/// <summary>
/// Initialises a LogisticRegressionGenerator object
/// </summary>
public LogisticRegressionGenerator()
{
Lambda = 1;
MaxIterations = 500;
PolynomialFeatures = 0;
LearningRate = 0.3;
LogisticFunction = new Logistic();
NormalizeFeatures = true;
}
private Layer ConvertLogistic(tflite.Operator op)
{
var inputs = op.GetInputsArray();
var input = _graph.Tensors(inputs[0]).Value;
var layer = new Logistic(input.GetShapeArray().ToNCHW());
_inputs.Add(layer.Input, inputs[0]);
_outputs.Add(op.Outputs(0), layer.Output);
return(layer);
}
public IHttpActionResult GetLogistic(string id)
{
Logistic logistic = db.Logistics.Find(id);
if (logistic == null)
{
return(NotFound());
}
return(Ok(logistic));
}
public async Task <IActionResult> Create([Bind("Id,Verlengkabels,Voltage110,Comments")] Logistic logistic)
{
if (ModelState.IsValid)
{
_context.Add(logistic);
await _context.SaveChangesAsync();
return(RedirectToAction(nameof(Index)));
}
return(View(logistic));
}
string type = String.Empty; // 对象类型
protected void Page_Load(object sender, EventArgs e)
{
op = RequestData.Get <string>("op");
id = RequestData.Get <string>("id");
OtherPayBill ent = null;
switch (RequestActionString)
{
case "update":
ent = OtherPayBill.Find(id);
string nowPayAmount = RequestData.Get <string>("NowPayAmount");
if (!string.IsNullOrEmpty(nowPayAmount))
{
ent.AcctualPayAmount = (ent.AcctualPayAmount.HasValue ? ent.AcctualPayAmount : 0) + Convert.ToDecimal(nowPayAmount);
if (ent.AcctualPayAmount == ent.ShouldPayAmount)
{
ent.PayState = "已付款";
ent.PayTime = System.DateTime.Now;
ent.PayUserId = UserInfo.UserID;
ent.PayUserName = UserInfo.Name;
}
ent.DoUpdate();
}
if (ent.PayType == "物流付款" && ent.PayState == "已付款")
{
string[] temparray = ent.InterfaceArray.Split(new string[] { "," }, StringSplitOptions.RemoveEmptyEntries);
for (int i = 0; i < temparray.Length; i++)
{
Logistic lEnt = Logistic.Find(temparray[i]);
lEnt.PayState = "已付款";
lEnt.DoUpdate();
}
}
break;
default:
break;
}
if (op != "c" && op != "cs")
{
if (!String.IsNullOrEmpty(id))
{
ent = OtherPayBill.Find(id);
SetFormData(ent);
PageState.Add("PayType", ent.PayType);
if (ent.PayType == "物流付款")
{
string[] array = ent.InterfaceArray.Split(new string[] { "," }, StringSplitOptions.RemoveEmptyEntries);
PageState.Add("DataList", Logistic.FindAllByPrimaryKeys(array));
}
}
}
}
public async Task <IHttpActionResult> GetLogistic(int id)
{
Logistic logistic = await db.Logistics.FindAsync(id);
if (logistic == null)
{
return(NotFound());
}
await db.Entry(logistic).Collection(log => log.LogisticInfos).LoadAsync();
return(Ok(logistic));
}
void ITrainer.Fit(Func <bool> HasCtrlBreak)
{
for (var k = 0; k < POSITIVES; k++)
{
var Yes = GetRandomSample();
var y = Model[Yes.Item2];
if (y != null)
{
const float POSITIVE = 1.0f;
Logistic.BinaryLogistic(y.GetVector(), Yes.Item1, POSITIVE, lr, null);
for (var h = 0; h < NEGATIVES; h++)
{
var No = GetRandomSample();
while (No != null && string.Equals(No.Item2, Yes.Item2))
{
No = GetRandomSample();
}
if (No != null)
{
const float NEGATIVE = 0.0f;
Logistic.BinaryLogistic(y.GetVector(), No.Item1, NEGATIVE, lr, null);
}
}
}
}
double pct = 0,
cc = 0d;
foreach (var y in Model)
{
foreach (var s in Samples)
{
var σ = Logistic.BinaryLogistic(y, s.Item1);
if (y.Id.Equals(s.Item2))
{
if (σ >= 0.5)
{
pct++;
}
}
else
{
if (σ < 0.5)
{
pct++;
}
}
cc++;
}
}
_accuracy = Math.Round((pct / cc) * 100);
}
public IActionResult Put(int id, [FromBody] Logistic logistic)
{
if (logistic != null)
{
using (var scope = new TransactionScope())
{
_logisticRepository.UpdateLogistic(logistic);
scope.Complete();
return(new OkResult());
}
}
return(new NoContentResult());
}
