Namespaces
	SOM

Classes

class ArrayHolder

Just like Holder, except for arrays. More...

struct ArrayWrapper

struct ComplexNumber

class Ex

The class of all exceptions thrown by this library A simple exception object that wraps a string message. More...

class FileHolder

Closes a file when this object goes out of scope. More...

class G2DRegionGraph

Implements a region adjacency graph for 2D images, and lets you merge similar regions to create a hierarchical breakdown of the image. More...

class G3dLetterMaker

class G3DMatrix

Represents a 3x3 matrix. More...

class G3DVector

Represents a 3D vector. More...

class GActionPath

class GActionPathSearch

This is the base class of search algorithms that can only perform a discreet set of actions (as opposed to jumping to anywhere in the search space), and seeks to minimize the error of a path of actions. More...

class GActionPathState

class GAdamOptimizer

Trains a neural network by ADAM. See Diederik P. Kingma and Jimmy Lei Ba, "Adam: A Method for Stochastic Optimization", 2015. More...

class GAgentActionIterator

Iterates through all the actions that are valid in the current state. If actions are continuous or very numerous, this should sample valid actions in a random order. The caller may decide that it has sampled enough at any time. More...

class GAgglomerativeClusterer

This merges each cluster with its closest neighbor. (The distance between clusters is computed as the distance between the closest members of the clusters times (n^b), where n is the total number of points from both clusters, and b is a balancing factor. More...

class GAgglomerativeTransducer

This is a semi-supervised agglomerative clusterer. It can only handle one output, and it must be nominal. All inputs must be continuous. Also, it assumes that all output values are represented in the training set. More...

class GAnnealing

Perturbs the current vector in a random direction. If it made the vector worse, restores the previous vector. Decays the deviation of perturbation over time. More...

class GApp

Contains some generally useful functions for launching applications. More...

class GArffAttribute

class GArffRelation

ARFF = Attribute-Relation File Format. This stores richer information than GRelation. This includes a name, a name for each attribute, and names for each supported nominal value. More...

class GArgReader

Parses command-line args and provides methods to conveniently process them. More...

class GAssignment

An abstract base class defining an assignment between two sets. More...

class GAtomicCycleFinder

This finds all of the atomic cycles (cycles that cannot be divided into two smaller cycles) in a graph. More...

class GAttributeSelector

Generates subsets of data that contain only the most relevant features for predicting the labels. The train method of this class produces a ranked ordering of the feature attributes by training a single-layer neural network, and deselecting the weakest attribute until all attributes have been deselected. The transform method uses only the highest-ranked attributes. More...

class GAutoFilter

class GBag

BAG stands for bootstrap aggregator. It represents an ensemble of voting models. Each model is trained with a slightly different training set, which is produced by drawing randomly from the original training set with replacement until we have a new training set of the same size. Each model is given equal weight in the vote. More...

class GBagOfRecommenders

This class performs bootstrap aggregation with collaborative filtering algorithms. More...

class GBallTree

An efficient algorithm for finding neighbors. Empirically, this class seems to be a little bit slower than GKdTree. More...

class GBaselineLearner

Always outputs the label mean (for continuous labels) and the most common class (for nominal labels). More...

class GBaselineRecommender

This class always predicts the average rating for each item, no matter to whom it is making the recommendation. The purpose of this algorithm is to serve as a baseline for comparison. More...

class GBayesianModelAveraging

This is an ensemble that uses the bagging approach for training, and Bayesian Model Averaging to combine the models. That is, it trains each model with data drawn randomly with replacement from the original training data. It combines the models with weights proporitional to their likelihood as computed using Bayes' law. More...

class GBayesianModelCombination

class GBayesNet

This class provides a platform for Bayesian belief networks. It allocates nodes in its own heap using placement new, so you don't have to worry about deleting the nodes. You can allocate your nodes manually and use them separately from this class if you want, but it is a lot easier if you use this class to manage it all. More...

class GBetaDistribution

The Beta distribution. More...

class GBezier

Represents a Bezier curve. More...

class GBigInt

Represents an integer of arbitrary size, and provides basic arithmetic functionality. Also contains functionality for implementing RSA symmetric-key cryptography. More...

