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G

gegenie_object() - Method in class Tests.Gini
 
generate_distribution(double, double) - Method in class Assisted_Classes.ChiSquareDistribution
This method returns the one minus the cumulative probability for the chi squared distribution
generate_distribution(double, double, double) - Method in class Assisted_Classes.FDistribution
 
generate_distribution(double, double) - Method in class Assisted_Classes.Tdistribution
 
get_Betas_only(double[][], double[], boolean) - Method in class models.LeastSquare_Regression
This method will compute the beta and it should be chosen when there is no interest for any other of the stats provided in this class (e.g.
get_Correlation(double[], double[]) - Method in class Assisted_Classes.Pearson_Correlation
This class computes the Pearson's Correlation Coefficient given two double arrays are provided
get_Correlation(double[][], int, int) - Method in class Assisted_Classes.Pearson_Correlation
This class computes the Pearson's Correlation Coefficient given a double array [] and some indications for the selected columns
get_Covariance(double[], double[]) - Method in class Assisted_Classes.Pearson_Correlation
This class computes the Covariance given two double arrays are provided
get_Covariance(double[][], int, int) - Method in class Assisted_Classes.Pearson_Correlation
This class computes the covariance given a double array [] and some indications for the selected columns
get_distinct_values() - Method in class models.Multinomial_Logistic_Regression
 
get_information_value() - Method in class Tests.Make_Woe
 
Get_means() - Method in class Cluster.Naive_Bayes_classifier
 
get_odds() - Method in class models.Logistic_Regression
 
get_target() - Method in class Cluster.Naive_Bayes_classifier
 
get_TOL() - Method in class Tests.VIF_Tolerance
returns the double array [] with Tolerance values.
Get_variances() - Method in class Cluster.Naive_Bayes_classifier
 
get_VIF() - Method in class Tests.VIF_Tolerance
returns the double array [] with VIF values.
get_VIF_TOL(double[][]) - Method in class Tests.VIF_Tolerance
Computes VIF (variance inflation factor) and Tolerance, but it uses a correlation matrix as its base for the computations.
get_VIF_TOL_old(double[][]) - Method in class Tests.VIF_Tolerance
Computes VIF (variance inflation factor) and Tolerance, but it uses a (one_out regression approach) Warning: It might be quite inefficient in large sets with many rows and columns.
get_woe_classes() - Method in class Tests.Make_Woe
 
get_woe_classes_ob() - Method in class Tests.Make_Woe
 
get_woe_names_st() - Method in class Tests.Make_Woe
 
get_woe_variable() - Method in class Tests.Make_Woe
 
getabsMax() - Method in class Assisted_Classes.DescriptiveStatistics
This method returns the absolute Max
getabsMin() - Method in class Assisted_Classes.DescriptiveStatistics
This method returns the absolute Min
getadjRSquared() - Method in class models.LeastSquare_Regression
 
getAIC() - Method in class models.Logistic_Regression
 
getArrayList() - Method in class Assisted_Classes.DescriptiveStatistics
This method returns all the elements of the DescriptiveStatistics as a double arraylist
getB(int, double, double, double) - Method in class Assisted_Classes.FDistribution
 
getbetas() - Method in class models.Discriminant_Analysis
 
getBetas() - Method in class models.LeastSquare_Regression
 
getbetas() - Method in class models.Logistic_Regression
 
getbetas() - Method in class models.Multinomial_Logistic_Regression
 
getBIC() - Method in class models.Logistic_Regression
 
GetClassification() - Method in class Cluster.KMeansCluster
 
getclassification(double[][]) - Method in class models.Multinomial_Logistic_Regression
This method will return the classification/prediction of each observation of the provided set that may be any of the K categories of the target.
Getcluster_means() - Method in class Cluster.KMeansCluster
 
