/// <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(); }