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java.lang.Objectweka.core.matrix.Matrix
tpp.LinearProjection
public class LinearProjection
A linear projection. the projection is defined by a matrix and projection pursuit is achieved by application of the perceptron learning rule. In other words we treat the projection as a bunch of linear neurons.
Constructor Summary | |
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LinearProjection(int inputDimensions,
int outputDimensions)
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LinearProjection(Matrix m)
Construct a LinearProjection from a matrix |
Method Summary | |
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void |
normalise()
Scale the projection so that all values are in the range [-1,1]. |
void |
PCA(Matrix data)
Find the principle components of the given data. |
Matrix |
project(Matrix data)
Apply the projection to the given data. |
double |
pursueTarget(Matrix data,
Matrix target)
Find the projection that best maps the data to the target. |
double |
pursueTarget(Matrix data,
Matrix target,
boolean[] inTrainingSet)
Try to find a projection that best maps the data to the target. |
double |
pursueTargetSingleShot(Matrix data,
Matrix target,
boolean[] inTrainingSet)
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void |
randomize()
Set all projection values to a random values in [-1,1] |
LinearProjection |
removeAttribute(int i)
Create a new projection by removing a single attribute (row) |
void |
train(Matrix data,
Matrix target,
boolean[] selected,
double[][] previousWeights,
double rate)
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Methods inherited from class weka.core.matrix.Matrix |
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arrayLeftDivide, arrayLeftDivideEquals, arrayRightDivide, arrayRightDivideEquals, arrayTimes, arrayTimesEquals, chol, clone, cond, constructWithCopy, copy, det, eig, get, getArray, getArrayCopy, getColumnDimension, getColumnPackedCopy, getMatrix, getMatrix, getMatrix, getMatrix, getRevision, getRowDimension, getRowPackedCopy, identity, inverse, isSquare, isSymmetric, lu, main, minus, minusEquals, norm1, norm2, normF, normInf, parseMatlab, plus, plusEquals, print, print, print, print, qr, random, rank, read, regression, regression, set, setMatrix, setMatrix, setMatrix, setMatrix, solve, solveTranspose, sqrt, svd, times, times, timesEquals, toMatlab, toString, trace, transpose, uminus, write |
Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
Constructor Detail |
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public LinearProjection(int inputDimensions, int outputDimensions)
public LinearProjection(Matrix m)
Method Detail |
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public Matrix project(Matrix data)
Projection
project
in interface Projection
public double pursueTarget(Matrix data, Matrix target)
Projection
pursueTarget
in interface Projection
public double pursueTargetSingleShot(Matrix data, Matrix target, boolean[] inTrainingSet)
public double pursueTarget(Matrix data, Matrix target, boolean[] inTrainingSet)
data
- target
- inTrainingSet
- which points have been selected -- we ignore the other ones.
(if ==null then we use all the points)
public void train(Matrix data, Matrix target, boolean[] selected, double[][] previousWeights, double rate)
public void randomize()
public void normalise()
public void PCA(Matrix data)
public LinearProjection removeAttribute(int i)
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