GetColumnDimension() - Method in class Matrix.TwoDmatrix
A simple getter method for the column dimension
getCorrelationMatrix(double[][]) - Method in class Assisted_Classes.Pearson_Correlation
This method will produce the Pearson's Correlation matrix
getCovarianceMatrix(double[][]) - Method in class Assisted_Classes.Pearson_Correlation
This method will produce the Covariancen matrix
GetDataPoint(int, int) - Method in class Matrix.TwoDmatrix
This method is used to extract specific data points from the matrix by requesting a specific column and specific row.
getDeterminant() - Method in class Matrix.EigenDecomposition
Computes the determinant of the matrix.
getDevSumsq() - Method in class Assisted_Classes.DescriptiveStatistics
Thus method returns the sum of deviations squares (also known as second moment) namely : sumDevsquares = Ói=1(Xi=1-m)2
getdoubleArray() - Method in class Assisted_Classes.DescriptiveStatistics
This method returns all the elements of the DescriptiveStatistics as a double array []
GetDoubleArray() - Method in class Matrix.TwoDmatrix
This method returns all the data in the matrix as a double array.
getDW() - Method in class models.LeastSquare_Regression
 
getEigenvector(int) - Method in class Matrix.EigenDecomposition
Gets a copy of the ith eigenvector of the original matrix.
getElement(int) - Method in class Assisted_Classes.DescriptiveStatistics
A getter class for a specific index in the DescriptiveStatistics arraylist
getFStatistic() - Method in class models.LeastSquare_Regression
 
getFStatisticPvalue() - Method in class models.LeastSquare_Regression
 
getgenie(double[], String[]) - Method in class Tests.Gini
This is the main method of the class that computes all the statistics assuming there is a score-type variable
getgenieString(String[], String[]) - Method in class Tests.Gini
This is the method that calculates the Gini coefficient by converting a categorical variable to numeric by using the odds of 1 category versus the other
getGini() - Method in class Tests.Gini
 
GetKFuzzyClassifixationProbabilities() - Method in class Cluster.KMeansCluster
 
getKurtosis() - Method in class Assisted_Classes.DescriptiveStatistics
This method returns the kurtosis of the variable, same as SPSS does : kurtosis = { [n(n+1)sum(xi - m)4- 3(n-1)3st.dev4] /[ st.dev4 (n-1)(n-2)(n-3)]}
getMax() - Method in class Assisted_Classes.DescriptiveStatistics
This method returns the Max
getMAXIMUMlikelihood() - Method in class models.Logistic_Regression
 
getmaximumlikelihood() - Method in class models.Multinomial_Logistic_Regression
 
getMean() - Method in class Assisted_Classes.DescriptiveStatistics
Returns the average or mean namely: m=Ói=1xi / N
getMedian(boolean) - Method in class Assisted_Classes.DescriptiveStatistics
This method returns the median
getMin() - Method in class Assisted_Classes.DescriptiveStatistics
This method returns the Min
GetMinimumSquaredDifference() - Method in class Cluster.KMeansCluster
 
getN() - Method in class Assisted_Classes.DescriptiveStatistics
Returns the Count laballed as N
getPC() - Method in class models.LeastSquare_Regression
 
getPercentile(double, boolean) - Method in class Assisted_Classes.DescriptiveStatistics
This method returns the percentile given a provided double number.
getpredicted_values() - Method in class models.Discriminant_Analysis
 
getPredictedValues() - Method in class models.LeastSquare_Regression
 
getprobabilites(double[][]) - Method in class models.Multinomial_Logistic_Regression
This method will return the probabilities of each observation of the provided set to belong in any of the K categories of the target.
getprobabilities(double[][]) - Method in class Cluster.Naive_Bayes_classifier
This method will provide the probability of each observation to belong in each class
getprobabilities() - Method in class models.Logistic_Regression
 
getProduct() - Method in class Assisted_Classes.DescriptiveStatistics
This method returns the Product as : Sum=Ði=1xi Warning as it may be too big on big sets.
getQ() - Method in class Matrix.EigenDecomposition
Returns the matrix Q of the transform.
getQT() - Method in class Matrix.EigenDecomposition
Returns the transpose of the matrix Q of the transform.
getQuantile1(boolean) - Method in class Assisted_Classes.DescriptiveStatistics
This method returns the first Quantile (25%)
getQuantile3(boolean) - Method in class Assisted_Classes.DescriptiveStatistics
This method returns the thrid Quantile (75%)
getR() - Method in class models.LeastSquare_Regression
 
