1

DeviceLibrary/DeviceLibrary.csproj

@@ -94,6 +94,7 @@
     <Compile Include="deviceLibrary\IO\kangnaide\MasterTcpClient.cs" />
     <Compile Include="deviceLibrary\IO\AIOBOX\AIOBOXManager.cs" />
     <Compile Include="deviceLibrary\IO\IOBase.cs" />
+    <Compile Include="deviceLibrary\IO\NanjingSDotIO.cs" />
     <Compile Include="manager\LedManager.cs" />
     <Compile Include="manager\LineWidthManager.cs" />
     <Compile Include="manager\ResourceControl.cs" />

DeviceLibrary/bean/ProjectorPInfo.cs

@@ -16,6 +16,7 @@ namespace TSA_V.DeviceLibrary
             this.SizeX = sizeX;
             this.SizeY = sizeY;
             this.PenWidth = lineWidth;
+            this.ShowText = showText;
 
         }
         public int PX = 2;

DeviceLibrary/deviceLibrary/IO/IOBase.cs

@@ -21,11 +21,15 @@ namespace TSA_V.DeviceLibrary
 
         public static void Init()
         {
-            bool isAIOBox = ConfigAppSettings.GetIntValue(Setting_Init.UseAIOBOX).Equals(1);
-            if (isAIOBox)
+            int isAIOBox = ConfigAppSettings.GetIntValue(Setting_Init.UseAIOBOX);
+            if (isAIOBox==1)
             {
                 instance = new AIOBOXManager();
             }
+            else if (isAIOBox == 2)
+            {
+                instance = new NanjingSDotIO();
+            }
             else
             {
                 instance = new KNDManager();

DeviceLibrary/deviceLibrary/IO/NanjingSDotIO.cs

+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Net.Sockets;
+using System.Text;
+using System.Threading;
+using System.Threading.Tasks;
+using TSA_V.DeviceLibrary;
+
+class NanjingSDotIO : IOBase
+{
+    byte station = 1;
+    public string IP;
+    TcpClient tcpClient = new TcpClient();
+    Thread iomonitorThread;
+    ushort seq = 0;
+    /// <summary>
+    /// 
+    /// </summary>
+    /// <param name="changindex">变化的io列表</param>
+    /// <param name="sta">所有IO状态</param>
+    public delegate void IO_Changed(List<int> changindex, bool[] sta);
+    public event IO_Changed DI_Changed_Event;
+    public event IO_Changed DO_Changed_Event;
+    /// <summary>
+    /// 连接状态变化, 手动连接不触发
+    /// </summary>
+    public event EventHandler<bool> ConnectionState_Event;
+    public NanjingSDotIO()
+    {
+        DIStartAddress = 0;
+        DoStartAddress = 0;
+    }
+    ~NanjingSDotIO()
+    {
+        iomonitorrun = false;
+        Close();
+    }
+    bool systemrun = false;
+    /// <summary>
+    /// 打开IO
+    /// </summary>
+    /// <returns></returns>
+    public bool Open()
+    {
+
+        tcpClient.Dispose();
+        tcpClient = new TcpClient();
+        tcpClient.ReceiveTimeout = 50;
+        tcpClient.SendTimeout = 50;
+        lock (tcpClient)
+        {
+            try
+            {
+                var connectResult = tcpClient.ConnectAsync(IP, 502);
+                if (connectResult.Wait(1000))
+                {
+                    systemrun = true;
+                    iomonitorrun = true;
+                    iomonitorThread = new Thread(new ThreadStart(iomonitor));
+                    iomonitorThread.Start();
+                    ConnectionState_Event?.Invoke(this, true);
+                    return true;
+                }
+                else
+                {
+
+                    tcpClient.EndConnect(connectResult);
+                    tcpClient.Close();
+                    return false;
+                }
+            }
+            catch
+            {
+                return false;
+            }
+        }
+
+    }
+    /// <summary>
+    /// 关闭IO
+    /// </summary>
+    public void Close()
+    {
+        try
+        {
+            systemrun = false;
+            iomonitorrun = false;
+            if (tcpClient.Connected)
+                tcpClient.Close();
+        }
+        catch { }
+    }
+    /// <summary>
+    /// 连接状态
+    /// </summary>
+    public bool IsConn
+    {
+        get => iomonitorrun && systemrun;
+    }
+
+    bool iomonitorrun = false;
+    /// <summary>
+    /// 循环读全部IO
+    /// </summary>
+    void iomonitor()
+    {
+        iomonitorrun = true;
+        while (iomonitorrun && systemrun)
+        {
+            Thread.Sleep(2);
+            try
+            {
+                ReadDO();
+            }
+            catch (SocketException)
+            {
+                if (tcpClient!=null && tcpClient.Connected)
+                    tcpClient.Close();
+                ConnectionState_Event?.Invoke(this, false);
+                iomonitorrun = false;
+            }
+        }
+        if (systemrun)
+        {
+            do
+            {
+                Thread.Sleep(1000);
+            } while (systemrun && !Open());
+        }
+    }
+
+    /// <summary>
+    /// 写io
+    /// </summary>
+    /// <param name="donum">地址</param>
+    /// <param name="state">On/Off</param>
+    /// <returns></returns>
+    public bool WriteDO(int donum, bool state)
+    {
+        byte funCode = 0x0F;//批量写圈
+        byte startAddress = (byte)(64 + donum);//写圈起始地址
+        byte startLength = 1;
+        var seqhead = BitConverter.GetBytes(seq);
+        byte iostate = (byte)(state ? 1 : 0);
+        byte[] by = new byte[]
+        {
+                //事物标识符
+                seqhead[0],
+                seqhead[1],
+                //协议标识符 固定值
+                0x00,
+                0x00,
+                //长度
+                0x00,
+                0x06,
+                //从站地址
+                station,
+                //功能码
+                funCode,
+                0x00,
+                startAddress, //起始地址
+                0x00,
+                startLength, //读个数
+                0x01,
+                iostate
+        };
+        by[5] = (byte)(by.Length - 6);
+        bool check = true;
+        lock (tcpClient)
+        {
+            try
+            {
+                seqadd();
+                tcpClient.Client.Send(by);
+                byte[] result = new byte[100];
+                var ulength = tcpClient.Client.Receive(result);
+                var newResult = result.ToList().Take(ulength).ToArray();
+                if (newResult[0] == seqhead[0] && newResult[1] == seqhead[1])
+                {
+                    check = true;
+                }
+                else
+                {
+                    return false;
+                }
+            }
+            catch
+            {
+                return false;
+            }
+        }
+
+
+        /*
+        var newResult = result.ToList().Take(ulength).ToArray();
+        //输出报文
+        var output = string.Join(" ", newResult.Select(x => x.ToString("X2")));
+
+        */
+        return check;
+    }
+    /// <summary>
+    /// 所有DI状态
+    /// </summary>
+    public bool[] DIstate = new bool[64];
+    /// <summary>
+    /// 所有DO状态
+    /// </summary>
+    public bool[] DOstate = new bool[64];
+    bool[] lastDIstate = new bool[64];
+    bool[] lastDOstate = new bool[64];
+    /// <summary>
+    /// 读全部IO
+    /// </summary>
+    void ReadDO()
+    {
+        byte funCode = 0x01;//批量写圈
+        byte startAddress = 0;//写圈起始地址
+        byte startLength = 128;
+        var seqhead = BitConverter.GetBytes(seq);
+        byte[] by = new byte[]
+        {
+                //事物标识符
+                seqhead[0],
+                seqhead[1],
+                //协议标识符 固定值
+                0x00,
+                0x00,
+                //长度
+                0x00,
+                0x06,
+                //从站地址
+                station,
+                //功能码
+                funCode,
+                0x00,
+                startAddress, //起始地址
+                0x00,
+                startLength, //读个数
+        };
+        by[5] = (byte)(by.Length - 6);
+        byte[] result = new byte[100];
+        int ulength = 0;
+
+        lock (tcpClient)
+        {
+            try
+            {
+                seqadd();
+                tcpClient.Client.Send(by);
+                ulength = tcpClient.Client.Receive(result);
+
+            }
+            catch (SocketException se)
+            {
+                throw se;
+            }
+        }
+        var newResult = result;//.ToList().Take(ulength).ToArray();
+                               //aa = BitConverter.ToString(newResult);
+                               //Console.WriteLine(aa);
+        if (newResult[0] == seqhead[0] && newResult[1] == seqhead[1])
+        {
+            int diIndex = 9;
+            int doIndex = 17;
+
+            for (int i = 0; i < 32; i++)
+            {
+                int a = (int)Math.Floor(i / 8d);
+                int b = a * 2;
+                DIstate[i + 8 * a] = Convert.ToBoolean(newResult[diIndex + b] >> i & 0x01);
+                DIstate[i + 8 + 8 * a] = Convert.ToBoolean(newResult[diIndex + 1 + b] >> i & 0x01);
+                DOstate[i + 8 * a] = Convert.ToBoolean(newResult[doIndex + b] >> i & 0x01);
+                DOstate[i + 8 + 8 * a] = Convert.ToBoolean(newResult[doIndex + 1 + b] >> i & 0x01);
+            }
+            for (int i = 0; i < 64; i++)
+            {
+                List<int> dichgindex = new List<int>();
+                List<int> dochgindex = new List<int>();
+                if (lastDIstate[i] != DIstate[i])
+                {
+                    dichgindex.Add(i);
+                    lastDIstate[i] = DIstate[i];
+                }
+                if (lastDOstate[i] != DOstate[i])
+                {
+                    dochgindex.Add(i);
+                    lastDOstate[i] = DOstate[i];
+                }
+                if (dichgindex.Count > 0)
+                    DI_Changed_Event?.BeginInvoke(dichgindex, DIstate, null, null);
+                if (dochgindex.Count > 0)
+                    DO_Changed_Event?.BeginInvoke(dichgindex, DOstate, null, null);
+            }
+        }
+        else
+        {
+
+        }
+        //输出报文
+        //var output = string.Join(" ", newResult.Select(x => x.ToString("X2")));
+
+    }
+
+    void seqadd()
+    {
+        seq++;
+        if (seq >= ushort.MaxValue - 10)
+            seq = 0;
+    }
+
+    public override void ConnectionKND(List<string> dIODeviceNameList)
+    {
+    }
+
+    public override void ReadAllDI(string deviceName, byte slaveId)
+    {
+        ReadDO();
+    }
+
+    public override void ReadAllDO(string deviceName, byte slaveId)
+    {
+        ReadDO();
+    }
+
+    public override void ConnectionIP(string kNDIP, ushort kNDPort)
+    {
+        this.IP = kNDIP;
+        Open();
+        iomonitorThread = new Thread(new ThreadStart(iomonitor));
+    }
+
+    public override void WriteSingleDO(string deviceName, byte slaveId, ushort index, IO_VALUE value, int time)
+    {
+        WriteDO(index, value == IO_VALUE.HIGH);
+    }
+
+    public override bool IsConnection(string kNDIP)
+    {
+        return IsConn;
+    }
+
+    public override void WriteSingleDO(string deviceName, byte slaveId, ushort index, IO_VALUE value)
+    {
+        WriteDO(index, value == IO_VALUE.HIGH);
+    }
+
+    public override IO_VALUE GetDIValue(string deviceName, byte slaveID, ushort v)
+    {
+        return DIstate[v] ? IO_VALUE.HIGH : IO_VALUE.LOW;
+    }
+
+    public override IO_VALUE GetDOValue(string deviceName, byte slaveID, ushort v)
+    {
+        return DOstate[v] ? IO_VALUE.HIGH : IO_VALUE.LOW;
+    }
+
+    public override IO_VALUE GetIOValue(ConfigIO configIO)
+    {
+        if (configIO.ProType.Equals(ConfigItemType.DI))
+        {
+            return DIstate[configIO.IOIndex] ? IO_VALUE.HIGH : IO_VALUE.LOW;
+        }
+        else {
+            return DOstate[configIO.IOIndex] ? IO_VALUE.HIGH : IO_VALUE.LOW;
+        }
+
+            
+    }
+
+    public override void CloseAllDO()
+    {
+        for (int i = 0; i < 32; i++)
+        {
+            WriteDO(i, false);
+        }
+    }
+
+    public override void CloseAllConnection()
+    {
+        Close();
+    }
+}

