All Classes and Interfaces
Class
Description
Reads a text or binary file as a
ResultSet.A
SQLException thrown by the AbstractFlatFile class.A
SQLFeatureNotSupportedException thrown by the AbstractFlatFile class.Interface implemented by perceptron activation functions.
Generates a decision tree using the CARTTM method of Breiman,
Friedman, Olshen and Stone (1984).
Analyzes a one-way classification model with covariates.
Performs a one-way analysis of covariance.
Performs one-way analysis of covariance and tests for parallelism.
Analysis of Variance table and related statistics.
Performs a one-way analysis of variance.
Analyzes a balanced factorial design with fixed effects.
Performs a two-way factorial analysis of variance.
Performs a two-way factorial analysis of variance with
additional printed output.
Performs a three-way factorial analysis of variance.
Performs the Apriori algorithm for association rule discovery.
Finds frequent itemsets and strong association rules
for a small set of transactions.
Applies the Apriori algorithm to separate sets of
transactions.
Automatically determines the best autoregressive time series model using
Akaike's Information Criterion.
Deprecated.
The input triangular matrix is singular.
Finds the minimum AIC autoregressive model for the Wolfer sunspot data.
Finds the minimum AIC autoregressive model for the Canadian lynx data.
Computes least-square estimates of parameters for an ARMA model.
The problem is ill-conditioned.
The bound for the relative error is too small.
The input matrix is singular.
The iteration has not made good progress.
The residuals have become too large in one step of the Least Squares
estimation of the ARMA coefficients.
The number of calls to the function has exceeded the maximum number of
iterations times the number of moving average (MA) parameters + 1.
Maximum number of function evaluations exceeded.
Maximum number of iterations exceeded.
Maximum number of Jacobian evaluations exceeded.
Estimates missing values in a time series collected with equal spacing.
Estimates missing values for a generated
\( \text{AR}(1) \) series.
Fits an \(\text{ARMA}(2,1)\) to the Wolfer sunspot data using
the method of moments.
Fits an \(\text{ARMA}(2,1)\) to the Wolfer sunspot data
using the method of least squares.
Fits an \(\text{ARMA}(2,1)\) to the Wolfer sunspot data and
produces a forecast table.
Computes maximum likelihood estimates of parameters for an ARMA model with
p and q autoregressive and moving average terms respectively.
The solution is noninvertible.
The solution is nonstationary.
Fits an \(\text{ARMA}(2,1)\) to the Wolfer
sunspot data using the method of maximum likelihood.
Detects and determines outliers and simultaneously estimates the model parameters in a time
series whose underlying outlier free series follows a general seasonal or nonseasonal ARMA
model.
Performs outlier identification and simultaneously
fits an \(\text{ARIMA}(2,2,0)\) to the Canadian Lynx dataset.
Performs outlier identification on an
\( \text{ARMA}(1,1)\) process contaminated by outliers.
Forecasts an \(\text{ARMA}(2,1)\)
time series contaminated by outliers.
Estimates the optimum seasonality parameters for a time series using an
autoregressive model, AR(p), to represent the time series.
Searches for the best fit seasonality for the Airline
data.
Contains association rules discovered by the Apriori algorithm.
Automatically identifies time series outliers, determines parameters of a
multiplicative seasonal \(\text{ARIMA}(p,0,q)\times(0,d,0)_s
\) model and produces forecasts that incorporate the effects of
outliers whose effects persist beyond the end of the series.
No appropriate ARIMA model could be found.
Searches for the best fitting non-seasonal \(
\text{ARIMA} \).
Searches for the best fitting \(\text{ARIMA}(p,d,q)\)
model.
Fits an \(\text{ARIMA}(p,d,q)\) model with fixed
parameter values.
Computes the sample autocorrelation function of a stationary time
series.
The problem is ill-conditioned.
Computes autocorrelations of the Wolfer sunspot data.
Component of
DayCountBasis.Collection of Bessel functions.
Evaluates the Bessel functions I, J, and K.
The beta probability distribution.
Evaluates the beta probability distribution.
Classifies patterns into two classes.
Trains a 3-layer network with a binary output variable and 4 categorical
input attributes.
The binomial probability distribution.
Evaluates the binomial probability distribution.
Collection of bond functions.
Computes the accrued interest on a bond paying semiannually.
Computes the accrued interest on a bond paying at maturity.
Computes the convexity of a 10 year bond.
Computes the number of days from the beginning of the period to the
settlement date.
Computes the number of days in a coupon period.
Computes the number of days between the settlement date and the next coupon
date.
Computes the next coupon date after the settlement date.
Computes the number of payable coupons between the settlement date and the
maturity date.
Computes the previous coupon date before the settlement date.
Computes the depreciation for the second accounting period.
Computes the depreciation for the second accounting period.
Computes the discount rate for a security.
Computes the annual duration of a 10 year bond.
Computes the discount rate of a 10 year bond.
Computes the modified Macauley duration of a 10 year bond.
Computes the price of a discounted bond.
Computes the price of a 10 year bond paying semiannual interest.
Computes the price of a bond with an odd long first coupon.
Computes the price of a bond with an odd long last coupon and multiple
coupon periods.
Computes price of a bond with an odd long last coupon
and one coupon period.
Computes the price of a bond with an odd short first
coupon.
Computes the price of a bond with an odd short last coupon
and multiple coupon periods.
Computes the price of a bond with an odd short last coupon
and one or less coupon periods to redemption.
Computes the price of a bond paying interest at maturity.
Computes the price of a discounted 1 year bond.
Computes the amount to be received at maturity for a 10 year bond.
Computes the bond-equivalent yield for a treasury bill.
Computes the price of a 1 year treasury bill.
Computes the yield for a 1 year treasury bill.
Computes a specified year fraction.
Computes the yield on a discounted 10 year bond.
Computes the yield on a 10 year bond paying semiannually.
Computes the yield of a bond with an odd long first coupon.
Computes the yield of a bond with an odd long last coupon and multiple
periods.
