public ActionResult Index()
{
var t = _dbContext.BasicInformations.First();
var CalCount = MatlabReader.Read <double>(t.PathToData, "Vk_record").Column(0);
return(Ok(CalCount.ToList().Where((c, i) => i % 50 == 0)));
}
public static void Main(string[] args)
{
const string dataset = "nips12raw_str602";
string filename = Path.Combine(DataPath, dataset + ".mat");
var dataList = MatlabReader.List(filename);
var counts = MatlabReader.Read <double>(filename, "counts");
var wc = MatlabReader.Read <double>(filename, "wc");
// var lda = new LDA(wc.RowCount, 20);
const int batches = 100;
var lda = new LDAShared(batches, wc.RowCount, 20);
var wordsInDoc = counts.EnumerateColumns()
.Select(row => row.EnumerateIndexed().ToDictionary(ia => ia.Item1, ia => (int)ia.Item2))
.ToArray();
Dirichlet[] postTheta;
Dirichlet[] postPhi;
lda.Infer(wordsInDoc, 1.0, 1.0, out postTheta, out postPhi);
var ldaPredict = new LDAPredictionModel(wc.RowCount, 20);
var predictions = ldaPredict.Predict(postTheta, postPhi);
foreach (var prediction in predictions)
{
Console.WriteLine(prediction);
}
}
private void BrowseTransferFunctionButton_Click(object sender, EventArgs e)
{
OpenFileDialog ofd = new OpenFileDialog();
ofd.InitialDirectory = @"C:\Users\ConraN01\Documents\Spyder_WS\MRI_RF_TF_Tool_Project\Test Files for Python Utility\Neuro Orion MRI RF Heating TF Files";
ofd.Filter = "Matlab MAT (*.mat)|*.mat|All Files (*.*)|*.*";
ofd.Title = "Select TF files...";
if (ofd.ShowDialog() != DialogResult.OK)
{
return;
}
if (ofd.FileNames.Length == 0)
{
MessageBox.Show(this, "No files selected", "TF Reading Error");
return;
}
try {
var f = ofd.FileName;
var shortfname = System.IO.Path.GetFileName(f);
var mz = MatlabReader.Read <double>(f, "z");
var msr = MatlabReader.Read <Complex>(f, "Sr");
// Take either first row or column....
if (mz.ColumnCount == 1 && mz.RowCount > 1)
{
TFz = mz.Column(0);
}
else if (mz.RowCount == 1 && mz.ColumnCount > 1)
{
TFz = mz.Row(0);
}
else
{
throw new FormatException("Input matrix must be 1xN or Nx1, with N>=2");
}
if (msr.ColumnCount == 1 && msr.RowCount > 1)
{
TFSr = msr.Column(0);
}
else if (mz.RowCount == 1 && msr.ColumnCount > 1)
{
TFSr = msr.Row(0);
}
else
{
throw new FormatException("Input matrix must be 1xN or Nx1, with N>=2");
}
if (TFz.Count != TFSr.Count)
{
throw new FormatException("Input matrices must be of equal dimensions");
}
TransferFunctionFilenameLabel.Text = shortfname;
TFFileFullPath = ofd.FileName;
ViewTFButton.Enabled = true;
} catch (Exception ex) {
MessageBox.Show(this, "TF File could not be opened!\n\n" + ex.Message,
"Error loading TF file",
MessageBoxButtons.OK, MessageBoxIcon.Error);
}
}
public void SolidPoint3D()
{
var projcoord = MatlabReader.Read <double>("..\\..\\..\\MGroup.IGA.Tests\\InputFiles\\SolidPointProjectiveCP.mat", "projcoord");
var projectiveCPCoordinates = new double[735, 4];
var counter = 0;
for (int i = 0; i < numberOfCPKsi3D + 1; i++)
{
for (int j = 0; j < numberOfCPHeta3D + 1; j++)
{
for (int k = 0; k < numberofCPZeta3D + 1; k++)
{
var index = k * (numberOfCPHeta3D + 1) * (numberOfCPKsi3D + 1) + j * (numberOfCPKsi3D + 1) + i;
projectiveCPCoordinates[counter, 0] = projcoord.At(index, 0);
projectiveCPCoordinates[counter, 1] = projcoord.At(index, 1);