public void EditLogistic(string name, string description, decimal amount, int quantity, int programId, int logisticId, int eventId)
{
// FUNCTION: Edit a new logistic
// PRE-CONDITIONS:
// POST-CONDITIONS:
Logistic logistic = server.GetEvent(eventId).EditLogistic(logisticId);
logistic.name = name;
logistic.description = description;
logistic.amount = amount;
logistic.quantity = quantity;
logistic.programId = programId;
}
public IHttpActionResult DeleteLogistic(string id)
{
Logistic logistic = db.Logistics.Find(id);
if (logistic == null)
{
return(NotFound());
}
db.Logistics.Remove(logistic);
db.SaveChanges();
return(Ok(logistic));
}
//Редактиране на данни
public bool Update(Logistic logistic)
{
bool isSuccess = false;
SqlConnection conn = new SqlConnection(myconnstrng);
try
{
String sql = "UPDATE table_logistic SET employee=@employee, first_name_employee=@first_name_employee, last_name_employee=@last_name_employee, address=@address, contact=@contact, date=@date, description=@description, price=@price, added_date=@added_date, added_by=@added_by, added_by_name=@added_by_name WHERE id=@id";
SqlCommand cmd = new SqlCommand(sql, conn);
cmd.Parameters.AddWithValue("@employee", logistic.Empleyee);
cmd.Parameters.AddWithValue("@first_name_employee", logistic.FirstNameEmployee);
cmd.Parameters.AddWithValue("@last_name_employee", logistic.LastNameEmployee);
cmd.Parameters.AddWithValue("@address", logistic.Address);
cmd.Parameters.AddWithValue("@contact", logistic.Contact);
cmd.Parameters.AddWithValue("@date", logistic.Date);
cmd.Parameters.AddWithValue("@description", logistic.Description);
cmd.Parameters.AddWithValue("@price", logistic.Price);
cmd.Parameters.AddWithValue("@added_date", logistic.AddedDate);
cmd.Parameters.AddWithValue("@added_by", logistic.AddedBy);
cmd.Parameters.AddWithValue("@added_by_name", logistic.AddedByName);
cmd.Parameters.AddWithValue("@id", logistic.Id);
conn.Open();
int rows = cmd.ExecuteNonQuery();
if (rows > 0)
{
isSuccess = true;
}
else
{
isSuccess = false;
}
}
catch (Exception ex)
{
MessageBox.Show(ex.Message);
}
finally
{
conn.Close();
}
return(isSuccess);
}
public static void logistic_cdf_values_test()
//****************************************************************************80
//
// Purpose:
//
// LOGISTIC_CDF_VALUES_TEST tests LOGISTIC_CDF_VALUES.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 13 June 2007
//
// Author:
//
// John Burkardt
//
{
double beta = 0;
double fx = 0;
double mu = 0;
int n_data;
double x = 0;
Console.WriteLine("");
Console.WriteLine("LOGISTIC_CDF_VALUES_TEST:");
Console.WriteLine(" LOGISTIC_CDF_VALUES returns values of ");
Console.WriteLine(" the Logistic Cumulative Density Function.");
Console.WriteLine("");
Console.WriteLine(" Mu Beta X CDF(X)");
Console.WriteLine("");
n_data = 0;
for (;;)
{
Logistic.logistic_cdf_values(ref n_data, ref mu, ref beta, ref x, ref fx);
if (n_data == 0)
{
break;
}
Console.WriteLine(" "
+ mu.ToString(CultureInfo.InvariantCulture).PadLeft(8) + " "
+ beta.ToString(CultureInfo.InvariantCulture).PadLeft(8) + " "
+ x.ToString(CultureInfo.InvariantCulture).PadLeft(8) + " "
+ fx.ToString("0.################").PadLeft(24) + "");
}
}
//Добавяне
public bool Insert(Logistic logistic)
{
bool isSuccess = false;
SqlConnection conn = new SqlConnection(myconnstrng);
try
{
String sql = "INSERT INTO table_logistic_archive (employee, first_name_employee, last_name_employee, address, contact, date, description, price, added_date, added_by, added_by_name) VALUES (@employee, @first_name_employee, @last_name_employee, @address, @contact, @date, @description, @price, @added_date, @added_by, @added_by_name)";
SqlCommand cmd = new SqlCommand(sql, conn);