class GBillboard

This is a billboard (a 2-D image in a 3-D world) for use with GBillboardWorld. You can set m_repeatX and/or m_repeatY to make the image repeat across the billboard. More...

class GBillboardWorld

This class represents a world of billboards, and provides a rendering engine. More...

struct GBitReverser_imp

Template used for reversing numBits of type T. You shouldn't need this, use the function reverseBits. More...

struct GBitReverser_imp< T, 1 >

Base case of template used for reversing numBits of type T. You shouldn't need this, use the function reverseBits. More...

class GBits

Contains various functions for bit analysis. More...

class GBitTable

Represents a table of bits. More...

class GBlobIncoming

This class is for deserializing blobs. It takes care of Endianness issues and protects against buffer overruns. This class would be particularly useful for writing a network protocol. More...

class GBlobOutgoing

This class is for serializing objects. It is the complement to GBlobIncoming. More...

class GBlobQueue

This is a special queue for handling blobs that come in and go out in varying sizes. It is particulary designed for streaming things that must travel or be parsed in packets that may differ in size from how they are sent or transmitted. More...

class GBlock

Represents a block of network units (artificial neurons) in a neural network. More...

class GBlockActivation

The base class of blocks that apply an activation function, such as tanh, in an element-wise manner. More...

class GBlockAllPairings

Consumes n values and produces a big vector in the form of two vectors (concatenated together) whose corresponding values form all pairs of inputs, plus each input is also paired with each of "lo" and "hi". Example: If the input is {1,2,3}, then the output will be {1,1,1,1,2,2,2,3,3, 2,3,lo,hi,3,lo,hi,lo,hi}. Thus, if there are n inputs, there will be n(n-1)+4n outputs. More...

class GBlockBentIdentity

Applies the Bent identity element-wise to the input.

Equation	Plot
$f(x) = \frac{\sqrt{x^2+1}-1}{2}+x$

More...

class GBlockConvolutional1D

class GBlockConvolutional2D

class GBlockFeatureSelector

A linear block with no bias. All weights are constrained to fall between 0 and 1, and to sum to 1. Regularization is implicitly applied during training to drive the weights such that each output will settle on giving a weight of 1 to exactly one input unit. More...

class GBlockFuzzy

A block of fuzzy logic units. Treats the inputs as two concatenated vectors, whose corresponding values each form a pair to be combined with fuzzy logic to produce one output value. Example: The input {1,2,3,4,5,6} will apply fuzzy logic to the three pairs {1,4}, {2,5} and {3,6} to produce a vector of 3 output values. More...

class GBlockGaussian

Applies the Gaussian function element-wise to the input.

Equation	Plot
$f(x) = e^{-x^2}$

More...

class GBlockGRU

A block of Gated Recurrent Units. More...

class GBlockIdentity

Applies the Identity function element-wise to the input. Serves as a pass-through block of units in a neural network.

Equation	Plot
$f(x) = x$

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class GBlockLeakyRectifier

Applies the Leaky rectified linear unit (Leaky ReLU) element-wise to the input.

Equation	Plot
$f(x) = \left \{ \begin{array}{rcl} 0.01x & \mbox{for} & x < 0\\ x & \mbox{for} & x \ge 0\end{array} \right.$

More...

class GBlockLinear

Standard fully-connected block of weights. Often followed by a GBlockActivation. More...

class GBlockLogistic

Applies the Logistic function element-wise to the input.

Equation	Plot
$f(x) = \frac{1}{1 + e^{-x}}$

More...

class GBlockLSTM

A classic Long Short Term Memory block (with coupled forget and input gates) More...

class GBlockRectifier

Applies the Rectified linear unit (ReLU) element-wise to the input.

Equation	Plot
$f(x) = \left \{ \begin{array}{rcl} 0 & \mbox{for} & x < 0 \\ x & \mbox{for} & x \ge 0\end{array} \right.$

More...

class GBlockRecurrent

Base class of recurrent blocks. More...

class GBlockRestrictedBoltzmannMachine

class GBlockScalarProduct

Treats the input as two concatenated vectors. Multiplies each corresponding pair of values together to produce the output. More...

class GBlockScalarSum

Treats the input as two concatenated vectors. Adds each corresponding pair of values together to produce the output. More...

class GBlockScaledTanh

Applies a scaled TanH function element-wise to the input.