projectiveCPCoordinates[counter, 2] = projcoord.At(index, 2);
projectiveCPCoordinates[counter, 3] = projcoord.At(index, 3);
}
}
}
var point = ParaviewNurbs3D.SolidPoint3D(numberOfCPKsi3D, degreeKsi3D, knotValueVectorKsi3D,
numberOfCPHeta3D, degreeHeta3D, knotValueVectorHeta3D, numberofCPZeta3D, degreeZeta3D,
knotValueVectorZeta3D, projectiveCPCoordinates, coordinateKsi3D, coordinateHeta3D, coordinateZeta3D);
var expectedPoint = new double[] { 0.75, 0, 0, 1 };
for (int i = 0; i < point.Length; i++)
{
Utilities.AreValuesEqual(expectedPoint[i], point[i], 1e-14);
}
}
//[ValidateAntiForgeryToken]
public ActionResult Create([FromBody] ddd collection)
{
Console.WriteLine(collection);
var dateTimes = collection.selectedOptiont.Select(s =>
{
if (DateTime.TryParse(s, out DateTime dt))
{
return(dt);
}
else
{
// 这种时间应该是不会有对应的记录的
return(DateTime.MinValue);
}
});
var t = _dbContext.BasicInformations
.Where(s => dateTimes.Any(t => s.DateTime == t));
List <IEnumerable <double> > data = new(t.Count());
foreach (var item in t)
{
// https://stackoverflow.com/questions/267033/getting-odd-even-part-of-a-sequence-with-linq
data.Add(MatlabReader.Read <double>(item.PathToData, collection.dataItem).Column(0)
.ToList().Where((c, i) => i % 50 == 0));
}
//t.Select(s => );
//var CalCount = MatlabReader.Read<double>(t.First().PathToData, "Vk_record").Column(0);
var dates = _dbContext.BasicInformations.Take(3).Select(s => s.DateTime);
return(Ok(new { collection, data, dates }));
//return Ok(collection);
}
private void BrowseRefButton_Click(object sender, EventArgs e)
{
OpenFileDialog ofd = new OpenFileDialog();
if (Directory.Exists(@"C: \Users\ConraN01\Documents\"))
{
ofd.InitialDirectory = @"C:\Users\ConraN01\Documents\Spyder_WS\MRI_RF_TF_Tool_Project\Test Files for Python Utility\Neuro Orion MRI RF Heating TF Files";
}
ofd.Filter = "Matlab MAT (*.mat)|*.mat|All Files (*.*)|*.*";
if (ofd.ShowDialog() != DialogResult.OK)
{
return;
}
try {
Bkgz = MatlabReader.Read <double>(ofd.FileName, "z").Column(0);
BkgSr = MatlabReader.Read <Complex>(ofd.FileName, "Sr").Column(0);
BkgFilename = ofd.FileName;
BackgroundTFFilenameLabel.Text = System.IO.Path.GetFileNameWithoutExtension(ofd.FileName);
}
catch (Exception ex) {
Bkgz = null;
BkgSr = null;
BackgroundTFFilenameLabel.Text = "...";
BkgFilename = null;
Replot();
MessageBox.Show(this, ex.ToString(), "TF Reading Error");
return;
}
Replot();
}
public static void TestReadingMat()
{
// test reading a .mat file
// string path = "/nfs/nhome/live/wittawat/SHARE/gatsby/research/code/saved/test_mat.mat";
string path = "../../Saved/test_mat1.mat";
Dictionary <string, Object> dict = MatlabReader.Read(path);
// a struct is read as a Dictionary<string, Object>
Console.WriteLine("s: {0}", dict["s"]);
Dictionary <string, Object> s = (Dictionary <string, Object>)dict["s"];
Object aobj = s["a"];
Console.WriteLine("s.a: {0}", aobj);
Console.WriteLine("s.st: {0}", s["st"]);
// cell array (1d) is read as Object[,] (2d)
Object[,] cobj = (Object[, ])s["c"];
Console.WriteLine("s.c: {0}", cobj[0, 1]);
// a numerical array is read as MicrosoftResearch.Infer.Maths.Matrix
Object lobj = s["l"];
Console.WriteLine("s.l: {0}", lobj.GetType());
// mat file cannot contain Matlab objects. Otherwise, an exception
// is thrown.