cmd.Parameters.AddWithValue("@employee", logistic.Empleyee);
cmd.Parameters.AddWithValue("@first_name_employee", logistic.FirstNameEmployee);
cmd.Parameters.AddWithValue("@last_name_employee", logistic.LastNameEmployee);
cmd.Parameters.AddWithValue("@address", logistic.Address);
cmd.Parameters.AddWithValue("@contact", logistic.Contact);
cmd.Parameters.AddWithValue("@date", logistic.Date);
cmd.Parameters.AddWithValue("@description", logistic.Description);
cmd.Parameters.AddWithValue("@price", logistic.Price);
cmd.Parameters.AddWithValue("@added_date", logistic.AddedDate);
cmd.Parameters.AddWithValue("@added_by", logistic.AddedBy);
cmd.Parameters.AddWithValue("@added_by_name", logistic.AddedByName);
conn.Open();
int rows = cmd.ExecuteNonQuery();
if (rows > 0)
{
isSuccess = true;
}
else
{
isSuccess = false;
}
}
catch (Exception ex)
{
MessageBox.Show(ex.Message);
}
finally
{
conn.Close();
}
return(isSuccess);
}
public int AddLogistic(string name, string description, decimal amount, int quantity, int programId, int eventId)
{
// FUNCTION: Add a new logistic
// PRE-CONDITIONS:
// POST-CONDITIONS:
Logistic logistic = new Logistic();
logistic.name = name;
logistic.description = description;
logistic.amount = amount;
logistic.quantity = quantity;
logistic.programId = programId;
server.GetEvent(eventId).AddLogistic(logistic);
return(logistic.logisticId);
}
public void SingleInput_WeightOne()
{
var connList = new List <WeightedDirectedConnection <double> >();
connList.Add(new WeightedDirectedConnection <double>(0, 1, 1.0));
// Create graph.
var digraph = WeightedDirectedGraphAcyclicBuilder <double> .Create(connList, 1, 1);
// Create neural net and run tests.
var actFn = new Logistic();
var net = new NeuralNetAcyclic(digraph, actFn.Fn);
SingleInput_WeightOne_Inner(net, actFn);
// Create vectorized neural net and run tests.
var vnet = new NeuralNet.Double.Vectorized.NeuralNetAcyclic(digraph, actFn.Fn);
SingleInput_WeightOne_Inner(vnet, actFn);
}
public async Task <IHttpActionResult> AddLogisticInfo(int LogisticID, [FromBody] LogisticInfo logisticInfo)
{
if (!ModelState.IsValid)
{
return(BadRequest(ModelState));
}
Logistic logistic = await db.Logistics.FindAsync(LogisticID);
if (logistic == null)
{
return(NotFound());
}
logisticInfo.LogisticID = logistic.LogisticID;
db.LogisticInfoes.Add(logisticInfo);
await db.SaveChangesAsync();
var httpResp = Request.CreateResponse(HttpStatusCode.NoContent);
return(ResponseMessage(httpResp));
}
string type = String.Empty; // 对象类型
protected void Page_Load(object sender, EventArgs e)
{
op = RequestData.Get <string>("op");
id = RequestData.Get <string>("id");
OtherPayBill ent = null;
if (op != "c" && op != "cs")
{
if (!String.IsNullOrEmpty(id))
{
ent = OtherPayBill.Find(id);
PageState.Add("PayType", ent.PayType);
SetFormData(ent);
if (ent.PayType == "物流付款")
{
string[] array = ent.InterfaceArray.Split(new string[] { "," }, StringSplitOptions.RemoveEmptyEntries);
PageState.Add("DataList", Logistic.FindAllByPrimaryKeys(array));
}
}
}
}
/// <summary>
/// 查询
/// </summary>
private void DoSelect()
{
if (!SearchCriterion.Orders.Exists(en => en.PropertyName == "CreateTime"))
{
SearchCriterion.Orders.Add(new OrderCriterionItem("CreateTime", false));
}
string paystate = RequestData.Get <string>("paystate");
if (paystate == "1")
{
SearchCriterion.AddSearch("PayState", "1");
ents = Logistic.FindAll(SearchCriterion);
}
else
{
ents = Logistic.FindAll(SearchCriterion, Expression.Sql(" isnull(PayState,0) <> '1' "));
}
this.PageState.Add("LogisticList", ents);
}
public void SingleInput_WeightZero()
{
var connList = new List <WeightedDirectedConnection <double> >
{
new WeightedDirectedConnection <double>(0, 1, 0.0)
};
// Create graph.
