public abstract class StatUtils extends Object
Modifier and Type | Field and Description |
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static double[] |
CRITICAL
Critical value used for Chi^2 test.
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Constructor and Description |
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StatUtils() |
Modifier and Type | Method and Description |
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static double[][] |
chi2(double[][][] M,
double[][][] Exp)
Chi^2 - Chi-squared test.
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static double[][] |
chi2(weka.core.Instances D)
Chi^2 - Do the chi-squared test on all pairs of labels.
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static double |
chi2(weka.core.Instances Y,
int j,
int k)
Chi^2 - Do the chi-squared test on the j-th and k-th labels in Y.
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static double[][] |
condDepMatrix(weka.core.Instances D,
Result result)
CondDepMatrix - Get a Conditional Dependency Matrix.
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static double[][] |
F(weka.core.Instances D)
F - Relative frequency matrix (between p(j),p(k) and p(j,k)) in dataset D.
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static int[][] |
getApproxC(weka.core.Instances D)
GetApproxP - A fast version of getC(D), based on frequent sets.
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static double[][] |
getApproxP(weka.core.Instances D)
GetApproxP - A fast version of getP(D), based on frequent sets.
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static int[][] |
getC(weka.core.Instances D)
GetC - Get pairwise co-ocurrence counts from the training data D.
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static double[][] |
getP(weka.core.Instances D)
GetP - Get a pairwise empirical joint-probability matrix P[][] from dataset D.
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static double[][] |
getP(int[][] C,
int N) |
static double[][] |
H(weka.core.Instances D)
H - Get a Conditional Entropy Matrix.
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static double[][] |
H(int[][] C,
int N)
H - Get a Conditional Entropy Matrix.
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static double |
H(int[][] C,
int j,
int k,
int Ncount)
H - Conditional Entropy H(y_j|y_k).
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static double[][] |
I(double[][] P)
I - Mutual Information -- fast version, must calcualte P[][] = getP(D) first.
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static double |
I(double[][] P,
int j,
int k)
I - Mutual Information.
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static double[][] |
I(weka.core.Instances D,
int L)
I - Get an Unconditional Depndency Matrix.
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static double |
I(weka.core.Instances D,
int j,
int k)
I - Mutual Information.
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static double[][] |
I(int[][] C,
int N)
I - Get a Mutual Information Matrix.
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static double |
I(int[][] C,
int j,
int k,
int Ncount)
I - Mutual Information I(y_j;y_k).
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static double[][] |
jPMF(weka.core.Instances D,
int j,
int k)
jPMF - Joint PMF.
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static double[][][] |
jPMF(weka.core.Instances D,
int j,
int k,
int l)
Joint Distribution.
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static double[][] |
LEAD(weka.core.Instances D,
weka.classifiers.Classifier h,
Random r)
LEAD - Performs LEAD on dataset 'D', using BR with base classifier 'h', under random seed 'r'.
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static double[][] |
LEAD(weka.core.Instances D,
weka.classifiers.Classifier h,
Random r,
String MDType) |
static double[][] |
LEAD(weka.core.Instances D,
Result result) |
static double[][] |
LEAD(weka.core.Instances D,
Result R,
String MDType)
LEAD - Performs LEAD on dataset 'D', with corresponding gresult 'R', and dependency measurement type 'MDType'.
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static double[][] |
LEAD2(weka.core.Instances D,
Result result)
LEAD.
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static void |
main(String[] args)
Main - do some tests.
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static double[][] |
margDepMatrix(weka.core.Instances D,
String op)
MargDepMatrix - Get an Unconditional Depndency Matrix.
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static double[] |
P(double[][] Y,
int[] x)
P - Empirical prior.
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static double[] |
P(double[][] Y,
int[] j,
int[] x)
P - Empirical prior.
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static double |
p(double[][] Y,
int j,
int k)
p - Empirical prior.
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static double |
P(double[][] Y,
int j,
int v,
int k,
int w)
P - Empirical joint.
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static double |
P(weka.core.Instances D,
int[] j,
int[] v)
P - Empirical joint.
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static double |
p(weka.core.Instances D,
int j,
int j_)
p - Empirical prior.
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static double |
P(weka.core.Instances D,
int j,
int v,
int k,
int w)
p - Empirical joint.
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public static double[] P(double[][] Y, int[] x)
Y
- label matrixx
- label valuespublic static double[] P(double[][] Y, int[] j, int[] x)
Y
- label matrixx
- label valuesj
- label indicespublic static double p(double[][] Y, int j, int k)
Y
- label matrixj
- label indexk
- label valuepublic static double p(weka.core.Instances D, int j, int j_)
D
- Instancesj
- label indexj_
- label valuepublic static double P(double[][] Y, int j, int v, int k, int w)
Y
- label matrixj
- 1st label indexv
- 1st label valuek
- 2nd label indexw
- 2nd label valuepublic static double P(weka.core.Instances D, int j, int v, int k, int w)
D
- Instancesj
- 1st label indexv
- 1st label valuek
- 2nd label indexw
- 2nd label valuepublic static double P(weka.core.Instances D, int[] j, int[] v)
D
- Instancesj
- label indices, e.g., 1,2,3v
- label values, e.g., 0,0,1public static double[][] jPMF(weka.core.Instances D, int j, int k)
public static double[][][] jPMF(weka.core.Instances D, int j, int k, int l)
public static double[][] getP(weka.core.Instances D)
public static int[][] getApproxC(weka.core.Instances D)
public static double[][] getApproxP(weka.core.Instances D)
public static double[][] getP(int[][] C, int N)
public static int[][] getC(weka.core.Instances D)
public static double I(int[][] C, int j, int k, int Ncount)
C
- count matrixj
- j-th label indexk
- k-th label indexNcount
- number of instances in the training setpublic static double H(int[][] C, int j, int k, int Ncount)
C
- count matrixj
- j-th label indexk
- k-th label indexNcount
- number of instances in the training setpublic static double[][] I(double[][] P)
I(double[][], int, int)
public static double I(double[][] P, int j, int k)
public static double I(weka.core.Instances D, int j, int k)
public static double[][] I(weka.core.Instances D, int L)
D
- datasetL
- number of labelspublic static double chi2(weka.core.Instances Y, int j, int k)
public static double[][] chi2(weka.core.Instances D)
D
- datasetchi2(Instances, int, int)
public static double[][] chi2(double[][][] M, double[][][] Exp)
M
- measured joint P(Y_1,Y_2)Exp
- expect joint P(Y_1)P(Y_2) given null hypothesispublic static double[][] margDepMatrix(weka.core.Instances D, String op)
D
- datasetop
- how we will measure the dependencypublic static double[][] I(int[][] C, int N)
public static double[][] H(int[][] C, int N)
public static double[][] H(weka.core.Instances D)
public static double[][] F(weka.core.Instances D)
public static double[][] condDepMatrix(weka.core.Instances D, Result result)
D
- datasetpublic static double[][] LEAD2(weka.core.Instances D, Result result)
public static double[][] LEAD(weka.core.Instances D, Result R, String MDType)
public static double[][] LEAD(weka.core.Instances D, Result result)
public static double[][] LEAD(weka.core.Instances D, weka.classifiers.Classifier h, Random r) throws Exception
Exception
public static double[][] LEAD(weka.core.Instances D, weka.classifiers.Classifier h, Random r, String MDType) throws Exception
Exception
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