}
public ETan(string filename)
{
z = MatlabReader.Read <double>(filename, "etan_z").Column(0);
rms = MatlabReader.Read <Complex>(filename, "etan_rms").Column(0);
this.filename = filename;
name = System.IO.Path.GetFileName(filename);
}
private void CompareTFButton_Click(object sender, EventArgs e)
{
OpenFileDialog ofd = new OpenFileDialog();
List <Vector <double> > ZList = new List <Vector <double> >();
List <Vector <Complex> > SrList = new List <Vector <Complex> >();
ofd.InitialDirectory = @"C:\Users\ConraN01\Documents\Spyder_WS\MRI_RF_TF_Tool_Project\Test Files for Python Utility\Neuro Orion MRI RF Heating TF Files";
ofd.Multiselect = true;
ofd.Filter = "Matlab MAT (*.mat)|*.mat|All Files (*.*)|*.*";
if (ofd.ShowDialog() != DialogResult.OK)
{
return;
}
if (ofd.FileNames.Length == 0)
{
MessageBox.Show(this, "No files selected", "TF Reading Error");
}
try
{
foreach (string f in ofd.FileNames)
{
ZList.Add(MatlabReader.Read <double>(f, "z").Column(0));
SrList.Add(MatlabReader.Read <Complex>(f, "Sr").Column(0));
}
}
catch (Exception ex)
{
MessageBox.Show(this, ex.ToString(), "TF Reading Error");
return;
}
TFComparisonForm tfcf = new TFComparisonForm(ofd.FileNames, ZList, SrList);
tfcf.Show();
}
public void CanReadFirstMatrix()
{
var matrix = MatlabReader.Read <double>("./data/Matlab/A.mat");
Assert.AreEqual(100, matrix.RowCount);
Assert.AreEqual(100, matrix.ColumnCount);
Assert.AreEqual(typeof(LinearAlgebra.Double.DenseMatrix), matrix.GetType());
AssertHelpers.AlmostEqual(100.108979553704, matrix.FrobeniusNorm(), 5);
}
public void CanReadFloatNamedSparseMatrix()
{
var matrix = MatlabReader.Read <float>("./data/Matlab/sparse-small.mat", "S");
Assert.AreEqual(100, matrix.RowCount);
Assert.AreEqual(100, matrix.ColumnCount);
Assert.AreEqual(typeof(LinearAlgebra.Single.SparseMatrix), matrix.GetType());
AssertHelpers.AlmostEqual(17.6385090630805f, matrix.FrobeniusNorm(), 6);
}
public Disturbance()
{
IsWindEnabled = Configuration.IsWindEnabled;
if (IsWindEnabled)
{
CalCount = MatlabReader.Read <double>("./WindModel.mat", "cal_count").Column(0);
WindModelState = MatlabReader.Read <double>("./WindModel.mat", "wind_model_state");
Wind = MatlabReader.Read <double>("./WindModel.mat", "wind").Column(0);
WindLateral = MatlabReader.Read <double>("./WindModel.mat", "wind_lat").Column(0);
}
}
public void CanReadFloatNamedSparseMatrix()
{
using (var stream = TestData.Data.ReadStream("Matlab.sparse-small.mat"))
{
var matrix = MatlabReader.Read <float>(stream, "S");
Assert.AreEqual(100, matrix.RowCount);
Assert.AreEqual(100, matrix.ColumnCount);
Assert.AreEqual(typeof(LinearAlgebra.Single.SparseMatrix), matrix.GetType());
AssertHelpers.AlmostEqual(17.6385090630805f, matrix.FrobeniusNorm(), 6);
}
}
public void CanReadFirstMatrix()
{
using (var stream = TestData.Data.ReadStream("Matlab.A.mat"))
{
var matrix = MatlabReader.Read <double>(stream);
Assert.AreEqual(100, matrix.RowCount);
Assert.AreEqual(100, matrix.ColumnCount);
Assert.AreEqual(typeof(LinearAlgebra.Double.DenseMatrix), matrix.GetType());
AssertHelpers.AlmostEqual(100.108979553704, matrix.FrobeniusNorm(), 5);
}
}
public static void TestLoadingMatData()
{
string path = Config.PathToSavedFile("data/banknote_norm.mat");
Dictionary <string, object> dict = MatlabReader.Read(path);