DeviceLibrary/manager/LineWidthManager.cs

@@ -95,6 +95,8 @@ namespace TSA_V.DeviceLibrary
                 while (true)
                 {
                     Thread.Sleep(200);
+                    if (!IOManager.ShuddenOK())
+                        break;
                     if (IsStop)
                     { 
                         result = ResourceControl.GetString(ResourceControl.UserStop, "用户中止");

DeviceLibrary/manager/TSAVBean.cs

@@ -281,6 +281,8 @@ namespace TSA_V.DeviceLibrary
                 {
                     foreach (NodeInfo node in map.Values)
                     {
+                        if (!IOManager.ShuddenOK())
+                            return;
                         if (!PUSICANControl.IsHomeEnd(node.NodeId))
                         {
                             timeOutMsg = ResourceControl.GetString(ResourceControl.RNodeGoHome, " 旋转轴[{0}]原点返回完成", node.NodeId);

DeviceLibrary/manager/TSAVStatus.cs

@@ -179,7 +179,10 @@ namespace TSA_V.DeviceLibrary
 
         public static bool GetWaitResult(List<WaitResultInfo> waitList, TimeSpan span,bool oneOkCanEnd, out string waitMsg)
         {
+
             waitMsg = "";
+            if (!IOManager.ShuddenOK())
+                return false;
             try
             {
                 if (waitList.Count <= 0)

DeviceLibrary/manager/WorkInfo.cs

@@ -265,7 +265,7 @@ namespace TSA_V.DeviceLibrary
             if (TSAVBean.IsValidPosition(currPoint.NodePositionX, currPoint.NodePositionY))
             {
                 //LogUtil.info(" 程序【" + currBoard.boardName + "】插件【" + currPoint.pointName + "】，X轴【" + currPoint.NodePositionX + "】，Y轴【" + currPoint.NodePositionY + "】");
-                 TSAVBean.ShowPoint(currPoint.NodePositionX, currPoint.NodePositionY,currPoint.PointType,currPoint.PointSizeX,currPoint.PointSizeY,currPoint.PenWidth,currPoint.PN);
+                 TSAVBean.ShowPoint(currPoint.NodePositionX, currPoint.NodePositionY,currPoint.PointType,currPoint.PointSizeX,currPoint.PointSizeY,currPoint.PenWidth,currPoint.ShowText);
                 waitList.Add(WaitResultInfo.WaitTime(1000)); 
             }
             else

PUSICANLibrary/PUSICANControl.cs

@@ -853,24 +853,31 @@ namespace PUSICANLibrary
         /// </summary> 
         public static bool IsHomeEnd(uint nodeId)
         {
-            //判断忙碌状态=0并且外部停止2=1
-            int value = 0;
-            //获取状态
-            if (PUSIResult.RET_SUCCESS == PUSICANControl.ReadSDO(nodeId, CAN_Address.ControlStatus, out value))
+            try
             {
-                string busyStr = Convert.ToString(value, 2).PadLeft(5, '0').Substring(1, 1);
-                string stop2Str = Convert.ToString(value, 2).PadLeft(5, '0').Substring(3, 1);
-                string stop1Str = Convert.ToString(value, 2).PadLeft(5, '0').Substring(4, 1);
-                if (busyStr.Equals("0") && (stop2Str.Equals("1") || stop1Str.Equals("1")))
+                //判断忙碌状态=0并且外部停止2=1
+                int value = 0;
+                //获取状态
+                if (PUSIResult.RET_SUCCESS == PUSICANControl.ReadSDO(nodeId, CAN_Address.ControlStatus, out value))
                 {
-                    return true ;
+                    string busyStr = Convert.ToString(value, 2).PadLeft(5, '0').Substring(1, 1);
+                    string stop2Str = Convert.ToString(value, 2).PadLeft(5, '0').Substring(3, 1);
+                    string stop1Str = Convert.ToString(value, 2).PadLeft(5, '0').Substring(4, 1);
+                    if (busyStr.Equals("0") && (stop2Str.Equals("1") || stop1Str.Equals("1")))
+                    {
+                        return true;
+                    }
+                    else
+                    {
+                        return false;
+                    }
                 }
-                else
-                {
-                    return false ;
-                } 
-            }  
-            return false ;
+                return false;
+            }
+            catch (Exception e) {
+                LogUtil.error("IsHomeEnd:" + e.ToString());
+            }
+            return false;
         }
         public  static void DefatutPosMove(uint node, int targetDefaultPosition, int movetype = 0,bool isLineSlv=false )
         {

PUSICANLibrary/PUSICANLibrary.csproj

@@ -18,11 +18,11 @@
     <DebugType>full</DebugType>
     <Optimize>false</Optimize>
     <OutputPath>bin\Debug\</OutputPath>
-    <DefineConstants>TRACE;DEBUG;x86</DefineConstants>
+    <DefineConstants>TRACE;DEBUG;x64</DefineConstants>
     <ErrorReport>prompt</ErrorReport>
     <WarningLevel>4</WarningLevel>
     <Prefer32Bit>false</Prefer32Bit>
-    <PlatformTarget>x86</PlatformTarget>
+    <PlatformTarget>AnyCPU</PlatformTarget>
   </PropertyGroup>
   <PropertyGroup Condition=" '$(Configuration)|$(Platform)' == 'Release|AnyCPU' ">
     <DebugType>pdbonly</DebugType>

TSA-V/AOIManager.cs

@@ -21,7 +21,8 @@ namespace TSA_V
 
                 //cmbAoiFile.Items.Clear();
                 FileList = new List<string>();
-
+                if (cmbAoiFile!=null)
+                    FileList.Add(ResourceCulture.GetString("不使用AOI"));
                 string appPath = Application.StartupPath;
                 string pathName = ConfigAppSettings.GetValue(Setting_Init.AOIFileConfig);
                 string filePath = appPath + pathName;
@@ -47,7 +48,7 @@ namespace TSA_V
                 if (String.IsNullOrEmpty(DefaultName).Equals(false)&& FileList.Contains(DefaultName))
                 {
                     FileList.Remove(DefaultName);
-                    FileList.Insert(0, DefaultName);
+                    FileList.Insert(1, DefaultName);
                 }
                 if (cmbAoiFile != null)
                 {

TSA-V/App.config

@@ -109,11 +109,11 @@
     <!--组装工作前，需要输入pcb板条码-->
     <add key="NeedPCBCode" value="0" />
     <!--是否调整气缸顺序，配置1时，侧挡气缸前进，底部气缸在上升-->
-    <add key ="SideCylinderMoveFirst" value ="1"/>
+    <add key="SideCylinderMoveFirst" value="1" />
     <!--是否启用工作区信号触发开始工作-->
-    <add key ="WorkSingleStart" value ="0"/>
+    <add key="WorkSingleStart" value="0" />
     <!--点位描述显示类型-->
-    <add key ="PointDisplayType" value="1"/>
+    <add key="PointDisplayType" value="1" />
   </appSettings>
   <log4net>
     <appender name="defaultAppender" type="log4net.Appender.RollingFileAppender">
@@ -146,7 +146,7 @@
     </logger>
   </log4net>
   <startup>
-    <supportedRuntime version="v4.0" sku=".NETFramework,Version=v4.6.1" />
+    <supportedRuntime version="v4.0" sku=".NETFramework,Version=v4.7.2" />
   </startup>
   <runtime>
     <assemblyBinding xmlns="urn:schemas-microsoft-com:asm.v1">
@@ -478,4 +478,4 @@
       </dependentAssembly>
     </assemblyBinding>
   </runtime>
-</configuration>
\ No newline at end of file
+</configuration>