Equation
$f(x) = tanh(x \times 0.66666667) \times 1.7159$

LeCun et al. suggest scale_in=2/3 and scale_out=1.7159. By carefully matching scale_in and scale_out, the nonlinearity can also be tuned to preserve the mean and variance of its input: More...

class GBlockSigExp

An element-wise nonlinearity block This activation function forms a sigmoid shape by splicing exponential and logarithmic functions together. More...

class GBlockSine

Applies the Sinusoid element-wise to the input.

Equation	Plot
$f(x) = \sin(x)$

More...

class GBlockSoftExp

A parameterized activation function (a.k.a. adaptive transfer function). More...

class GBlockSoftPlus

Applies the SoftPlus function element-wise to the input.

Equation	Plot
$f(x)=\ln(1+e^x)$

More...

class GBlockSoftRoot

An element-wise nonlinearity block. This function is shaped like a sigmoid, but with both a co-domain and domain that spans the continuous values. At very negative values, it is shaped like y=-sqrt(-2x). Near zero, it is shaped like y=x. At very positive values, it is shaped like y=sqrt(2x). More...

class GBlockSparse

A layer with random sparse connections. More...

class GBlockSwitch

Treats the input as three concatenated vectors: a, b, and c. (The values in 'a' typically fall in the range [0,1].) The output is computed element-wise as a*b + (1-a)*c. More...

class GBlockTanh

Applies the TanH function element-wise to the input.

Equation	Plot
$f(x) = tanh(x)$

More...

class GBlockWeightless

The base class of blocks that have no weights. More...

class GBNBeta

A node in a belief network that represents a beta distribution. More...

class GBNCategorical

A node in a belief network that represents a categorical distribution. Instances of this class can serve as parent-nodes to any GBNVariable node. The child node must specify distribution parameters for every category that the parent node supports (or for every combination of categories if there are multiply categorical parents). More...

class GBNConstant

A node in a belief network that represents a constant value. More...

class GBNExponential

A node in a belief network that represents an exponential distribution. More...

class GBNGamma

A node in a belief network that represents a gamma distribution. More...

class GBNInverseGamma

A node in a belief network that represents an inverse-gamma distribution. More...

class GBNLogNormal

A node in a belief network that represents a lognormal distribution. More...

class GBNMath

A node in a belief network that applies some math operation to the output of its one parent node. More...

class GBNMetropolisNode

This is the base class for nodes in a belief network that are sampled using the Metropolis algorithm. More...

class GBNNode

The base class of all nodes in a Bayesian belief network. More...

class GBNNormal

A node in a belief network that represents a Gaussian, or normal, distribution. More...

class GBNPareto

A node in a belief network that represents a Pareto distribution. More...

class GBNPoisson

A node in a belief network that represents a Poisson distribution. More...

class GBNProduct

A node in a belief network that always returns the product of its parent nodes. More...

class GBNSum

A node in a belief network that always returns the sum of its parent nodes. More...

class GBNUniformContinuous

A node in a belief network that represents a uniform continuous distribution. More...

class GBNUniformDiscrete

A node in a belief network that represents a uniform discrete distribution. More...

class GBNVariable

The base class of nodes in a belief network that represent variable values. More...

class GBomb

An experimental ensemble technique. More...

class GBouncyBalls

class GBrandesBetweennessCentrality

Computes the number of times that the shortest-path between every pair of points passes over each edge and vertex. More...

class GBreadthFirstUnfolding

A manifold learning algorithm that reduces dimensionality in local neighborhoods, and then stitches the reduced local neighborhoods together using the Kabsch algorithm. More...

class GBruteForceNeighborFinder

Finds neighbors by measuring the distance to all points. This one should work properly even if the distance metric does not support the triangle inequality. More...

class GBucket

When Train is called, this performs cross-validation on the training set to determine which learner is the best. It then trains that learner with the entire training set. More...

class GCalibrator

class GCamera

This camera assumes the canvas is specified in cartesian coordinates. The 3D space is based on a right-handed coordinate system. (So if x goes to the right and y goes up, then z comes out of the screen toward you.) More...

class GCategoricalDistribution

This is a distribution that specifies a probability for each value in a set of nominal values. More...