var digraph = WeightedDirectedGraphBuilder <double> .Create(connList, 1, 1);
// Create neural net and run tests.
var actFn = new Logistic();
var net = new NeuralNetCyclic(digraph, actFn.Fn, 2);
SingleInput_WeightZero_Inner(net);
// Create vectorized neural net and run tests.
var vnet = new NeuralNets.Double.Vectorized.NeuralNetCyclic(digraph, actFn.Fn, 2);
SingleInput_WeightZero_Inner(vnet);
}
public void TwoInputs_WeightHalf()
{
var connList = new List <WeightedDirectedConnection <double> >
{
new WeightedDirectedConnection <double>(0, 2, 0.5),
new WeightedDirectedConnection <double>(1, 2, 0.5)
};
// Create graph.
var digraph = WeightedDirectedGraphAcyclicBuilder <double> .Create(connList, 2, 1);
// Create neural net and run tests.
var actFn = new Logistic();
var net = new NeuralNetAcyclic(digraph, actFn.Fn);
TwoInputs_WeightHalf_Inner(net, actFn);
// Create vectorized neural net and run tests.
var vnet = new NeuralNet.Double.Vectorized.NeuralNetAcyclic(digraph, actFn.Fn);
TwoInputs_WeightHalf_Inner(vnet, actFn);
}
/// <summary>
/// Compute the error gradient of the given Theta parameter for the training and label sets
/// </summary>
/// <param name="theta">Learning Theta parameters</param>
/// <param name="X">Training set</param>
/// <param name="y">Training labels</param>
/// <param name="lambda">Regularisation constant</param>
/// <param name="regularizer">Regularization term function.</param>
/// <returns></returns>
public Vector ComputeGradient(Vector theta, Matrix X, Vector y, double lambda, IRegularizer regularizer)
{
int m = X.Rows;
Vector gradient = Vector.Zeros(theta.Length);
Vector s = (X * theta).ToVector();
IFunction function = new Logistic();
s = s.Each(v => function.Compute(v));
for (int i = 0; i < theta.Length; i++)
{
gradient[i] = (1.0 / m) * ((s - y) * X[i, VectorType.Col]).Sum();
}
if (lambda != 0)
{
gradient = regularizer.Regularize(theta, gradient, m, lambda);
}
return gradient;
}
/// <summary>
/// Compute the error cost of the given Theta parameter for the training and label sets
/// </summary>
/// <param name="theta">Learning Theta parameters</param>
/// <param name="X">Training set</param>
/// <param name="y">Training labels</param>
/// <param name="lambda">Regularization constant</param>
/// <param name="regularizer">Regularization term function.</param>
/// <returns></returns>
public double ComputeCost(Vector theta, Matrix X, Vector y, double lambda, IRegularizer regularizer)
{
int m = X.Rows;
double j = 0.0;
Vector s = (X * theta).ToVector();
IFunction function = new Logistic();
s = s.Each(v => function.Compute(v));
Vector slog = s.Copy().Each(v => System.Math.Log(System.Math.Abs(1.0 - v)));
j = (-1.0 / m) * ( (y.Dot(s.Log())) + (-1.0 * ((1.0 - y).Dot(slog))) );
if (lambda != 0)
{
j = regularizer.Regularize(j, theta, m, lambda);
}
return j;
}
public void CyclicOutput()
{
var connList = new List <WeightedDirectedConnection <double> >
{
new WeightedDirectedConnection <double>(0, 1, 1.0),
new WeightedDirectedConnection <double>(1, 1, 1.0)
};
// Create graph.
var digraph = WeightedDirectedGraphBuilder <double> .Create(connList, 1, 1);
// Create neural net and run tests.
var actFn = new Logistic();
var net = new NeuralNetCyclic(digraph, actFn.Fn, 1);
CyclicOutput_Inner(net, actFn);
// Create vectorized neural net and run tests.
