Uses of Class weka.classifiers.functions.pace.DoubleVector (Documentation for extended WEKA including Ensembles of Hierarchically Nested Dichotomies)

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Uses of Class
weka.classifiers.functions.pace.DoubleVector

Packages that use DoubleVector
weka.classifiers.functions.pace

Uses of DoubleVector in weka.classifiers.functions.pace

Fields in weka.classifiers.functions.pace declared as DoubleVector
`protected DoubleVector`	`DiscreteFunction.points`
`protected DoubleVector`	`DiscreteFunction.values`

Methods in weka.classifiers.functions.pace that return DoubleVector
`protected DoubleVector`	`DiscreteFunction.getPointValues()` Gets all point values
`protected DoubleVector`	`DiscreteFunction.getFunctionValues()` Gets all function values
`DoubleVector`	`DoubleVector.square()` Returns the squared vector
`DoubleVector`	`DoubleVector.sqrt()` Returns the square-root of all the elements in the vector
`DoubleVector`	`DoubleVector.copy()` Makes a deep copy of the vector
`DoubleVector`	`DoubleVector.sign()` Returns the signs of all elements in terms of -1, 0 and +1.
`DoubleVector`	`DoubleVector.subvector(int i0, int i1)` Returns a subvector.
`DoubleVector`	`DoubleVector.subvector(IntVector index)` Returns a subvector.
`DoubleVector`	`DoubleVector.unpivoting(IntVector index, int length)` Returns a vector from the pivoting indices.
`DoubleVector`	`DoubleVector.plus(double x)` Adds a value to all the elements
`DoubleVector`	`DoubleVector.plusEquals(double x)` Adds a value to all the elements in place
`DoubleVector`	`DoubleVector.plus(DoubleVector v)` Adds another vector element by element
`DoubleVector`	`DoubleVector.plusEquals(DoubleVector v)` Adds another vector in place element by element
`DoubleVector`	`DoubleVector.minus(double x)` Subtracts a value
`DoubleVector`	`DoubleVector.minusEquals(double x)` Subtracts a value in place
`DoubleVector`	`DoubleVector.minus(DoubleVector v)` Subtracts another DoubleVector element by element
`DoubleVector`	`DoubleVector.minusEquals(DoubleVector v)` Subtracts another DoubleVector element by element in place
`DoubleVector`	`DoubleVector.times(double s)` Multiplies a scalar
`DoubleVector`	`DoubleVector.timesEquals(double s)` Multiply a vector by a scalar in place, u = s * u
`DoubleVector`	`DoubleVector.times(DoubleVector v)` Multiplies another DoubleVector element by element
`DoubleVector`	`DoubleVector.timesEquals(DoubleVector v)` Multiplies another DoubleVector element by element in place
`DoubleVector`	`DoubleVector.dividedBy(DoubleVector v)` Divided by another DoubleVector element by element
`DoubleVector`	`DoubleVector.dividedByEquals(DoubleVector v)` Divided by another DoubleVector element by element in place
`DoubleVector`	`DoubleVector.cumulate()` Returns a vector that stores the cumulated values of the original vector
`DoubleVector`	`DoubleVector.cumulateInPlace()` Cumulates the original vector in place
`DoubleVector`	`DoubleVector.cat(DoubleVector v)` Combine two vectors together
`DoubleVector`	`DoubleVector.map(java.lang.String className, java.lang.String method)` Applies a method to the vector
`DoubleVector`	`DoubleVector.rev()` Returns the reverse vector
`static DoubleVector`	`DoubleVector.random(int n)` Returns a random vector of uniform distribution
`DoubleVector`	`NormalMixture.supportPoints(DoubleVector data, int ne)` Contructs the set of support points for mixture estimation.
`DoubleVector`	`NormalMixture.empiricalBayesEstimate(DoubleVector x)` Returns the empirical Bayes estimate of a vector.
`DoubleVector`	`NormalMixture.nestedEstimate(DoubleVector x)` Returns the optimal nested model estimate of a vector.
`DoubleVector`	`NormalMixture.subsetEstimate(DoubleVector x)` Returns the estimate of optimal subset selection.
`DoubleVector`	`NormalMixture.h(DoubleVector x)` Computes the value of h(x) given the mixture, where x is a vector.
`DoubleVector`	`NormalMixture.f(DoubleVector x)` Computes the value of f(x) given the mixture, where x is a vector.
`static DoubleVector`	`Maths.pnorm(double x, DoubleVector mean, double sd)` Returns the cumulative probability of a set of normal distributions with different means.
`static DoubleVector`	`Maths.dnorm(double x, DoubleVector mean, double sd)` Returns the density values of a set of normal distributions with different means.
`static DoubleVector`	`Maths.dnormLog(double x, DoubleVector mean, double sd)` Returns the log-density values of a set of normal distributions with different means.
`static DoubleVector`	`Maths.rnorm(int n, double mean, double sd, java.util.Random random)` Generates a sample of a normal distribution.
`static DoubleVector`	`Maths.pchisq(double x, DoubleVector ncp)` Returns the cumulative probability of a set of noncentral Chi-squared distributions.
`static DoubleVector`	`Maths.dchisq(double x, DoubleVector ncp)` Returns the density of the noncentral Chi-squared distribution.
`static DoubleVector`	`Maths.dchisqLog(double x, DoubleVector ncp)` Returns the log-density of a set of noncentral Chi-squared distributions.
`static DoubleVector`	`Maths.rchisq(int n, double ncp, java.util.Random random)` Generates a sample of a Chi-square distribution.
`DoubleVector`	`PaceMatrix.getColumn(int j)` Return a DoubleVector that stores a column of the matrix
`DoubleVector`	`PaceMatrix.getColumn(int i0, int i1, int j)` Return a DoubleVector that stores some elements of a column of the matrix
`DoubleVector`	`PaceMatrix.nnls(PaceMatrix b, IntVector pvt)` Solves the nonnegative linear squares problem.
`DoubleVector`	`PaceMatrix.nnlse(PaceMatrix b, PaceMatrix c, PaceMatrix d, IntVector pvt)` Solves the nonnegative least squares problem with equality constraint.
`DoubleVector`	`PaceMatrix.nnlse1(PaceMatrix b, IntVector pvt)` Solves the nonnegative least squares problem with equality constraint.
`DoubleVector`	`ChisqMixture.supportPoints(DoubleVector data, int ne)` Contructs the set of support points for mixture estimation.
`DoubleVector`	`ChisqMixture.pace6(DoubleVector x)` Returns the pace6 estimate of a vector.
`DoubleVector`	`ChisqMixture.pace2(DoubleVector x)` Returns the pace2 estimate of a vector.
`DoubleVector`	`ChisqMixture.pace4(DoubleVector x)` Returns the pace4 estimate of a vector.
`DoubleVector`	`ChisqMixture.h(DoubleVector AHat)` Computes the value of h(x) given the mixture, where x is a vector.
`DoubleVector`	`ChisqMixture.f(DoubleVector x)` Computes the value of f(x) given the mixture, where x is a vector.
`abstract DoubleVector`	`MixtureDistribution.supportPoints(DoubleVector data, int ne)` Contructs the set of support points for mixture estimation.