Matrix x = (Matrix)dict["X"];
Console.WriteLine(x);
Console.WriteLine();
Matrix y = (Matrix)dict["Y"];
Console.WriteLine(y);
}
internal void LogisticIrtTest()
{
Variable <int> numStudents = Variable.New <int>().Named("numStudents");
Range student = new Range(numStudents);
VariableArray <double> ability = Variable.Array <double>(student).Named("ability");
ability[student] = Variable.GaussianFromMeanAndPrecision(0, 1e-6).ForEach(student);
Variable <int> numQuestions = Variable.New <int>().Named("numQuestions");
Range question = new Range(numQuestions);
VariableArray <double> difficulty = Variable.Array <double>(question).Named("difficulty");
difficulty[question] = Variable.GaussianFromMeanAndPrecision(0, 1e-6).ForEach(question);
VariableArray <double> discrimination = Variable.Array <double>(question).Named("discrimination");
discrimination[question] = Variable.Exp(Variable.GaussianFromMeanAndPrecision(0, 1).ForEach(question));
VariableArray2D <bool> response = Variable.Array <bool>(student, question).Named("response");
response[student, question] = Variable.BernoulliFromLogOdds(((ability[student] - difficulty[question]).Named("minus") * discrimination[question]).Named("product"));
bool[,] data;
double[] discriminationTrue = new double[0];
bool useDummyData = false;
if (useDummyData)
{
data = new bool[4, 2];
for (int i = 0; i < data.GetLength(0); i++)
{
for (int j = 0; j < data.GetLength(1); j++)
{
data[i, j] = (i > j);
}
}
}
else
{
// simulated data
// also try IRT2PL_10_250.mat
//TODO: change path for cross platform using
Dictionary <string, object> dict = MatlabReader.Read(@"..\..\..\Tests\Data\IRT2PL_10_1000.mat");
Matrix m = (Matrix)dict["Y"];
data = ConvertToBool(m.ToArray());
m = (Matrix)dict["discrimination"];
discriminationTrue = Util.ArrayInit(data.GetLength(1), i => m[i]);
}
numStudents.ObservedValue = data.GetLength(0);
numQuestions.ObservedValue = data.GetLength(1);
response.ObservedValue = data;
InferenceEngine engine = new InferenceEngine();
engine.Algorithm = new VariationalMessagePassing();
Console.WriteLine(StringUtil.JoinColumns(engine.Infer(discrimination), " should be ", StringUtil.ToString(discriminationTrue)));
}
// event RecordShipStateEvent;
public Ship()
{
DeckEnable = Configuration.IsDeckCompensationEnabled;
omega_d_2i = vb.Dense(new double[] { omega_dx_2i, omega_dy_2i, omega_dz_2i });
DeckPosition = Position;
if (DeckEnable)
{
ForwardFilterState = MatlabReader.Read <double>("./ForwardFilter.mat", "forward_filter_state");
CurrentDeckPredict = MatlabReader.Read <double>("./ForwardFilter.mat", "current_deck_predict").Column(0);
CurrentDeckControl = MatlabReader.Read <double>("./ForwardFilter.mat", "current_deck_control").Column(0);
CurrentDeckLateralControl = MatlabReader.Read <double>("./ForwardFilter.mat", "current_deck_control_lat").Column(0);
}
}
public void MatlabWriteNumericNameTest()
{
string fileName = $"{System.IO.Path.GetTempPath()}MatlabWriteNumericNameTest{Environment.CurrentManagedThreadId}.mat";
using (MatlabWriter writer = new MatlabWriter(fileName))
{
writer.Write("24", new int[0]);
}
Dictionary <string, object> vars = MatlabReader.Read(fileName);
int[] ints = (int[])vars["24"];
Assert.Empty(ints);
}
//[DeploymentItem(@"Data\test.mat", "Data")]
