static void Main()
{
ML.Init();
Application.EnableVisualStyles();
Application.SetCompatibleTextRenderingDefault(false);
Application.Run(new Main());
}
public void TestDetectMovingDown()
{
Assert.IsFalse(ML.DetectMovingDown(null));
var notD1 = new int[32] {
-1, -1, -3, -15, -1, 1, 0, -1, -1, 1, -10, -18, 0, 0, 1, 0, 0, -1, -7, 0, 7, 0, 1, 1, 0, -1, 0, -1, 0, 0, 1, 0
};
var notD2 = new int[32] {
0, 1, 1, 2, 0, -1, 0, 0, 0, 1, 18, 23, -2, -1, -1, -1, 0, 1, 7, -12, -10, -1, 0, 0, 0, -1, 1, 0, -1, -1, -1, 1
};
var notD3 = new int[32] {
0, 0, 0, 1, 12, 24, 2, 1, 1, 0, 0, 3, -20, 29, 4, 1, 0, 0, -3, 3, -10, 9, 2, 1, 0, 0, 0, 0, 0, 1, 1, 0
};
Assert.IsFalse(ML.DetectMovingDown(notD1));
Assert.IsFalse(ML.DetectMovingDown(notD2));
Assert.IsFalse(ML.DetectMovingDown(notD3));
var movD1 = new int[32] {
0, -1, -2, -9, 0, -1, 0, -1, -1, -2, -12, -22, 0, 0, 0, 0, 0, 0, -6, 3, 5, 1, 0, 0, -1, 0, -1, 0, 0, 0, 0, 0
};
var movD2 = new int[32] {
0, 0, 0, 1, -12, 24, 2, 1, 1, 0, 0, 3, -20, 29, 4, 1, 0, 0, -3, 3, 10, 9, 2, 1, 0, 0, 0, 0, 0, 1, 1, 0
};
var movD3 = new int[32] {
0, 1, 1, 2, 0, -1, 0, 0, 0, 1, 18, 23, -2, -1, -1, -1, 0, 1, 7, -12, -10, -1, -2, 0, 0, -1, 1, 0, -1, -1, 20, 1
};
Assert.IsTrue(ML.DetectMovingDown(movD1));
Assert.IsTrue(ML.DetectMovingDown(movD2));
Assert.IsTrue(ML.DetectMovingDown(movD3));
}
public void MatrixFactorization_Estimator()
{
string labelColumnName = "Label";
string matrixColumnIndexColumnName = "Col";
string matrixRowIndexColumnName = "Row";
// This data contains three columns, Label, Col, and Row where Col and Row will be treated as the expected input names
// of the trained matrix factorization model.
var data = new TextLoader(Env, GetLoaderArgs(labelColumnName, matrixColumnIndexColumnName, matrixRowIndexColumnName))
.Read(new MultiFileSource(GetDataPath(TestDatasets.trivialMatrixFactorization.trainFilename)));
// "invalidData" is not compatible to "data" because it contains columns Label, ColRenamed, and RowRenamed (no column is Col or Row).