TSA-V/FrmBoardInfo.Designer.cs

@@ -318,10 +318,8 @@
             // 
             this.groupBox2.Anchor = ((System.Windows.Forms.AnchorStyles)(((System.Windows.Forms.AnchorStyles.Top | System.Windows.Forms.AnchorStyles.Left) 
             | System.Windows.Forms.AnchorStyles.Right)));
-            this.groupBox2.Controls.Add(this.lblLineW);
+            this.groupBox2.Controls.Add(this.btnConfigAOI);
             this.groupBox2.Controls.Add(this.label8);
-            this.groupBox2.Controls.Add(this.txtLineWidth);
-            this.groupBox2.Controls.Add(this.lbllinews);
             this.groupBox2.Controls.Add(this.label122);
             this.groupBox2.Controls.Add(this.btnUpdate);
             this.groupBox2.Controls.Add(this.label9);
@@ -344,9 +342,9 @@
             // lblLineW
             // 
             this.lblLineW.Font = new System.Drawing.Font("微软雅黑", 9F, System.Drawing.FontStyle.Regular, System.Drawing.GraphicsUnit.Point, ((byte)(134)));
-            this.lblLineW.Location = new System.Drawing.Point(489, 25);
+            this.lblLineW.Location = new System.Drawing.Point(367, 63);
             this.lblLineW.Name = "lblLineW";
-            this.lblLineW.Size = new System.Drawing.Size(80, 20);
+            this.lblLineW.Size = new System.Drawing.Size(112, 20);
             this.lblLineW.TabIndex = 297;
             this.lblLineW.Text = "线体宽度：";
             this.lblLineW.TextAlign = System.Drawing.ContentAlignment.MiddleRight;
@@ -365,7 +363,7 @@
             // txtLineWidth
             // 
             this.txtLineWidth.Font = new System.Drawing.Font("微软雅黑", 12F, System.Drawing.FontStyle.Regular, System.Drawing.GraphicsUnit.Point, ((byte)(134)));
-            this.txtLineWidth.Location = new System.Drawing.Point(573, 21);
+            this.txtLineWidth.Location = new System.Drawing.Point(483, 59);
             this.txtLineWidth.MaxLength = 8;
             this.txtLineWidth.Name = "txtLineWidth";
             this.txtLineWidth.Size = new System.Drawing.Size(70, 29);
@@ -375,7 +373,7 @@
             // 
             this.lbllinews.AutoSize = true;
             this.lbllinews.Font = new System.Drawing.Font("微软雅黑", 9F, System.Drawing.FontStyle.Regular, System.Drawing.GraphicsUnit.Point, ((byte)(134)));
-            this.lbllinews.Location = new System.Drawing.Point(646, 27);
+            this.lbllinews.Location = new System.Drawing.Point(556, 65);
             this.lbllinews.Name = "lbllinews";
             this.lbllinews.Size = new System.Drawing.Size(30, 17);
             this.lbllinews.TabIndex = 296;
@@ -881,10 +879,12 @@
             // 
             // groupBox1
             // 
-            this.groupBox1.Controls.Add(this.btnConfigAOI);
             this.groupBox1.Controls.Add(this.lblAoi);
+            this.groupBox1.Controls.Add(this.lblLineW);
             this.groupBox1.Controls.Add(this.cmbAOIFile);
             this.groupBox1.Controls.Add(this.txtCode);
+            this.groupBox1.Controls.Add(this.txtLineWidth);
+            this.groupBox1.Controls.Add(this.lbllinews);
             this.groupBox1.Controls.Add(this.txtBoardName);
             this.groupBox1.Controls.Add(this.label14);
             this.groupBox1.Controls.Add(this.label2);
@@ -900,7 +900,7 @@
             // 
             this.btnConfigAOI.FlatStyle = System.Windows.Forms.FlatStyle.Flat;
             this.btnConfigAOI.Font = new System.Drawing.Font("微软雅黑", 10.5F, System.Drawing.FontStyle.Regular, System.Drawing.GraphicsUnit.Point, ((byte)(134)));
-            this.btnConfigAOI.Location = new System.Drawing.Point(454, 58);
+            this.btnConfigAOI.Location = new System.Drawing.Point(-47, 15);
             this.btnConfigAOI.Name = "btnConfigAOI";
             this.btnConfigAOI.Size = new System.Drawing.Size(100, 30);
             this.btnConfigAOI.TabIndex = 284;

TSA-V/FrmBoardInfo.cs

@@ -554,8 +554,10 @@ namespace TSA_V
             board.LineWidth = FormUtil.GetIntValue(txtLineWidth); 
             board.boardCode = FormUtil.getValue(txtCode); 
             board.orgType = orgType;
-
-            board.AOIProName = cmbAOIFile.Text;
+            if (cmbAOIFile.SelectedIndex == 0)            
+                board.AOIProName = "";            
+            else
+                board.AOIProName = cmbAOIFile.Text;
             if (board.boardName.Equals(""))
             {
                 MessageBox.Show(ResourceCulture.GetString(ResourceCulture.WritePName, "请输入程序名称"));
@@ -1030,7 +1032,7 @@ namespace TSA_V
         {
             if (e.Button == MouseButtons.Right)
             {
-                if (e.RowIndex >= 0)
+                if (e.RowIndex >= 0 && e.ColumnIndex>=0)
                 {
                     this.dgvList.ClearSelection();
                     dgvList.Rows[e.RowIndex].Selected = true;

TSA-V/FrmBoardList.cs

@@ -323,7 +323,7 @@ namespace TSA_V
                 }
                 if (BoardManager.getBoardByName(board.boardName) != null)
                 {
-                    MessageBox.Show(ResourceCulture.GetString("导入失败：程序名已存在"), ResourceCulture.GetString("提示"), MessageBoxButtons.OK, MessageBoxIcon.Error);
+                    MessageBox.Show(ResourceCulture.GetString("导入失败：程序名已存在")+"["+ board.boardName + "]", ResourceCulture.GetString("提示"), MessageBoxButtons.OK, MessageBoxIcon.Error);
                     return;
                 }
                 BoardManager.ImportBoard(board);

TSA-V/FrmMenu.Designer.cs

@@ -95,7 +95,7 @@ namespace TSA_V
             this.btnWork.FlatStyle = System.Windows.Forms.FlatStyle.Flat;
             this.btnWork.Font = new System.Drawing.Font("微软雅黑", 21.75F, System.Drawing.FontStyle.Regular, System.Drawing.GraphicsUnit.Point, ((byte)(134)));
             this.btnWork.ForeColor = System.Drawing.SystemColors.ButtonHighlight;
-            this.btnWork.Location = new System.Drawing.Point(87, 107);
+            this.btnWork.Location = new System.Drawing.Point(119, 116);
             this.btnWork.Name = "btnWork";
             this.btnWork.Size = new System.Drawing.Size(261, 312);
             this.btnWork.TabIndex = 0;
@@ -159,7 +159,7 @@ namespace TSA_V
             this.btnHistory.FlatStyle = System.Windows.Forms.FlatStyle.Flat;
             this.btnHistory.Font = new System.Drawing.Font("微软雅黑", 15F, System.Drawing.FontStyle.Regular, System.Drawing.GraphicsUnit.Point, ((byte)(134)));
             this.btnHistory.ForeColor = System.Drawing.Color.White;
-            this.btnHistory.Location = new System.Drawing.Point(354, 319);
+            this.btnHistory.Location = new System.Drawing.Point(386, 328);
             this.btnHistory.Name = "btnHistory";
             this.btnHistory.Size = new System.Drawing.Size(180, 100);
             this.btnHistory.TabIndex = 20;
@@ -254,7 +254,7 @@ namespace TSA_V
             this.btnReplenish.FlatStyle = System.Windows.Forms.FlatStyle.Flat;
             this.btnReplenish.Font = new System.Drawing.Font("微软雅黑", 12F, System.Drawing.FontStyle.Regular, System.Drawing.GraphicsUnit.Point, ((byte)(134)));
             this.btnReplenish.ForeColor = System.Drawing.Color.White;
-            this.btnReplenish.Location = new System.Drawing.Point(540, 213);
+            this.btnReplenish.Location = new System.Drawing.Point(572, 222);
             this.btnReplenish.Name = "btnReplenish";
             this.btnReplenish.Size = new System.Drawing.Size(180, 100);
             this.btnReplenish.TabIndex = 8;
@@ -270,7 +270,7 @@ namespace TSA_V
             this.btnMaintenance.FlatStyle = System.Windows.Forms.FlatStyle.Flat;
             this.btnMaintenance.Font = new System.Drawing.Font("微软雅黑", 12F, System.Drawing.FontStyle.Regular, System.Drawing.GraphicsUnit.Point, ((byte)(134)));
             this.btnMaintenance.ForeColor = System.Drawing.Color.White;
-            this.btnMaintenance.Location = new System.Drawing.Point(540, 107);
+            this.btnMaintenance.Location = new System.Drawing.Point(572, 116);
             this.btnMaintenance.Name = "btnMaintenance";
             this.btnMaintenance.Size = new System.Drawing.Size(180, 100);
             this.btnMaintenance.TabIndex = 8;
@@ -286,7 +286,7 @@ namespace TSA_V
             this.btnCom.FlatStyle = System.Windows.Forms.FlatStyle.Flat;
             this.btnCom.Font = new System.Drawing.Font("微软雅黑", 12F, System.Drawing.FontStyle.Regular, System.Drawing.GraphicsUnit.Point, ((byte)(134)));
             this.btnCom.ForeColor = System.Drawing.Color.White;
-            this.btnCom.Location = new System.Drawing.Point(540, 319);
+            this.btnCom.Location = new System.Drawing.Point(572, 328);
             this.btnCom.Name = "btnCom";
             this.btnCom.Size = new System.Drawing.Size(180, 100);
             this.btnCom.TabIndex = 5;
@@ -302,7 +302,7 @@ namespace TSA_V
             this.btnAOI.FlatStyle = System.Windows.Forms.FlatStyle.Flat;
             this.btnAOI.Font = new System.Drawing.Font("微软雅黑", 15F, System.Drawing.FontStyle.Regular, System.Drawing.GraphicsUnit.Point, ((byte)(134)));
             this.btnAOI.ForeColor = System.Drawing.Color.White;
-            this.btnAOI.Location = new System.Drawing.Point(354, 213);
+            this.btnAOI.Location = new System.Drawing.Point(386, 222);
             this.btnAOI.Name = "btnAOI";
             this.btnAOI.Size = new System.Drawing.Size(180, 100);
             this.btnAOI.TabIndex = 1;
@@ -318,7 +318,7 @@ namespace TSA_V
             this.btnProduct.FlatStyle = System.Windows.Forms.FlatStyle.Flat;
             this.btnProduct.Font = new System.Drawing.Font("微软雅黑", 15F, System.Drawing.FontStyle.Regular, System.Drawing.GraphicsUnit.Point, ((byte)(134)));
             this.btnProduct.ForeColor = System.Drawing.Color.White;
-            this.btnProduct.Location = new System.Drawing.Point(354, 107);
+            this.btnProduct.Location = new System.Drawing.Point(386, 116);
             this.btnProduct.Name = "btnProduct";
             this.btnProduct.Size = new System.Drawing.Size(180, 100);
             this.btnProduct.TabIndex = 1;

