public static FNode BindNode(FNode Equation, CellVector Bindings, Dictionary<string, int> Map)
{
FNode t = Equation.CloneOfMe();
foreach(KeyValuePair<string,int> kv in Map)
{
string name = kv.Key;
int idx = kv.Value;
FNode b = new FNodeValue(null, Bindings[idx]);
t = FNodeCompacter.Bind(t, b, name);
}
return t;
}
public override void Invoke()
{
// Get data!!!! //
string name = this._Parameters["@NAME"].Expression.Evaluate().valueSTRING;
DataSet data = this._Parameters["@DATA"].Data;
FNodeSet x = this._Parameters["@INPUT"].ExpressionSet;
int Clusters = (int)this._Parameters["@CLUSTERS"].Expression.Evaluate().INT;
FNode w = new FNodeValue(null, new Cell(1D));
if (this._Parameters.Exists("@WEIGHT"))
w = this._Parameters["@WEIGHT"].Expression;
int MaxITT = 25;
if (this._Parameters.Exists("@MAXITT"))
MaxITT = (int)this._Parameters["@MAXITT"].Expression.Evaluate().valueINT;
Predicate p = Predicate.TrueForAll;
if (this._Parameters.Exists("@WHERE"))
p = new Predicate(this._Parameters["@WHERE"].Expression);
// Model //
Thoroughbred.Seabiscut.RowCluster clus = new Thoroughbred.Seabiscut.RowCluster(name, data, p, x, w, Clusters);
clus.MaxItterations = MaxITT;
// Fit //
clus.Render();
// Push the model to the home heap //
this.Home.Models.Reallocate(clus.Name, clus);
// Append the notificiations //
this.Home.IO.AppendBuffer(clus.Statistics());
}
public override void Invoke()
{
// Get data!!!! //
string name = this._Parameters["@NAME"].Expression.Evaluate().valueSTRING;
DataSet data = this._Parameters["@DATA"].Data;
FNodeSet x = this._Parameters["@INPUT"].ExpressionSet;
FNode y = this._Parameters["@OUTPUT"].Expression;
FNode w = new FNodeValue(null, new Cell(1D));
if (this._Parameters.Exists("@WEIGHT"))
w = this._Parameters["@WEIGHT"].Expression;
Lambda link = this._Parameters["@LINK"].Lambda;
int MaxITT = 10;
if (this._Parameters.Exists("@MAXITT"))
MaxITT = (int)this._Parameters["@MAXITT"].Expression.Evaluate().valueINT;
Predicate p = Predicate.TrueForAll;
if (this._Parameters.Exists("@WHERE"))
p = new Predicate(this._Parameters["@WHERE"].Expression);
// Model //
Thoroughbred.ARizenTalent.GeneralizedLinearModel glm = new Thoroughbred.ARizenTalent.GeneralizedLinearModel(name, data, p, y, x, w, link);
glm.MaximumIterations = MaxITT;
glm.Render();
// Push the model to the home heap //
//RecordSet rs1 = glm.ParameterData;
//RecordSet rs2 = glm.ModelData;
//this.Home.ChunkHeap.Reallocate(rs1.Name, rs1);
//this.Home.ChunkHeap.Reallocate(rs2.Name, rs2);
//this.Home.Lambdas.Reallocate(glm.Name + "_LINK", link);
this.Home.Models.Reallocate(glm.Name, glm);
}
public override void Invoke()
{
// Get data!!!! //
string name = this._Parameters["@NAME"].Expression.Evaluate().valueSTRING;
DataSet data = this._Parameters["@DATA"].Data;
FNode x = this._Parameters["@INPUT"].Expression;
FNode y = this._Parameters["@OUTPUT"].Expression;
FNode w = new FNodeValue(null, new Cell(1D));
if (this._Parameters.Exists("@WEIGHT"))
w = this._Parameters["@WEIGHT"].Expression;
Cell scale = new Cell(10D);
if (this._Parameters.Exists("@SCALE"))
scale = this._Parameters["@SCALE"].Expression.Evaluate();
int MaxITT = 10;
if (this._Parameters.Exists("@MAXITT"))
MaxITT = (int)this._Parameters["@MAXITT"].Expression.Evaluate().valueINT;
bool Strict = false;
if (this._Parameters.Exists("@STRICT"))
Strict = this._Parameters["@STRICT"].Expression.Evaluate().valueBOOL;
Predicate p = Predicate.TrueForAll;
if (this._Parameters.Exists("@WHERE"))
p = new Predicate(this._Parameters["@WHERE"].Expression);
// Model //
Thoroughbred.ARizenTalent.NonlinearRegressionModel nlm = new Thoroughbred.ARizenTalent.NonlinearRegressionModel(name, data, p, y, x, w);
nlm.Scale = scale;
nlm.MaximumIterations = MaxITT;
nlm.IsStrict = Strict;
if (this._Parameters.Exists("@BETA"))
nlm.Beta = this._Parameters["@BETA"].Matrix.Evaluate().ToVector;
// Fit //
nlm.Render();
// Push the model to the home heap //
//RecordSet rs1 = nlm.ParameterData;
//RecordSet rs2 = nlm.ModelData;
//this.Home.ChunkHeap.Reallocate(rs1.Name, rs1);
//this.Home.ChunkHeap.Reallocate(rs2.Name, rs2);
this.Home.Models.Reallocate(nlm.Name, nlm);
}
public override void Invoke()
{
// Get data!!!! //
string name = this._Parameters["@NAME"].Expression.Evaluate().valueSTRING;
DataSet data = this._Parameters["@DATA"].Data;
FNodeSet x = this._Parameters["@INPUT"].ExpressionSet;
FNode y = this._Parameters["@OUTPUT"].Expression;
FNode w = new FNodeValue(null, new Cell(1D));
if (this._Parameters.Exists("@WEIGHT"))
w = this._Parameters["@WEIGHT"].Expression;
Predicate p = Predicate.TrueForAll;
if (this._Parameters.Exists("@WHERE"))
p = new Predicate(this._Parameters["@WHERE"].Expression);
int threads = 1;
if (this._Parameters.Exists("@PARTITIONS"))
threads = (int)this._Parameters["@PARTITIONS"].Expression.Evaluate().valueINT;
// Model //
Thoroughbred.ARizenTalent.LinearRegression lm = new Thoroughbred.ARizenTalent.LinearRegression(name, data, p, y, x, w);
if (threads == 1)
{
lm.Render();
}
else
{
lm.PartitionedRender(threads);
}
// Push the model to the home heap //
//RecordSet rs1 = lm.ParameterData;
//RecordSet rs2 = lm.ModelData;
//this.Home.ChunkHeap.Reallocate(rs1.Name, rs1);
//this.Home.ChunkHeap.Reallocate(rs2.Name, rs2);
this.Home.Models.Reallocate(lm.Name, lm);
this.Home.IO.AppendBuffer(lm.Statistics());
}