public void Compute(decimal[] vectror, int lag)
{
if (vectror == null)
{
throw new ArgumentNullException("vector");
}
if (lag >= vectror.Length)
{
throw new IndexOutOfRangeException("lag can't be more or equal x.Length");
}
decimal[] x = new decimal[vectror.Length - lag];
for (int i = 0; i < vectror.Length - lag; i++)
{
x[i] = vectror[i];
}
decimal[] y = new decimal[vectror.Length - lag];
for (int i = lag; i < vectror.Length; i++)
{
y[i - lag] = vectror[i];
}
RegressionMethod.Compute(y, x);
}
public PredictionEngine <ModelInput, ModelOutput> CreatePredictionEngine(RegressionMethod method)
{
if (Transformers.ContainsKey(method))
{
var modelData = Transformers[method];
return(modelData.Context.Model.CreatePredictionEngine <ModelInput, ModelOutput>(modelData.Model, inputSchema: modelData.Schema));
}
else
{
var path = $"{_path}\\ML\\Models\\{method.ToString()}.zip";
if (File.Exists(path))
{
MLContext context = new MLContext();
var model = context.Model.Load(path, out var schema);
Transformers[method] = new ModelData()
{
Context = context,
Schema = schema,
Model = model
};
var predictEngine = context.Model.CreatePredictionEngine <ModelInput, ModelOutput>(model, inputSchema: schema);
return(predictEngine);
}
}
throw new InvalidOperationException($"Regression Model ({method} couldn't be found.");
}
public RegressionWithRanking(RegressionMethod regressionMethod, RegressionFeatureRankingMethod ranker, int nfeatures,
Parameters regressionParam, Parameters rankerParam)
{
this.regressionMethod = regressionMethod;
this.ranker = ranker;
this.nfeatures = nfeatures;
this.regressionParam = regressionParam;
this.rankerParam = rankerParam;
}
public RegressionWithRankingMultiSizes(RegressionMethod classifier, RegressionFeatureRankingMethod ranker,
double reductionFactor, int maxFeatures, Parameters classifierParam, Parameters rankerParam)
{
this.classifier = classifier;
this.ranker = ranker;
this.reductionFactor = reductionFactor;
this.maxFeatures = maxFeatures;
this.classifierParam = classifierParam;
this.rankerParam = rankerParam;
}
public void Compute(decimal[] y, decimal[] x)
{
if (y == null)
{
throw new ArgumentNullException("y");
}
if (x == null)
{
throw new ArgumentNullException("x");
}
if (x.Length != y.Length)
{
throw new DifferentLengthException();
}
RegressionMethod.Compute(y, x);
}
public void Compute(decimal[] y, params decimal[][] xn)
{
RegressionMethod.Compute(y, xn);
}
public void Compute(bool[] y, params decimal[][] xn)
{
RegressionMethod.Compute(y.Select(i => Convert.ToDecimal(i)).ToArray(), xn);
}
