public void Wrap_Original_Stream_On_Over()
{
var sut = SequenceStream.Using(
stream => new StreamWriter(
stream,
Encoding.UTF8,
leaveOpen: false,
bufferSize: 128
)
);
var compressed = sut.Over(
stream => new GZipStream(
stream,
leaveOpen: false,
compressionLevel: CompressionLevel.Fastest
)
);
using var wordsStream = compressed
.On(new[] { "Read", "the", "content" })
.Writing((output, word) => output.Write($"{word} "));
using var reader = new StreamReader(
new GZipStream(wordsStream, CompressionMode.Decompress),
Encoding.UTF8
);
var result = reader.ReadToEnd();
result.Should().Be("Read the content ");
}
public Stream Create <TItem, TContext>(
Func <Stream, TContext> contextFactory,
Action <TContext> dispose,
IEnumerable <TItem> sequence,
ArrayPool <byte> pool,
Action <TContext, TItem> write)
=> SequenceStream
.Using(contextFactory, dispose)
.On(sequence.ToAsyncEnumerable())
.Pooling(pool)
.Writing(write);
public static Stream BinaryContext(IEnumerable <Person> persons) =>
SequenceStream.Using(
stream => new BinaryWriter(stream, Encoding.UTF8)
)
.On(persons)
.Writing(
(binaryWriter, person) =>
{
binaryWriter.Write(person.Name);
binaryWriter.Write(person.Age);
}
);
public async Task Read_Async()
{
using var concatenation = SequenceStream.Concatenation(
new Stream[]
{
new MemoryStream(new byte[] { 1, 2, 3 }),
new MemoryStream(new byte[] { 7, 40, 80, 78 })
}.ToAsyncEnumerable(),
ArrayPool <byte> .Shared,
2
);
var buffer = new byte[5];
await concatenation.ReadAsync(buffer, 0, 5);
buffer.Should().Equal(1, 2, 3, 7, 40);
}
public void Read_Piece()
{
using var concatenation = SequenceStream.Concatenation(
new Stream[]
{
new MemoryStream(new byte[] { 1, 2, 3 }),
new MemoryStream(new byte[] { 7, 40, 80, 78 })
},
ArrayPool <byte> .Shared,
2
);
var buffer = new byte[5];
concatenation.Read(buffer, 0, 5);
buffer.Should().Equal(1, 2, 3, 7, 40);
}
public void Dispose_Used_Streams_On_Dispose([Range(1, 5)] int chunkSize, [Values(1, 3, 5, 7, 10, 12, 17)] int read)
{
var streams = new[]
{
new Disposable(new MemoryStream(new byte[] { 7, 40, 80, 78 })),
new Disposable(new MemoryStream(new byte[] { 20, 40, 70 })),
new Disposable(new MemoryStream(new byte[] { 17, 50, 233, 89, 54 }))
};
using (var concatenation = SequenceStream.Concatenation(streams, ArrayPool <byte> .Shared, chunkSize))
{
concatenation.Read(new byte[read], 0, read);
}
streams.Should().OnlyContain(
stream => stream.Position == 0 || stream.Disposed
);
}
public void Read_Entire_Content()
{
using var concatenation = SequenceStream.Concatenation(
new Stream[]
{
new MemoryStream(new byte[] { 1, 2, 3 }),
new MemoryStream(new byte[] { 7, 40, 80, 78 })
},
ArrayPool <byte> .Shared,
3
);
var buffer = new byte[100];
var read = concatenation.Read(buffer, 0, 100);
read.Should().Be(7);
buffer.Take(read).Should().Equal(1, 2, 3, 7, 40, 80, 78);
}
public void Test()
{
using var source = new CancellationTokenSource();
var cancelled = false;
using var strings = SequenceStream.Using(stream => new StreamWriter(stream, Encoding.UTF8))
.On(Yield())
.WithCancellation(source.Token)
.Writing(
(output, @string) => output.Write(@string)
);
using var streamReader = new StreamReader(strings);
Assert.ThrowsAsync <TaskCanceledException>(
streamReader.ReadToEndAsync
);
cancelled.Should().BeTrue();
async IAsyncEnumerable <string> Yield(
public static AbstractModel train(SequenceStream <Event> events, IDictionary <string, string> trainParams,
IDictionary <string, string> reportMap)
{
if (!isValid(trainParams))
{
throw new System.ArgumentException("trainParams are not valid!");
}
if (!isSequenceTraining(trainParams))
{
throw new System.ArgumentException("Algorithm must be a sequence algorithm!");
}
int iterations = getIntParam(trainParams, ITERATIONS_PARAM, ITERATIONS_DEFAULT, reportMap);
int cutoff = getIntParam(trainParams, CUTOFF_PARAM, CUTOFF_DEFAULT, reportMap);
bool useAverage = getBooleanParam(trainParams, "UseAverage", true, reportMap);
return((new SimplePerceptronSequenceTrainer()).trainModel(iterations, events, cutoff, useAverage));
}
public static Stream ItemMetaInformationOnBuilder(IEnumerable <string> names)
