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java.lang.Objectjml.regression.Regression
public abstract class Regression
Abstract super class for all regression methods.
Field Summary | |
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double |
epsilon
Convergence tolerance. |
int |
maxIter
Maximal number of iterations. |
int |
n
Number of samples. |
int |
ny
Number of dependent variables. |
int |
p
Number of independent variables. |
org.apache.commons.math.linear.RealMatrix |
W
Unknown parameters represented as a matrix (p x ny). |
org.apache.commons.math.linear.RealMatrix |
X
Training data matrix (n x p) with each row being a data example. |
org.apache.commons.math.linear.RealMatrix |
Y
Dependent variable matrix for training (n x ny). |
Constructor Summary | |
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Regression()
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Regression(double epsilon)
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Regression(int maxIter,
double epsilon)
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Regression(Options options)
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Method Summary | |
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void |
feedData(double[][] data)
Feed training data for this regression model. |
void |
feedData(org.apache.commons.math.linear.RealMatrix X)
Feed training data for the regression model. |
void |
feedDependentVariables(double[][] depVars)
Feed training dependent variables for this regression model. |
void |
feedDependentVariables(org.apache.commons.math.linear.RealMatrix Y)
Feed training dependent variables for this regression model. |
abstract void |
loadModel(java.lang.String filePath)
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org.apache.commons.math.linear.RealMatrix |
predict(double[][] Xt)
Predict the dependent variables for test data Xt. |
org.apache.commons.math.linear.RealMatrix |
predict(org.apache.commons.math.linear.RealMatrix Xt)
Predict the dependent variables for test data Xt. |
abstract void |
saveModel(java.lang.String filePath)
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abstract void |
train()
Train the regression model. |
abstract org.apache.commons.math.linear.RealMatrix |
train(org.apache.commons.math.linear.RealMatrix X,
org.apache.commons.math.linear.RealMatrix Y)
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Methods inherited from class java.lang.Object |
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clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Field Detail |
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public int ny
public int p
public int n
public org.apache.commons.math.linear.RealMatrix X
public org.apache.commons.math.linear.RealMatrix Y
public org.apache.commons.math.linear.RealMatrix W
public double epsilon
public int maxIter
Constructor Detail |
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public Regression()
public Regression(double epsilon)
public Regression(int maxIter, double epsilon)
public Regression(Options options)
Method Detail |
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public void feedData(org.apache.commons.math.linear.RealMatrix X)
X
- data matrix with each row being a data examplepublic void feedData(double[][] data)
data
- an n x d 2D double
array with each
row being a data examplepublic void feedDependentVariables(org.apache.commons.math.linear.RealMatrix Y)
Y
- dependent variable matrix for training with each row being
the dependent variable vector for each data training data
examplepublic void feedDependentVariables(double[][] depVars)
depVars
- an n x c 2D double
arraypublic abstract void train()
public abstract org.apache.commons.math.linear.RealMatrix train(org.apache.commons.math.linear.RealMatrix X, org.apache.commons.math.linear.RealMatrix Y)
public org.apache.commons.math.linear.RealMatrix predict(org.apache.commons.math.linear.RealMatrix Xt)
Xt
- test data matrix with each row being a
data example.
public org.apache.commons.math.linear.RealMatrix predict(double[][] Xt)
Xt
- an n x d 2D double
array with each
row being a data example
public abstract void loadModel(java.lang.String filePath)
public abstract void saveModel(java.lang.String filePath)
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