Methods in weka.classifiers.functions.pace with parameters of type DoubleVector
`void`	`DoubleVector.set(DoubleVector v)` Set the elements using a DoubleVector
`void`	`DoubleVector.set(int i0, int i1, DoubleVector v, int j0)` Set some elements using a DoubleVector.
`double`	`DoubleVector.innerProduct(DoubleVector v)` Returns the inner product of two DoubleVectors
`double`	`DoubleVector.sum2(DoubleVector v)` Returns \|\|u-v\|\|^2
`DoubleVector`	`DoubleVector.plus(DoubleVector v)` Adds another vector element by element
`DoubleVector`	`DoubleVector.plusEquals(DoubleVector v)` Adds another vector in place element by element
`DoubleVector`	`DoubleVector.minus(DoubleVector v)` Subtracts another DoubleVector element by element
`DoubleVector`	`DoubleVector.minusEquals(DoubleVector v)` Subtracts another DoubleVector element by element in place
`DoubleVector`	`DoubleVector.times(DoubleVector v)` Multiplies another DoubleVector element by element
`DoubleVector`	`DoubleVector.timesEquals(DoubleVector v)` Multiplies another DoubleVector element by element in place
`DoubleVector`	`DoubleVector.dividedBy(DoubleVector v)` Divided by another DoubleVector element by element
`DoubleVector`	`DoubleVector.dividedByEquals(DoubleVector v)` Divided by another DoubleVector element by element in place
`DoubleVector`	`DoubleVector.cat(DoubleVector v)` Combine two vectors together
`boolean`	`NormalMixture.separable(DoubleVector data, int i0, int i1, double x)` Return true if a value can be considered for mixture estimatino separately from the data indexed between i0 and i1
`DoubleVector`	`NormalMixture.supportPoints(DoubleVector data, int ne)` Contructs the set of support points for mixture estimation.
`PaceMatrix`	`NormalMixture.fittingIntervals(DoubleVector data)` Contructs the set of fitting intervals for mixture estimation.
`PaceMatrix`	`NormalMixture.probabilityMatrix(DoubleVector s, PaceMatrix intervals)` Contructs the probability matrix for mixture estimation, given a set of support points and a set of intervals.
`DoubleVector`	`NormalMixture.empiricalBayesEstimate(DoubleVector x)` Returns the empirical Bayes estimate of a vector.
`DoubleVector`	`NormalMixture.nestedEstimate(DoubleVector x)` Returns the optimal nested model estimate of a vector.
`DoubleVector`	`NormalMixture.subsetEstimate(DoubleVector x)` Returns the estimate of optimal subset selection.
`void`	`NormalMixture.trim(DoubleVector x)` Trims the small values of the estaimte
`DoubleVector`	`NormalMixture.h(DoubleVector x)` Computes the value of h(x) given the mixture, where x is a vector.
`DoubleVector`	`NormalMixture.f(DoubleVector x)` Computes the value of f(x) given the mixture, where x is a vector.
`static DoubleVector`	`Maths.pnorm(double x, DoubleVector mean, double sd)` Returns the cumulative probability of a set of normal distributions with different means.
`static DoubleVector`	`Maths.dnorm(double x, DoubleVector mean, double sd)` Returns the density values of a set of normal distributions with different means.
`static DoubleVector`	`Maths.dnormLog(double x, DoubleVector mean, double sd)` Returns the log-density values of a set of normal distributions with different means.
`static DoubleVector`	`Maths.pchisq(double x, DoubleVector ncp)` Returns the cumulative probability of a set of noncentral Chi-squared distributions.
`static DoubleVector`	`Maths.dchisq(double x, DoubleVector ncp)` Returns the density of the noncentral Chi-squared distribution.
