/// <summary>Generate Linear Regression model based on a set of examples.</summary>
/// <param name="x">The Matrix to process.</param>
/// <param name="y">The Vector to process.</param>
/// <returns>Model.</returns>
public override IModel Generate(Matrix x, Vector y)
{
// create initial theta
Vector theta = Vector.Ones(x.Cols + 1);
Matrix copy = x.Copy();
// normalise features
for (int i = 0; i < copy.Cols; i++)
{
var j = FeatureNormalizer.FeatureScale(copy[i, VectorType.Col]);
for (int k = 0; k < copy.Rows; k++)
{
copy[k, i] = j[k];
}
}
// add intercept term
copy = copy.Insert(Vector.Ones(copy.Rows), 0, VectorType.Col);
// run gradient descent
var run = GradientDescent.Run(theta, copy, y, this.MaxIterations, this.LearningRate, new LinearCostFunction(), this.Lambda, new Regularization());
// once converged create model and apply theta
LinearRegressionModel model = new LinearRegressionModel(x.Mean(VectorType.Row), x.StdDev(VectorType.Row))
{
Descriptor = this.Descriptor,
Theta = run.Item2
};
return(model);
}
/// <summary>Generate Linear Regression model based on a set of examples.</summary>
/// <param name="x">The Matrix to process.</param>
/// <param name="y">The Vector to process.</param>
/// <returns>Model.</returns>
public override IModel Generate(Matrix x, Vector y)
{
// create initial theta
Vector theta = Vector.Ones(x.Cols + 1);
Matrix copy = x.Copy();
// normalise features
for (int i = 0; i < copy.Cols; i++)
{
var j = FeatureNormalizer.FeatureScale(copy[i, VectorType.Col]);
for (int k = 0; k < copy.Rows; k++)
{
copy[k, i] = j[k];
}
}
// add intercept term
copy = copy.Insert(Vector.Ones(copy.Rows), 0, VectorType.Col);
// run gradient descent
var run = GradientDescent.Run(theta, copy, y, MaxIterations, LearningRate, new LinearCostFunction(),
Lambda, new Regularization());
// once converged create model and apply theta
LinearRegressionModel model = new LinearRegressionModel(x.Mean(VectorType.Row), x.StdDev(VectorType.Row))
{
Descriptor = Descriptor,
Theta = run.Item2
};
return model;
}
/// <summary>Generate Linear Regression model based on a set of examples.</summary>
/// <param name="X">The Matrix to process.</param>
/// <param name="y">The Vector to process.</param>
/// <returns>Model.</returns>
public override IModel Generate(Matrix X, Vector y)
{
Preprocess(X);
// copy matrix
var copy = X.Copy();
// add intercept term
copy = copy.Insert(Vector.Ones(copy.Rows), 0, VectorType.Col);
// create initial theta
var theta = Vector.Rand(copy.Cols);
// run gradient descent
var optimizer = new Optimizer(theta, MaxIterations, LearningRate)
{
CostFunction = new LinearCostFunction
{
X = copy,
Y = y,
Lambda = Lambda,
Regularizer = new L2Regularizer()
}
};
optimizer.Run();
// once converged create model and apply theta
var model = new LinearRegressionModel
{
Descriptor = Descriptor,
NormalizeFeatures = NormalizeFeatures,
FeatureNormalizer = FeatureNormalizer,
FeatureProperties = FeatureProperties,
Theta = optimizer.Properties.Theta
};
return(model);
}
/// <summary>Generate Linear Regression model based on a set of examples.</summary>
/// <param name="X">The Matrix to process.</param>
/// <param name="y">The Vector to process.</param>
/// <returns>Model.</returns>
public override IModel Generate(Matrix X, Vector y)
{
this.Preprocess(X);
// copy matrix
Matrix copy = X.Copy();
// add intercept term
copy = copy.Insert(Vector.Ones(copy.Rows), 0, VectorType.Col);
// create initial theta
Vector theta = Vector.Rand(copy.Cols);
// run gradient descent
var optimizer = new numl.Math.Optimization.Optimizer(theta, this.MaxIterations, this.LearningRate)
{
CostFunction = new numl.Math.Functions.Cost.LinearCostFunction()
{
X = copy,
Y = y,
Lambda = this.Lambda,
Regularizer = new numl.Math.Functions.Regularization.L2Regularizer()
}
};
optimizer.Run();
// once converged create model and apply theta
LinearRegressionModel model = new LinearRegressionModel()
{
Descriptor = this.Descriptor,
NormalizeFeatures = base.NormalizeFeatures,
FeatureNormalizer = base.FeatureNormalizer,
FeatureProperties = base.FeatureProperties,
Theta = optimizer.Properties.Theta
};
return(model);
}
public void Load_LinearRegression()
{
string xml = @"<?xml version=""1.0""?>
<LinearRegressionModel>
<Descriptor Type=""None"" Name="""">
<Features Length=""2"">
<Property Name=""LeftOperand"" Type=""Double"" Discrete=""False"" Start=""0"" />
<Property Name=""RightOperand"" Type=""Double"" Discrete=""False"" Start=""1"" />
</Features>
<Label>
<Property Name=""Result"" Type=""Double"" Discrete=""False"" Start=""-1"" />
</Label>
</Descriptor>
<v size=""3"">
<e>73299.802339155649</e>
<e>13929.858323609986</e>
<e>28235.048808708329</e>
</v>
<v size=""2"">
<e>22155.108339050836</e>
<e>25812.304093938921</e>
</v>
<v size=""2"">
<e>14120.242563388447</e>
<e>14302.3670376599</e>
</v>
</LinearRegressionModel>";
LinearRegressionModel model = new LinearRegressionModel();
model.LoadXml(xml);
}
/// <summary>Generate Linear Regression model based on a set of examples.</summary>
/// <param name="X">The Matrix to process.</param>
/// <param name="y">The Vector to process.</param>
/// <returns>Model.</returns>
public override IModel Generate(Matrix X, Vector y)
{
this.Preprocess(X);
// copy matrix
Matrix copy = X.Copy();
// add intercept term
copy = copy.Insert(Vector.Ones(copy.Rows), 0, VectorType.Col);
// create initial theta
Vector theta = Vector.Rand(copy.Cols);
// run gradient descent
var optimizer = new numl.Math.Optimization.Optimizer(theta, this.MaxIterations, this.LearningRate)
{
CostFunction = new numl.Math.Functions.Cost.LinearCostFunction()
{
X = copy,
Y = y,
Lambda = this.Lambda,
Regularizer = new numl.Math.Functions.Regularization.L2Regularizer()
}
};
optimizer.Run();
// once converged create model and apply theta
LinearRegressionModel model = new LinearRegressionModel()
{
Descriptor = this.Descriptor,
NormalizeFeatures = base.NormalizeFeatures,
FeatureNormalizer = base.FeatureNormalizer,
FeatureProperties = base.FeatureProperties,
Theta = optimizer.Properties.Theta
};
return model;
}
