public void RffWorkout()
{
Random rand = new Random();
var data = new[] {
new TestClass()
{
A = Enumerable.Range(0, 100).Select(x => (float)rand.NextDouble()).ToArray()
},
new TestClass()
{
A = Enumerable.Range(0, 100).Select(x => (float)rand.NextDouble()).ToArray()
}
};
var invalidData = ComponentCreation.CreateDataView(Env, new[] { new TestClassInvalidSchema {
A = 1
}, new TestClassInvalidSchema {
A = 1
} });
var validFitInvalidData = ComponentCreation.CreateDataView(Env, new[] { new TestClassBiggerSize {
A = new float[200]
}, new TestClassBiggerSize {
A = new float[200]
} });
var dataView = ComponentCreation.CreateDataView(Env, data);
var generator = new GaussianFourierSampler.Arguments();
var pipe = new RffEstimator(Env, new[] {
new RffTransform.ColumnInfo("A", "RffA", 5, false),
new RffTransform.ColumnInfo("A", "RffB", 10, true, new LaplacianFourierSampler.Arguments())
});
TestEstimatorCore(pipe, dataView, invalidInput: invalidData, validForFitNotValidForTransformInput: validFitInvalidData);
Done();
}
public void TestOldSavingAndLoading()
{
Random rand = new Random();
var data = new[] {
new TestClass()
{
A = Enumerable.Range(0, 100).Select(x => (float)rand.NextDouble()).ToArray()
},
new TestClass()
{
A = Enumerable.Range(0, 100).Select(x => (float)rand.NextDouble()).ToArray()
}
};
var dataView = ComponentCreation.CreateDataView(Env, data);
var est = new RffEstimator(Env, new[] {
new RffTransform.ColumnInfo("A", "RffA", 5, false),
new RffTransform.ColumnInfo("A", "RffB", 10, true, new LaplacianFourierSampler.Arguments())
});
var result = est.Fit(dataView).Transform(dataView);
var resultRoles = new RoleMappedData(result);
using (var ms = new MemoryStream())
{
TrainUtils.SaveModel(Env, Env.Start("saving"), ms, null, resultRoles);
ms.Position = 0;
var loadedView = ModelFileUtils.LoadTransforms(Env, dataView, ms);
}
}
