/// <summary>Generate Logistic 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)
{
X = IncreaseDimensions(X, this.PolynomialFeatures);
this.Preprocess(X);
// guarantee 1/0 based label vector
y = y.ToBinary(f => f == 1d, falseValue: 0d);
// add intercept term
X = X.Insert(Vector.Ones(X.Rows), 0, VectorType.Col, false);
Vector theta = Vector.Rand(X.Cols);
// run gradient descent
var optimizer = new numl.Math.Optimization.Optimizer(theta, this.MaxIterations, this.LearningRate)
{
CostFunction = new numl.Math.Functions.Cost.LogisticCostFunction()
{
X = X,
Y = y,
Lambda = this.Lambda,
Regularizer = new numl.Math.Functions.Regularization.L2Regularizer(),
LogisticFunction = this.LogisticFunction
}
};
optimizer.Run();
LogisticRegressionModel model = new LogisticRegressionModel()
{
Descriptor = this.Descriptor,
NormalizeFeatures = base.NormalizeFeatures,
FeatureNormalizer = base.FeatureNormalizer,
FeatureProperties = base.FeatureProperties,
Theta = optimizer.Properties.Theta,
LogisticFunction = this.LogisticFunction,
PolynomialFeatures = this.PolynomialFeatures
};
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);
}
/// <summary>
/// Generates a new Collaborative Filtering model.
/// </summary>
/// <param name="X">Training matrix values.</param>
/// <param name="y">Vector of entity identifiers.</param>
/// <returns></returns>
public override IModel Generate(Matrix X, Vector y)
{
this.Preprocess(X.Copy());
// inputs are ratings from each user (X = entities x ratings), y = entity id.
// create rating range in case we don't have one already
if (this.Ratings == null)
this.Ratings = new Range(X.Min(), X.Max());
// indicator matrix of 1's where rating was provided otherwise 0's.
Matrix R = X.ToBinary(f => this.Ratings.Test(f));
// The mean needs to be values within rating range only.
Vector mean = X.GetRows().Select(s =>
s.Where(w => this.Ratings.Test(w)).Sum() /
s.Where(w => this.Ratings.Test(w)).Count()
).ToVector();
// update feature averages before preprocessing features.
this.FeatureProperties.Average = mean;
this.Preprocess(X);
// where references could be user ratings and entities are movies / books, etc.
int references = X.Cols, entities = X.Rows;
// initialize Theta parameters
Matrix ThetaX = Matrix.Rand(entities, this.CollaborativeFeatures, -1d);
Matrix ThetaY = Matrix.Rand(references, this.CollaborativeFeatures, -1d);
numl.Math.Functions.Cost.ICostFunction costFunction = new numl.Math.Functions.Cost.CofiCostFunction()
{
CollaborativeFeatures = this.CollaborativeFeatures,
Lambda = this.Lambda,
R = R,
Regularizer = null,
X = ThetaX,
Y = X.Unshape()
};
// we're optimising two params so combine them
Vector Theta = Vector.Combine(ThetaX.Unshape(), ThetaY.Unshape());
numl.Math.Optimization.Optimizer optimizer = new numl.Math.Optimization.Optimizer(Theta, this.MaxIterations, this.LearningRate)
{
CostFunction = costFunction
};
optimizer.Run();
// extract the optimised parameter Theta
ThetaX = optimizer.Properties.Theta.Slice(0, (ThetaX.Rows * ThetaX.Cols) - 1).Reshape(entities, VectorType.Row);
ThetaY = optimizer.Properties.Theta.Slice(ThetaX.Rows * ThetaX.Cols, Theta.Length - 1).Reshape(references, VectorType.Row);
// create reference mappings, each value is the original index.