public void MatlabWriterTest()
{
Dictionary <string, object> dict = MatlabReader.Read(Path.Combine(TestUtils.DataFolderPath, "test.mat"));
string fileName = $"{System.IO.Path.GetTempPath()}MatlabWriterTest{Environment.CurrentManagedThreadId}.mat";
using (MatlabWriter writer = new MatlabWriter(fileName))
{
foreach (var entry in dict)
{
writer.Write(entry.Key, entry.Value);
}
}
MatlabReaderTester(fileName);
}
private void OpenMatFile()
{
//TODO: Change to button reaction or.. as a resource in debug
using (var reader = new StreamReader(@"C:\Users\Lenovo\Desktop\ImageData.mat"))
{
_faceCoordinates = MatlabReader.Read <double>(reader.BaseStream, "SubDir_Data");
List <double> list = new List <double>();
for (int i = 0; i < 8; ++i)
{
list.Add(_faceCoordinates[i, 0]);
}
}
;
}
static private void TestSparseGeneration(string Path)
{
var UniformMat = MatlabReader.Read <double> (Path, "Tri");
var NodeCount = MatlabReader.Read <double> (Path, "NodeCount");
var Count = NodeCount.ToArray();
var Delaunay = new int[UniformMat.RowCount, UniformMat.ColumnCount];
for (int i = 0; i < Delaunay.GetLength(0); i++)
{
for (int j = 0; j < Delaunay.GetLength(1); j++)
{
Delaunay [i, j] = (int)UniformMat [i, j] - 1;
}
}
var myArray = new Sparse(Delaunay, (int)Count[0, 0]);
}
//[DeploymentItem(@"Data\IRT2PL_10_250.mat", "Data")]
public void MatlabReaderTest2()
{
Dictionary <string, object> dict = MatlabReader.Read(Path.Combine(TestUtils.DataFolderPath, "IRT2PL_10_250.mat"));
Assert.Equal(5, dict.Count);
Matrix m = (Matrix)dict["Y"];
Assert.True(m.Rows == 250);
Assert.True(m.Cols == 10);
Assert.True(m[0, 1] == 0.0);
Assert.True(m[1, 0] == 1.0);
m = (Matrix)dict["difficulty"];
Assert.True(m.Rows == 10);
Assert.True(m.Cols == 1);
Assert.True(MMath.AbsDiff(m[1], 0.7773) < 2e-4);
}
//[DeploymentItem(@"Data\test.mat", "Data")]
public void MatlabWriterTest()
{
Dictionary <string, object> dict = MatlabReader.Read(Path.Combine(
#if NETCORE
Path.GetDirectoryName(typeof(PsychTests).Assembly.Location), // work dir is not the one with Microsoft.ML.Probabilistic.Tests.dll on netcore and neither is .Location on netfull
#endif
"Data", "test.mat"));
string fileName = $"{System.IO.Path.GetTempPath()}MatlabWriterTest{Environment.CurrentManagedThreadId}.mat";
using (MatlabWriter writer = new MatlabWriter(fileName))
{
foreach (var entry in dict)
{
writer.Write(entry.Key, entry.Value);
}
}
MatlabReaderTester(fileName);
}
public void MatlabMatrixRoundtrip()
{
var denseDouble = Matrix <double> .Build.Random(20, 20);
var denseComplex = Matrix <Complex> .Build.Random(10, 10);
var diagonalDouble = Matrix <double> .Build.DiagonalOfDiagonalArray(new[] { 1.0, 2.0, 3.0 });
var sparseDouble = Matrix <double> .Build.Sparse(20, 20, (i, j) => i % (j + 1) == 2?i + 10 *j : 0);
var denseDoubleP = MatlabWriter.Pack(denseDouble, "denseDouble");
var denseComplexP = MatlabWriter.Pack(denseComplex, "denseComplex");
var diagonalDoubleP = MatlabWriter.Pack(diagonalDouble, "diagonalDouble");
var sparseDoubleP = MatlabWriter.Pack(sparseDouble, "sparseDouble");
Assert.That(MatlabReader.Unpack <double>(denseDoubleP).Equals(denseDouble));
Assert.That(MatlabReader.Unpack <Complex>(denseComplexP).Equals(denseComplex));
Assert.That(MatlabReader.Unpack <double>(diagonalDoubleP).Equals(diagonalDouble));