class GCategoricalSampler

This class is for efficiently drawing random values from a categorical distribution with a large number of categories. More...

class GCategoricalSamplerBatch

class GCharSet

This class represents a set of characters. More...

class GClusterer

The base class for clustering algorithms. Classes that inherit from this class must implement a method named "cluster" which performs clustering, and a method named "whichCluster" which reports which cluster the specified row is determined to be a member of. More...

class GCollaborativeFilter

The base class for collaborative filtering recommender systems. More...

class GCompressor

This implements a simple compression/decompression algorithm. More...

class GConstStringHashTable

Hash table based on keys of constant strings (or at least strings that won't change during the lifetime of the hash table). It's a good idea to use a GHeap in connection with this class. More...

class GConstStringToIndexHashTable

Hash table based on keys of constant strings (or at least strings that won't change during the lifetime of the hash table). It's a good idea to use a GHeap in connection with this class. More...

class GConstVecWrapper

This class temporarily wraps a GVec around a const array of doubles. You should take care to ensure this object is destroyed before the array it wraps. More...

class GContentBasedFilter

class GContentBoostedCF

class GContext

The base class for the buffers that a thread needs to use (train or predict with) a neural network component. More...

class GContextGRU

Context class for GRU blocks. More...

class GContextLayer

Contains the buffers that a thread needs to train or use a GLayer. Each thread should use a separate GContextLayer object. Call GLayer::newContext to obtain a new GContextLayer object. More...

class GContextLSTM

Context class for LSTM blocks. More...

class GContextNeuralNet

Contains the buffers that a thread needs to train or use a GNeuralNet. Each thread should use a separate GContextNeuralNet object. Call GNeuralNet::newContext to obtain a new GContextNeuralNet object. More...

class GContextRecurrent

A special context object for recurrent blocks. More...

class GContextRecurrentInstance

A single instance of a recurrent context that has been unfolded through time. More...

class GCoordVectorIterator

An iterator for an n-dimensional coordinate vector. For example, suppose you have a 4-dimensional 2x3x2x1 grid, and you want to iterate through its coordinates: (0000, 0010, 0100, 0110, 0200, 0210, 1000, 1010, 1100, 1110, 1200, 1210). This class will iterate over coordinate vectors in this manner. (For 0-dimensional coordinate vectors, it behaves as though the origin is the only valid coordinate.) More...

class GCosineSimilarity

This is a similarity metric that computes the cosine of the angle bewtween two sparse vectors. More...

class GCrypto

This is a symmetric-key block-cypher. It utilizes a 2048-byte internal state which is initialized using the passphrase. It uses repeated applications of sha-512 to advance the internal state, and to generate an 1024-byte pad that it xor's with your data to encrypt or decrypt it. Warning: You use this algorithm at your own risk. Many encryption algorithms eventually turn out to be insecure, and to my knowledge, this algorithm has not yet been extensively scrutinized. More...

class GCSVParser

A class for parsing CSV files (or tab-separated files, or whitespace separated files, etc.). (This class does not support Mac line endings, so you should replace all '' with '
' before using this class if your data comes from a Mac.) More...

class GCycleCut

This finds the shortcuts in a table of neighbors and replaces them with INVALID_INDEX. More...

class GDataAugmenter

This class augments data. That is, it transforms the data according to some provided transformation, and attaches the transformed data as new attributes to the existing data. More...

class GDataColSplitter

This class divides a matrix into two parts. The left-most columns are the features. The right-most columns are the labels. More...

class GDataPreprocessor

This class facilitates automatic data preprocessing. The constructor requires a dataset and some parameters that specify which portion of the data to use, and how the data should be transformed. A transform will automatically be generated to preprocess the data to meet the specified requirements, and the data will be transformed. Additional datasets can be added with the "add" method. These will be processed by the same transform. The processed data can be retrieved with the "get" method, with an index corresponding to the order of the data. More...

class GDataRowSplitter

This class divides a features and labels matrix into two parts by randomly assigning each row to one of the two parts, keeping the corresponding rows together. The rows are shallow-copied. The destructor of this class releases all of the row references. More...

class GDecisionTree

This is an efficient learning algorithm. It divides on the attributes that reduce entropy the most, or alternatively can make random divisions. More...

