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java.lang.Object | +--weka.estimators.KDConditionalEstimator
Conditional probability estimator for a numeric domain conditional upon a discrete domain (utilises separate kernel estimators for each discrete conditioning value).
Constructor Summary | |
KDConditionalEstimator(int numCondSymbols,
double precision)
Constructor |
Method Summary | |
void |
addValue(double data,
double given,
double weight)
Add a new data value to the current estimator. |
Estimator |
getEstimator(double given)
Get a probability estimator for a value |
double |
getProbability(double data,
double given)
Get a probability estimate for a value |
static void |
main(java.lang.String[] argv)
Main method for testing this class. |
java.lang.String |
toString()
Display a representation of this estimator |
Methods inherited from class java.lang.Object |
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
Constructor Detail |
public KDConditionalEstimator(int numCondSymbols, double precision)
numCondSymbols
- the number of conditioning symbolsprecision
- the precision to which numeric values are given. For
example, if the precision is stated to be 0.1, the values in the
interval (0.25,0.35] are all treated as 0.3.Method Detail |
public void addValue(double data, double given, double weight)
addValue
in interface ConditionalEstimator
data
- the new data valuegiven
- the new value that data is conditional uponweight
- the weight assigned to the data valuepublic Estimator getEstimator(double given)
getEstimator
in interface ConditionalEstimator
given
- the new value that data is conditional upon
public double getProbability(double data, double given)
getProbability
in interface ConditionalEstimator
data
- the value to estimate the probability ofgiven
- the new value that data is conditional upon
public java.lang.String toString()
toString
in class java.lang.Object
public static void main(java.lang.String[] argv)
argv
- should contain a sequence of pairs of integers which
will be treated as numeric, symbolic.
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Copyright (c) 2003 David Lindsay, Computer Learning Research Centre, Dept. Computer Science, Royal Holloway, University of London