var vnet = new NeuralNet.Double.Vectorized.NeuralNetCyclic(digraph, actFn.Fn, 1);
CyclicOutput_Inner(vnet, actFn);
}
//Изтриване на данни
public bool Delete(Logistic logistic)
{
bool isSuccess = false;
SqlConnection conn = new SqlConnection(myconnstrng);
try
{
String sql = "DELETE FROM table_logistic WHERE id=@id";
SqlCommand cmd = new SqlCommand(sql, conn);
cmd.Parameters.AddWithValue("@id", logistic.Id);
conn.Open();
int rows = cmd.ExecuteNonQuery();
if (rows > 0)
{
isSuccess = true;
}
else
{
isSuccess = false;
}
}
catch (Exception ex)
{
MessageBox.Show(ex.Message);
}
finally
{
conn.Close();
}
return(isSuccess);
}
public void SingleInput_WeightOne()
{
var connList = new List <WeightedDirectedConnection <double> >
{
new WeightedDirectedConnection <double>(0, 1, 1.0)
};
// Create graph.
var digraph = WeightedDirectedGraphBuilder <double> .Create(connList, 1, 1);
// Create neural net
var actFn = new Logistic();
var net = new CyclicNeuralNet(digraph, actFn.Fn, 1, false);
// Activate and test.
net.InputVector[0] = 0.0;
for (int i = 0; i < 10; i++)
{
net.Activate();
Assert.AreEqual(0.5, net.OutputVector[0]);
}
// Activate and test.
net.InputVector[0] = 1.0;
for (int i = 0; i < 10; i++)
{
net.Activate();
Assert.AreEqual(actFn.Fn(1), net.OutputVector[0]);
}
// Activate and test.
net.InputVector[0] = 10.0;
for (int i = 0; i < 10; i++)
{
net.Activate();
Assert.AreEqual(actFn.Fn(10), net.OutputVector[0]);
}
}
public void HiddenNode()
{
var connList = new List <WeightedDirectedConnection <double> >
{
new WeightedDirectedConnection <double>(0, 3, 0.5),
new WeightedDirectedConnection <double>(1, 3, 0.5),
new WeightedDirectedConnection <double>(3, 2, 2.0)
};
// Create graph.
var digraph = WeightedDirectedGraphAcyclicBuilder <double> .Create(connList, 2, 1);
// Create neural net and run tests.
var actFn = new Logistic();
var net = new NeuralNetAcyclic(digraph, actFn.Fn);
HiddenNode_Inner(net, actFn);
// Create vectorized neural net and run tests.
var vnet = new NeuralNets.Double.Vectorized.NeuralNetAcyclic(digraph, actFn.Fn);
HiddenNode_Inner(vnet, actFn);
}
public void MultipleInputsOutputs()
{
var connList = new List <WeightedDirectedConnection <double> >
{
new WeightedDirectedConnection <double>(0, 5, 1.0),
new WeightedDirectedConnection <double>(1, 3, 1.0),
new WeightedDirectedConnection <double>(2, 4, 1.0)
};
// Create graph.
var digraph = WeightedDirectedGraphAcyclicBuilder <double> .Create(connList, 3, 3);
// Create neural net and run tests.
var actFn = new Logistic();
var net = new NeuralNetAcyclic(digraph, actFn.Fn);
MultipleInputsOutputs_Inner(net, actFn);
// Create vectorized neural net and run tests.