var invalidData = new TextLoader(Env, GetLoaderArgs(labelColumnName, matrixColumnIndexColumnName + "Renamed", matrixRowIndexColumnName + "Renamed"))
.Read(new MultiFileSource(GetDataPath(TestDatasets.trivialMatrixFactorization.testFilename)));
var options = new MatrixFactorizationTrainer.Options
{
MatrixColumnIndexColumnName = matrixColumnIndexColumnName,
MatrixRowIndexColumnName = matrixRowIndexColumnName,
LabelColumnName = labelColumnName,
NumIterations = 3,
NumThreads = 1,
ApproximationRank = 4,
};
var est = ML.Recommendation().Trainers.MatrixFactorization(options);
TestEstimatorCore(est, data, invalidInput: invalidData);
Done();
}
public void UgroupTest()
{
var data = new List <UngroupExample> {
new UngroupExample {
Age = 18, UserName = new[] { "Amy", "Willy" }, Gender = new[] { "Girl", "Boy" }
},
new UngroupExample {
Age = 20, UserName = new[] { "Dori", "Ariel" }, Gender = new[] { "Fish", "Mermaid" }
}
};
var dataView = ComponentCreation.CreateDataView(Env, data);
var ungroupTransform = new UngroupTransform(Env, dataView, UngroupTransform.UngroupMode.Inner, "UserName", "Gender");
var ungrouped = ML.CreateEnumerable <GroupExample>(ungroupTransform, false).ToList();
Assert.Equal(4, ungrouped.Count);
Assert.Equal(18, ungrouped[0].Age);
Assert.Equal("Amy", ungrouped[0].UserName);
Assert.Equal("Girl", ungrouped[0].Gender);
Assert.Equal(18, ungrouped[1].Age);
Assert.Equal("Willy", ungrouped[1].UserName);
Assert.Equal("Boy", ungrouped[1].Gender);
Assert.Equal(20, ungrouped[2].Age);
Assert.Equal("Dori", ungrouped[2].UserName);
Assert.Equal("Fish", ungrouped[2].Gender);
Assert.Equal(20, ungrouped[3].Age);
Assert.Equal("Ariel", ungrouped[3].UserName);
Assert.Equal("Mermaid", ungrouped[3].Gender);
}
// POST: api/Prediction
public string Post([FromBody] byte[] data)
{
if (data == null)
{
return(null);
}
using var stream = new MemoryStream(data);
var image = Image.FromStream(stream);
if (image == null)
{
return(null);
}
//ml processing
image.Save("path");
var model = ML.model("path");
model.run();
return(JsonConvert.SerializeObject(data));
}
public ActionResult DeleteConfirmed(int id)
{
ML mL = db.MLs.Find(id);
db.MLs.Remove(mL);
db.SaveChanges();
return(RedirectToAction("Index"));
}
public Main()
{
InitializeComponent();
mainThreadId = System.Threading.Thread.CurrentThread.ManagedThreadId;
AllowDrop = true;
ML.Logger += Log;
ML.Init();
}
protected override Parser.Result Evaluate(string data, ref int from)
{
var list = Utils.GetFunctionArgs(data, ref from, ParaCnt);
uint.TryParse(list[0], out uint arg1);
ML.ER_SetErr((ei)arg1);
Interpreter.Instance.AppendOutput("Error_Set(" + arg1.ToString() + ")");
return(new Parser.Result());
}
protected override Parser.Result Evaluate(string data, ref int from)
{
var list = Utils.GetFunctionArgs(data, ref from, ParaCnt);
uint.TryParse(list[0], out uint arg1);
bool bRet = ML.MT_GetHomeDone((mi)arg1);
Interpreter.Instance.AppendOutput("Motor_CheckHome(" + arg1.ToString() + ") = " + bRet.ToString());
return(new Parser.Result(bRet == true ? 1 : 0, null, null));
}
public ActionResult Edit([Bind(Include = "UserID,DogID,Age,Gender,FirstName,Breed")] ML mL)
{
if (ModelState.IsValid)
{
db.Entry(mL).State = EntityState.Modified;
db.SaveChanges();
return(RedirectToAction("Index"));
}
return(View(mL));
}
protected override Parser.Result Evaluate(string data, ref int from)
{
var list = Utils.GetFunctionArgs(data, ref from, ParaCnt);
uint.TryParse(list[0], out uint arg1);
uint.TryParse(list[1], out uint arg2);
bool bRet = ML.CL_Complete((ci)arg1, (fb)arg2);
Interpreter.Instance.AppendOutput("Cylinder_CheckStop(" + arg1.ToString() + "," + arg2.ToString() + ") = " + bRet.ToString());