Computes the yield of a bond with an odd long last coupon and
one coupon period.
Computes the yield of a bond with an odd short first coupon.
Computes the yield of a bond with an odd short last coupon and multiple
coupon periods.
Computes the yield of a bond with an odd short last coupon and one or fewer
coupon periods.
Computes the yield on a bond paying at maturity.
Performs bootstrap aggregation to generate predictions using predictive
models.
Performs bootstrap aggregation on a decision tree.
Performs bootstrap aggregation on a logistic regression model.
Solves a nonlinear least-squares problem subject to bounds on the variables
using a modified Levenberg-Marquardt algorithm.
False convergence - The iterates appear to be converging to a noncritical
point.
Public interface for the user-supplied function to evaluate the function
that defines the least-squares problem.
Public interface for the user-supplied function to compute the Jacobian.
Solves a nonlinear least squares problem subject to bounds.
Solves a nonlinear least squares problem
subject to bounds with a supplied Jacobian and initial guess.
Solve a linear least-squares problem with bounds on the variables.
Maximum number of iterations exceeded.
Solves a linear least squares problem
with bounds on the variables.
Extension of the BSpline class to interpolate data points.
Fits a B-spline to data.
Extension of the BSpline class to compute a least squares spline approximation
to data points.
Fits a least squares B-spline to data.
BSpline represents and evaluates univariate B-splines.
Generates a decision tree using the C4.5 algorithm for a categorical response
variable and categorical or quantitative predictor variables.
Analyzes categorical data using logistic, probit, Poisson, and other linear
models.
The ClassificationVariable vector has not been initialized.
The Classification Variable limit set by the user through
setUpperBound has been exceeded.The number of distinct values for each Classification Variable must be
greater than 1.
The number of observations to be deleted (set by
setObservationMax) has grown too large.The model has been determined to be rank deficient.
Fits a logit and probit categorical model to beetle mortality
data.
Analyzes interval type data with the Poisson model.
Cumulative probability distribution functions.
Evaluates various cumulative distribution functions.
Public interface for the user-supplied cumulative distribution function
to be used by InverseCdf and ChiSquaredTest.
Generates a decision tree using CHAID for categorical or discrete ordered
predictor variables.
Chi-squared goodness-of-fit test.
The iteration did not converge
There are no observations.
The function is not a Cumulative Distribution Function (CDF).
Performs a chi-squared test on simulated data.
Cholesky factorization of a matrix of type
double.The matrix is not symmetric, positive definite.
Solves a system using Cholesky factorization.
A public interface for probability distributions that provide a method for a
closed form solution of the maximum likelihood function
Performs a hierarchical cluster analysis from a distance matrix.
Performs hierarchical clustering on Fisher's
iris data.
Perform a K-means (centroid) cluster analysis.
There is a cluster with no points
Convergence did not occur within the maximum number of iterations.
Deprecated.
No longer used, replaced with an
IllegalArgumentException.Deprecated.
No longer used, replaced with an
IllegalArgumentException.
Performs K-Means clustering on Fisher's iris data.
Performs K-Means++ clustering on Fisher's iris data.
Perform a k-Nearest Neighbor classification.
Performs K-Nearest Neighbor clustering on Fisher's iris data.
Set of mathematical functions for complex numbers.
Collection of complex Eigen System functions.
The iteration did not converge.
Computes the eigenvalues and eigenvectors of a complex matrix.
Converts a real matrix to a complex matrix.
Complex FFT.
Finds the Fourier coefficients of a complex
sequence.
LU factorization of a matrix of type
Complex.
Computes the LU factorization of a complex matrix.
Complex matrix manipulation functions.
Indicates which matrix type is used.
Initializes and prints a complex matrix.
Sparse Cholesky factorization of a matrix of type
ComplexSparseMatrix.The matrix is not Hermitian, positive definite.
Data structures and functions for the numeric Cholesky factor.
Data structures and functions for the symbolic Cholesky factor.
Uses the Cholesky factorization of a complex
sparse matrix to solve a linear system.
Sparse matrix of type
Complex.The
SparseArray class uses public fields to hold
the data for a sparse matrix in the Java Sparse Array format.
Performs operations on a sparse complex matrix.
Computes the LU factorization of a general sparse matrix of type
ComplexSparseMatrix by a column method and solves a sparse
linear system of equations \(Ax=b\).
Computes the LU factorization of a sparse complex matrix.
Singular Value Decomposition (SVD) of a rectangular matrix of type
Complex.The iteration did not converge
Computes the SVD factorization of a complex matrix.
Solves a real symmetric definite linear system using the conjugate gradient
method with optional preconditioning.
Public interface for the user supplied function to
ConjugateGradient.The conjugate gradient method did not converge within the allowed maximum
number of iterations.
The input matrix A is indefinite, that is the matrix is not positive or
negative definite.
The Jacobi preconditioner is not strictly positive or negative definite.
The Precondition matrix is indefinite.
Public interface for the user supplied function to
ConjugateGradient used for preconditioning.The Precondition matrix is singular.
Solves a positive definite linear system using the conjugate gradient
method.
Solves a sparse linear system using the conjugate gradient method with
preconditioning.
Performs a chi-squared analysis of a two-way contingency table.
Performs a chi-squared test for independence.
Calculates a number of statistics associated with a contingency table.
The continuous uniform probability distribution.
Evaluates the continuous uniform probability distribution.
Computes the sample variance-covariance or correlation matrix.
Deprecated.
Deprecated.
Frequencies must be nonnegative.
Weights must be nonnegative.
Deprecated.
Calculates a variance-covariance matrix.
Computes the sample cross-correlation function of two stationary time
series.
The problem is ill-conditioned.
Computes the cross-covariances and cross-correlations for the gas furnace
data.
Performs V-Fold cross-validation for predictive models.
Uses cross-validation to determine the
optimally pruned decision tree.
Extension of the Spline class to handle the Akima cubic spline.
Computes the Akima cubic spline.
Extension of the Spline class to interpolate data points.