Assert.That(MatlabReader.Unpack <double>(sparseDoubleP).Equals(sparseDouble));
Assert.That(MatlabReader.Unpack <double>(denseDoubleP).Storage, Is.TypeOf <DenseColumnMajorMatrixStorage <double> >());
Assert.That(MatlabReader.Unpack <Complex>(denseComplexP).Storage, Is.TypeOf <DenseColumnMajorMatrixStorage <Complex> >());
Assert.That(MatlabReader.Unpack <double>(diagonalDoubleP).Storage, Is.TypeOf <SparseCompressedRowMatrixStorage <double> >());
Assert.That(MatlabReader.Unpack <double>(sparseDoubleP).Storage, Is.TypeOf <SparseCompressedRowMatrixStorage <double> >());
if (File.Exists("testrt.mat"))
{
File.Delete("testrt.mat");
}
MatlabWriter.Store("testrt.mat", new[] { denseDoubleP, denseComplexP, diagonalDoubleP, sparseDoubleP });
Assert.That(MatlabReader.Read <double>("testrt.mat", "denseDouble").Equals(denseDouble));
Assert.That(MatlabReader.Read <Complex>("testrt.mat", "denseComplex").Equals(denseComplex));
Assert.That(MatlabReader.Read <double>("testrt.mat", "diagonalDouble").Equals(diagonalDouble));
Assert.That(MatlabReader.Read <double>("testrt.mat", "sparseDouble").Equals(sparseDouble));
Assert.That(MatlabReader.Read <double>("testrt.mat", "denseDouble").Storage, Is.TypeOf <DenseColumnMajorMatrixStorage <double> >());
Assert.That(MatlabReader.Read <Complex>("testrt.mat", "denseComplex").Storage, Is.TypeOf <DenseColumnMajorMatrixStorage <Complex> >());
Assert.That(MatlabReader.Read <double>("testrt.mat", "diagonalDouble").Storage, Is.TypeOf <SparseCompressedRowMatrixStorage <double> >());
Assert.That(MatlabReader.Read <double>("testrt.mat", "sparseDouble").Storage, Is.TypeOf <SparseCompressedRowMatrixStorage <double> >());
File.Delete("testrt.mat");
}
private void MatlabReaderTester(string fileName)
{
Dictionary <string, object> dict = MatlabReader.Read(fileName);
Assert.Equal(12, dict.Count);
Matrix aScalar = (Matrix)dict["aScalar"];
Assert.Equal(1, aScalar.Rows);
Assert.Equal(1, aScalar.Cols);
Assert.Equal(5.0, aScalar[0, 0]);
Assert.Equal("string", (string)dict["aString"]);
MatlabReader.ComplexMatrix aComplexScalar = (MatlabReader.ComplexMatrix)dict["aComplexScalar"];
Assert.Equal(5.0, aComplexScalar.Real[0, 0]);
Assert.Equal(3.0, aComplexScalar.Imaginary[0, 0]);
MatlabReader.ComplexMatrix aComplexVector = (MatlabReader.ComplexMatrix)dict["aComplexVector"];
Assert.Equal(1.0, aComplexVector.Real[0, 0]);
Assert.Equal(2.0, aComplexVector.Imaginary[0, 0]);
Assert.Equal(3.0, aComplexVector.Real[0, 1]);
Assert.Equal(4.0, aComplexVector.Imaginary[0, 1]);
var aStruct = (Dictionary <string, object>)dict["aStruct"];
Assert.Equal(2, aStruct.Count);
Assert.Equal(1.0, ((Matrix)aStruct["field1"])[0]);
Assert.Equal("two", (string)aStruct["field2"]);
object[,] aCell = (object[, ])dict["aCell"];
Assert.Equal(1.0, ((Matrix)aCell[0, 0])[0]);
int[] intArray = (int[])dict["intArray"];
Assert.Equal(1, intArray[0]);
int[] uintArray = (int[])dict["uintArray"];
Assert.Equal(1, uintArray[0]);
bool[] aLogical = (bool[])dict["aLogical"];
Assert.True(aLogical[0]);
Assert.True(aLogical[1]);
Assert.False(aLogical[2]);
object[,,] aCell3D = (object[, , ])dict["aCell3D"];
Assert.Null(aCell3D[0, 0, 0]);
Assert.Equal(7.0, ((Matrix)aCell3D[0, 0, 1])[0, 0]);
Assert.Equal(6.0, ((Matrix)aCell3D[0, 1, 0])[0, 0]);