TSA-V/FrmMenu.cs

@@ -74,10 +74,10 @@ namespace TSA_V
             //    MessageBox.Show(ResourceCulture.GetString( "清先等待预热结束"));
             //    return;
             //}
-            isClick = true;  
+            isClick = true;
 
             FrmBoardSelect fw = new FrmBoardSelect();
-            this.Visible = false; ;
+            this.Visible = false;
             fw.ShowDialog();
             this.Visible = true;
         }

TSA-V/Properties/Resource.en-US.resx

@@ -2542,4 +2542,10 @@
   <data name="DeviceInGohome" xml:space="preserve">
     <value>11:34Backing to origin.</value>
   </data>
+  <data name="不使用AOI" xml:space="preserve">
+    <value>Do not use AOI</value>
+  </data>
+  <data name="加载AOI失败." xml:space="preserve">
+    <value>Load AOI profile fail.</value>
+  </data>
 </root>
\ No newline at end of file

TSA-V/Properties/Settings.Designer.cs

@@ -12,7 +12,7 @@ namespace TSA_V.Properties {
     
     
     [global::System.Runtime.CompilerServices.CompilerGeneratedAttribute()]
-    [global::System.CodeDom.Compiler.GeneratedCodeAttribute("Microsoft.VisualStudio.Editors.SettingsDesigner.SettingsSingleFileGenerator", "15.6.0.0")]
+    [global::System.CodeDom.Compiler.GeneratedCodeAttribute("Microsoft.VisualStudio.Editors.SettingsDesigner.SettingsSingleFileGenerator", "16.10.0.0")]
     internal sealed partial class Settings : global::System.Configuration.ApplicationSettingsBase {
         
         private static Settings defaultInstance = ((Settings)(global::System.Configuration.ApplicationSettingsBase.Synchronized(new Settings())));

TSA-V/Smart-Workstation.csproj

 <?xml version="1.0" encoding="utf-8"?>
 <Project ToolsVersion="12.0" DefaultTargets="Build" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
-  <Import Project="..\packages\Microsoft.ML.0.11.0\build\netstandard2.0\Microsoft.ML.props" Condition="Exists('..\packages\Microsoft.ML.0.11.0\build\netstandard2.0\Microsoft.ML.props')" />
   <Import Project="..\packages\Microsoft.ML.CpuMath.0.11.0\build\netstandard2.0\Microsoft.ML.CpuMath.props" Condition="Exists('..\packages\Microsoft.ML.CpuMath.0.11.0\build\netstandard2.0\Microsoft.ML.CpuMath.props')" />
   <Import Project="$(MSBuildExtensionsPath)\$(MSBuildToolsVersion)\Microsoft.Common.props" Condition="Exists('$(MSBuildExtensionsPath)\$(MSBuildToolsVersion)\Microsoft.Common.props')" />
   <PropertyGroup>
@@ -11,14 +10,14 @@
     <AppDesignerFolder>Properties</AppDesignerFolder>
     <RootNamespace>TSA_V</RootNamespace>
     <AssemblyName>Neo Station</AssemblyName>
-    <TargetFrameworkVersion>v4.6.1</TargetFrameworkVersion>
+    <TargetFrameworkVersion>v4.7.2</TargetFrameworkVersion>
     <FileAlignment>512</FileAlignment>
     <TargetFrameworkProfile />
     <NuGetPackageImportStamp>
     </NuGetPackageImportStamp>
   </PropertyGroup>
   <PropertyGroup Condition=" '$(Configuration)|$(Platform)' == 'Debug|AnyCPU' ">
-    <PlatformTarget>x86</PlatformTarget>
+    <PlatformTarget>AnyCPU</PlatformTarget>
     <DebugSymbols>true</DebugSymbols>
     <DebugType>full</DebugType>
     <Optimize>false</Optimize>
@@ -629,14 +628,12 @@
       <ErrorText>这台计算机上缺少此项目引用的 NuGet 程序包。使用“NuGet 程序包还原”可下载这些程序包。有关更多信息，请参见 http://go.microsoft.com/fwlink/?LinkID=322105。缺少的文件是 {0}。</ErrorText>
     </PropertyGroup>
     <Error Condition="!Exists('..\packages\Microsoft.ML.CpuMath.0.11.0\build\netstandard2.0\Microsoft.ML.CpuMath.props')" Text="$([System.String]::Format('$(ErrorText)', '..\packages\Microsoft.ML.CpuMath.0.11.0\build\netstandard2.0\Microsoft.ML.CpuMath.props'))" />
-    <Error Condition="!Exists('..\packages\Microsoft.ML.0.11.0\build\netstandard2.0\Microsoft.ML.props')" Text="$([System.String]::Format('$(ErrorText)', '..\packages\Microsoft.ML.0.11.0\build\netstandard2.0\Microsoft.ML.props'))" />
-    <Error Condition="!Exists('..\packages\Microsoft.ML.0.11.0\build\netstandard2.0\Microsoft.ML.targets')" Text="$([System.String]::Format('$(ErrorText)', '..\packages\Microsoft.ML.0.11.0\build\netstandard2.0\Microsoft.ML.targets'))" />
     <Error Condition="!Exists('..\packages\Stub.System.Data.SQLite.Core.NetFramework.1.0.113.3\build\net46\Stub.System.Data.SQLite.Core.NetFramework.targets')" Text="$([System.String]::Format('$(ErrorText)', '..\packages\Stub.System.Data.SQLite.Core.NetFramework.1.0.113.3\build\net46\Stub.System.Data.SQLite.Core.NetFramework.targets'))" />
   </Target>
-  <Import Project="..\packages\Microsoft.ML.0.11.0\build\netstandard2.0\Microsoft.ML.targets" Condition="Exists('..\packages\Microsoft.ML.0.11.0\build\netstandard2.0\Microsoft.ML.targets')" />
   <Import Project="..\packages\Stub.System.Data.SQLite.Core.NetFramework.1.0.113.3\build\net46\Stub.System.Data.SQLite.Core.NetFramework.targets" Condition="Exists('..\packages\Stub.System.Data.SQLite.Core.NetFramework.1.0.113.3\build\net46\Stub.System.Data.SQLite.Core.NetFramework.targets')" />
   <PropertyGroup>
-    <PostBuildEvent>"C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\VC\Tools\MSVC\14.29.30133\bin\Hostx64\x86\editbin" /largeaddressaware "$(TargetPath)"</PostBuildEvent>
+    <PostBuildEvent>echo "C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\VC\Tools\MSVC\14.29.30133\bin\Hostx64\x86\editbin" /largeaddressaware "$(TargetPath)"
+start $(TargetDir)</PostBuildEvent>
   </PropertyGroup>
   <!-- To modify your build process, add your task inside one of the targets below and uncomment it. 
        Other similar extension points exist, see Microsoft.Common.targets.

TSA-V/packages.config

 <?xml version="1.0" encoding="utf-8"?>
 <packages>
   <package id="Microsoft.Data.DataView" version="0.11.0" targetFramework="net461" />
-  <package id="Microsoft.ML" version="0.11.0" targetFramework="net461" />
   <package id="Microsoft.ML.CpuMath" version="0.11.0" targetFramework="net461" />
   <package id="Newtonsoft.Json" version="12.0.1" targetFramework="net461" />
   <package id="Stub.System.Data.SQLite.Core.NetFramework" version="1.0.113.3" targetFramework="net461" />

TSA-V/projector/FrmProjectorScreen.cs

@@ -35,7 +35,7 @@ namespace TSA_V
             this.Text = "显示测试_" + ScreenIndex;
             this.StartPosition = FormStartPosition.Manual; 
         }
-        public bool  CloseShowName=true;
+        public bool  CloseShowName=false;
         public bool ShowForm()
         {
             Screen[] sc = Screen.AllScreens;
@@ -280,6 +280,7 @@ namespace TSA_V
             if (!name.Equals(""))
             {
                 int size = (sizeX + sizeY) / 2;
+                size = 30;
                 if (!CloseShowName)
                 {
                     g.DrawString(name, new Font("Arial ", size, FontStyle.Regular), Brushes.White, x - size + 5, y + size + 2);

TSA-V/workForm/FrmBoardSelect.cs

@@ -315,11 +315,11 @@ namespace TSA_V
                 chbOffLine.Checked = false;
                 chbOffLine.Visible = false;
             }
-            //else
-            //{
-            //    chbWorkSingleStart.Visible = true;
-            //    chbOffLine.Visible = true;
-            //}
+            else
+            {
+                chbWorkSingleStart.Visible = true;
+                chbOffLine.Visible = true;
+            }
         }
 
         private void chbOffLine_CheckedChanged(object sender, EventArgs e)