return(new Parser.Result(bRet == true ? 1 : 0, null, null));
}
public void ViewDetails()
{
//Populate the Excel Sheet
GlobalDefinitions.ExcelLib.PopulateInCollection(Base.ExcelPath, "ManageListings");
Thread.Sleep(2000);
//page Refresh
Global.GlobalDefinitions.driver.Navigate().Refresh();
ML.Click();
Thread.Sleep(4000);
while (true)
{
var j = 1;
while (j <= 10)
{
// identify 2nd row of table. Then extact the text and assign to a variable
var Category1 = Global.GlobalDefinitions.driver.FindElement(By.XPath("//*[@id='listing-management-section']/div[2]/div[1]/table/tbody/tr[" + j + "]/td[2]")).Text;
var ViewBtn = Global.GlobalDefinitions.driver.FindElement(By.XPath("//*[@id='listing-management-section']/div[2]/div[1]/table/tbody/tr[" + j + "]/td[8]/i[1]"));
//compare the text with the expected text. "Graphics & Design"
if (Category1 == (Global.GlobalDefinitions.ExcelLib.ReadData(2, "Category1")))
{
//Clickin on View Button
ViewBtn.Click();
break;
}
j++;
}
var btnNext = Global.GlobalDefinitions.driver.FindElement(By.XPath("//*[@id='listing-management-section']/div[2]/div[1]/div/button[4]"));
btnNext.Click();
}
//Validation for View Button
var Text = (Global.GlobalDefinitions.driver.FindElement(By.XPath("//*[@id='service-detail-section']/div[3]/div/div[1]/div/div/div/a[1]")).Text);
Assert.AreEqual("Graphics & Design ", Text);
Console.WriteLine(" user can see the details");
Global.Base.test.Log(RelevantCodes.ExtentReports.LogStatus.Info, " can view the details");
//string text = Global.GlobalDefinitions.driver.FindElement(By.XPath("//*[@id='service-detail-section']/div[2]/div/div[2]/div[1]/div[1]/div[2]/h1/span")).Text;
// if (text == "QA")
// {
// Global.Base.test.Log(RelevantCodes.ExtentReports.LogStatus.Pass, " can view the details");
// }
// else
// Global.Base.test.Log(RelevantCodes.ExtentReports.LogStatus.Fail, " can't see the details");
}
protected override Parser.Result Evaluate(string data, ref int from)
{
var list = Utils.GetFunctionArgs(data, ref from, ParaCnt);
uint.TryParse(list[0], out uint arg1);
if (!Constants.CHECK)
{
ML.MT_GoHome((mi)arg1);
}
Interpreter.Instance.AppendOutput("Motor_Home(" + arg1.ToString() + ")");
return(new Parser.Result());
}
protected override Parser.Result Evaluate(string data, ref int from)
{
var list = Utils.GetFunctionArgs(data, ref from, ParaCnt);
uint.TryParse(list[0], out uint arg1);
uint.TryParse(list[1], out uint arg2);
if (!Constants.CHECK)
{
ML.CL_Move((ci)arg1, (fb)arg2);
}
Interpreter.Instance.AppendOutput("Cylinder_Move(" + arg1.ToString() + "," + arg2.ToString() + ")");
return(new Parser.Result());
}
protected override Parser.Result Evaluate(string data, ref int from)
{
var list = Utils.GetFunctionArgs(data, ref from, ParaCnt);
uint.TryParse(list[0], out uint arg1);
double.TryParse(list[1], out double arg2);
if (!Constants.CHECK)
{
ML.MT_GoAbsRun(arg1, arg2);
}
Interpreter.Instance.AppendOutput("MoveMotr(" + arg1.ToString() + ", " + arg2.ToString() + ")");
return(new Parser.Result());
}
public void WordTokenizeWorkout()
{
var data = new[] { new TestClass()
{
A = "This is a good sentence.", B = new string[2] {
"Much words", "Wow So Cool"
}
} };
var dataView = ML.Data.ReadFromEnumerable(data);
var invalidData = new[] { new TestWrong()
{
A = 1, B = new float[2] {
2, 3
}
} };
var invalidDataView = ML.Data.ReadFromEnumerable(invalidData);
var pipe = new WordTokenizingEstimator(Env, new[] {
new WordTokenizingEstimator.ColumnInfo("TokenizeA", "A"),
new WordTokenizingEstimator.ColumnInfo("TokenizeB", "B"),
});
TestEstimatorCore(pipe, dataView, invalidInput: invalidDataView);
// Reuse the pipe trained on dataView in TestEstimatorCore to make prediction.
