = Count1 - 1; i >= 1; i--)
{
StartUp.DsTrans.Tables[0].Rows.RemoveAt(i);
}
for (int i = 0; i < Count2; i++)
{
StartUp.DsTrans.Tables[1].Rows.RemoveAt(0);
}
int Count = 0;
Count = newDs.Tables[0].Rows.Count;
for (int i = 0; i < Count; i++)
{
StartUp.DsTrans.Tables[0].Rows.Add(newDs.Tables[0].Rows[i].ItemArray);
}
Count = newDs.Tables[1].Rows.Count;
for (int i = 0; i < Count; i++)
{
StartUp.DsTrans.Tables[1].Rows.Add(newDs.Tables[1].Rows[i].ItemArray);
}
// ko xoá dòng thứ 0 của table[0] vì dòng đó là dòng tạm.
if (newDs.Tables[0].Rows.Count > 0)
{
//Xét lại irow
if (FrmTT_SOCTHDA_HDDT.iRow > newDs.Tables[0].Rows.Count - 1)
{
FrmTT_SOCTHDA_HDDT.iRow = newDs.Tables[0].Rows.Count - 1;
}
StartUp.DataFilter(StartUp.DsTrans.Tables[0].Rows[FrmTT_SOCTHDA_HDDT.iRow]["stt_rec"].ToString());
}
// Set lai irow va rowfilter ...
if (_frmView.DataGrid.ActiveRecord != null)
{
int select_irow = (_frmView.DataGrid.ActiveRecord as DataRecord).Index;
if (select_irow >= 0)
{
string selected_stt_rec = (_frmView.DataGrid.DataSource as DataView)[select_irow]["stt_rec"].ToString();
FrmTT_SOCTHDA_HDDT.iRow = select_irow + 1;
//refresh lại rowfilter
StartUp.DataFilter(selected_stt_rec);
}
}
this.Close();
}
}
}
catch (Exception ex)
{
SmErrorLib.ErrorLog.CatchMessage(ex);
}
}
public void TestGridSearch()
{
//https://www.hackerrank.com/challenges/the-grid-search/problem
/*
* string[] G = new string[]
* {
* "7283455864",
* "6731158619",
* "8988242643",
* "3830589324",
* "2229505813",
* "5633845374",
* "6473530293",
* "7053106601",
* "0834282956",
* "4607924137"
* };
*
* string[] P = new string[]
* {
* "9505",
* "3845",
* "3530"
* };
*/
string[] G = new string[]
{
"123412",
"561212",
"123634",
"7812889"
};
string[] P = new string[]
{
"12",
"34"
};
/*
* string[] G = new string[]
* {
* "111111111111111",
* "111111111111111",
* "111111011111111",
* "111111111111111",
* "111111111111111"
* };
*
* string[] P = new string[]
* {
* "11111",
* "11111",
* "11110"
* };
*/
string result = GridSearch.Search(G, P);
}
void InsertRowCT(string nd51)
{
DataRow[] _row = DsPrint.Tables["TableCT"].DefaultView.ToTable().Select("tag = 1");
if (_row.Count() > 0)
{
return;
}
string stt_rec = DsPrint.Tables["TablePH"].DefaultView[0]["stt_rec"].ToString();
int _index = 1;
foreach (DataRowView dr in DsPrint.Tables["TableCT"].DefaultView)
{
dr["stt"] = _index;
_index++;
}
/*Chị VANTT bảo không lên đoạn này 130734187
* if (nd51 == "1")
* {
* //Thêm dòng ghi chú
* string gc_thue = DsPrint.Tables["TablePH"].DefaultView[0]["gc_thue"].ToString().Trim();
* if (gc_thue != "")
* {
* DataRow row = DsPrint.Tables["TableCT"].NewRow();
* row["stt_rec"] = stt_rec;
* row["ten_vt"] = "(" + gc_thue + ")";
* row["tag"] = 1;
* DsPrint.Tables["TableCT"].Rows.Add(row);
* }
* }
*/
//thêm dòng chiết khấu
decimal t_ck = Convert.ToDecimal(DsPrint.Tables["TablePH"].DefaultView[0]["t_ck"]);
decimal t_ck_nt = Convert.ToDecimal(DsPrint.Tables["TablePH"].DefaultView[0]["t_ck_nt"]);
if (t_ck != 0 || t_ck_nt != 0)
{
DataRow newrow = DsPrint.Tables["TableCT"].NewRow();
newrow["stt_rec"] = stt_rec;
