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java.lang.Objectorg.openjump.core.attributeoperations.statistics.CorrelationCoefficients
public class CorrelationCoefficients
Class that calculates various correlation coefficients.
| Nested Class Summary | |
|---|---|
class |
CorrelationCoefficients.CorrelationInformation
|
class |
CorrelationCoefficients.RankCorrelationInformation
|
protected class |
CorrelationCoefficients.SpearmanRankNumberPair
|
| Field Summary | |
|---|---|
protected java.lang.String |
attrName1
|
protected java.lang.String |
attrName2
|
protected java.lang.Object[] |
dataArray
|
protected double[] |
means
|
protected Feature[] |
rawFeatures
|
| Constructor Summary | |
|---|---|
CorrelationCoefficients(Feature[] features,
java.lang.String attr1,
java.lang.String attr2)
|
|
| Method Summary | |
|---|---|
protected double |
aritmeticMiddle(Feature[] features,
int attr)
|
static double |
getDeviation(Feature[] features,
java.lang.String attr,
double mean)
Returns the deviation of the values of the given attribute. |
CorrelationCoefficients.RankCorrelationInformation |
getKendalsTauRankCoefficient()
"Spearman Rank Order Correlations (or "rho") and Kendall's Tau-b (or "tau") Correlations are used when the variables are measured as ranks (from highest-to-lowest or lowest-to-highest)" http://www.themeasurementgroup.com/datamining/definitions/correlation.htm |
double |
getMean(int nr)
Get the aritmetic middle for the nr-th attribut given |
CorrelationCoefficients.CorrelationInformation |
getPearsonCoefficient()
get Pearson's correlation coefficient (good, dimension-less measure, if there is a linear relation between the attributes) see: http://www.netzwelt.de/lexikon/Korrelationskoeffizient.html |
protected java.util.HashMap<java.lang.Integer,java.lang.Double> |
getRank2SpearmanRankMap(java.lang.Object[] sortedValues,
java.util.HashMap<java.lang.Object,java.lang.Integer> value2NumAppearances)
|
CorrelationCoefficients.RankCorrelationInformation |
getSpearmansRhoCoefficient()
get Pearson's correlation coefficient (good, dimension-less measure, if there is a linear relation between the attributes) see: http://www.netzwelt.de/lexikon/Korrelationskoeffizient.html |
protected double |
getVariance(java.lang.String attr)
|
protected CorrelationDataPair[] |
initializeDataStorage(Feature[] features)
|
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
|---|
protected java.lang.Object[] dataArray
protected java.lang.String attrName1
protected java.lang.String attrName2
protected double[] means
protected Feature[] rawFeatures
| Constructor Detail |
|---|
public CorrelationCoefficients(Feature[] features,
java.lang.String attr1,
java.lang.String attr2)
| Method Detail |
|---|
protected CorrelationDataPair[] initializeDataStorage(Feature[] features)
public static double getDeviation(Feature[] features,
java.lang.String attr,
double mean)
features - array containing the features we want the deviation forattr - name of the attribute to calculate the deviation formean - the mean for the given features
java.lang.IllegalArgumentException - if the attribute is not of a numerical typeFeatureCollectionToolsprotected double getVariance(java.lang.String attr)
protected double aritmeticMiddle(Feature[] features,
int attr)
public double getMean(int nr)
nr - index number of attribut to calculate the mean for
public CorrelationCoefficients.CorrelationInformation getPearsonCoefficient()
protected java.util.HashMap<java.lang.Integer,java.lang.Double> getRank2SpearmanRankMap(java.lang.Object[] sortedValues,
java.util.HashMap<java.lang.Object,java.lang.Integer> value2NumAppearances)
public CorrelationCoefficients.RankCorrelationInformation getSpearmansRhoCoefficient()
public CorrelationCoefficients.RankCorrelationInformation getKendalsTauRankCoefficient()
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