var result = pipe.Fit(dataView).Transform(dataView);
// Extract the transformed result of the first row (the only row we have because data contains only one TestClass) as a native class.
var nativeResult = ML.CreateEnumerable <NativeResult>(result, false).First();
// Check the tokenization of A. Expected result is { "This", "is", "a", "good", "sentence." }.
var tokenizeA = new[] { "This", "is", "a", "good", "sentence." };
Assert.True(tokenizeA.Length == nativeResult.TokenizeA.Length);
for (int i = 0; i < tokenizeA.Length; ++i)
{
Assert.Equal(tokenizeA[i], nativeResult.TokenizeA[i]);
}
// Check the tokenization of B. Expected result is { "Much", "words", "Wow", "So", "Cool" }. One may think that the expected output
// should be a 2-D array { { "Much", "words"}, { "Wow", "So", "Cool" } }, but please note that ML.NET may flatten all outputs if
// they are high-dimension tensors.
var tokenizeB = new[] { "Much", "words", "Wow", "So", "Cool" };
Assert.True(tokenizeB.Length == nativeResult.TokenizeB.Length);
for (int i = 0; i < tokenizeB.Length; ++i)
{
Assert.Equal(tokenizeB[i], nativeResult.TokenizeB[i]);
}
Done();
}
// GET: MLs/Edit/5
public ActionResult Edit(int?id)
{
if (id == null)
{
return(RedirectToAction("MyAccount", "Users"));
}
ML mL = db.MLs.Find(id);
if (mL == null)
{
return(HttpNotFound());
}
return(View(mL));
}
void DoCopyTree(ref TPredicateItemBig ABig, ref TDynamicArray AStack)
{
int m_who;
TAlternativeParserItemList ML;
TAlternativeParserItemBase mBase;
TAlternativeParserItemTFS mTFS;
TAlternativeParserItemBig mBig;
TPredicateItemTFE mTFE;
TAlternativeParserItemBig mRfc = ABig.Rfc;
ML = mRfc.MainList;
for (int i = 0; i <= ML.Count - 1; i++)
{
mBase = ML.GetItems(i);
m_who = mBase.Who();
if (m_who == 0)
{
mTFS = (TAlternativeParserItemTFS)(mBase);
TPredicateItemTFS iTfs = new TPredicateItemTFS();
iTfs.Assign(mTFS);
ABig.AddItem(iTfs);
for (int j = 0; j <= iTfs.TFECount - 1; j++)
{
mTFE = iTfs.GetTFEItems(j);
if (mTFE.RfcTFE.Big != null)
{
TPredicateItemBig iBig = NewBig(mTFE.RfcTFE.Big);
mTFE.Big = iBig;
AStack.InsertToFirst(iBig);
}
}
}
if (m_who == 1)
{
mBig = (TAlternativeParserItemBig)(mBase);
TPredicateItemBig iBig = NewBig(mBig);
ABig.AddItem(iBig);
AStack.InsertToFirst(iBig);
}
}
for (int i = 0; i <= mRfc.CountBig - 1; i++)
{
mBig = mRfc.GetItemsBig(i);
TPredicateItemBig iBig = NewBig(mBig);
ABig.AddItem(iBig);
AStack.InsertToFirst(iBig);
}
}
public void TestTextPredictAndSet()
{
var intention = new ML <MyTextRow, bool>();
IEnumerable <MyTextRow> data = GetTextData();
var msg = "Parking is terrible";
var result = intention.Using(MLType.TextFeaturizingEstimator)
.LearnFrom(data)
.PredictAndSet(new MyTextRow {
