public void RandomIndexSampler_Sample_SampleSize_Too_Large()
{
var values = new int[] { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3 };
var sampleSize = 100;
var sut = new RandomIndexSampler <int>(42);
var sampleIndices = sut.Sample(values, sampleSize);
}
public void RandomIndexSampler_Sample_Indexed_DataIndices_Too_Large()
{
var values = new int[] { 1, 1, 1 };
var indices = new int[] { 0, 1, 2, 3, 10, 11, 12, 13, 18, 19, 20 };
var sampleSize = 2;
var sut = new RandomIndexSampler <int>(42);
var sampleIndices = sut.Sample(values, sampleSize, indices);
}
public void RandomIndexSampler_Sample()
{
var values = new int[] { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3 };
var sampleSize = values.Length / 2;
var sut = new RandomIndexSampler <int>(42);
var sampleIndices = sut.Sample(values, sampleSize);
var actual = values.GetIndices(sampleIndices);
var expected = new int[] { 1, 2, 1, 2, 1, 3, 1, 2, 2, 1, 2, };
CollectionAssert.AreEqual(expected, actual);
}
public void RandomIndexSampler_Sample_Indexed()
{
var values = new int[] { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3 };
var indices = new int[] { 0, 1, 2, 3, 10, 11, 12, 13, 18, 19, 20 };
var sampleSize = 6;
var sut = new RandomIndexSampler <int>(42);
var sampleIndices = sut.Sample(values, sampleSize, indices);
var actual = values.GetIndices(sampleIndices);
var expected = new int[] { 3, 2, 1, 1, 1, 2 };
CollectionAssert.AreEqual(expected, actual);
}
public void SharpLearning_With_Cntk_Example()
{
// Load data
var(observations, targets) = DataSetUtilities.LoadWinequalityWhite();
// transform data for neural net
var transform = new MinMaxTransformer(0.0, 1.0);
transform.Transform(observations, observations);
var featureCount = observations.ColumnCount;
var observationCount = observations.RowCount;
var targetCount = 1;
var inputShape = new int[] { featureCount, 1 };
var outputShape = new int[] { targetCount };
// Convert data to float, and wrap as minibatch data.
var observationsFloat = observations.Data().Select(v => (float)v).ToArray();
var observationsData = new MemoryMinibatchData(observationsFloat, inputShape, observationCount);
var targetsFloat = targets.Select(v => (float)v).ToArray();
var targetsData = new MemoryMinibatchData(targetsFloat, outputShape, observationCount);
var dataType = DataType.Float;
var device = DeviceDescriptor.CPUDevice;
// setup input and target variables.
var inputVariable = Layers.Input(inputShape, dataType);
var targetVariable = Variable.InputVariable(outputShape, dataType);
// setup name to variable
var nameToVariable = new Dictionary <string, Variable>
{
{ "observations", inputVariable },
{ "targets", targetVariable },
};
// Get cross validation folds.
var sampler = new RandomIndexSampler <double>(seed: 24);
var crossValidationIndexSets = CrossValidationUtilities
.GetKFoldCrossValidationIndexSets(sampler, foldCount: 10, targets: targets);
var predictions = new double[observationCount];
// Run cross validation loop.
foreach (var set in crossValidationIndexSets)
{
// setup data.
var trainingNameToData = new Dictionary <string, MemoryMinibatchData>
{
{ "observations", observationsData.GetSamples(set.trainingIndices) },
{ "targets", targetsData.GetSamples(set.trainingIndices) }
};
var validationNameToData = new Dictionary <string, MemoryMinibatchData>
{
{ "observations", observationsData.GetSamples(set.validationIndices) },
{ "targets", targetsData.GetSamples(set.validationIndices) }
};
var trainSource = new MemoryMinibatchSource(nameToVariable, trainingNameToData, seed: 232, randomize: true);
var validationSource = new MemoryMinibatchSource(nameToVariable, validationNameToData, seed: 232, randomize: false);
// Create model and fit.
var model = CreateModel(inputVariable, targetVariable, targetCount, dataType, device);
model.Fit(trainSource, batchSize: 128, epochs: 10);
// Predict.
var predictionsRaw = model.Predict(validationSource);
var currentPredictions = predictionsRaw.Select(v => (double)v.Single()).ToArray();
// set cross-validation predictions
var validationIndices = set.validationIndices;
for (int i = 0; i < validationIndices.Length; i++)
{
predictions[validationIndices[i]] = currentPredictions[i];
}
}
Trace.WriteLine(FormatErrorString(targets, predictions));
}
