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public interface DataReducer
This class maps a higher dimensional vector to a lower dimensional vector (and vice-versa) @Note there is not requirement for the mapping to be exactly invertable i.e. M.M^-1 != I. Only an appropriate approximation of the higher dimension is required
Nested Class Summary | |
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static class |
DataReducer.DataReducerInformation
Contains information about the implementation of the DataReducer class that can be used to determine the classes abilities at run-time. |
Method Summary | |
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double |
getEpsilon()
Get epsilon, the very small error margin used the determine if the function has finished minimising. |
double |
getTargetDependentVariance()
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double |
getTargetIndpendentVariance()
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BigMat |
reconstructDependent(BigMat in)
Reconstruct (approximately) a high dimensional input given the low dimensional output |
double[] |
reconstructDependent(double[] in)
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BigMat |
reconstructIndependent(BigMat in)
Reconstruct (approximately) a high dimensional input given the low dimensional output |
double[] |
reconstructIndependent(double[] in)
Reconstruct (approximately) a high dimensional input given the low dimensional output |
BigMat |
reduceDependent(BigMat in)
Perform dimensionality reduction on the BigMat |
double[] |
reduceDependent(double[] in)
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BigMat |
reduceIndependent(BigMat in)
Perform dimensionality reduction on the BigMat |
double[] |
reduceIndependent(double[] in)
Perform dimensionality reduction on a single sample of variables |
void |
setEpsilon(double e)
Set epsilon, the very small error margin used the determine if the function has finished minimising. |
void |
setOutputDimensions(int d)
Set the maximum number of output dimensions in the model (pre-build, undefined if set after building some classes will alter the dimensionality of the reduction others will not) If d is greater than the number input variables, the model will truncate at the maximum number of variables. |
void |
setTargetDependentVariance(double var)
Stop calculating components when the variance explained in the dependent variable is greater than var |
void |
setTargetIndependentVariance(double var)
Stop calculating components when the variance explained in the independent variable is greater than var |
void |
setTargetKaiserGuttman()
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Methods inherited from interface Facemorph.Stats.StatisticalModel |
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apply, getInputDimensions, getModelInformation, getModelInformation, getOutputDimensions, outputStatistics, outputStatistics, setDependentData, setDependentData, setIndependentData, train |
Methods inherited from interface Facemorph.DataBase.IniHandler |
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read, write |
Method Detail |
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double[] reduceIndependent(double[] in)
in
- vector to reduce
double[] reconstructIndependent(double[] in)
in
- low dimensional vector
double[] reduceDependent(double[] in)
double[] reconstructDependent(double[] in)
BigMat reduceIndependent(BigMat in)
in
- BigMat to reduce
BigMat reconstructIndependent(BigMat in)
in
- low dimensional matrix
BigMat reduceDependent(BigMat in)
in
- BigMat to reduce
BigMat reconstructDependent(BigMat in)
in
- low dimensional matrix
void setOutputDimensions(int d)
d
- target number of dimensions.void setTargetDependentVariance(double var)
var
- variance to explaindouble getTargetDependentVariance()
void setTargetIndependentVariance(double var)
var
- variance to explaindouble getTargetIndpendentVariance()
void setTargetKaiserGuttman()
void setEpsilon(double e)
e
- error valuedouble getEpsilon()
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