Text = msg
});
Assert.IsTrue(result.Label);
}
public ActionResult Create(int UserID, int DogID, Double Age, DogGender?Gender, string FirstName, String Breed)
{
ML mL = new ML();
mL.UserID = UserID;
mL.DogID = DogID;
mL.Age = Age;
mL.Gender = Gender;
mL.FirstName = FirstName;
mL.Breed = Breed;
db.MLs.Add(mL);
db.SaveChanges();
return(RedirectToAction("Index"));
}
public void TestLinearPredictWithOutput()
{
var intention = new ML <MyLinearRow, float>();
IEnumerable <MyLinearRow> data = GetFloatData();
var result = intention.Using(MLType.LightGbm)
.WhereScoreIs("Score")
.LearnFrom(data)
.PredictWithOutput(new MyLinearRow {
Val1 = 1, Val2 = 0, Val3 = 0
});
Assert.IsTrue(result.Score > 0.75);
}
private void RestoreSavedNote()
{
IEnumerable <NoteNode> noteslist = ML.RetrieveNotes(null);
if (noteslist.Count() > 0)
{
foreach (var note in noteslist)
{
note.IsSaved = true;
TrVwNotesList.Nodes.Add(note);
}
TrVwNotesList.SelectedNode = TrVwNotesList.Nodes?[0];
TxtTitle.Text = (TrVwNotesList.SelectedNode as NoteNode)?.Title;
RTxtDesc.Text = (TrVwNotesList.SelectedNode as NoteNode)?.Desc;
}
}
public void CacheTest()
{
var src = Enumerable.Range(0, 100).Select(c => new MyData()).ToArray();
var data = ML.CreateDataView(src);
data.GetColumn <float[]>(ML, "Features").ToArray();
data.GetColumn <float[]>(ML, "Features").ToArray();
Assert.True(src.All(x => x.AccessCount == 2));
src = Enumerable.Range(0, 100).Select(c => new MyData()).ToArray();
data = ML.CreateDataView(src);
data = ML.Data.Cache(data);
data.GetColumn <float[]>(ML, "Features").ToArray();
data.GetColumn <float[]>(ML, "Features").ToArray();
Assert.True(src.All(x => x.AccessCount == 1));
}
public void Reset()
{
code[0] = dout.Addres;
if (code[1] == 0x10)
{
for (int a = 8; a < code.Length; a += 2)
{
code[a] = 0;
}
}
else
{
code[5] = 0;
}
ML.Interrupt(code);
}
public void GroupTest()
{
var data = new List <GroupExample> {
new GroupExample {
Age = 18, UserName = "******", Gender = "Girl"
},
new GroupExample {
Age = 18, UserName = "******", Gender = "Boy"
},
new GroupExample {
Age = 20, UserName = "******", Gender = "Fish"
},
new GroupExample {
Age = 20, UserName = "******", Gender = "Mermaid"
}
};
var dataView = ComponentCreation.CreateDataView(Env, data);
var groupTransform = new GroupTransform(Env, dataView, "Age", "UserName", "Gender");
var grouped = ML.CreateEnumerable <UngroupExample>(groupTransform, false).ToList();
// Expected content of grouped should contains two rows.
// Age, UserName, Gender
// 18, {"Amy", "Willy"}, {"Girl", "Boy"}
// 20, {"Dori", "Ariel"}, {"Fish", "Mermaid"}
// Note that "Age, UserName, Gender" is not a row; it just shows column names per row below it.