this.ReferenceFeatureMap = (this.ReferenceFeatureMap == null ? Vector.Create(references, i => i) : this.ReferenceFeatureMap);
this.EntityFeatureMap = (this.EntityFeatureMap == null ? Vector.Create(entities, i => i) : this.EntityFeatureMap);
return new CofiRecommenderModel()
{
Descriptor = this.Descriptor,
NormalizeFeatures = this.NormalizeFeatures,
FeatureNormalizer = this.FeatureNormalizer,
FeatureProperties = this.FeatureProperties,
Ratings = this.Ratings,
ReferenceFeatureMap = this.ReferenceFeatureMap,
EntityFeatureMap = this.EntityFeatureMap,
Mu = mean,
Y = y,
Reference = X,
ThetaX = ThetaX,
ThetaY = ThetaY
};
}
/// <summary>Generate Logistic 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)
{
X = IncreaseDimensions(X, this.PolynomialFeatures);
this.Preprocess(X);
// guarantee 1/0 based label vector
y = y.ToBinary(f => f == 1d, falseValue: 0d);
// add intercept term
X = X.Insert(Vector.Ones(X.Rows), 0, VectorType.Col, false);
Vector theta = Vector.Rand(X.Cols);
// run gradient descent
var optimizer = new numl.Math.Optimization.Optimizer(theta, this.MaxIterations, this.LearningRate)
{
CostFunction = new numl.Math.Functions.Cost.LogisticCostFunction()
{
X = X,
Y = y,
Lambda = this.Lambda,
Regularizer = new numl.Math.Functions.Regularization.L2Regularizer(),
LogisticFunction = this.LogisticFunction
}
};
optimizer.Run();
LogisticRegressionModel model = new LogisticRegressionModel()
{
Descriptor = this.Descriptor,
NormalizeFeatures = base.NormalizeFeatures,
FeatureNormalizer = base.FeatureNormalizer,
FeatureProperties = base.FeatureProperties,
Theta = optimizer.Properties.Theta,
LogisticFunction = this.LogisticFunction,
PolynomialFeatures = this.PolynomialFeatures
};
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;
}
/// <summary>
/// Generates a new Collaborative Filtering model.
/// </summary>
/// <param name="X">Training matrix values.</param>
/// <param name="y">Vector of entity identifiers.</param>
/// <returns></returns>
public override IModel Generate(Matrix X, Vector y)
{
this.Preprocess(X.Copy());
// inputs are ratings from each user (X = entities x ratings), y = entity id.
// create rating range in case we don't have one already
if (this.Ratings == null)
{
this.Ratings = new Range(X.Min(), X.Max());
}
// indicator matrix of 1's where rating was provided otherwise 0's.
Matrix R = X.ToBinary(f => this.Ratings.Test(f));
// The mean needs to be values within rating range only.
Vector mean = X.GetRows().Select(s =>
s.Where(w => this.Ratings.Test(w)).Sum() /
s.Where(w => this.Ratings.Test(w)).Count()
).ToVector();
// update feature averages before preprocessing features.
this.FeatureProperties.Average = mean;
this.Preprocess(X);
// where references could be user ratings and entities are movies / books, etc.
int references = X.Cols, entities = X.Rows;
// initialize Theta parameters
Matrix ThetaX = Matrix.Rand(entities, this.CollaborativeFeatures, -1d);
Matrix ThetaY = Matrix.Rand(references, this.CollaborativeFeatures, -1d);
numl.Math.Functions.Cost.ICostFunction costFunction = new numl.Math.Functions.Cost.CofiCostFunction()
{
CollaborativeFeatures = this.CollaborativeFeatures,
Lambda = this.Lambda,
R = R,
Regularizer = null,
X = ThetaX,
Y = X.Unshape()
};
// we're optimising two params so combine them
Vector Theta = Vector.Combine(ThetaX.Unshape(), ThetaY.Unshape());
numl.Math.Optimization.Optimizer optimizer = new numl.Math.Optimization.Optimizer(Theta, this.MaxIterations, this.LearningRate)
{
CostFunction = costFunction
};
optimizer.Run();
// extract the optimised parameter Theta
ThetaX = optimizer.Properties.Theta.Slice(0, (ThetaX.Rows * ThetaX.Cols) - 1).Reshape(entities, VectorType.Row);
ThetaY = optimizer.Properties.Theta.Slice(ThetaX.Rows * ThetaX.Cols, Theta.Length - 1).Reshape(references, VectorType.Row);
// create reference mappings, each value is the original index.
this.ReferenceFeatureMap = (this.ReferenceFeatureMap == null ? Vector.Create(references, i => i) : this.ReferenceFeatureMap);
this.EntityFeatureMap = (this.EntityFeatureMap == null ? Vector.Create(entities, i => i) : this.EntityFeatureMap);
return(new CofiRecommenderModel()
{
Descriptor = this.Descriptor,
NormalizeFeatures = this.NormalizeFeatures,
FeatureNormalizer = this.FeatureNormalizer,
FeatureProperties = this.FeatureProperties,
Ratings = this.Ratings,
ReferenceFeatureMap = this.ReferenceFeatureMap,
EntityFeatureMap = this.EntityFeatureMap,
Mu = mean,
Y = y,
Reference = X,
ThetaX = ThetaX,
ThetaY = ThetaY
});
}