newrow["stt_rec0"] = -1;
newrow["ten_vt"] = "Chiết khấu";
newrow["ten_vt2"] = "Discount";
newrow["tien2"] = t_ck;
newrow["tien_nt2"] = t_ck_nt;
newrow["tag"] = 1;
DsPrint.Tables["TableCT"].Rows.Add(newrow);
}
int rowCountCT = DsPrint.Tables["TableCT"].DefaultView.Count;
GridSearch.InsertSubRow("HDA", "TableCT");
////Thêm số dòng cho đủ ngầm định
//if (rowCountCT < so_dong_in)
//{
// for (int k = rowCountCT; k < so_dong_in; k++)
// {
// DataRow row = DsPrint.Tables["TableCT"].NewRow();
// row["stt_rec"] = stt_rec;
// row["stt_rec0"] = "999";
// row["tag"] = 1;
// DsPrint.Tables["TableCT"].Rows.Add(row);
// }
//}
DsPrint.Tables["TableCT"].DefaultView.RowFilter = "stt_rec= '" + stt_rec + "'";
DsPrint.Tables["TableCT"].DefaultView.Sort = "stt_rec0";
GridSearch.DSource = DsPrint;
}
public void GridsearchConstructorTest()
{
Accord.Math.Tools.SetupGenerator(0);
// Example binary data
double[][] inputs =
{
new double[] { -1, -1 },
new double[] { -1, 1 },
new double[] { 1, -1 },
new double[] { 1, 1 }
};
int[] xor = // xor labels
{
-1, 1, 1, -1
};
// Declare the parameters and ranges to be searched
GridSearchRange[] ranges =
{
new GridSearchRange("complexity", new double[] { 0.00000001, 5.20, 0.30, 0.50 } ),
new GridSearchRange("degree", new double[] { 1, 10, 2, 3, 4, 5 } ),
new GridSearchRange("constant", new double[] { 0, 1, 2 } )
};
// Instantiate a new Grid Search algorithm for Kernel Support Vector Machines
var gridsearch = new GridSearch<KernelSupportVectorMachine>(ranges);
// Set the fitting function for the algorithm
gridsearch.Fitting = delegate(GridSearchParameterCollection parameters, out double error)
{
// The parameters to be tried will be passed as a function parameter.
int degree = (int)parameters["degree"].Value;
double constant = parameters["constant"].Value;
double complexity = parameters["complexity"].Value;
// Use the parameters to build the SVM model
Polynomial kernel = new Polynomial(degree, constant);
KernelSupportVectorMachine ksvm = new KernelSupportVectorMachine(kernel, 2);
// Create a new learning algorithm for SVMs
SequentialMinimalOptimization smo = new SequentialMinimalOptimization(ksvm, inputs, xor);
smo.Complexity = complexity;
// Measure the model performance to return as an out parameter
error = smo.Run();
return ksvm; // Return the current model
};
// Declare some out variables to pass to the grid search algorithm
GridSearchParameterCollection bestParameters; double minError;
// Compute the grid search to find the best Support Vector Machine
KernelSupportVectorMachine bestModel = gridsearch.Compute(out bestParameters, out minError);
// A linear kernel can't solve the xor problem.
Assert.AreNotEqual((int)bestParameters["degree"].Value, 1);
// The minimum error should be zero because the problem is well-known.
Assert.AreEqual(minError, 0.0);
Assert.IsNotNull(bestModel);
Assert.IsNotNull(bestParameters);
Assert.AreEqual(bestParameters.Count, 3);
}
public void Dijkstra()
{
var path = GridSearch.Dijkstra(_grid, _startCell.GetPosition(), _endCell.GetPosition());
StartCoroutine(ShowPath(path));
}