Assert.Equal(2, grouped.Count);
// grouped[0] is the first output row --- 18, {"Amy", "Willy"}, {"Girl", "Boy"}
Assert.Equal(18, grouped[0].Age);
Assert.Equal(2, grouped[0].UserName.Length);
Assert.Equal("Amy", grouped[0].UserName[0]);
Assert.Equal("Willy", grouped[0].UserName[1]);
Assert.Equal(2, grouped[0].Gender.Length);
Assert.Equal("Girl", grouped[0].Gender[0]);
Assert.Equal("Boy", grouped[0].Gender[1]);
// grouped[1] is the second output row --- 20, {"Dori", "Ariel"}, {"Fish", "Mermaid"}
Assert.Equal(20, grouped[1].Age);
Assert.Equal(2, grouped[1].Gender.Length);
Assert.Equal("Dori", grouped[1].UserName[0]);
Assert.Equal("Ariel", grouped[1].UserName[1]);
Assert.Equal(2, grouped[1].Gender.Length);
Assert.Equal("Fish", grouped[1].Gender[0]);
Assert.Equal("Mermaid", grouped[1].Gender[1]);
}
private void StartCamera()
{
ML.OnFrameResult += result =>
{
if (Disposing || !Visible)
{
return;
}
Invoke(new MethodInvoker(delegate
{
CurrentParrot = result.ParrotCount;
CurrentRabbit = result.RabbitCount;
CurrentSergeant = result.SergeantCount;
}));
};
ML.StartFile(imageBoxFrameGrabber1, imageBoxFrameGrabber2, file);
}
public void Run()
{
lock (Lab3.locker)
{
Matrix MF = new Matrix(N);
Matrix MG = new Matrix(N);
Matrix MH = new Matrix(N);
Matrix ML;
MF.MatrixFillsOne(1);
MG.MatrixFillsOne(1);
MH.MatrixFillsOne(1);
Console.WriteLine("F2 started.");
ML = MF.sum(MG.multiply(MH));
Console.WriteLine("ML = \n" + ML.toString());
Console.WriteLine("Function 2 end.");
}
}
private void CtrlDHandler()
{
NoteNode note = TrVwNotesList.SelectedNode as NoteNode;
if (note?.IsSaved ?? false)
{
if (ML.DeleteNoteNode(note))
{
TxtTitle.Text = RTxtDesc.Text = string.Empty;
note.IsSaved = false;
TrVwNotesList.Nodes.Remove(note);
}
else
{
MessageBox.Show("failed deletion");
}
}
}
void NextGenome()
{
Random.InitState((int)69);
GameObject[] carts = GameObject.FindGameObjectsWithTag("Cart");
foreach (GameObject cart in carts)
{
Destroy(cart);
}
bool new_gene = false;
Debug.Log("Next Genome!");
genome++;
if (genome == MAX_GENOMES)
{
new_gene = true;
generation++;
genome = 0;
}
GameObject[] tunnels = GameObject.FindGameObjectsWithTag("Tunnel");
foreach (GameObject tunnel in tunnels)
{
Spawner spawner = tunnel.GetComponentInChildren <Spawner>();
if (spawner != null)
{
spawner.NewRound();
}
}
GameObject[] intersections = GameObject.FindGameObjectsWithTag("Intersection");
foreach (GameObject intersection in intersections)
{
ML ml = intersection.GetComponent <ML> ();
if (ml != null)
{
ml.cost = 0;
if (new_gene == true)
{
ml.NewGeneration();
}
}
}
new_gene = false;
}
private void CtrlEHandler()
{
NoteNode note = TrVwNotesList.SelectedNode as NoteNode;
if (note?.IsSaved ?? false)
{
if (ML.DeleteNoteNode(note))
{
TxtTitle.Enabled = RTxtDesc.Enabled = true;
note.IsSaved = false;
note.ForeColor = Color.Yellow;
note.BackColor = Color.Red;
}
else
{
MessageBox.Show("failed deletion");
}
}
}
//------------------------------------------------------------------------
// currection method
//------------------------------------------------------------------------
public ML.LineSegment3 CorrectionLineSegment(MonoDrop srcDrop , ML.LineSegment3 lineSeg3)
{
if (null == srcDrop)
return lineSeg3;
//Correction value
//PairInt parameter is array index(0 start , 1 is not ).
