[SuppressMessage("ReSharper", "AccessToModifiedClosure")] // Closure is always modified after use
public void SpinWaitForExchangeWithContext()
{
const int NumIterations = 10_000;
var runner = NewRunner(new DummyImmutableRef("0", 0L));
// (Func<T, TContext, T>, T)
var valuesArr = Enumerable.Range(0, NumIterations + 1)
.Select(i => new DummyImmutableRef((-i).ToString(), i))
.ToArray();
runner.GlobalSetUp = (target, _) => target.Value = valuesArr[0];
runner.AllThreadsTearDown = target => {
FastAssertEqual(NumIterations, target.Value.LongProp);
FastAssertEqual((-NumIterations).ToString(), target.Value.StringProp);
};
runner.ExecuteSingleWriterSingleReaderTests(
target => {
for (var i = 0; i < NumIterations; i += 2)
{
var nextVal = i;
var exchRes = target.SpinWaitForExchange((c, ctx) => ctx[c.LongProp + 1], valuesArr, valuesArr[nextVal]);
AssertAreEqual(nextVal, exchRes.PreviousValue.LongProp);
AssertAreEqual(nextVal + 1, exchRes.CurrentValue.LongProp);
FastAssertEqual <string>((-nextVal).ToString(), exchRes.PreviousValue.StringProp);
FastAssertEqual <string>((-(nextVal + 1)).ToString(), exchRes.CurrentValue.StringProp);
}
},
target => {
for (var i = 1; i < NumIterations; i += 2)
{
var nextVal = i;
var exchRes = target.SpinWaitForExchange((c, ctx) => valuesArr[c.LongProp + 1], valuesArr, valuesArr[nextVal]);
AssertAreEqual(nextVal, exchRes.PreviousValue.LongProp);
AssertAreEqual(nextVal + 1, exchRes.CurrentValue.LongProp);
FastAssertEqual <string>((-nextVal).ToString(), exchRes.PreviousValue.StringProp);
FastAssertEqual <string>((-(nextVal + 1)).ToString(), exchRes.CurrentValue.StringProp);
}
}
);
runner.AllThreadsTearDown = null;
// (T, Func<T, T, TContext, bool>)
valuesArr = Enumerable.Range(0, NumIterations + 1)
.Select(i => new DummyImmutableRef(i.ToString(), i))
.ToArray();
runner.GlobalSetUp = (target, _) => target.Value = valuesArr[0];
runner.AllThreadsTearDown = target => {
FastAssertEqual(NumIterations, target.Value.LongProp);
FastAssertEqual(NumIterations.ToString(), target.Value.StringProp);
};
runner.ExecuteSingleWriterSingleReaderTests(
target => {
for (var i = 0; i < NumIterations; i += 2)
{
var nextVal = i;
var exchRes = target.SpinWaitForExchange(valuesArr[nextVal + 1], (c, n, ctx) => n.LongProp == c.LongProp + ctx, 1);
AssertAreEqual(nextVal, exchRes.PreviousValue.LongProp);
AssertAreEqual(nextVal + 1, exchRes.CurrentValue.LongProp);
FastAssertEqual <string>(nextVal.ToString(), exchRes.PreviousValue.StringProp);
FastAssertEqual <string>((nextVal + 1).ToString(), exchRes.CurrentValue.StringProp);
}
},
target => {
for (var i = 1; i < NumIterations; i += 2)
{
var nextVal = i;
var exchRes = target.SpinWaitForExchange(valuesArr[nextVal + 1], (c, n, ctx) => n.LongProp == c.LongProp + ctx, 1);
AssertAreEqual(nextVal, exchRes.PreviousValue.LongProp);
AssertAreEqual(nextVal + 1, exchRes.CurrentValue.LongProp);
FastAssertEqual <string>(nextVal.ToString(), exchRes.PreviousValue.StringProp);
FastAssertEqual <string>((nextVal + 1).ToString(), exchRes.CurrentValue.StringProp);
}
}
);
runner.AllThreadsTearDown = null;
// (Func<T, TMapContext, T>, Func<T, T, TPredicateContext, bool>)
valuesArr = Enumerable.Range(0, NumIterations + 1)
.Select(i => new DummyImmutableRef((-i).ToString(), i))
.ToArray();
runner.GlobalSetUp = (target, _) => target.Value = valuesArr[0];
runner.AllThreadsTearDown = target => {
FastAssertEqual(NumIterations, target.Value.LongProp);
FastAssertEqual((-NumIterations).ToString(), target.Value.StringProp);
};
runner.ExecuteSingleWriterSingleReaderTests(
target => {
for (var i = 0; i < NumIterations; i += 2)
{
var nextVal = i;
var exchRes = target.SpinWaitForExchange((c, ctx) => ctx[c.LongProp + 1], valuesArr, (c, n, ctx) => n.LongProp == c.LongProp + ctx && c.LongProp == nextVal, 1);
AssertAreEqual(nextVal, exchRes.PreviousValue.LongProp);
AssertAreEqual(nextVal + 1, exchRes.CurrentValue.LongProp);
FastAssertEqual <string>((-nextVal).ToString(), exchRes.PreviousValue.StringProp);