TSA-V/workForm/FrmTSAV.cs

@@ -67,14 +67,17 @@ namespace TSA_V
             else
             {
                 LogUtil.info("配置程序之后才能工作！");
+                
                 this.Close();
             }
-            if (TSAVBean.IsNeedAOI)
+            if (TSAVBean.IsNeedAOI && !string.IsNullOrEmpty(board.AOIProName))
             {
                 LoadAoi();
                 if (CurrProject == null)
                 {
-                    this.Close();
+                    LogUtil.info("没有读取到AOI,结束,IsNeedAOI=true！");
+                    MessageBox.Show(ResourceCulture.GetString("加载AOI失败."));
+                    //this.Close();
                 }
             }
             if (!IsSet)
@@ -526,14 +529,15 @@ namespace TSA_V
                 lblPartNum.Text = smtPoint.TagNo;
                 lblPointName.Text = smtPoint.PN;
                 ComponetInfo com = CSVBomManager.GetCom(BoardManager.CurrBoard.bomName, smtPoint);
-                TSAVPosition position = CSVPositionReader<TSAVPosition>.GetPositonByNum(com.PositionNum);
-
-                if (position != null)
-                {
-                    lblPositionNum.Text = position.PositionNum;
-                }
                 if (com != null)
                 {
+                    TSAVPosition position = CSVPositionReader<TSAVPosition>.GetPositonByNum(com.PositionNum);
+
+                    if (position != null)
+                    {
+                        lblPositionNum.Text = position.PositionNum;
+                    }
+
                     lblComDes.Text = com.ComponentDes;
                     this.lblComName.Text = com.PN;
                     lblCount.Text = com.ComCount.ToString();

packages/Microsoft.ML.0.11.0/LICENSE

-MIT License
-
-Copyright (c) 2018 .NET Foundation
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

packages/Microsoft.ML.0.11.0/THIRD-PARTY-NOTICES.TXT

-ML.NET uses third-party libraries or other resources that may be
-distributed under licenses different than the ML.NET software.
-
-In the event that we accidentally failed to list a required notice, please
-bring it to our attention. Post an issue or email us:
-
-           dotnet@microsoft.com
-
-The attached notices are provided for information only.
-
-License notice for LIBMF
-------------------------
-
-https://github.com/cjlin1/libmf
-
-Copyright (c) 2014-2015 The LIBMF Project.
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions
-are met:
-
-1. Redistributions of source code must retain the above copyright
-notice, this list of conditions and the following disclaimer.
-
-2. Redistributions in binary form must reproduce the above copyright
-notice, this list of conditions and the following disclaimer in the
-documentation and/or other materials provided with the distribution.
-
-3. Neither name of copyright holders nor the names of its contributors
-may be used to endorse or promote products derived from this software
-without specific prior written permission.
-
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
-A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE REGENTS OR
-CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
-EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
-PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
-PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
-LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
-NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file

packages/Microsoft.ML.0.11.0/build/netstandard2.0/Microsoft.ML.props

-<Project xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
-
-  <!--
-  NuGet packages.config doesn't support native assemblies automatically,
-  so copy the native assemblies to the output directory.
-  -->
-  <ItemGroup Condition="Exists('packages.config') OR
-                        Exists('$(MSBuildProjectName).packages.config') OR
-                        Exists('packages.$(MSBuildProjectName).config')">
-    <Content Include="$(MSBuildThisFileDirectory)\..\..\runtimes\win-x64\native\*.dll"
-             Condition="'$(PlatformTarget)' == 'x64'">
-      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
-      <Visible>false</Visible>
-      <Link>%(Filename)%(Extension)</Link>
-    </Content>
-    <Content Include="$(MSBuildThisFileDirectory)\..\..\runtimes\win-x86\native\*.dll"
-             Condition="'$(PlatformTarget)' == 'x86'">
-      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
-      <Visible>false</Visible>
-      <Link>%(Filename)%(Extension)</Link>
-    </Content>
-  </ItemGroup>
-
-</Project>
\ No newline at end of file

packages/Microsoft.ML.0.11.0/build/netstandard2.0/Microsoft.ML.targets

-<Project xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
-
-  <PropertyGroup>
-    <EnableMLUnsupportedPlatformTargetCheck Condition="'$(EnableMLUnsupportedPlatformTargetCheck)' == ''">true</EnableMLUnsupportedPlatformTargetCheck>
-  </PropertyGroup>
-
-  <Target Name="_CheckForUnsupportedPlatformTarget"
-          Condition="'$(EnableMLUnsupportedPlatformTargetCheck)' == 'true'"
-          AfterTargets="_CheckForInvalidConfigurationAndPlatform">
-
-    <!--
-    Special case .NET Core portable applications.  When building a portable .NET Core app,
-    the PlatformTarget is empty, and you don't know until runtime (i.e. which dotnet.exe)
-    what processor architecture will be used.
-    -->
-    <Error Condition="('$(PlatformTarget)' != 'x64' AND '$(PlatformTarget)' != 'x86') AND
-                      ('$(OutputType)' == 'Exe' OR '$(OutputType)'=='WinExe') AND
-                      !('$(TargetFrameworkIdentifier)' == '.NETCoreApp' AND '$(PlatformTarget)' == '')"
-           Text="Microsoft.ML currently supports 'x64' and 'x86' processor architectures. Please ensure your application is targeting 'x64' or 'x86'." />
-  </Target>
-  
-</Project>
\ No newline at end of file

packages/Microsoft.ML.0.11.0/lib/netstandard2.0/Microsoft.ML.KMeansClustering.xml