Computes a cubic spline.
Extension of the Spline class to interpolate data points with
periodic boundary conditions.
Computes a cubic spline interpolant with
periodic boundary conditions.
Extension of the Spline class to interpolate data points consistent
with the concavity of the data.
Too many iterations.
Computes a shape preserving cubic spline.
Extension of the Spline class to construct a smooth cubic spline
from noisy data points.
Extension of the Spline class used to construct a spline for noisy data
points using an alternate method.
Computes a smooth cubic spline on noisy data.
Computes a smooth cubic spline on noisy data using an "optimized" smoothing
parameter value.
Extension of the Spline class to handle a tension-continuity-bias (TCB) cubic spline,
also known as a Kochanek-Bartels spline and is a generalization of the Catmull-Rom spline.
Computes the Kochanek-Bartels cubic spline.
Specifies a data node for a support vector machine.
The Day Count Basis.
Perform a DBSCAN cluster analysis.
Class that holds the minimum number of points and epsilon parameters of
the
DBSCAN algorithm.Public interface for the user-supplied function to compute the distances
between points.
Performs DBSCAN clustering on Fisher's iris data.
Performs DBSCAN clustering on an artificial data set.
Abstract class for generating a decision tree for a single response variable
and one or more predictor variables.
Exception thrown when the maximum tree size has been exceeded.
Exception thrown when pruning fails to converge.
Exception thrown when attempting to split a node that is already pure
(response variable is constant).
Fits a decision tree to the golf data using C45 and ALACART.
Fits a decision tree to categorical data using CHAID and
prints the decision tree.
Fits a decision tree to categorical data using C45 and prints the decision
tree.
Fits a decision tree to the Kyphosis data using QUEST.
Fits a decision tree to mixed-type data using QUEST and
prunes the decision tree.
Abstract class that extends
DecisionTree for classes that use an
information gain criteria.Specifies which information gain criteria to use in determining the best
split at each node.
Methods to account for missing values in predictor variables.
Solves a linear programming problem using an active set strategy.
All constraints are not satisfied.
The bounds given are inconsistent.
The algorithm appears to be cycling.
The problem has multiple solutions giving essentially the same
minimum.
No acceptable pivot could be found.
The LP problem has no constraints.
The problem is unbounded.
The problem is vacuous.
Some constraints were discarded because they were too linearly
dependent on other active constraints.
Deprecated.
No longer used, replaced with an
IllegalArgumentException.
Solves a linear programming problem.
Solves a linear programming problem.
Solves a linear programming problem in \(5\) variables.
Differences a seasonal or nonseasonal time series.
Computes a lagged difference formula for the airline data.
Computes a lagged difference formula for the airline data excluding
the lost observations.
The discrete uniform probability distribution.
Evaluates the discrete uniform probability distribution.
Performs a linear or a quadratic discriminant function analysis among
several known groups.
The variance-covariance matrix is singular.
There are no observations in a group.
The sum of the weights have become negative.
Performs a discriminant analysis on Fisher's iris data.
Computes a matrix of dissimilarities (or similarities) between the columns
(or rows) of a matrix.
No variable has positive variance.
The computations cannot continue because a scale factor is zero.
The computations cannot continue because the Euclidean norm of the
column is equal to zero.
Computes a dissimilarity matrix using the Euclidean distance.
Public interface for the user-supplied distribution function.
Implements the exponential GARCH (EGARCH) model.
Fits an EGARCH(1, 1) to a segment of S&P 500 returns.
Fits an EGARCH(1, 1) with a user defined distribution on \(z_t\).
Fits an EGARCH(1, 1) with an ARMA(1,1) on the mean.
Collection of Eigen System functions.
The iteration did not converge
Computes the eigenvalues and eigenvectors of a matrix.
Computes empirical quantiles.
The computations cannot continue because a scale factor is zero.
Computes empirical quantiles for rainfall data.
Performs two-stage training using randomly selected training patterns in stage I.
Trains a 2-layer network using the 2-stage Epoch trainer.
The class is used to determine the limit of a sequence of
approximations, by means of the Epsilon algorithm of
P.
Accelerates a series of partial sums using the Epsilon algorithm.
The exponential probability distribution.
Evaluates the exponential probability distribution.
Abstract class for extended GARCH models.
An enumeration of the types of solvers available to the estimation
procedure.
Public interface for specifying the distribution of \(z_t\).
The extreme value/Gumbel probability distribution.
Evaluates the extreme value probability distribution.
Performs Principal Component Analysis or Factor Analysis on a covariance or correlation matrix.
Bad variance error.
Eigenvalue error.
Non positive eigenvalue error.
Matrix not positive definite.
Covariance matrix not positive semi-definite.
Hessian matrix not semi-definite.
Rank of covariance matrix error.
Indicates which method is used for computing the factor score
coefficients.
Covariance matrix singular error.
Computes the principal components on a 9 variable correlation matrix.
Compute the factors on 9 variables by the method of maximum likelihood.
Generates the low-discrepancy Faure sequence.
Generates points of the Faure sequence.
A representation of a feed forward neural network.
Solves the generalized Feynman-Kac PDE.
Public interface for user supplied boundary coefficients and terminal condition
the PDE must satisfy.
The boundary conditions are inconsistent.
The constraints are inconsistent.
Corrector failed to converge.
Error test failure detected.
Public interface for non-zero forcing term in the Feynman-Kac equation.
The constraints at the initial point are inconsistent.
Public interface for adjustment of initial data or as an opportunity
for output during the integration steps.
Iteration matrix is singular.
Public interface for user supplied PDE coefficients in the Feynman-Kac PDE.
The end value for the integration in time, tout, is not consistent with
the current time value, t.
The current integration point in time and the end point are equal.
Distance between starting time point and end point for the integration is
too small.
Tolerance is too small.
Too many iterations required by the DAE solver.
Compares American vs European options on a vanilla put.
Applies a diffusion model for options pricing.
Evaluates the price of a European option with two payoff strategies.
Evaluates the price of a convertible bond.