//Index2 startPos = PairInt.Start_C5_R0;
Vector3 putPos_left_up = m_boardInfo.GetPositionAt_ViewLeftUp () + Single.UIRoot.transform.position;
Vector3 putPos_right_up = m_boardInfo.GetPositionAt_ViewRightUp () + Single.UIRoot.transform.position;
Vector3 putPos_left_bottom = m_boardInfo.GetPositionAt_ViewLeftBottom () + Single.UIRoot.transform.position;
Vector3 putPos_right_bottom = m_boardInfo.GetPositionAt_ViewRightBottom () + Single.UIRoot.transform.position;
//Vector3 putPos_left_up = GetPositionOfPutDrop (new PairInt (0, 0));
//Vector3 putPos_right_up = GetPositionOfPutDrop (new PairInt (0, (int)ConstBoard.Max_Row-1));
//Vector3 putPos_left_bottom = GetPositionOfPutDrop (new PairInt ((int)ConstBoard.Max_Column-1, 0));
//Vector3 putPos_right_bottom = GetPositionOfPutDrop (new PairInt ((int)ConstBoard.Max_Column-1, (int)ConstBoard.Max_Row-1));
#if UNITY_EDITOR
//-------------------------------------------------------------------------
//20140906 chamto test
//-------------------------------------------------------------------------
Single.MonoDebug.cube_LeftUp.transform.position = putPos_left_up;
Single.MonoDebug.cube_RightUp.transform.position = putPos_right_up;
Single.MonoDebug.cube_LeftBottom.transform.position = putPos_left_bottom;
Single.MonoDebug.cube_RightBottom.transform.position = putPos_right_bottom;
//-------------------------------------------------------------------------
#endif
Bounds bob = m_boardInfo.GetBoundaryOfView (Single.UIRoot.transform.position);
ML.LineSegment3 result = new ML.LineSegment3();
result.origin = srcDrop.gotoWorldPosition;
result.last = lineSeg3.last;
//CDefine.DebugLog ("Bounds " + bob + bob.min + bob.max);
if (lineSeg3.last.y >= bob.max.y)
{ //------------- correction up -------------
//CDefine.DebugLog("----correction up");
result.last_y = putPos_left_up.y;
if(lineSeg3.last.x <= bob.min.x)
{ //correction left-up
result.last = putPos_left_up;
//CDefine.DebugLog("----correction left up" + putPos_left_up);
}
if(lineSeg3.last.x >= bob.max.x)
{ //correction right-up
result.last = putPos_right_up;
//CDefine.DebugLog("----correction right up");
}
}else
if (lineSeg3.last.y <= bob.min.y)
{ ////------------- correction bottom -------------
//CDefine.DebugLog("----correction bottom");
result.last_y = putPos_left_bottom.y;
if(lineSeg3.last.x <= bob.min.x)
{ //correction left-bottom
result.last = putPos_left_bottom;
//CDefine.DebugLog("----correction left bottom"+putPos_left_bottom);
}
if(lineSeg3.last.x >= bob.max.x)
{ //correction right-bottom
result.last = putPos_right_bottom;
//CDefine.DebugLog("----correction right bottom");
}
}else
if(lineSeg3.last.x <= bob.min.x)
{ ////------------- correction left -------------
result.last_x = putPos_left_up.x;
//CDefine.DebugLog("----correction left");
}else
if(lineSeg3.last.x >= bob.max.x)
{ ////------------- correction right -------------
result.last_x = putPos_right_up.x;
//CDefine.DebugLog("----correction right");
}else
{
////------------- correction is not required
//CDefine.DebugLog("----correction is not required");
return lineSeg3;
}
//CDefine.DebugLog ("Correction LineSegement : " + result);
return result;
}
public override void Compute(ML.Data.IMLData input, double[] output)
{
if(input is ML.Data.Basic.BasicMLData)
Compute(((ML.Data.Basic.BasicMLData)input).Data, output);
else
{
// TODO: make this more efficient
var tmp = new double[input.Count];
input.CopyTo(tmp, 0, input.Count);
Compute(tmp, output);
}
}