-<?xml version="1.0"?>
-<doc>
-    <assembly>
-        <name>Microsoft.ML.KMeansClustering</name>
-    </assembly>
-    <members>
-        <member name="T:Microsoft.ML.KMeansClusteringExtensions">
-            <summary>
-            The trainer context extensions for the <see cref="T:Microsoft.ML.Trainers.KMeansPlusPlusTrainer"/>.
-            </summary>
-        </member>
-        <member name="M:Microsoft.ML.KMeansClusteringExtensions.KMeans(Microsoft.ML.ClusteringCatalog.ClusteringTrainers,System.String,System.String,System.Int32)">
-            <summary>
-            Train a KMeans++ clustering algorithm.
-            </summary>
-            <param name="catalog">The clustering catalog trainer object.</param>
-            <param name="featureColumnName">The name of the feature column.</param>
-            <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
-            <param name="clustersCount">The number of clusters to use for KMeans.</param>
-            <example>
-            <format type="text/markdown">
-            <![CDATA[
-             [!code-csharp[KMeans](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/KMeans.cs)]
-            ]]></format>
-            </example>
-        </member>
-        <member name="M:Microsoft.ML.KMeansClusteringExtensions.KMeans(Microsoft.ML.ClusteringCatalog.ClusteringTrainers,Microsoft.ML.Trainers.KMeansPlusPlusTrainer.Options)">
-            <summary>
-            Train a KMeans++ clustering algorithm.
-            </summary>
-            <param name="catalog">The clustering catalog trainer object.</param>
-            <param name="options">Algorithm advanced options.</param>
-        </member>
-        <member name="T:Microsoft.ML.Trainers.KMeansModelParameters">
-            <example>
-            <format type="text/markdown">
-            <![CDATA[
-             [!code-csharp[KMeans](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/KMeans.cs)]
-            ]]></format>
-            </example>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.KMeansModelParameters.GetVersionInfo">
-            <summary>
-            Version information to be saved in binary format
-            </summary>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.KMeansModelParameters.#ctor(Microsoft.ML.IHostEnvironment,System.Int32,Microsoft.ML.Data.VBuffer{System.Single}[],System.Boolean)">
-            <summary>
-            Initialize predictor with a trained model.
-            </summary>
-            <param name="env">The host environment</param>
-            <param name="k">Number of centroids</param>
-            <param name="centroids">Coordinates of the centroids</param>
-            <param name="copyIn">If true then the <paramref name="centroids"/> vectors will be subject to
-            a deep copy, if false then this constructor will take ownership of the passed in centroid vectors.
-            If false then the caller must take care to not use or modify the input vectors once this object
-            is constructed, and should probably remove all references.</param>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.KMeansModelParameters.#ctor(Microsoft.ML.IHostEnvironment,Microsoft.ML.ModelLoadContext)">
-            <summary>
-            Initialize predictor from a binary file.
-            </summary>
-            <param name="ctx">The load context</param>
-            <param name="env">The host environment</param>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.KMeansModelParameters.SaveCore(Microsoft.ML.ModelSaveContext)">
-            <summary>
-            Save the predictor in binary format.
-            </summary>
-            <param name="ctx">The context to save to</param>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.KMeansModelParameters.Create(Microsoft.ML.IHostEnvironment,Microsoft.ML.ModelLoadContext)">
-            <summary>
-            This method is called by reflection to instantiate a predictor.
-            </summary>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.KMeansModelParameters.InitPredictor">
-            <summary>
-            Initialize internal parameters: L2 norms of the _centroids.
-            </summary>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.KMeansModelParameters.GetClusterCentroids(Microsoft.ML.Data.VBuffer{System.Single}[]@,System.Int32@)">
-            <summary>
-            Copies the centroids to a set of provided buffers.
-            </summary>
-            <param name="centroids">The buffer to which to copy. Will be extended to
-            an appropriate length, if necessary.</param>
-            <param name="k">The number of clusters, corresponding to the logical size of
-            <paramref name="centroids"/>.</param>
-        </member>
-        <member name="T:Microsoft.ML.Trainers.KMeansPlusPlusTrainer">
-            <summary>
-        K-means is a popular clustering algorithm. With K-means, the data is clustered into a specified 
-        number of clusters in order to minimize the within-cluster sum of squares.
-      </summary><remarks>
-        K-means++ improves upon K-means by using the <a href="https://research.microsoft.com/apps/pubs/default.aspx?id=252149">Yinyang K-Means</a> method for choosing the initial cluster centers.
-        YYK-Means accelerates K-Means up to an order of magnitude while producing exactly the same clustering results (modulo floating point precision issues).
-        YYK-Means observes that there is a lot of redundancy across iterations in the KMeans algorithms and most points do not change their clusters during an iteration.
-        It uses various bounding techniques to identify this redundancy and eliminate many distance computations and optimize centroid computations.
-        <para>For more information on K-means, and K-means++ see:</para>
-        <list type="bullet">
-          <item><description><a href="https://en.wikipedia.org/wiki/K-means_clustering">K-means</a></description></item>
-          <item><description><a href="https://en.wikipedia.org/wiki/K-means%2b%2b">K-means++</a></description></item>
-        </list>
-      </remarks>
-        </member>
-        <member name="F:Microsoft.ML.Trainers.KMeansPlusPlusTrainer.Defaults.ClustersCount">
-            <value>The number of clusters.</value>
-        </member>
-        <member name="F:Microsoft.ML.Trainers.KMeansPlusPlusTrainer.Options.ClustersCount">
-            <summary>
-            The number of clusters.
-            </summary>
-        </member>
-        <member name="F:Microsoft.ML.Trainers.KMeansPlusPlusTrainer.Options.InitAlgorithm">
-            <summary>
-            Cluster initialization algorithm.
-            </summary>
-        </member>
-        <member name="F:Microsoft.ML.Trainers.KMeansPlusPlusTrainer.Options.OptimizationTolerance">
-            <summary>
-            Tolerance parameter for trainer convergence. Low = slower, more accurate.
-            </summary>
-        </member>
-        <member name="F:Microsoft.ML.Trainers.KMeansPlusPlusTrainer.Options.MaxIterations">
-            <summary>
-            Maximum number of iterations.
-            </summary>
-        </member>
-        <member name="F:Microsoft.ML.Trainers.KMeansPlusPlusTrainer.Options.AccelerationMemoryBudgetMb">
-            <summary>
-            Memory budget (in MBs) to use for KMeans acceleration.
-            </summary>
-        </member>
-        <member name="F:Microsoft.ML.Trainers.KMeansPlusPlusTrainer.Options.NumThreads">
-            <summary>
-            Degree of lock-free parallelism. Defaults to automatic. Determinism not guaranteed.
-            </summary>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.KMeansPlusPlusTrainer.#ctor(Microsoft.ML.IHostEnvironment,Microsoft.ML.Trainers.KMeansPlusPlusTrainer.Options)">
-            <summary>
-            Initializes a new instance of <see cref="T:Microsoft.ML.Trainers.KMeansPlusPlusTrainer"/>
-            </summary>
-            <param name="env">The <see cref="T:Microsoft.ML.IHostEnvironment"/> to use.</param>
-            <param name="options">The advanced options of the algorithm.</param>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.KMeansPlusPlusInit.Initialize(Microsoft.ML.IHost,System.Int32,Microsoft.ML.IChannel,Microsoft.ML.Trainers.FeatureFloatVectorCursor.Factory,System.Int32,System.Int32,Microsoft.ML.Data.VBuffer{System.Single}[],System.Int64@,System.Int64@,System.Boolean)">
-            <summary>
-            Initialize starting centroids via KMeans++ algorithm. This algorithm will always run single-threaded,
-            regardless of the value of <paramref name="numThreads" />.
-            </summary>
-        </member>
-        <member name="T:Microsoft.ML.Trainers.KMeansAcceleratedRowMap">
-            <summary>
-            An instance of this class is used by SharedStates in YinYangTrainer
-            and KMeansBarBarInitialization. It effectively bounds MaxInstancesToAccelerate and
-            initializes RowIndexGetter.
-            </summary>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.KMeansAcceleratedRowMap.BuildParallelIndexLookup(Microsoft.ML.Trainers.FeatureFloatVectorCursor.Factory)">
-            <summary>
-            Initializes the parallel index lookup HashArray using a sequential RowCursor. We
-            preinitialize the HashArray so we can perform lock-free lookup operations during
-            the primary KMeans pass.
-            </summary>
-        </member>
-        <member name="T:Microsoft.ML.Trainers.KMeansBarBarInitialization.SharedState">
-             <summary>
-             Data for optimizing KMeans|| initialization. Very similar to SharedState class
-             For every instance, there is a space for the best weight and best cluster computed.
-            
-             In this class, new clusters mean the clusters that were added to the cluster set
-             in the previous round of KMeans|| and old clusters are the rest of them (the ones
-             that were added in the rounds before the previous one).
-            
-             In every round of KMeans||, numSamplesPerRound new clusters are added to the set of clusters.
-             There are 'numRounds' number of rounds. We compute and store the distance of each new
-             cluster from every round to all of the previous clusters and use it
-             to avoid unnecessary computation by applying the triangle inequality.
-             </summary>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.KMeansBarBarInitialization.SharedState.SetInstanceCluster(System.Int32,System.Single,System.Int32)">
-            <summary>
-            When assigning an accelerated row to a cluster, we store away the weight
-            to its closest cluster, as well as the identity of the new
-            closest cluster. Note that bestWeight can be negative since it is
-            corresponding to the weight of a distance which does not have
-            the L2 norm of the point itself.
-            </summary>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.KMeansBarBarInitialization.SharedState.SetClusterDistance(System.Int32,Microsoft.ML.Data.VBuffer{System.Single}@,System.Single,System.Int32,Microsoft.ML.Data.VBuffer{System.Single}@,System.Single)">
-            <summary>
-            Computes and stores the distance of a new cluster to an old cluster
