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public interface MLDataPair
Training data is stored in two ways, depending on if the data is for supervised, or unsupervised training. For unsupervised training just an input value is provided, and the ideal output values are null. For supervised training both input and the expected ideal outputs are provided. This interface abstracts classes that provide a holder for both of these two data items.
Method Summary | |
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MLData |
getIdeal()
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double[] |
getIdealArray()
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MLData |
getInput()
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double[] |
getInputArray()
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double |
getSignificance()
Get the significance, 1.0 is neutral. |
boolean |
isSupervised()
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void |
setIdealArray(double[] data)
Set the ideal data, the desired output. |
void |
setInputArray(double[] data)
Set the input. |
void |
setSignificance(double s)
Set the significance, 1.0 is neutral. |
Methods inherited from interface org.encog.util.kmeans.CentroidFactory |
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createCentroid |
Method Detail |
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double[] getIdealArray()
double[] getInputArray()
void setIdealArray(double[] data)
data
- The ideal data.void setInputArray(double[] data)
data
- The input.boolean isSupervised()
MLData getIdeal()
MLData getInput()
double getSignificance()
void setSignificance(double s)
s
- The significance.
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