FastAssertEqual <string>((-(nextVal + 1)).ToString(), exchRes.CurrentValue.StringProp);
}
},
target => {
for (var i = 1; i < NumIterations; i += 2)
{
var nextVal = i;
var exchRes = target.SpinWaitForExchange((c, ctx) => ctx[c.LongProp + 1], valuesArr, (c, n, ctx) => n.LongProp == c.LongProp + ctx && c.LongProp == nextVal, 1);
AssertAreEqual(nextVal, exchRes.PreviousValue.LongProp);
AssertAreEqual(nextVal + 1, exchRes.CurrentValue.LongProp);
FastAssertEqual <string>((-nextVal).ToString(), exchRes.PreviousValue.StringProp);
FastAssertEqual <string>((-(nextVal + 1)).ToString(), exchRes.CurrentValue.StringProp);
}
}
);
runner.AllThreadsTearDown = null;
// (Func<T, TContext, T>, Func<T, T, bool>)
valuesArr = Enumerable.Range(0, NumIterations + 1)
.Select(i => new DummyImmutableRef((-i).ToString(), i))
.ToArray();
runner.GlobalSetUp = (target, _) => target.Value = valuesArr[0];
runner.AllThreadsTearDown = target => {
FastAssertEqual(NumIterations, target.Value.LongProp);
FastAssertEqual((-NumIterations).ToString(), target.Value.StringProp);
};
runner.ExecuteSingleWriterSingleReaderTests(
target => {
for (var i = 0; i < NumIterations; i += 2)
{
var nextVal = i;
var exchRes = target.SpinWaitForExchange((c, ctx) => valuesArr[c.LongProp + ctx], 1, (c, n) => n.LongProp == c.LongProp + 1 && c.LongProp == nextVal);
AssertAreEqual(nextVal, exchRes.PreviousValue.LongProp);
AssertAreEqual(nextVal + 1, exchRes.CurrentValue.LongProp);
FastAssertEqual <string>((-nextVal).ToString(), exchRes.PreviousValue.StringProp);
FastAssertEqual <string>((-(nextVal + 1)).ToString(), exchRes.CurrentValue.StringProp);
}
},
target => {
for (var i = 1; i < NumIterations; i += 2)
{
var nextVal = i;
var exchRes = target.SpinWaitForExchange((c, ctx) => valuesArr[c.LongProp + ctx], 1, (c, n) => n.LongProp == c.LongProp + 1 && c.LongProp == nextVal);
AssertAreEqual(nextVal, exchRes.PreviousValue.LongProp);
AssertAreEqual(nextVal + 1, exchRes.CurrentValue.LongProp);
FastAssertEqual <string>((-nextVal).ToString(), exchRes.PreviousValue.StringProp);
FastAssertEqual <string>((-(nextVal + 1)).ToString(), exchRes.CurrentValue.StringProp);
}
}
);
runner.AllThreadsTearDown = null;
// (Func<T, T>, Func<T, T, TContext, bool>)
valuesArr = Enumerable.Range(0, NumIterations + 1)
.Select(i => new DummyImmutableRef((-i).ToString(), i))
.ToArray();
runner.GlobalSetUp = (target, _) => target.Value = valuesArr[0];
runner.AllThreadsTearDown = target => {
FastAssertEqual(NumIterations, target.Value.LongProp);
FastAssertEqual((-NumIterations).ToString(), target.Value.StringProp);
};
runner.ExecuteSingleWriterSingleReaderTests(
target => {
for (var i = 0; i < NumIterations; i += 2)
{
var nextVal = i;
var exchRes = target.SpinWaitForExchange(c => valuesArr[c.LongProp + 1], (c, n, ctx) => n.LongProp == c.LongProp + ctx && c.LongProp == nextVal, 1);
AssertAreEqual(nextVal, exchRes.PreviousValue.LongProp);
AssertAreEqual(nextVal + 1, exchRes.CurrentValue.LongProp);
FastAssertEqual <string>((-nextVal).ToString(), exchRes.PreviousValue.StringProp);
FastAssertEqual <string>((-(nextVal + 1)).ToString(), exchRes.CurrentValue.StringProp);
}
},
target => {
for (var i = 1; i < NumIterations; i += 2)
{
var nextVal = i;
var exchRes = target.SpinWaitForExchange(c => valuesArr[c.LongProp + 1], (c, n, ctx) => n.LongProp == c.LongProp + ctx && c.LongProp == nextVal, 1);
AssertAreEqual(nextVal, exchRes.PreviousValue.LongProp);
AssertAreEqual(nextVal + 1, exchRes.CurrentValue.LongProp);
FastAssertEqual <string>((-nextVal).ToString(), exchRes.PreviousValue.StringProp);
FastAssertEqual <string>((-(nextVal + 1)).ToString(), exchRes.CurrentValue.StringProp);
}
}
);
runner.AllThreadsTearDown = null;
}
/////////////////////////////////////////////////////////////////////
// Private Helper Methods
/////////////////////////////////////////////////////////////////////
/// <summary>
/// Constructs and trains a neural network, using the user selected
/// settings, to fit a model to the selected dataset.
