public class LinearBinaryCategorizer extends AbstractDiscriminantBinaryCategorizer<Vectorizable> implements VectorInputEvaluator<Vectorizable,java.lang.Boolean>, ThresholdBinaryCategorizer<Vectorizable>
LinearBinaryCategorizer
class implements a binary
categorizer that is implemented by a linear function. More formally, the
classifier is parameterized by a weight vector (w) and a bias (b) and
computes the output for a given input (x) as:
f(x) = w * x + b
The categorization is done by:
f(x) >= 0Modifier and Type | Field and Description |
---|---|
protected double |
bias
The bias term.
|
static double |
DEFAULT_BIAS
The default bias is 0.0.
|
protected Vector |
weights
The weight vector.
|
BINARY_CATEGORIES
Constructor and Description |
---|
LinearBinaryCategorizer()
Creates a new instance of LinearBinaryCategorizer.
|
LinearBinaryCategorizer(LinearBinaryCategorizer other)
Creates a new copy of a LinearBinaryCategorizer.
|
LinearBinaryCategorizer(Vector weights,
double bias)
Creates a new instance of LinearBinaryCategorizer with the given weights
and bias.
|
Modifier and Type | Method and Description |
---|---|
LinearBinaryCategorizer |
clone()
This makes public the clone method on the
Object class and
removes the exception that it throws. |
double |
evaluateAsDouble(Vector input)
A convenience method for evaluating a Vector object as a double, thus
avoiding the convertToVector call from Vectorizable.
|
double |
evaluateAsDouble(Vectorizable input)
Categorizes the given input vector as a double by:
weights * input + bias
|
double |
getBias()
Gets the bias term.
|
int |
getInputDimensionality()
Gets the expected dimensionality of the input vector to the evaluator,
if it is known.
|
double |
getThreshold()
Gets the threshold, which is the negative of the bias.
|
Vector |
getWeights()
Gets the weight vector.
|
void |
setBias(double bias)
Sets the bias term.
|
void |
setThreshold(double threshold)
Sets the threshold, which is the negative of the bias.
|
void |
setWeights(Vector weights)
Sets the weight vector.
|
java.lang.String |
toString() |
evaluate, evaluateWithDiscriminant
getCategories
equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
evaluateWithDiscriminant
getCategories
public static final double DEFAULT_BIAS
protected Vector weights
protected double bias
public LinearBinaryCategorizer()
public LinearBinaryCategorizer(Vector weights, double bias)
weights
- The weight vector.bias
- The bias term.public LinearBinaryCategorizer(LinearBinaryCategorizer other)
other
- The LinearBinaryCategorizer to copy.public LinearBinaryCategorizer 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 CloneableSerializable
clone
in class AbstractCloneableSerializable
public double evaluateAsDouble(Vectorizable input)
evaluateAsDouble
in interface DiscriminantBinaryCategorizer<Vectorizable>
input
- The input vector to categorize.public double evaluateAsDouble(Vector input)
input
- The input value to convert to a vector.public int getInputDimensionality()
VectorInputEvaluator
getInputDimensionality
in interface VectorInputEvaluator<Vectorizable,java.lang.Boolean>
public double getThreshold()
getThreshold
in interface ThresholdBinaryCategorizer<Vectorizable>
public void setThreshold(double threshold)
setThreshold
in interface ThresholdBinaryCategorizer<Vectorizable>
threshold
- the threshold, which is the negative of the bias.public Vector getWeights()
public void setWeights(Vector weights)
weights
- The weight vector.public double getBias()
public void setBias(double bias)
bias
- The bias term.public java.lang.String toString()
toString
in class java.lang.Object