Solves for the "Greeks" of mathematical finance.
FFT functions.
Computes the Fourier coefficients of a periodic sequence.
Collection of finance functions.
Computes the amount of interest paid in the first year of a 30 year fixed
mortgage.
Computes the amount of principal paid in the first year of a 30 year fixed
rate mortgage.
Computes the depreciation of an asset.
Computes the depreciation of an asset using the double-declining balance
method.
Converts a fractional dollar price to a decimal price.
Converts a decimal dollar price to a fractional dollar price.
Computes the effective rate from a nominal rate compounded quarterly.
Computes the future value of an investment.
Computes the future value of an investment with scheduled rate of growth.
Computes the interest due the second year of a loan.
Computes the internal rate of return on an investment.
Computes the modified internal rate of return on an investment.
Computes the nominal interest rate.
Computes the number of payment periods for a loan.
Computes the net present value of a lottery prize using the stream of
payments as input.
Computes the payment due each year on a loan.
Computes the payment on principal the first year of a loan.
Computes the net present value of a lottery prize.
Computes the interest rate on a loan.
Computes the straight line depreciation of an asset.
Computes sum-of-year's depreciation.
Computes the depreciation of an asset using the variable-declining balance
method.
Computes the internal rate of return of an investment with variable
schedule.
Computes the net present value for a schedule of payments.
Reads a text file as a
ResultSet.Defines a method that parses a
String into an Object. Reads Fisher's Iris data set from a CSV file.
Reads in a data set in a space separated form.
Evaluates a gamma probability density for a given set of data.
The gamma probability distribution.
Evaluates the gamma probability distribution.
Computes estimates of the parameters of a GARCH(p,q) model.
The equality constraints are inconsistent.
The equality constraints and the bounds on the variables are
found to be inconsistent.
No vector X satisfies all of the constraints.
Number of function evaluations exceeded 1000.
The variables are determined by the equality constraints.
Estimates a \(\text{GARCH}(p,q)\) model from simulated data.
The generalized Gaussian probability distribution.
Linear system solver using the restarted Generalized Minimum Residual (GMRES)
method.
Deprecated.
Use
IMSLFormatter instead.Public interface for the user supplied function to
GenMinRes.Public interface for the user supplied function to the
GenMinRes object used for the norm \( \Vert X \Vert \)
when the Gram-Schmidt implementation is used.Public interface for the user supplied function to
GenMinRes used for preconditioning.Maximum number of iterations exceeded.
Public interface for the user supplied function to the
GenMinRes object used for the inner
product when the Gram-Schmidt implementation is used.
Solves a small linear system with the Generalized Minimum Residual (GMRES)
method.
Solves a small linear system with user supplied inner
product.
Solves a small linear system stored in sparse form.
Solves a small linear system stored in sparse form with
preconditioning.
Solves the Poisson equation using the second Householder
implementation.
Solves the Poisson equation using the second Householder
implementation and preconditioning.
Solves a small linear system with logging.
The geometric probability distribution.
Evaluates the geometric probability distribution.
Performs stochastic gradient boosting for a single response variable and
multiple predictor variables.
The loss function type as specified by the error measure.
Predicts a regression response
variable based on 6 predictor variables.
Predicts a binary response variable based on 4 predictor variables.
Selects the number of iterations using
cross-validation.
Uses an input model to set the configuration of
the base learner.
Predicts a data set using a trained gradient boosting model.
Uses a trained gradient boosting model
to predict a new data set.
Reads in a trained gradient boosting
model object to predict a new data set.
Hidden layer in a neural network.
Calculates parameters and forecasts using the Holt-Winters
Multiplicative or Additive forecasting method for seasonal data.
Applies Holt-Winter's exponential smoothing to a series.
Pure Java implementation of the hyperbolic functions and their inverses.
Evaluates the hyperbolic functions.
HyperRectangleQuadrature integrates a function over a hypercube.
Public interface function for the HyperRectangleQuadrature class.
Evaluates a multi-dimensional integral.
Pure Java implementation of the IEEE 754 functions
as specified in IEEE Standard for Binary Floating-Point Arithmetic,
ANSI/IEEE Standard 754-1985 (IEEE, New York).
Signals that a mathematical exception has occurred.
Simple formatter for classes that implement logging.
Signals that an error has occurred.
Signals that an unexpected error has occurred.
Input layer in a neural network.
A
Node in the InputLayer.Inverse cumulative probability distribution functions.
Evaluates the inverse CDF for the beta and chi-squared random variables.
Inverse of user-supplied cumulative distribution function.
The iteration did not converge
Computes the inverse of a user-supplied CDF at a probability value.
The inverse Gaussian (Wald) probability distribution.
Evaluates the inverse Gaussian (Wald) probability distribution.
Object containing a set of frequent items and the number of transactions
examined to obtain the frequent item set.
Pure Java implementation of the standard java.lang.Math class.
Performs Kalman filtering and evaluates the likelihood function for the
state-space model.
Computes the filtered estimates and the one-step-ahead estimates using the
Kalman filter.
Estimates a moving average model \(\text{MA}(1)\) using the Kalman filter.
Computes the Kaplan-Meier reliability function estimates or the CDF based on
failure data that may be multi-censored.
Computes the survival curve for units under life-testing.
Computes Kaplan-Meier (or product-limit) estimates of survival probabilities
for a sample of failure times that possibly contain right censoring.
Computes the Kaplan-Meier probability estimates for censored data.
Abstract class to specify a kernel function for support vector machines.
A Kohonen self organizing map.
Creates and trains a Kohonen self-organizing
map.
Trains a Kohonen network.
The class
KolmogorovOneSample performs a Kolmogorov-Smirnov
goodness-of-fit test in one sample.
Performs a Kolmogorov one-sample test.
Performs a Kolmogorov-Smirnov two-sample test.
Performs a Kolmogorov two-sample test.
Performs lack-of-fit test for a univariate time series or transfer function
given the appropriate correlation function.