{
return(SequenceStream.Using(
stream => new StreamWriter(stream, Encoding.UTF8)
)
.On(names)
.AsItems()
.Writing(
(writer, item) =>
{
if (item.Kind.IsFirst())
{
writer.WriteLine("Names");
}
writer.Write($"{item.Index}: {item.Value}");
if (!item.Kind.IsLast())
{
writer.WriteLine();
}
}
));
}
private void mPlayButton_Click(object sender, EventArgs e)
{
mPlaybackDevice.stopPlayback();
if (mInputFilesListView.Items.Count == 0)
{
MessageBox.Show(this, "There are no input files to play", "Play");
return;
}
/* Playback of all files using both SubStream and SequenceStream */
List <Stream> ifStreams = new List <Stream>(mInputFilesListView.Items.Count);
foreach (ListViewItem inputFileItem in mInputFilesListView.Items)
{
FileStream ifs = new FileStream(inputFileItem.SubItems[2].Text, FileMode.Open, FileAccess.Read);
PCMDataInfo pcmInfo = PCMDataInfo.ParseRiffWaveHeader(ifs);
long startPos = ifs.Position;
ifs.Position = 0;
SubStream subIfs = new SubStream(ifs, startPos, pcmInfo.DataLength);
ifStreams.Add(subIfs);
}
mPCMInputStream = new SequenceStream(ifStreams);
mPlaybackDevice.play(mPCMInputStream);
}
public static SequenceStream.Builder <JsonWriter> Using(JsonWriterFactory factory) => SequenceStream.Using(factory.Create);
public virtual AbstractModel trainModel(int iterations, SequenceStream sequenceStream, int cutoff,
bool useAverage)
{
this.iterations = iterations;
this.sequenceStream = sequenceStream;
DataIndexer di = new OnePassDataIndexer(new SequenceStreamEventStream(sequenceStream), cutoff, false);
numSequences = 0;
foreach (Sequence <Event> s in sequenceStream)
{
numSequences++;
}
outcomeList = di.OutcomeList;
predLabels = di.PredLabels;
pmap = new IndexHashTable <string>(predLabels, 0.7d);
display("Incorporating indexed data for training... \n");
this.useAverage = useAverage;
numEvents = di.NumEvents;
this.iterations = iterations;
outcomeLabels = di.OutcomeLabels;
omap = new Dictionary <string, int?>();
for (int oli = 0; oli < outcomeLabels.Length; oli++)
{
omap[outcomeLabels[oli]] = oli;
}
outcomeList = di.OutcomeList;
numPreds = predLabels.Length;
numOutcomes = outcomeLabels.Length;
if (useAverage)
{
updates = RectangularArrays.ReturnRectangularIntArray(numPreds, numOutcomes, 3);
}
display("done.\n");
display("\tNumber of Event Tokens: " + numEvents + "\n");
display("\t Number of Outcomes: " + numOutcomes + "\n");
display("\t Number of Predicates: " + numPreds + "\n");
parameters = new MutableContext[numPreds];
if (useAverage)
{
averageParams = new MutableContext[numPreds];
}
allOutcomesPattern = new int[numOutcomes];
for (int oi = 0; oi < numOutcomes; oi++)
{
allOutcomesPattern[oi] = oi;
}
for (int pi = 0; pi < numPreds; pi++)
{
parameters[pi] = new MutableContext(allOutcomesPattern, new double[numOutcomes]);
if (useAverage)
{
averageParams[pi] = new MutableContext(allOutcomesPattern, new double[numOutcomes]);
}
for (int aoi = 0; aoi < numOutcomes; aoi++)
{
parameters[pi].setParameter(aoi, 0.0);
if (useAverage)
{
averageParams[pi].setParameter(aoi, 0.0);
}
}
}
modelDistribution = new double[numOutcomes];
display("Computing model parameters...\n");
findParameters(iterations);
display("...done.\n");
/// <summary>
///************* Create and return the model ***************** </summary>
string[] updatedPredLabels = predLabels;
/*
* String[] updatedPredLabels = new String[pmap.size()];
* for (String pred : pmap.keySet()) {
* updatedPredLabels[pmap.get(pred)]=pred;
* }
*/
if (useAverage)
{
return(new PerceptronModel(averageParams, updatedPredLabels, outcomeLabels));
}
else
{
return(new PerceptronModel(parameters, updatedPredLabels, outcomeLabels));
}
}
public static Stream Strings(IEnumerable <string> strings) => SequenceStream.FromStrings(strings, Encoding.UTF8, Environment.NewLine);
public static Stream Bytes(IEnumerable <byte[]> chunks) =>
SequenceStream.UsingStream()
.On(chunks)
.Writing(
(stream, bytes) => { stream.Write(bytes, 0, bytes.Length); }
);
public SequenceStreamEventStream(SequenceStream <Event> sequenceStream)
{
this.sequenceIterator = sequenceStream.GetEnumerator();
}