double[,,,] array4D = (double[, , , ])dict["array4D"];
Assert.Equal(4.0, array4D[0, 0, 1, 0]);
Assert.Equal(5.0, array4D[0, 0, 0, 1]);
long[] aLong = (long[])dict["aLong"];
Assert.Equal(1234567890123456789L, aLong[0]);
}
//[DeploymentItem(@"Data\IRT2PL_10_250.mat", "Data")]
public void MatlabReaderTest2()
{
Dictionary <string, object> dict = MatlabReader.Read(Path.Combine(
#if NETCORE
Path.GetDirectoryName(typeof(PsychTests).Assembly.Location), // work dir is not the one with Microsoft.ML.Probabilistic.Tests.dll on netcore and neither is .Location on netfull
#endif
"Data", "IRT2PL_10_250.mat"));
Assert.Equal(5, dict.Count);
Matrix m = (Matrix)dict["Y"];
Assert.True(m.Rows == 250);
Assert.True(m.Cols == 10);
Assert.True(m[0, 1] == 0.0);
Assert.True(m[1, 0] == 1.0);
m = (Matrix)dict["difficulty"];
Assert.True(m.Rows == 10);
Assert.True(m.Cols == 1);
Assert.True(MMath.AbsDiff(m[1], 0.7773) < 2e-4);
}
public void MatlabWriteStringDictionaryTest()
{
Dictionary <string, string> dictString = new Dictionary <string, string>();
dictString["a"] = "a";
dictString["b"] = "b";
string fileName = $"{System.IO.Path.GetTempPath()}MatlabWriteStringDictionaryTest{Environment.CurrentManagedThreadId}.mat";
using (MatlabWriter writer = new MatlabWriter(fileName))
{
writer.Write("dictString", dictString);
}
Dictionary <string, object> vars = MatlabReader.Read(fileName);
Dictionary <string, object> dict = (Dictionary <string, object>)vars["dictString"];
foreach (var entry in dictString)
{
Assert.Equal(dictString[entry.Key], dict[entry.Key]);
}
}
public void MatlabWriteStringListTest()
{
List <string> strings = new List <string>();
strings.Add("a");
strings.Add("b");
string fileName = $"{System.IO.Path.GetTempPath()}MatlabWriteStringListTest{Environment.CurrentManagedThreadId}.mat";
using (MatlabWriter writer = new MatlabWriter(fileName))
{
writer.Write("strings", strings);
}
Dictionary <string, object> vars = MatlabReader.Read(fileName);
string[] array = (string[])vars["strings"];
for (int i = 0; i < array.Length; i++)
{
Assert.Equal(strings[i], array[i]);
}
}
private void button1_Click(object sender, EventArgs e)
{
int i = 0;
double x, h, a, wu;
Matrix <double> data = MatlabReader.Read <double>("data.mat");
Matrix <double> time = MatlabReader.Read <double>("time.mat");
Matrix <double> kp = MatlabReader.Read <double>("kp.mat");
while (data[i, 0] == 0)
{
i += 1;
}
txtdelay.Text = time[i, 0].ToString();//计算延时
txtkp.Text = kp[10, 0].ToString();
x = Convert.ToDouble(txtkp.Text);
h = Convert.ToDouble(txth.Text);
a = Convert.ToDouble(txta.Text);
wu = Convert.ToDouble(txtwu.Text);
txttime.Text = (System.Math.Sqrt((x * 4 * h / 3.1415 / a) * (x * 4 * h / 3.1415 / a) - 1) / wu).ToString();//计算时间常数
}
public void LoadDataFromMat(string filePath, out Vector[] X, out bool[] Y)
{
// Expect X (dxn) and Y (1xn) in the mat file
Dictionary <string, object> dict = MatlabReader.Read(filePath);
Matrix xmat = (Matrix)dict["X"];
X = MatrixUtils.SplitColumns(xmat);
int n = X.Length;
// Y is actually a 1xn vector
Matrix ymat = (Matrix)dict["Y"];
double[,] yarr = ymat.ToArray();
if (yarr.GetLength(1) != n)
{
throw new ArgumentException("length of X and Y do not match.");
}
Y = new bool[n];
for (int i = 0; i < n; i++)
{
// expect 0, 1
Y[i] = yarr[0, i] > 0.5;
}
}