Performs a lack-of-fit test between an \(\text{ARMA}(2,1)\) and Wolfer's
sunspot data.
The base class for
Layers in a neural network.Trains a
FeedForwardNetwork using a Levenberg-Marquardt
algorithm for minimizing a sum of squares error.A LicenseException exception is thrown if
a license to use the product cannot be obtained.
Computes population (current) or cohort life tables based upon the observed
population sizes at the middle (for population table) or the beginning (for
cohort table) of some user specified age intervals.
Computes a cohort life table.
Specifies the linear kernel for support vector machines.
Deprecated.
LinearProgramming has been replaced by DenseLP.Deprecated.
Deprecated.
Deprecated.
Deprecated.
Deprecated.
No longer used, replaced with an
IllegalArgumentException.Deprecated.
LinearProgramming class has been deprecated.Deprecated.
LinearProgramming class has been deprecated.Fits a multiple linear regression model with or without an intercept.
Computes a simple linear regression model.
Computes case statistics in a simple linear regression.
A link in a neural network.
The logistic probability distribution.
Evaluates the logistic probability distribution.
Performs binomial or multinomial logistic regression.
Trains a logistic regression model for a binomial response variable.
Trains a logistic regression model for a multinomial response.
Trains a logistic regression model for multinomial count data.
Predicts a data set using a previously trained logistic regression model
object.
Uses a trained logistic regression model to predict new data.
Aggregates two separate fits of logistic regression.
The log-logistic probability distribution.
Evaluates the log-logistic probability distribution.
Evaluates a lognormal probability density for a given set of data.
The log-normal probability distribution.
Evaluates the log normal probability distribution.
LU factorization of a matrix of type
double.
Performs the LU factorization of a matrix.
Manipulation methods for real-valued rectangular matrices.
Indicates which matrix type is used.
Calculates the 1-norm of a simple matrix.
Maximum likelihood parameter estimation.
Indicates which optimization method to use in maximizing the likelihood.
Estimates the parameters of a beta probability distribution.
Estimates the parameters of a gamma probability distribution.
Estimates the parameters of the normal probability distribution.
Estimates the parameters of the generalized Gaussian distribution.
Estimates the parameters of the log-logistic probability distribution.
Estimates the parameters of the inverse Gaussian (Wald) probability
distribution.
Estimates the parameters of a discrete uniform probability distribution.
Estimates the parameter (probability) of a binomial probability
distribution.
Estimates the parameter (probability) of a negative binomial probability
distribution.
A 32-bit Mersenne Twister generator.
A 64-bit Mersenne Twister generator.
Generates a pseudorandom sequence using the Mersenne64 Twister.
Generates a pseudorandom sequence using the Mersenne Twister.
Retrieve and format message strings.
Minimizes a general objective function subject to linear equality/inequality
constraints.
The equality constraints are inconsistent.
No vector x satisfies all of the constraints.
the variables are determined by the equality constraints.
Public interface for the user-supplied function to evaluate the function to be minimized.
Public interface for the user-supplied function to compute the gradient.
The equality constraints and the bounds on the variables are found to be
inconsistent.
Solves a general minimization problem with constraints.
Minimizes a nonlinear function with constraints.
General nonlinear programming solver.
Penalty function point infeasible for original problem.
Constraint evaluation returns an error with current point.
Deprecated.
Use
IMSLFormatter instead.Public interface for the user supplied function to the
MinConNLP object.Public interface for the user supplied function to compute the gradient for
MinConNLP object.Problem is singular or ill-conditioned.
Limiting accuracy reached for a singular problem.
Working set gradients are linearly dependent.
No acceptable stepsize in [SIGMA,SIGLA].
Objective evaluation returns an error with current point.
Penalty function point infeasible.
QP problem seemingly infeasible.
Problem is singular.
Termination criteria are not satisfied.
Maximum number of iterations exceeded.
Maximum time allowed for solve exceeded.
Working set is singular in dual extended QP.
Solves a nonlinear programming problem using a finite
difference gradient.
MinConNLP Example 2: Solves a general nonlinear programming problem with
a user supplied gradient.
MinConNLP Example 3: Solves a general nonlinear programming problem using a
finite difference gradient.
Deprecated.
MinConNonlin has been replaced by MinConNLP.Deprecated.
MinConNonlin has been replaced by MinConNLP.Deprecated.
MinConNonlin has been replaced by MinConNLP.Deprecated.
MinConNonlin has been replaced by MinConNLP.Deprecated.
MinConNonlin has been replaced by MinConNLP.Deprecated.
MinConNonlin has been replaced by MinConNLP.Deprecated.
MinConNonlin has been replaced by MinConNLP.Deprecated.
MinConNonlin has been replaced by MinConNLP.Unconstrained minimization.
Public interface for the user supplied function to the
MinUncon object.Public interface for the user supplied function to the
MinUncon object.
MinUncon Example 1: Minimizes a single variable function.
MinUncon Example 2: Minimizes a single variable function
using the analytic derivative.
Unconstrained multivariate minimization.
Scaled step tolerance satisfied; the current point may be an approximate
local solution, or the algorithm is making very slow progress and is not
near a solution, or the scaled step tolerance is too big.
False convergence error; the iterates appear to be converging to a
noncritical point.
Public interface for the user supplied function to the
MinUnconMultiVar object.Public interface for the user supplied gradient to the
MinUnconMultiVar object.Public interface for the user supplied Hessian to the
MinUnconMultiVar object.Maximum number of iterations exceeded.
Five consecutive steps of the maximum allowable stepsize have been taken,
either the function is unbounded below, or has a finite asymptote in some
direction or the maximum allowable step size is too small.
MinUnconMultiVar Example 1: Minimizes a multivariate function.
MinUnconMultiVar Example 2: Minimizes a multivariate function with a user
supplied gradient.
MinUnconMultiVar Example 3: Minimizes a multivariate function with a user
supplied Hessian.
Reads a linear programming problem from an MPS file.
An element in the sparse contraint matrix.
The MPS file is invalid.
Reads an MPS file.