class GDenseClusterRecommender

This class clusters the rows according to a dense distance metric, then uses the baseline vector in each cluster to make predictions. More...

class GDenseCosineDistance

Returns 1 minus the cosine of the angle between the two vectors with the origin. More...

class GDiff

This class finds the differences between two text files It is case and whitespace sensitive, but is tolerant of Unix/Windows/Mac line endings. It uses lines as the atomic unit. It accepts matching lines in a greedy manner. More...

struct GDiffLine

This is a helper struct used by GDiff. More...

class GDijkstra

Finds the shortest path from an origin vertex to all other vertices. Implemented with a binary-heap priority-queue. If the graph is sparse on edges, it will run in about O(n log(n)) time. If the graph is dense, it runs in about O(n^2 log(n)) More...

class GDirList

This class contains a list of files and a list of folders. The constructor populates these lists with the names of files and folders in the current working directory. More...

class GDiscreteActionIterator

This is a simple and common action iterator that can be used when there is a discrete set of possible actions. More...

class GDiscretize

This transform uses buckets to convert continuous data into discrete data. It is common to use GFilter to combine this with your favorite modeler (which only supports discrete values) to create a modeler that can also support continuous values as well. More...

class GDistanceMetric

This class enables you to define a distance (or dissimilarity) metric between two vectors. pScaleFactors is an optional parameter (it can be NULL) that lets the calling class scale the significance of each dimension. Distance metrics that do not mix with this concept may simply ignore any scale factors. Typically, classes that use this should be able to assume that the triangle inequality will hold, but do not necessarily enforce the parallelogram law. More...

class GDistribution

class GDom

A Document Object Model. This represents a document as a hierarchy of objects. The DOM can be loaded-from or saved-to a file in JSON (JavaScript Object Notation) format. (See http://json.org.) In the future, support for XML and/or other formats may be added. More...

class GDomClient

This is a socket client that sends and receives DOM nodes. More...

class GDomListIterator

This class iterates over the items in a list node. More...

class GDomNode

Represents a single node in a DOM. More...

class GDomServer

This is a socket server that sends and receives DOM nodes. More...

class GDoubleRect

Represents a rectangular region with doubles. More...

class GDynamicPageConnection

class GDynamicPageServer

class GDynamicPageSession

class GDynamicPageSessionExtension

class GEmpiricalGradientDescent

This algorithm does a gradient descent by feeling a small distance out in each dimension to measure the gradient. For efficiency reasons, it only measures the gradient in one dimension (which it cycles round-robin style) per iteration and uses the remembered gradient in the other dimensions. More...

class GEnsemble

This is a base-class for ensembles that combine the predictions from multiple weighed models. More...

class GEuclidSimilarity

This computes the reciprocal of Euclidean distance, where all missing values are simply ignored. More...

class GEvolutionaryOptimizer

Uses an evolutionary process to optimize a vector. More...

class GExpectException

Instantiating an object of this class specifies that any exceptions thrown during the life of this object should be treated as "expected". More...

class GExtendedKalmanFilter

This is an implementation of the Extended Kalman Filter. This class is used by alternately calling advance and correct. More...

class GFeatureFilter

class GFile

Contains some useful routines for manipulating files. More...

class GFilter

class GFloatRect

Represents a rectangular region with floats. More...

class GFloydWarshall

Computes the shortest-cost path between all pairs of vertices in a graph. Takes O(n^3) time. More...

class GFolderDeserializer

This class complements GFolderSerializer. More...

class GFolderSerializer

This turns a file or a folder (and its contents recursively) into a stream of bytes. More...

class GFourier

Fourier transform. More...

class GFourierWaveProcessor

This is an abstract class that processes a wave file in blocks. Specifically, it divides the wave file up into overlapping blocks, converts them into Fourier space, calls the abstract "process" method with each block, converts back from Fourier space, and then interpolates to create the wave output. More...

class GFunction

This class represents a math function. (It might be used, for example, in a plotting tool.) More...

class GFunctionParser

This class parses math equations. (This is useful, for example, for plotting tools.) More...

