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 = ML.Data.ReadFromEnumerable(new[] { new TestClassInvalidSchema {
A = 1
}, new TestClassInvalidSchema {
A = 1
} });
var validFitInvalidData = ML.Data.ReadFromEnumerable(new[] { new TestClassBiggerSize {
A = new float[200]
}, new TestClassBiggerSize {
A = new float[200]
} });
var dataView = ML.Data.ReadFromEnumerable(data);
var generator = new GaussianFourierSampler.Arguments();
var pipe = new RandomFourierFeaturizingEstimator(Env, new[] {
new RandomFourierFeaturizingTransformer.ColumnInfo("RffA", 5, false, "A"),
new RandomFourierFeaturizingTransformer.ColumnInfo("RffB", 10, true, "A", 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 = ML.Data.ReadFromEnumerable(data);
var est = new RandomFourierFeaturizingEstimator(Env, new[] {
new RandomFourierFeaturizingTransformer.ColumnInfo("RffA", 5, false, "A"),
new RandomFourierFeaturizingTransformer.ColumnInfo("RffB", 10, true, "A", 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);
}
}