`static DoubleVector`	`Maths.dchisqLog(double x, DoubleVector ncp)` Returns the log-density of a set of noncentral Chi-squared distributions.
`void`	`PaceMatrix.setMatrix(int i0, int i1, int j, DoubleVector v)` Set the submatrix A[i0:i1][j] with the values stored in a DoubleVector
`boolean`	`ChisqMixture.separable(DoubleVector data, int i0, int i1, double x)` Return true if a value can be considered for mixture estimatino separately from the data indexed between i0 and i1
`DoubleVector`	`ChisqMixture.supportPoints(DoubleVector data, int ne)` Contructs the set of support points for mixture estimation.
`PaceMatrix`	`ChisqMixture.fittingIntervals(DoubleVector data)` Contructs the set of fitting intervals for mixture estimation.
`PaceMatrix`	`ChisqMixture.probabilityMatrix(DoubleVector s, PaceMatrix intervals)` Contructs the probability matrix for mixture estimation, given a set of support points and a set of intervals.
`DoubleVector`	`ChisqMixture.pace6(DoubleVector x)` Returns the pace6 estimate of a vector.
`DoubleVector`	`ChisqMixture.pace2(DoubleVector x)` Returns the pace2 estimate of a vector.
`DoubleVector`	`ChisqMixture.pace4(DoubleVector x)` Returns the pace4 estimate of a vector.
`void`	`ChisqMixture.trim(DoubleVector x)` Trims the small values of the estaimte
`DoubleVector`	`ChisqMixture.h(DoubleVector AHat)` Computes the value of h(x) given the mixture, where x is a vector.
`DoubleVector`	`ChisqMixture.f(DoubleVector x)` Computes the value of f(x) given the mixture, where x is a vector.
`void`	`MixtureDistribution.fit(DoubleVector data)` Fits the mixture (or mixing) distribution to the data.
`void`	`MixtureDistribution.fit(DoubleVector data, int method)` Fits the mixture (or mixing) distribution to the data.
`DiscreteFunction`	`MixtureDistribution.fitForSingleCluster(DoubleVector data, int method)` Fits the mixture (or mixing) distribution to the data.
`abstract boolean`	`MixtureDistribution.separable(DoubleVector data, int i0, int i1, double x)` Return true if a value can be considered for mixture estimatino separately from the data indexed between i0 and i1
`abstract DoubleVector`	`MixtureDistribution.supportPoints(DoubleVector data, int ne)` Contructs the set of support points for mixture estimation.
`abstract PaceMatrix`	`MixtureDistribution.fittingIntervals(DoubleVector data)` Contructs the set of fitting intervals for mixture estimation.
`abstract PaceMatrix`	`MixtureDistribution.probabilityMatrix(DoubleVector s, PaceMatrix intervals)` Contructs the probability matrix for mixture estimation, given a set of support points and a set of intervals.
`PaceMatrix`	`MixtureDistribution.empiricalProbability(DoubleVector data, PaceMatrix intervals)` Computes the empirical probabilities of the data over a set of intervals.

Constructors in weka.classifiers.functions.pace with parameters of type DoubleVector
`DiscreteFunction(DoubleVector p)` Constructs a discrete function with the point values provides and the function values are all 1/n.
`DiscreteFunction(DoubleVector p, DoubleVector v)` Constructs a discrete function with both the point values and function values provided.
`PaceMatrix(DoubleVector v)` Construct a PaceMatrix with a single column from a DoubleVector

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Uses of Classweka.classifiers.functions.pace.DoubleVector

Uses of Class
weka.classifiers.functions.pace.DoubleVector