class GFuzzyKMeans

A K-means clustering algorithm where every point has partial membership in each cluster. This algorithm is specified in Li, D. and Deogun, J. and Spaulding, W. and Shuart, B., Towards missing data imputation: A study of fuzzy K-means clustering method, In Rough Sets and Current Trends in Computing, Springer, pages 573–579, 2004. More...

class GGammaDistribution

The Gamma distribution. More...

class GGaussianProcess

A Gaussian Process model. This class was implemented according to the specification in Algorithm 2.1 on page 19 of chapter 2 of http://www.gaussianprocesses.org/gpml/chapters/ by Carl Edward Rasmussen and Christopher K. I. Williams. More...

class GGraphCut

This implements an optimized max-flow/min-cut algorithm described in "An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision" by Boykov, Y. and Kolmogorov, V. This implementation assumes that edges are undirected. More...

class GGraphCutTransducer

A transduction algorithm that uses a max-flow/min-cut graph-cut algorithm to partition the data until each class is in a separate cluster. Unlabeled points are then assigned the label of the cluster in which they fall. More...

class GGraphEdgeIterator

Iterates over the edges that connect to the specified node. More...

class GGridSearch

This performs a brute-force grid search with uniform sampling over the unit hypercube with increasing granularity. (Your target function should scale the candidate vectors as necessary to cover the desired space.) This grid-search increases the granularity after each pass, and carefully avoids sampling anywhere that it has sampled before. More...

class GHashTable

Implements a typical hash table. (It doesn't take ownership of the objects you add, so you must still delete them yourself.) More...

class GHashTableBase

The base class of hash tables. More...

class GHashTableEnumerator

This class iterates over the values in a hash table. More...

class GHeap

Provides a heap in which to put strings or whatever you need to store. If you need to allocate space for a lot of small objects, it's much more efficient to use this class than the C++ heap. Plus, you can delete them all by simply deleting the heap. You can't, however, reuse the space for individual objects in this heap. More...

class GHiddenMarkovModel

class GHillClimber

In each dimension, tries 5 candidate adjustments: a lot smaller, a little smaller, same spot, a little bigger, and a lot bigger. If it picks a smaller adjustment, the step size in that dimension is made smaller. If it picks a bigger adjustment, the step size in that dimension is made bigger. More...

class GHistogram

Gathers values and puts them in bins. More...

class GHtml

This class is for parsing HTML files. It's designed to be very simple. This class might be useful, for example, for building a web-crawler or for extracting readable text from a web page. More...

class GHttpClient

This class allows you to get files using the HTTP protocol. More...

class GHttpConnection

Each GHttpConnection represents an HTTP connection between a client and the server. A GHTTPServer is a collection of GHTTPConnection objects. To implement a HTTP server, you will typically override the doGet and doPost methods of this class to generate web pages. More...

class GHttpMultipartParser

class GHttpParamParser

A class for parsing the name/value pairs that follow the "?" in a URL. More...

class GHttpServer

This class allows you to implement a simple HTTP daemon. More...

class GIdentityFunction

This is an implementation of the identity function. It might be useful, for example, as the observation function in a GRecurrentModel if you want to create a Jordan network. More...

class GImage

Represents an image. More...

class GImputeMissingVals

class GIncrementalLearner

This is the base class of supervised learning algorithms that can learn one row at a time. More...

class GIncrementalLearnerQAgent

This is an implementation of GQLearner that uses an incremental learner for its Q-table and a SoftMax (usually pick the best action, but sometimes randomly pick the action) strategy to balance between exploration vs exploitation. To use this class, you need to supply an incremental learner (see the comment for the constructor for more details) and to implement the GetRewardForLastAction method. More...

class GIncrementalTransform

This is the base class of algorithms that can transform data one row at a time without supervision. More...

class GIncrementalTransformChainer

This wraps two two-way-incremental-transforms to form a single combination transform. More...

class GIndexedMultiSet

A multiset class that can be queried by index. It is implemented using a balanced tree structure, so most operations take O(log(n)) time. More...

class GIndexVec

Useful functions for operating on vectors of indexes. More...

class GInstanceRecommender

This class makes recommendations by finding the nearest-neighbors (as determined by evaluating only overlapping ratings), and assuming that the ratings of these neighbors will be predictive of your ratings. More...

class GInstanceTable

This represents a grid of values. It might be useful as a Q-table with Q-learning. More...

