public class MultivariateDiscriminantWithBias extends MultivariateDiscriminant
Modifier and Type | Field and Description |
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protected Vector |
bias
Bias term that gets added the output of the matrix multiplication.
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Constructor and Description |
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MultivariateDiscriminantWithBias()
Default constructor.
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MultivariateDiscriminantWithBias(int numInputs,
int numOutputs)
Creates a new MultivariateDiscriminantWithBias
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MultivariateDiscriminantWithBias(Matrix discriminant)
Creates a new instance of MultivariateDiscriminantWithBias.
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MultivariateDiscriminantWithBias(Matrix discriminant,
Vector bias)
Creates a new instance of MultivariateDiscriminantWithBias.
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Modifier and Type | Method and Description |
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MultivariateDiscriminantWithBias |
clone()
This makes public the clone method on the
Object class and
removes the exception that it throws. |
Matrix |
computeParameterGradient(Vector input)
Computes the derivative of the function about the input with respect
to the parameters of the function.
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void |
convertFromVector(Vector parameters)
Uploads a matrix from a row-stacked vector of parameters.
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Vector |
convertToVector()
Creates a row-stacked version of the discriminant.
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Vector |
evaluate(Vector input)
Evaluates the function on the given input and returns the output.
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Vector |
getBias()
Getter for bias
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void |
setBias(Vector bias)
Setter for bias
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computeParameterGradient, differentiate, getDiscriminant, getInputDimensionality, getOutputDimensionality, setDiscriminant, toString
protected Vector bias
public MultivariateDiscriminantWithBias()
public MultivariateDiscriminantWithBias(int numInputs, int numOutputs)
numInputs
- Number of inputs of the function (number of matrix columns)numOutputs
- Number of outputs of the function (number of matrix rows)public MultivariateDiscriminantWithBias(Matrix discriminant)
discriminant
- internal matrix to premultiply input vectors by.public MultivariateDiscriminantWithBias(Matrix discriminant, Vector bias)
discriminant
- internal matrix to premultiply input vectors by.bias
- Bias term that gets added the output of the matrix multiplication.public MultivariateDiscriminantWithBias clone()
AbstractCloneableSerializable
Object
class and
removes the exception that it throws. Its default behavior is to
automatically create a clone of the exact type of object that the
clone is called on and to copy all primitives but to keep all references,
which means it is a shallow copy.
Extensions of this class may want to override this method (but call
super.clone()
to implement a "smart copy". That is, to target
the most common use case for creating a copy of the object. Because of
the default behavior being a shallow copy, extending classes only need
to handle fields that need to have a deeper copy (or those that need to
be reset). Some of the methods in ObjectUtil
may be helpful in
implementing a custom clone method.
Note: The contract of this method is that you must use
super.clone()
as the basis for your implementation.clone
in interface GradientDescendable
clone
in interface Vectorizable
clone
in interface VectorizableVectorFunction
clone
in interface CloneableSerializable
clone
in class MultivariateDiscriminant
public Vector evaluate(Vector input)
Evaluator
public Vector getBias()
public void setBias(Vector bias)
bias
- Bias term that gets added the output of the matrix multiplication.public Vector convertToVector()
MultivariateDiscriminant
convertToVector
in interface Vectorizable
convertToVector
in class MultivariateDiscriminant
public void convertFromVector(Vector parameters)
MultivariateDiscriminant
convertFromVector
in interface Vectorizable
convertFromVector
in class MultivariateDiscriminant
parameters
- row-stacked version of discriminantpublic Matrix computeParameterGradient(Vector input)
GradientDescendable
computeParameterGradient
in interface GradientDescendable
computeParameterGradient
in interface ParameterGradientEvaluator<Vector,Vector,Matrix>
computeParameterGradient
in class MultivariateDiscriminant
input
- Point about which to differentiate w.r.t. the parameters.