-            <paramref name="newClusterFeatures"/> must be between 0..numSamplesPerRound-1.
-            </summary>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.KMeansBarBarInitialization.SharedState.CanWeightComputationBeAvoided(System.Single,System.Int32,System.Int32)">
-            <summary>
-            This function is the key to use triangle inequality. Given an instance x distance to the best
-            old cluster, cOld, and distance of a new cluster, cNew, to cOld, this function evaluates whether
-            the distance computation of dist(x,cNew) can be avoided.
-            </summary>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.KMeansBarBarInitialization.FindBestCluster(Microsoft.ML.Data.VBuffer{System.Single}@,System.Int32,Microsoft.ML.Trainers.KMeansBarBarInitialization.SharedState,System.Int32,System.Int32,Microsoft.ML.Data.VBuffer{System.Single}[],System.Single[],System.Boolean,System.Boolean,System.Single@,System.Int32@)">
-             <summary>
-             This function finds the best cluster and the best weight for an instance using
-             smart triangle inequality to avoid unnecessary weight computations.
-            
-             Note that <paramref name="needToStoreWeight"/> is used to avoid the storing the new cluster in
-             final round. After the final round, best cluster information will be ignored.
-             </summary>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.KMeansBarBarInitialization.ComputeAccelerationMemoryRequirement(System.Int64,System.Int32,System.Int32,System.Boolean,System.Int64@,System.Int64@)">
-            <summary>
-            This method computes the memory requirement for _clusterDistances in SharedState (clusterBytes) and
-            the maximum number of instances whose weight to the closest cluster can be memorized in order to avoid
-            recomputation later.
-            </summary>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.KMeansBarBarInitialization.Initialize(Microsoft.ML.IHost,System.Int32,Microsoft.ML.IChannel,Microsoft.ML.Trainers.FeatureFloatVectorCursor.Factory,System.Int32,System.Int32,Microsoft.ML.Data.VBuffer{System.Single}[],System.Int64,System.Int64@,System.Int64@)">
-             <summary>
-             KMeans|| Implementation, see https://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf
-             This algorithm will require:
-             - (k * overSampleFactor * rounds * diminsionality * 4) bytes for the final sampled clusters.
-             - (k * overSampleFactor * numThreads * diminsionality * 4) bytes for the per-round sampling.
-            
-             Uses memory in initializationState to cache distances and avoids unnecessary distance computations
-             akin to YinYang-KMeans paper.
-            
-             Everywhere in this function, weight of an instance x from a cluster c means weight(x,c) = dist(x,c)^2-norm(x)^2.
-             We store weight in most cases to avoid unnecessary computation of norm(x).
-             </summary>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.KMeansRandomInit.Initialize(Microsoft.ML.IHost,System.Int32,Microsoft.ML.IChannel,Microsoft.ML.Trainers.FeatureFloatVectorCursor.Factory,System.Int32,Microsoft.ML.Data.VBuffer{System.Single}[],System.Int64@,System.Int64@)">
-            <summary>
-            Initialize starting centroids via reservoir sampling.
-            </summary>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.KMeansLloydsYinYangTrain.WorkChunkStateBase.Reduce(Microsoft.ML.Trainers.KMeansLloydsYinYangTrain.WorkChunkState[],Microsoft.ML.Trainers.KMeansLloydsYinYangTrain.ReducedWorkChunkState)">
-            <summary>
-            Reduces the array of work chunks into this chunk, coalescing the
-            results from multiple worker threads partitioned over a parallel cursor set and
-            clearing their values to prepare them for the next iteration.
-            </summary>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.KMeansLloydsYinYangTrain.ReducedWorkChunkState.UpdateClusters(Microsoft.ML.Data.VBuffer{System.Single}[],System.Single[],System.Single[],System.Single@)">
-            <summary>
-            Updates all the passed in variables with the results of the most recent iteration
-            of cluster assignment. It is assumed that centroids will contain the previous results
-            of this call.
-            </summary>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.KMeansLloydsYinYangTrain.SharedState.SetYinYangCluster(System.Int32,Microsoft.ML.Data.VBuffer{System.Single}@,System.Single,System.Int32,System.Single)">
-            <summary>
-            When assigning an accelerated row to a cluster, we store away the distance
-            to its closer and second closed cluster, as well as the identity of the new
-            closest cluster. This method returns the last known closest cluster.
-            </summary>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.KMeansLloydsYinYangTrain.SharedState.UpdateYinYangBounds(System.Int32)">
-            <summary>
-            Updates the known YinYang bounds for the given row using the centroid position
-            deltas from the previous iteration.
-            </summary>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.KMeansLloydsYinYangTrain.SharedState.IsYinYangGloballyBound(System.Int32)">
-            <summary>
-            Determines if the triangle distance inequality still applies to the given row,
-            allowing us to avoid per-cluster distance computation.
-            </summary>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.KMeansLloydsYinYangTrain.ProcessChunk(Microsoft.ML.Trainers.FeatureFloatVectorCursor,Microsoft.ML.Trainers.KMeansLloydsYinYangTrain.SharedState,Microsoft.ML.Trainers.KMeansLloydsYinYangTrain.WorkChunkStateBase,System.Int32,Microsoft.ML.Data.VBuffer{System.Single}[],System.Single[])">
-            <summary>
-            Performs the 'update' step of KMeans. This method is passed a WorkChunkState. In the parallel version
-            this chunk will be one of _numThreads chunks and the RowCursor will be part of a RowCursorSet. In the
-            unthreaded version, this chunk will be the final chunk and hold state for the entire data set.
-            </summary>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.KMeansUtils.ParallelWeightedReservoirSample(Microsoft.ML.IHost,System.Int32,System.Int32,Microsoft.ML.Trainers.FeatureFloatVectorCursor.Factory,Microsoft.ML.Trainers.KMeansUtils.WeightFunc,Microsoft.ML.Trainers.KMeansUtils.RowIndexGetter,Microsoft.ML.Data.VBuffer{System.Single}[]@,Microsoft.ML.Internal.Utilities.Heap{Microsoft.ML.Trainers.KMeansUtils.WeightedPoint}[]@)">
-             <summary>
-             Performs a multithreaded version of weighted reservior sampling, returning
-             an array of numSamples, where each sample has been selected from the
-             data set with a probability of numSamples/N * weight/(sum(weight)). Buffer
-             is sized to the number of threads plus one and stores the minheaps needed to
-             perform the per-thread reservior samples.
-            
-             This method assumes that the numSamples is much smaller than the full dataset as
-             it expects to be able to sample numSamples * numThreads.
-            
-             This is based on the 'A-Res' algorithm in 'Weighted Random Sampling', 2005; Efraimidis, Spirakis:
-             https://utopia.duth.gr/~pefraimi/research/data/2007EncOfAlg.pdf
-             </summary>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.KMeansUtils.ParallelMapReduce``2(System.Int32,Microsoft.ML.IHost,Microsoft.ML.Trainers.FeatureFloatVectorCursor.Factory,Microsoft.ML.Trainers.KMeansUtils.RowIndexGetter,Microsoft.ML.Trainers.KMeansUtils.InitAction{``0},Microsoft.ML.Trainers.KMeansUtils.MapAction{``0},Microsoft.ML.Trainers.KMeansUtils.ReduceAction{``0,``1},``0[]@,``1@)">
-            <summary>
-            Takes a data cursor and perform an in-memory parallel aggregation operation on it. This
-            helper wraps some of the behavior common to parallel operations over a IRowCursor set,
-            including building the set, creating separate Random instances, and IRowCursor disposal.
-            </summary>
-            <typeparam name="TPartitionState">The type that each parallel cursor will be expected to aggregate to.</typeparam>
-            <typeparam name="TGlobalState">The type of the final output from combining each per-thread instance of TInterAgg.</typeparam>
-            <param name="numThreads"></param>
-            <param name="baseHost"></param>
-            <param name="factory"></param>
-            <param name="rowIndexGetter"></param>
-            <param name="initChunk">Initializes an instance of TInterAgg, or prepares/clears it if it is already allocated.</param>
-            <param name="mapper">Invoked for every row, should update TInterAgg using row cursor data.</param>
-            <param name="reducer">Invoked after all row cursors have completed, combines the entire array of TInterAgg instances into a final TAgg result.</param>
-            <param name="buffer">A reusable buffer array of TInterAgg.</param>
-            <param name="result">A reusable reference to the final result.</param>
-            <returns></returns>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.KMeansUtils.FindBestCluster(Microsoft.ML.Data.VBuffer{System.Single}@,Microsoft.ML.Data.VBuffer{System.Single}[],System.Single[],System.Int32,System.Boolean,System.Single@,System.Int32@,System.Single@,System.Int32@)">
-            <summary>
-            Given a point and a set of centroids this method will determine the closest centroid
-            using L2 distance. It will return a value equivalent to that distance, the index of the
-            closest cluster, and a value equivalent to the distance to the second-nearest cluster.
-            </summary>
-            <param name="features"></param>
-            <param name="centroids"></param>
-            <param name="centroidL2s">The L2 norms of the centroids. Used for efficiency and expected to be computed up front.</param>
-            <param name="centroidCount">The number of centroids. Must be less than or equal to the length of the centroid array.</param>
-            <param name="needRealDistance">Whether to return a real L2 distance, or a value missing the L2 norm of <paramref name="features"/>.</param>
-            <param name="minDistance">The distance between <paramref name="features"/> and the nearest centroid in <paramref name="centroids" />.</param>
-            <param name="cluster">The index of the nearest centroid.</param>
-            <param name="secMinDistance">The second nearest distance, or PosInf if <paramref name="centroids" /> only contains a single point.</param>
-            <param name="secCluster">The index of the second nearest centroid, or -1 if <paramref name="centroids" /> only contains a single point.</param>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.KMeansUtils.VerifyModelConsistency(Microsoft.ML.Data.VBuffer{System.Single}[])">
-            <summary>
-            Checks that all coordinates of all centroids are finite, and throws otherwise
-            </summary>
-        </member>
-    </members>
-</doc>