getRange() - Method in class Assisted_Classes.DescriptiveStatistics
Returns the range which stands for the MAX-MIN.
getRealEigenvalue(int) - Method in class Matrix.EigenDecomposition
Returns the real part of the ith eigenvalue of the original matrix.
getRealEigenvalues() - Method in class Matrix.EigenDecomposition
Gets a copy of the real parts of the eigenvalues of the original matrix.
getRegDegreesFreedom() - Method in class models.LeastSquare_Regression
 
getRegMeanSquares() - Method in class models.LeastSquare_Regression
 
getRegSumSquares() - Method in class models.LeastSquare_Regression
 
getResiDegreesFreedom() - Method in class models.LeastSquare_Regression
 
getResiduals() - Method in class models.LeastSquare_Regression
 
getresiduals() - Method in class models.Logistic_Regression
 
getResiMeanSquares() - Method in class models.LeastSquare_Regression
 
getResiSumSquares() - Method in class models.LeastSquare_Regression
 
getRoc() - Method in class Tests.Gini
 
getRoc_object() - Method in class Tests.Gini
 
GetRowDimension() - Method in class Matrix.TwoDmatrix
A simple getter method for the row dimension
getRSquared() - Method in class models.LeastSquare_Regression
 
getSBC() - Method in class models.LeastSquare_Regression
 
getSE() - Method in class models.LeastSquare_Regression
 
GetSingleColumn(int) - Method in class Matrix.TwoDmatrix
returns a full column of the data matrix
GetSingleRow(int) - Method in class Matrix.TwoDmatrix
returns a full row of the data matrix
getSkewness() - Method in class Assisted_Classes.DescriptiveStatistics
This method returns the kurtosis of the variable, same as many statistical packages do : skewness = [n / (n -1) (n - 2)] sum[(xi- mean)3] / std3
getsorted_Eigenvalues() - Method in class Cluster.pca_analysis
 
getsorted_EigenVecor() - Method in class Cluster.pca_analysis
 
getsorted_Proportions() - Method in class Cluster.pca_analysis
 
getStandardDeviation() - Method in class Assisted_Classes.DescriptiveStatistics
This method returns the Standard Deviation as : St.Dev=Sqrt(Ói=1(xi-m)2 / N)
getStandardErrors() - Method in class models.LeastSquare_Regression
 
getSum() - Method in class Assisted_Classes.DescriptiveStatistics
This method returns the Sum as : Sum=Ói=1xi
getSumsq() - Method in class Assisted_Classes.DescriptiveStatistics
Thus method returns the sum of squares namely : sumofsquares = Ói=1Xi=12
getTotalDegreesFreedom() - Method in class models.LeastSquare_Regression
 
getTotalSumSquares() - Method in class models.LeastSquare_Regression
 
getTstatistics() - Method in class models.LeastSquare_Regression
 
getTstatisticsPvalues() - Method in class models.LeastSquare_Regression
 
getV() - Method in class Matrix.EigenDecomposition
Gets the matrix V of the decomposition.
getvalue(double) - Method in class Assisted_Classes.Expected_value_expotential_function
 
getVariance() - Method in class Assisted_Classes.DescriptiveStatistics
This method returns the variance as : var=Ói=1(xi-m)2 / N-1
getWald() - Method in class models.Logistic_Regression
 
getWald_P_Values() - Method in class models.Logistic_Regression
 
getwaldpvalues() - Method in class models.Multinomial_Logistic_Regression
 
getwalds() - Method in class models.Multinomial_Logistic_Regression
 
Gini - Class in Tests
This class calculates the area under the ROC curve and the Gini coefficient statistics commonly used to asses the predictive power of a score (double array) versus a binary event (String Array) .
Gini() - Constructor for class Tests.Gini
 
give_me_woe(String[], String[]) - Method in class Tests.Make_Woe
This is the main method of the class that computes all the WoE related statistics
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