///
/// Training continues until the maximum number of iterations has
/// been exceeded or the total network error is less than the set
/// minimum network error value. In the former case the trained
/// neural network is the network that achieved the minimum network
/// error during the training process.
/// </summary>
/// <returns>0 if successful otherwise -1</returns>
///
private int FitModel()
{
int result = 0;
bool converged = false;
bool invalidResult = false;
double netError = -1;
double minErr = double.MaxValue;
NeuralNet minNet = new NeuralNet(); // keeps track of the network with the minimum network error
NNetTrainer trainer = new NNetTrainer(); // this object trains the neural net
// populate the training set data
PopulateTrainingSet();
// initialize the trainer
trainer.AddNewTrainingSet(mInputVecs, mTargetVecs);
trainer.LearningConstant = LearnConst;
trainer.Momentum = Momentum;
// clear the neural network ready to fit the model data
mNet.ClearNeuralNetwork();
// initialize the network
mNet.NumInputs = 1; // a single input value (the 'x-value')
mNet.NumOutputs = 1; // a single output value (the 'y-value')
mNet.OutputUnitType = OutputFunction;
mNet.OutputUnitSlope = OutputSlope;
mNet.OutputUnitAmplify = OutputAmplify;
// use a fixed architecture of one hidden layer
mNet.AddLayer(NumberOfHiddenUnits, HiddenFunction, InitRange, HiddenSlope, HiddenAmplify);
// carry out the training
for (int i = 1; i <= NumIterations; i++)
{
trainer.TrainNeuralNet(mNet);
netError = trainer.NetError * ScaleFactor;
// check for an invalid result from the network trainer
if (double.IsNaN(netError) || double.IsInfinity(netError))
{
MessageBox.Show("The network trainer has produced an invalid result:\nNetwork Error = " + netError.ToString() +
"\nThe training process has been stopped.\nPlease adjust the model settings and try again.",
"ModelFit", MessageBoxButtons.OK, MessageBoxIcon.Exclamation);
invalidResult = true;
result = -1;
break;
}
if (netError < MinNetError)
{
// the solution has converged
MessageBox.Show("The solution has converged after " + i.ToString() + " iterations.",
"ModelFit", MessageBoxButtons.OK, MessageBoxIcon.Information);
// update the status bar
this.PanelIterations.Text = "Iterations: " + i.ToString();
this.PanelNetError.Text = "Minimum Error: " + netError.ToString("G5");
converged = true;
break;
}
// keep track of the minimum error value
if (netError < minErr)
{
// copy the state of the neural net at the minimum error value
minNet.CopyNeuralNet(mNet);
minErr = netError;
}
// show the current progress
if (i % 100 == 0)
{
this.PanelIterations.Text = "Iterations: " + i.ToString();
this.PanelNetError.Text = "Network Error: " + netError.ToString("G5");
Update();
}
trainer.ResetNetError();
}
if (!converged && !invalidResult)
{
// the solution has not converged within the given number of iterations
MessageBox.Show("The solution has not converged.\n" +
"The minimum error value was: " + minErr.ToString("G5") + "\n" +
"The neural network that achieved this minimum will be used to fit the model.",
"ModelFit", MessageBoxButtons.OK, MessageBoxIcon.Information);
// copy the net with settings at the minimum error value
mNet.CopyNeuralNet(minNet);
// update the status bar
this.PanelIterations.Text = "Iterations: " + NumIterations.ToString();
this.PanelNetError.Text = "Minimum Error: " + minErr.ToString("G5");
}
// show the output in excel - if requested
if (ExcelOutputCkBox.Checked && !invalidResult)
{
ShowOutputInExcel(mNet);
}
return(result);
}