Classifies patterns into three or more classes.
Trains a 3-layer network to Fisher's iris data.
Computes the multichannel cross-correlation function of two mutually
stationary multichannel time series.
The problem is ill-conditioned.
Computes cross-correlations for a three-channel time series.
Performs metric multidimensional scaling using the Euclidean or individual
differences model.
A Hessian matrix is ill-defined.
The number of positive eigenvalues of the double-centered distance matrix
is too small.
Applies multidimensional scaling to a distance matrix.
Applies multidimensional scaling to rectangles of different size.
Performs Student-Newman-Keuls multiple comparisons test.
Performs the Student-Newman-Keuls multiple comparison test on a small set of
means.
Trains a naive Bayes classifier.
Trains a classifier to Fisher's Iris data.
Trains a classifier on nominal (categorical)
attributes.
Trains a classifier with a user supplied probability
function.
Defines the user supplied probability distribution.
The negative binomial probability distribution.
Evaluates the negative binomial probability distribution.
Minimizes a function of n variables with or without box constraints
using a direct search polytope algorithm.
Public interface for the user-supplied function to evaluate the objective
function of the minimization problem.
Solves an unconstrained optimization problem using the simplex method of
Nelder and Mead.
Solves a constrained optimization problem using a direct search complex
method.
Neural network base class.
A
Node in a neural network.Fits a multivariate nonlinear regression model using least squares.
Public interface for the user supplied function to compute the
derivative for
NonlinearRegression.Public interface for the user supplied function for
NonlinearRegression.A negative frequency was encountered.
A negative weight was encountered.
The number of iterations has exceeded the maximum allowed.
Fits a nonlinear regression using finite differences for the derivatives.
Fits a nonlinear regression using user supplied derivatives.
Fits a nonlinear regression on scaled data.
Nonlinear least squares.
Public interface for the user supplied function to the
NonlinLeastSquares object.Public interface for the user supplied function to the
NonlinLeastSquares object.Too many iterations.
Solves a nonlinear least squares problem using a finite difference Jacobian.
NonlinLeastSquares Example 2: Solves a nonlinear least squares problem with a
user supplied Jacobian.
Solves a linear least squares problem with nonnegativity constraints.
Maximum number of iterations has been exceeded.
Maximum time allowed for solve is exceeded.
Solves a nonnegative least squares problem.
Evaluates the normal (Gaussian) probability density for a given set of data.
Performs a test for normality.
There is no variation in the input data.
Performs a test of normality.
The normal (Gaussian) probability distribution.
Evaluates the normal probability distribution.
Computes statistics for mean and variance inferences using a sample
from a normal population.
Performs a hypothesis test for the mean of a normal distribution.
Computes statistics for mean and variance inferences using samples from
two normal populations.
Performs a hypothesis test for the difference in means
of two normal distributions.
Performs a difference in means test with incremental updates.
Compute the Jacobian matrix for a function \(f(y)\) with
m components in n independent variables.
Public interface function.
Public interface for the user-supplied function to compute the Jacobian.
NumericalDerivatives Example 1: Approximates the gradient of a function of
two variables using numerical differentiation.
Approximates one component of the gradient using numerical differentiation.
Approximates the gradient with a combination of
numerical derivatives and analytic derivatives.
Approximates the gradient using central
divided differences.
Approximates the Hessian of a function using
numerical differentiation.
Solves an optimization problem with supplied numerical gradients.
ODE represents and solves an initial-value problem for ordinary differential
equations.
Extension of the ODE class to solve a stiff initial-value problem for
ordinary differential equations using the Adams-Gear methods.
The iteration did not converge within the maximum number of steps allowed (default 500).
Public interface for user supplied function to
OdeAdamsGear object.Public interface for the user supplied function to
evaluate the Jacobian matrix.
Maximum function evaluations exceeded.
The interpolation matrix is singular.
Tolerance is too small or the problem is stiff.
Solves an ODE using the Adams-Gear method.
Solves an initial-value problem for ordinary differential
equations using the Runge-Kutta-Verner fifth-order and
sixth-order method.
The iteration did not converge within the maximum number of steps allowed (default 500).
Public interface for user supplied function to
OdeRungeKutta object.Tolerance is too small or the problem is stiff.
Solves an ODE using the Runge-Kutta-Verner method.
Output layer in a neural network.
A
Perceptron in the OutputLayer.The Pareto probability distribution.
Evaluates the Pareto probability distribution.
Class
PartialCovariances computes the partial covariances or partial
correlations from an input covariance or correlation matrix.Exception thrown if a computed correlation is greater than one for some pair of variables.
Exception thrown if a computed partial correlation is greater than one for some pair of variables.
Computes the partial covariances for a set of 9 variables.
Computes partial covariances after adjusting for specific variables.
Probability density functions.
The magnitude of alternating series sum is too small relative to the sum
of positive terms to permit a reliable accuracy.
Evaluates probability density functions.
A public interface for probability distributions that provide a method
to calculate the gradient of the density function
A public interface for probability distributions that provide methods
to calculate the gradient and hessian of the density function
A
Perceptron node in a neural network.Return the value of various mathematical and physical constants.
Displays the physical constant PI.
Evaluates a Poisson probability density of a given set of data.
The Poisson probability distribution.
Evaluates the Poisson probability distribution.
Specifies the polynomial kernel for support vector machines.
Computes a pooled variance-covariance matrix from one or more sets of
observations.
Computes a pooled variance-covariance matrix involving 2 groups.
Computes pooled variance-covariance for Fisher's iris data.
Specifies a predictive model.
Wraps the
java.lang.CloneNotSupportedException to indicate
that the clone method in class Object has been
called to clone an object, but that the object's class does not implement
the Cloneable interface.An exception class intended to be the parent of all nested Exception
classes where the enclosing class extends
PredictiveModel.Exception thrown when an input parameter has changed that might affect
the model estimates or predictions.
Exception thrown when the sum of probabilities is not approximately one.
Enumerates different variable types.