packages/Microsoft.ML.0.11.0/lib/netstandard2.0/Microsoft.ML.PCA.xml

-<?xml version="1.0"?>
-<doc>
-    <assembly>
-        <name>Microsoft.ML.PCA</name>
-    </assembly>
-    <members>
-        <member name="M:Microsoft.ML.PcaCatalog.ProjectToPrincipalComponents(Microsoft.ML.TransformsCatalog.ProjectionTransforms,System.String,System.String,System.String,System.Int32,System.Int32,System.Boolean,System.Nullable{System.Int32})">
-            <summary>Initializes a new instance of <see cref="T:Microsoft.ML.Transforms.PrincipalComponentAnalysisEstimator"/>.</summary>
-            <param name="catalog">The transform's catalog.</param>
-            <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
-            <param name="inputColumnName">Name of column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
-            <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
-            <param name="rank">The number of principal components.</param>
-            <param name="overSampling">Oversampling parameter for randomized PrincipalComponentAnalysis training.</param>
-            <param name="center">If enabled, data is centered to be zero mean.</param>
-            <param name="seed">The seed for random number generation.</param>
-        </member>
-        <member name="M:Microsoft.ML.PcaCatalog.ProjectToPrincipalComponents(Microsoft.ML.TransformsCatalog.ProjectionTransforms,Microsoft.ML.Transforms.PrincipalComponentAnalysisEstimator.ColumnOptions[])">
-            <summary>Initializes a new instance of <see cref="T:Microsoft.ML.Transforms.PrincipalComponentAnalysisEstimator"/>.</summary>
-            <param name="catalog">The transform's catalog.</param>
-            <param name="columns">Input columns to apply PrincipalComponentAnalysis on.</param>
-        </member>
-        <member name="M:Microsoft.ML.PcaCatalog.RandomizedPca(Microsoft.ML.AnomalyDetectionCatalog.AnomalyDetectionTrainers,System.String,System.String,System.Int32,System.Int32,System.Boolean,System.Nullable{System.Int32})">
-            <summary>
-            Trains an approximate PCA using Randomized SVD algorithm.
-            </summary>
-            <param name="catalog">The anomaly detection catalog trainer object.</param>
-            <param name="featureColumnName">The name of the feature column.</param>
-            <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
-            <param name="rank">The number of components in the PCA.</param>
-            <param name="oversampling">Oversampling parameter for randomized PCA training.</param>
-            <param name="center">If enabled, data is centered to be zero mean.</param>
-            <param name="seed">The seed for random number generation.</param>
-        </member>
-        <member name="M:Microsoft.ML.PcaCatalog.RandomizedPca(Microsoft.ML.AnomalyDetectionCatalog.AnomalyDetectionTrainers,Microsoft.ML.Trainers.RandomizedPcaTrainer.Options)">
-            <summary>
-            Trains an approximate PCA using Randomized SVD algorithm.
-            </summary>
-            <param name="catalog">The anomaly detection catalog trainer object.</param>
-            <param name="options">Advanced options to the algorithm.</param>
-        </member>
-        <member name="T:Microsoft.ML.Trainers.RandomizedPcaTrainer">
-            <summary>
-            This trainer trains an approximate PCA using Randomized SVD algorithm
-            Reference: https://web.stanford.edu/group/mmds/slides2010/Martinsson.pdf
-            </summary>
-            <remarks>
-            This PCA can be made into Kernel PCA by using Random Fourier Features transform
-            </remarks>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.RandomizedPcaTrainer.#ctor(Microsoft.ML.IHostEnvironment,System.String,System.String,System.Int32,System.Int32,System.Boolean,System.Nullable{System.Int32})">
-            <summary>
-            Initializes a new instance of <see cref="T:Microsoft.ML.Trainers.RandomizedPcaTrainer"/>.
-            </summary>
-            <param name="env">The local instance of the <see cref="T:Microsoft.ML.IHostEnvironment"/>.</param>
-            <param name="features">The name of the feature column.</param>
-            <param name="weights">The name of the weight column.</param>
-            <param name="rank">The number of components in the PCA.</param>
-            <param name="oversampling">Oversampling parameter for randomized PCA training.</param>
-            <param name="center">If enabled, data is centered to be zero mean.</param>
-            <param name="seed">The seed for random number generation.</param>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.RandomizedPcaTrainer.PostProcess(System.Single[][],System.Single[],System.Single[],System.Int32,System.Int32)">
-            <summary>
-            Modifies <paramref name="y"/> in place so it becomes <paramref name="y"/> * eigenvectors / eigenvalues.
-            </summary>
-        </member>
-        <member name="T:Microsoft.ML.Trainers.PcaModelParameters">
-            <summary>
-        PCA is a dimensionality-reduction transform which computes the projection of the feature vector onto a low-rank subspace. 
-      </summary><remarks>
-      <a href="https://en.wikipedia.org/wiki/Principal_component_analysis">Principle Component Analysis (PCA)</a> is a dimensionality-reduction algorithm which computes the projection of the feature vector to onto a low-rank subspace.
-      Its training is done using the technique described in the paper: <a href="https://arxiv.org/pdf/1310.6304v2.pdf">Combining Structured and Unstructured Randomness in Large Scale PCA</a>,
-      and the paper <a href="https://arxiv.org/pdf/0909.4061v2.pdf">Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions</a>
-        <para>For more information, see also:</para>
-        <list type="bullet">
-          <item><description>
-            <a href="https://web.stanford.edu/group/mmds/slides2010/Martinsson.pdf">Randomized Methods for Computing the Singular Value Decomposition (SVD) of very large matrices</a>
-          </description></item>
-          <item><description>
-            <a href="https://arxiv.org/abs/0809.2274">A randomized algorithm for principal component analysis</a>
-          </description></item>
-          <item><description>
-            <a href="http://users.cms.caltech.edu/~jtropp/papers/HMT11-Finding-Structure-SIREV.pdf">Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions</a>
-          </description></item>
-        </list>
-      </remarks>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.PcaModelParameters.#ctor(Microsoft.ML.IHostEnvironment,System.Int32,System.Single[][],Microsoft.ML.Data.VBuffer{System.Single}@)">
-            <summary>
-            Instantiate new model parameters from trained model.
-            </summary>
-            <param name="env">The host environment.</param>
-            <param name="rank">The rank of the PCA approximation of the covariance matrix. This is the number of eigenvectors in the model.</param>
-            <param name="eigenVectors">Array of eigenvectors.</param>
-            <param name="mean">The mean vector of the training data.</param>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.PcaModelParameters.GetEigenVectors(Microsoft.ML.Data.VBuffer{System.Single}[]@,System.Int32@)">
-            <summary>
-            Copies the top eigenvectors of the covariance matrix of the training data
-            into a set of buffers.
-            </summary>
-            <param name="vectors">A possibly reusable set of vectors, which will
-            be expanded as necessary to accomodate the data.</param>
-            <param name="rank">Set to the rank, which is also the logical length
-            of <paramref name="vectors"/>.</param>
-        </member>
-        <member name="M:Microsoft.ML.Trainers.PcaModelParameters.GetMean(Microsoft.ML.Data.VBuffer{System.Single}@)">
-            <summary>
-            Copies the mean vector of the training data.
-            </summary>
-        </member>
-        <member name="T:Microsoft.ML.Transforms.PrincipalComponentAnalysisTransformer">
-            <summary>
-        PCA is a dimensionality-reduction transform which computes the projection of the feature vector onto a low-rank subspace. 
-      </summary><remarks>
-      <a href="https://en.wikipedia.org/wiki/Principal_component_analysis">Principle Component Analysis (PCA)</a> is a dimensionality-reduction algorithm which computes the projection of the feature vector to onto a low-rank subspace.
-      Its training is done using the technique described in the paper: <a href="https://arxiv.org/pdf/1310.6304v2.pdf">Combining Structured and Unstructured Randomness in Large Scale PCA</a>,
-      and the paper <a href="https://arxiv.org/pdf/0909.4061v2.pdf">Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions</a>
-        <para>For more information, see also:</para>
-        <list type="bullet">
-          <item><description>
-            <a href="https://web.stanford.edu/group/mmds/slides2010/Martinsson.pdf">Randomized Methods for Computing the Singular Value Decomposition (SVD) of very large matrices</a>
-          </description></item>
-          <item><description>
-            <a href="https://arxiv.org/abs/0809.2274">A randomized algorithm for principal component analysis</a>
-          </description></item>
-          <item><description>
-            <a href="http://users.cms.caltech.edu/~jtropp/papers/HMT11-Finding-Structure-SIREV.pdf">Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions</a>
-          </description></item>
-        </list>
-      </remarks>
-        </member>
-        <member name="T:Microsoft.ML.Transforms.PrincipalComponentAnalysisEstimator">
-            <summary>
-        PCA is a dimensionality-reduction transform which computes the projection of the feature vector onto a low-rank subspace. 
-      </summary><remarks>
-      <a href="https://en.wikipedia.org/wiki/Principal_component_analysis">Principle Component Analysis (PCA)</a> is a dimensionality-reduction algorithm which computes the projection of the feature vector to onto a low-rank subspace.
-      Its training is done using the technique described in the paper: <a href="https://arxiv.org/pdf/1310.6304v2.pdf">Combining Structured and Unstructured Randomness in Large Scale PCA</a>,
-      and the paper <a href="https://arxiv.org/pdf/0909.4061v2.pdf">Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions</a>
-        <para>For more information, see also:</para>
-        <list type="bullet">
-          <item><description>
-            <a href="https://web.stanford.edu/group/mmds/slides2010/Martinsson.pdf">Randomized Methods for Computing the Singular Value Decomposition (SVD) of very large matrices</a>
-          </description></item>
-          <item><description>
-            <a href="https://arxiv.org/abs/0809.2274">A randomized algorithm for principal component analysis</a>
-          </description></item>
-          <item><description>
-            <a href="http://users.cms.caltech.edu/~jtropp/papers/HMT11-Finding-Structure-SIREV.pdf">Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions</a>
-          </description></item>
-        </list>
-      </remarks>
-        </member>
-        <member name="T:Microsoft.ML.Transforms.PrincipalComponentAnalysisEstimator.ColumnOptions">
-            <summary>
-            Describes how the transformer handles one column pair.
-            </summary>
-        </member>
-        <member name="F:Microsoft.ML.Transforms.PrincipalComponentAnalysisEstimator.ColumnOptions.Name">
-            <summary>
-            Name of the column resulting from the transformation of <see cref="F:Microsoft.ML.Transforms.PrincipalComponentAnalysisEstimator.ColumnOptions.InputColumnName"/>.
-            </summary>
-        </member>
-        <member name="F:Microsoft.ML.Transforms.PrincipalComponentAnalysisEstimator.ColumnOptions.InputColumnName">
-            <summary>
-            Name of column to transform.
-            </summary>
-        </member>
-        <member name="F:Microsoft.ML.Transforms.PrincipalComponentAnalysisEstimator.ColumnOptions.WeightColumn">
-            <summary>
-            The name of the weight column.
-            </summary>
-        </member>
-        <member name="F:Microsoft.ML.Transforms.PrincipalComponentAnalysisEstimator.ColumnOptions.Rank">
-            <summary>
-            The number of components in the PCA.
-            </summary>
-        </member>
-        <member name="F:Microsoft.ML.Transforms.PrincipalComponentAnalysisEstimator.ColumnOptions.Oversampling">
-            <summary>
-            Oversampling parameter for randomized PCA training.
-            </summary>
-        </member>
-        <member name="F:Microsoft.ML.Transforms.PrincipalComponentAnalysisEstimator.ColumnOptions.Center">
-            <summary>
-            If enabled, data is centered to be zero mean.
-            </summary>
-        </member>
-        <member name="F:Microsoft.ML.Transforms.PrincipalComponentAnalysisEstimator.ColumnOptions.Seed">
-            <summary>
-            The seed for random number generation.
-            </summary>
-        </member>
-        <member name="M:Microsoft.ML.Transforms.PrincipalComponentAnalysisEstimator.ColumnOptions.#ctor(System.String,System.String,System.String,System.Int32,System.Int32,System.Boolean,System.Nullable{System.Int32})">
-            <summary>
-            Describes how the transformer handles one column pair.
-            </summary>
-            <param name="name">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
-            <param name="inputColumnName">Name of column to transform.
-            If set to <see langword="null"/>, the value of the <paramref name="name"/> will be used as source.</param>
-            <param name="weightColumn">The name of the weight column.</param>
-            <param name="rank">The number of components in the PCA.</param>
-            <param name="overSampling">Oversampling parameter for randomized PCA training.</param>
-            <param name="center">If enabled, data is centered to be zero mean.</param>
-            <param name="seed">The random seed. If unspecified random state will be instead derived from the <see cref="T:Microsoft.ML.MLContext"/>.</param>
-        </member>
-        <member name="M:Microsoft.ML.Transforms.PrincipalComponentAnalysisEstimator.#ctor(Microsoft.ML.IHostEnvironment,System.String,System.String,System.String,System.Int32,System.Int32,System.Boolean,System.Nullable{System.Int32})">
-            <summary>
-        PCA is a dimensionality-reduction transform which computes the projection of the feature vector onto a low-rank subspace. 
-      </summary><remarks>
-      <a href="https://en.wikipedia.org/wiki/Principal_component_analysis">Principle Component Analysis (PCA)</a> is a dimensionality-reduction algorithm which computes the projection of the feature vector to onto a low-rank subspace.
-      Its training is done using the technique described in the paper: <a href="https://arxiv.org/pdf/1310.6304v2.pdf">Combining Structured and Unstructured Randomness in Large Scale PCA</a>,
-      and the paper <a href="https://arxiv.org/pdf/0909.4061v2.pdf">Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions</a>
-        <para>For more information, see also:</para>
-        <list type="bullet">
-          <item><description>
-            <a href="https://web.stanford.edu/group/mmds/slides2010/Martinsson.pdf">Randomized Methods for Computing the Singular Value Decomposition (SVD) of very large matrices</a>
-          </description></item>
-          <item><description>
-            <a href="https://arxiv.org/abs/0809.2274">A randomized algorithm for principal component analysis</a>
-          </description></item>
-          <item><description>
-            <a href="http://users.cms.caltech.edu/~jtropp/papers/HMT11-Finding-Structure-SIREV.pdf">Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions</a>
-          </description></item>
-        </list>
-      </remarks>
-            <param name="env">The environment to use.</param>
-            <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName" />.</param>
-            <param name="inputColumnName">Name of the column to transform.
-            If set to <see langword="null" />, the value of the <paramref name="outputColumnName" /> will be used as source.</param>
-            <param name="weightColumn">The name of the weight column.</param>
-            <param name="rank">The number of components in the PCA.</param>
-            <param name="overSampling">Oversampling parameter for randomized PCA training.</param>
-            <param name="center">If enabled, data is centered to be zero mean.</param>
-            <param name="seed">The seed for random number generation.</param>
-        </member>
-        <member name="M:Microsoft.ML.Transforms.PrincipalComponentAnalysisEstimator.#ctor(Microsoft.ML.IHostEnvironment,Microsoft.ML.Transforms.PrincipalComponentAnalysisEstimator.ColumnOptions[])">
-            <summary>
-        PCA is a dimensionality-reduction transform which computes the projection of the feature vector onto a low-rank subspace. 
-      </summary><remarks>
-      <a href="https://en.wikipedia.org/wiki/Principal_component_analysis">Principle Component Analysis (PCA)</a> is a dimensionality-reduction algorithm which computes the projection of the feature vector to onto a low-rank subspace.
-      Its training is done using the technique described in the paper: <a href="https://arxiv.org/pdf/1310.6304v2.pdf">Combining Structured and Unstructured Randomness in Large Scale PCA</a>,
-      and the paper <a href="https://arxiv.org/pdf/0909.4061v2.pdf">Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions</a>
-        <para>For more information, see also:</para>
-        <list type="bullet">
-          <item><description>
-            <a href="https://web.stanford.edu/group/mmds/slides2010/Martinsson.pdf">Randomized Methods for Computing the Singular Value Decomposition (SVD) of very large matrices</a>
-          </description></item>
-          <item><description>
-            <a href="https://arxiv.org/abs/0809.2274">A randomized algorithm for principal component analysis</a>
-          </description></item>
-          <item><description>
-            <a href="http://users.cms.caltech.edu/~jtropp/papers/HMT11-Finding-Structure-SIREV.pdf">Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions</a>
-          </description></item>
-        </list>
-      </remarks>
-            <param name="env">The environment to use.</param>
-            <param name="columns">The dataset columns to use, and their specific settings.</param>
-        </member>
-        <member name="M:Microsoft.ML.Transforms.PrincipalComponentAnalysisEstimator.Fit(Microsoft.Data.DataView.IDataView)">
-            <summary>
-            Trains and returns a <see cref="T:Microsoft.ML.Transforms.PrincipalComponentAnalysisTransformer"/>.
-            </summary>
-        </member>
-        <member name="M:Microsoft.ML.Transforms.PrincipalComponentAnalysisEstimator.GetOutputSchema(Microsoft.ML.SchemaShape)">
-            <summary>
-            Returns the <see cref="T:Microsoft.ML.SchemaShape"/> of the schema which will be produced by the transformer.
-            Used for schema propagation and verification in a pipeline.
-            </summary>
-        </member>
-    </members>
-</doc>