var vnet = new NeuralNet.Double.Vectorized.NeuralNetAcyclic(digraph, actFn.Fn);
MultipleInputsOutputs_Inner(vnet, actFn);
}
/// <summary>
/// Compute the error gradient of the given Theta parameter for the training and label sets
/// </summary>
/// <param name="theta">Learning Theta parameters</param>
/// <param name="X">Training set</param>
/// <param name="y">Training labels</param>
/// <param name="lambda">Regularisation constant</param>
/// <param name="regularizer">Regularization term function.</param>
/// <returns></returns>
public Vector ComputeGradient(Vector theta, Matrix X, Vector y, double lambda, IRegularizer regularizer)
{
var m = X.Rows;
var gradient = Vector.Zeros(theta.Length);
var s = (X * theta).ToVector();
IFunction function = new Logistic();
s = s.Each(v => function.Compute(v));
for (var i = 0; i < theta.Length; i++)
{
gradient[i] = (1.0 / m) * ((s - y) * X[i, VectorType.Col]).Sum();
}
if (lambda != 0)
{
gradient = regularizer.Regularize(theta, gradient, m, lambda);
}
return(gradient);
}
/// <summary>
/// Compute the error cost of the given Theta parameter for the training and label sets
/// </summary>
/// <param name="theta">Learning Theta parameters</param>
/// <param name="X">Training set</param>
/// <param name="y">Training labels</param>
/// <param name="lambda">Regularization constant</param>
/// <param name="regularizer">Regularization term function.</param>
/// <returns></returns>
public double ComputeCost(Vector theta, Matrix X, Vector y, double lambda, IRegularizer regularizer)
{
var m = X.Rows;
var j = 0.0;
var s = (X * theta).ToVector();
IFunction function = new Logistic();
s = s.Each(v => function.Compute(v));
var slog = s.Copy().Each(v => Math.Log(Math.Abs(1.0 - v)));
j = (-1.0 / m) * (y.Dot(s.Log()) + (-1.0 * (1.0 - y).Dot(slog)));
if (lambda != 0)
{
j = regularizer.Regularize(j, theta, m, lambda);
}
return(j);
}
protected void Page_Load(object sender, EventArgs e)
{
if (RequestActionString == "batchdelete")
{
DoBatchDelete();
}
else if (RequestActionString == "duiying")
{
string did = RequestData.Get <string>("DId");
string lid = RequestData.Get <string>("LId");
Logistic ent = Logistic.TryFind(lid);
if (ent != null)
{
ent.DeliveryId = did;
ent.DoUpdate();
}
}
else
{
DoSelect();
}
}
public void ComplexCyclic()
{
var connList = new List <WeightedDirectedConnection <double> >
{
new WeightedDirectedConnection <double>(0, 1, -2.0),
new WeightedDirectedConnection <double>(0, 2, 1.0),
new WeightedDirectedConnection <double>(1, 2, 1.0),
new WeightedDirectedConnection <double>(2, 1, 1.0)
};
// Create graph.
var digraph = WeightedDirectedGraphBuilder <double> .Create(connList, 1, 1);
// Create neural net
var actFn = new Logistic();
var net = new CyclicNeuralNet(digraph, actFn.Fn, 1, false);
// Simulate network in C# and compare calculated outputs with actual network outputs.
double[] preArr = new double[3];
double[] postArr = new double[3];
postArr[0] = 3.0;
net.InputVector[0] = 3.0;
for (int i = 0; i < 10; i++)
{
preArr[1] = postArr[0] * -2.0 + postArr[2];
preArr[2] = postArr[0] + postArr[1];
postArr[1] = actFn.Fn(preArr[1]);
postArr[2] = actFn.Fn(preArr[2]);
net.Activate();
Assert.AreEqual(postArr[1], net.OutputVector[0]);
}
}
string id = String.Empty; // 对象id
protected void Page_Load(object sender, EventArgs e)
{
id = RequestData.Get <string>("id");
Logistic ent = Logistic.Find(id);
SetFormData(ent);
DataTable tbl = new DataTable();
DataColumn col = new DataColumn("Code");
col.DataType = typeof(string);
tbl.Columns.Add(col);
col = new DataColumn("Name");
col.DataType = typeof(string);
tbl.Columns.Add(col);
col = new DataColumn("Unit");
col.DataType = typeof(string);
tbl.Columns.Add(col);
col = new DataColumn("OutCount");
col.DataType = typeof(string);
tbl.Columns.Add(col);
col = new DataColumn("Remark");
col.DataType = typeof(string);
tbl.Columns.Add(col);
JArray jsonarray = JsonHelper.GetObject <JArray>(ent.Child);
foreach (JObject json in jsonarray)
{
DataRow dr = tbl.NewRow();
dr["Code"] = json.Value <string>("Code");
dr["Name"] = json.Value <string>("Name");
dr["OutCount"] = json.Value <string>("OutCount");
dr["Unit"] = json.Value <string>("Unit");
dr["Remark"] = json.Value <string>("Remark");
tbl.Rows.Add(dr);
}
PageState.Add("DataList", tbl);
}
/// <summary>
/// Initializes a new LogisticCostFunction with the default sigmoid logistic function.
/// </summary>
public LogisticCostFunction()
{
if (LogisticFunction == null)
LogisticFunction = new Logistic();
}