Performs the PrefixSpan algorithm for sequential pattern mining.
Finds sequential patterns in a sequence database.
Finds sequential patterns in a sequence database.
Creates a sequence database from a transaction database.
Matrix printing utilities.
Prints a simple matrix.
This class can be used to customize the actions of PrintMatrix.
Prints a matrix with and without row and column labels.
Prints a matrix in CSV format.
The ProbabilityDistribution abstract class defines members and methods common
to univariate probability distributions and useful in parameter estimation.
Public interface for a user-supplied probability distribution.
Analyzes survival and reliability data using Cox's proportional hazards model.
The Classification Variable limit set by the user through
setUpperBound has been exceeded.Performs proportional-hazards data analysis on lung cancer data.
QR Decomposition of a matrix.
Performs the QR factorization of a matrix.
Solves the convex quadratic programming problem subject to equality or inequality
constraints.
The system of constraints is inconsistent.
No solution for the LP problem with h = 0 was found by
DenseLP.The objective value for the problem is unbounded.
A solution was not found.
Solves a quadratic programming problem in 4 variables.
Solves a quadratic programming problem with equality constraints.
Illustrates the exception thrown by the solver when it encounters
inconsistent style constraints.
Quadrature is a general-purpose integrator that uses a globally
adaptive scheme in order to reduce the absolute error.Public interface function for the Quadrature class.
Quadrature Example 1: Approximates an integral.
Quadrature Example 2: Approximates the integral of \(e^{-x}\).
Quadrature Example 3: Approximates the integral of the entire real line.
Quadrature Example 4: Approximates a trigonometric integral.
Trains a network using the quasi-Newton method,
MinUnconMultiVar.Error function to be minimized by trainer.
Generates a decision tree using the QUEST algorithm for a categorical
response variable and categorical or quantitative predictor variables.
RadialBasis computes a least-squares fit to scattered data in \(
{\bf R}^d\), where d is the dimension.
Public interface for the user supplied function to the
RadialBasis
object.The Gaussian basis function, \(e^{-ax^2}\).
The Hardy multiquadric basis function, \(\sqrt{r^2+\delta^2}
\).
RadialBasis Example 1: Approximates a function with a
Hardy multiquadric radial basis function.
Approximates a function with a polyharmonic spline radial basis function.
RadialBasis Example 2b: Defines a polyharmonic spline radial basis
function.
Approximates a function using a Hardy multiquadric radial basis function.
Approximates a function with a Gaussian radial basis function.
Specifies the radial basis kernel for support vector machines.
Generate uniform and non-uniform random number distributions.
Base pseudorandom number.
Generates a pseudorandom sample from a normal distribution and performs a
goodness of fit test.
Generates a pseudorandom multivariate sequence with user defined marginal
distributions.
Generates a pseudorandom sample from a discrete distribution.
Generates a pseudorandom sample from a discrete uniform distribution.
Generates a simple pseudorandom sample from a finite population, a sample
of indices, or a permutation of an array of indices.
Generates a pseudorandom permutation.
Generates a set of pseudorandom indices.
Selects a sample from a data set.
Selects a pseudorandom sample from a million records.
Selects a pseudorandom sample from Fisher's iris data.
Interface implemented by generators of random or quasi-random
multidimensional sequences.
Generates predictions using a random forest of decision trees.
Class that wraps exceptions thrown by reflective operations in core
reflection.
Fits a random forest to the Kyphosis data using ALACART decision trees and
generates predictions on a test set.
Fits a random forest to Fisher's Iris data using ALACART decision trees.
Fits a random forest using C45 decision trees and
calculates variable importance.
Compute the ranks, normal scores, or exponential scores
for a vector of observations.
Analyzes the ranks of a data set.
The Rayleigh probability distribution.
Evaluates the Rayleigh probability distribution.
Public interface for user supplied function to
UserBasisRegression object.Generates regressors for a general linear model.
Generates binary regressors for classification variables.
Sets up data for a two-way analysis of covariance.
Scales or unscales continuous data prior to its use in neural network
training, testing, or forecasting.
Applies scaling methods to three data sets.
Selects the best multiple linear regression models.
No Variables can enter the model.
Finds the best regressions using the \(R^2\) criterion.
Finds the best regressions using Mallow's \(C_p\) criterion.
Defines a sequence database for use with the
PrefixSpan algorithm.Collection of special functions.
Calculates various special functions.
Specifies the sigmoid kernel for support vector machines.
Performs a sign test.
Performs the sign test on a small data set.
Performs the sign test on a small data set.
The matrix is singular.
A collection of sorting functions.
Sorts an array and computes the permutation.
Sorts a matrix using columns as keys.
Sparse Cholesky factorization of a matrix of type
SparseMatrix.The matrix is not symmetric, positive definite.
The numeric Cholesky factorization of a matrix.
The symbolic Cholesky factorization of a matrix.
SparseCholesky Example 1: Computes the Cholesky factorization of a sparse
matrix.
Solves a sparse linear programming problem by an infeasible primal-dual
interior-point method.
The Cholesky factorization failed because of accuracy problems.
A diagonal element of the diagonal weight matrix is too small.
The dual problem is infeasible.
The lower bound is greater than the upper bound.
One or more LP variables are falsely characterized by the internal
presolver.
One or more LP variables are falsely characterized by the internal
presolver.
The initial solution for the one-row linear program is infeasible.
The primal problem is infeasible.
The primal problem is unbounded.
The problem is unbounded.
The maximum number of iterations has been exceeded.
A column of the constraint matrix has no entries.
A row of the constraint matrix has no entries.
Solves a linear programming problem with sparse representation.
SparseLP Example 2: Solves a linear programming problem defined in an MPS
file.
Sparse matrix of type
double.The
SparseArray class uses public fields to hold
the data for a sparse matrix in the Java Sparse Array format.
SparseMatrix Example 1: Computes the matrix product of two sparse
matrices.
SparseMatrix Example 2: Converts a matrix in market format to a sparse matrix
format.
Reads a file containing Market format data.
