- 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