Spline represents and evaluates univariate piecewise polynomial splines.
Represents and evaluates tensor-product splines.
Computes a two-dimensional, tensor-product spline interpolant from
two-dimensional, tensor-product data.
Spline2DInterpolate Example 1: Computes a tensor-product spline
interpolant.
Spline2DInterpolate Example 2: Computes the tensor-product spline interpolant.
Computes a spline interpolant on a function and evaluates the partial
derivatives.
Spline2DInterpolate Example 4: Integrates a tensor-product spline.
Computes a two-dimensional, tensor-product spline approximant using least squares.
Computes a tensor-product cubic spline least squares fit to a function.
Builds multiple linear regression models using forward selection, backward
selection, or stepwise selection.
Cycling is occurring.
No Variables can enter the model.
Performs stepwise regression variable selection.
Computes basic univariate statistics.
Computes summary statistics for a small data set.
Computes the LU factorization of a general sparse matrix of type
SparseMatrix by a column method and solves the real sparse linear
system of equations \(Ax=b\).
SuperLU Example 1: Computes the LU factorization of a sparse matrix.
Abstract class for generating a support vector machine.
Class that wraps exceptions thrown by reflective operations in core
reflection.
Trains a support vector machine on Fisher's iris data.
Classifies Fisher's iris data after first selecting
parameter values using cross-validation.
Performs goodness-of-fit using the one-class support vector machine.
Compares a regression and a classification support vector machine
for predicting a categorical response.
Illustrates the use of case weights on the
training data.
Specifies a support vector machine for classification (SVC).
Singular Value Decomposition (SVD) of a
rectangular matrix of type
double.The iteration did not converge
Computes the SVD factorization of a matrix.
Class to contain model estimates after training a support vector machine.
Specifies a support vector machine for the one class problem.
Specifies a support vector machine for regression (SVR).
Computes the eigenvalues and eigenvectors of a real
symmetric matrix.
Computes the eigenvalues and eigenvectors of a symmetric matrix.
Tallies observations into a multi-way frequency table.
Computes a two-way table in the presence of missing values.
Computes a two-way table and displays the balanced table.
Computes a two-way table and displays the unbalanced table.
Class
TableOneWay calculates a frequency table for a data array.
Computes a one-way table for a continuous scale variable.
Class
TableTwoWay calculates a two-dimensional frequency table for
a data array based upon two variables.
Computes a two-way table for continuous scale data.
A specialized class for time series data and analysis.
Converts time series data contained within nominal categories to a lagged
format for processing by a neural network.
Applies a time series filter to a classification
variable.
Sets up a time series object.
Sets up a time series with a different time zone.
Converts time series data to a lagged format used as input to a neural network.
Applies a time series filter.
A class of operations and methods for objects of class TimeSeries.
Public enum of methods for combining synchronous time series values.
Public interface for the user-supplied function that defines how to
combine two synchronous time series values.
Public enum of merge rules that defines how two time series should be
merged.
Merges two time series using different merging rules.
Merges two time series using different combining methods.
Performs the backshift operation on a time series.
Performs the stacking or vectorizing operation on a time series.
Breaks a line into tokens.
Standardizes the output format for the neural network log files.
Interface implemented by classes used to train a network.
Solves a Transportation problem.
Indicates which algorithm is used to solve the transportation problem.
Maximum number of iterations exceeded.
An unexpected error occurred.
Solves a transportation problem using the revised Simplex method.
Solves a random transportation problem using both the simplex and the
interior-point method.
Serves as the root node of a decision tree and contains information about the
relationship of child nodes.
A
DecisionTree node that is a child node of Tree.Converts nominal data into a series of binary encoded
columns for input to a neural network.
Filters a small data set on a nominal type variable.
Encodes ordinal data into percentages for input to a neural network.
Encodes a small ordinal data set using the arcsin square root transform.
Fits a linear function of the form \(y = c_0 + c_1 f_1 (x) + c_2 f_2 (x) + \cdots + c_k f_k (x) + \varepsilon\),
where \(f_1 (x),f_2 (x), \cdots ,f_k (x)\) are the user basis functions
\(f_i (x)\) evaluated at index values
\(i = 1,2, \ldots ,k,c_0 \) is the intercept, \(c_1 ,c_2 , \cdots ,c_k\)
are the coefficients associated with the basis functions, and is the random
error associated with y.
Fits a regression to a function without noise with user
defined basis functions.
Fits a regression to a polynomial with user defined basis functions.
Performs vector autoregression for a multivariate time series.
Fits a vector autoregression to a time series.
Print the version information.
Handle warning messages.
Captures a warning message and reprints the message later.
Handle warning messages.
The Weibull probability distribution.
Evaluates the Weibull probability distribution.
Performs Welch's t-test for testing the difference in means between
two normal populations with unequal variances.
The form of the alternate hypothesis.
Performs Welch's t-test for three example data sets.
Performs a Wilcoxon rank sum test.
Performs a rank sum test.
Performs a rank sum test and displays all the statistics.
Deprecated.
ZeroFunction has been replaced by ZerosFunction.Deprecated.
ZeroFunction has been replaced by ZerosFunction.Deprecated.
ZeroFunction class has been deprecated.The ZeroPolynomial class computes the zeros of a polynomial
with complex coefficients, Aberth's method.
The iteration did not converge
Finds the zeros of a polynomial.
Finds the zeros of a polynomial with complex coefficients.
Finds the real zeros of a real, continuous, univariate function,
f(x).
Public interface for the user supplied function to
ZerosFunction.
Finds zeros of the \(\sin\) function.
Solves a system of n nonlinear equations f(x) = 0 using a modified Powell
hybrid algorithm.
The iteration did not converge.
Public interface for user supplied function to
ZeroSystem
object.Public interface for user supplied function to
ZeroSystem
object.Tolerance too small
Too many iterations.
Solves a system of nonlinear equations.
ZeroSystem Example 2: Solves a system of nonlinear equations with logging
enabled.
IMSLFormatterinstead.