class GInverseGammaDistribution

The inverse Gamma distribution. More...

class GIsomap

Isomap is a manifold learning algorithm that uses the Floyd-Warshall algorithm to compute an estimate of the geodesic distance between every pair of points using local neighborhoods, and then uses classic multidimensional scaling to compute a low-dimensional projection. More...

class GKdTree

An efficient algorithm for finding neighbors. More...

class GKernel

The base class for kernel functions. Classes which implement this must provide an "apply" method that applies the kernel to two vectors. Kernels may be combined together to form a more complex kernel, to which the kernel trick will still apply. More...

class GKernelAdd

An addition kernel. More...

class GKernelChiSquared

Chi Squared kernel. More...

class GKernelDistance

Returns 1 minus the cosine of the angle between the two vectors with the origin. More...

class GKernelExp

The Exponential kernel. More...

class GKernelGaussianRBF

A Gaussian RBF kernel. More...

class GKernelIdentity

The identity kernel. More...

class GKernelMultiply

A multiplication kernel. More...

class GKernelNormalize

A Normalizing kernel. More...

class GKernelPolynomial

A polynomial kernel. More...

class GKernelPow

A power kernel. More...

class GKernelScale

A scalar kernel. More...

class GKernelTranslate

A translation kernel. More...

class GKeyPair

class GKMeans

An implementation of the K-means clustering algorithm. More...

class GKMeansSparse

An implementation of the K-means clustering algorithm. More...

class GKMedoids

An implementation of the K-medoids clustering algorithm. More...

class GKMedoidsSparse

An implementation of the K-medoids clustering algorithm for sparse data. More...

class GKNN

The k-Nearest Neighbor learning algorithm. More...

class GLabelFilter

class GLayer

GNeuralNet contains GLayers stacked upon each other. GLayer contains GBlocks concatenated beside each other. (GNeuralNet is a type of GBlock.) Each GBlock is an array of differentiable network units (artificial neurons). The user must add at least one GBlock to each GLayer. More...

class GLearnerLib

Provides some useful functions for instantiating learning algorithms from the command line. More...

class GLearnerLoader

This class is for loading various learning algorithms from a DOM. When any learning algorithm is saved, it calls baseDomNode, which creates (among other things) a field named "class" which specifies the class name of the algorithm. This class contains methods that will recognize any of the classes in this library and load them. If it doesn't recognize a class, it will either return NULL or throw and exception, depending on the flags you pass to the constructor. Obviously this loader won't recognize any classes that you make. Therefore, you should overload the corresponding method in this class with a new method that will first recognize and load your classes, and then call these methods to handle other types. More...

class GLinearDistribution

A linear regression model that predicts a distribution. (This algorithm also differs from GLinearRegressor in that it computes its model in closed form instead of using a numerical approach to find it. In general, GLinearRegressor seems to be a bit more accurate. Perhaps the method used in this algorithm is not very numerically stable.) More...

class GLinearProgramming

class GLinearRegressor

A linear regression model. Let f be a feature vector of real values, and let l be a label vector of real values, then this model estimates l=Bf+e, where B is a matrix of real values, and e is a vector of real values. (In the Wikipedia article on linear regression, B is called "beta", and e is called "epsilon". The approach used by this model to compute beta and epsilon, however, is much more efficient than the approach currently described in that article.) More...

class GLLE

Locally Linear Embedding is a manifold learning algorithm that uses sparse matrix techniques to efficiently compute a low-dimensional projection. More...

class GLNormDistance

Interpolates between manhattan distance (norm=1), Euclidean distance (norm=2), and Chebyshev distance (norm=infinity). For nominal attributes, Hamming distance is used. More...

class GLogify

This transform converts continuous values into logarithmic space. More...

class GManifold

This class stores static methods that are useful for manifold learning. More...

class GManifoldSculpting

Manifold Sculpting. A non-linear dimensionality reduction algorithm. (See Gashler, Michael S. and Ventura, Dan and Martinez, Tony. Iterative non-linear dimensionality reduction with manifold sculpting. In Advances in Neural Information Processing Systems 20, pages 513–520, MIT Press, Cambridge, MA, 2008.) More...

class GMasterThread

Manages a pool of GWorkerThread objects. To use this class, first call addWorker one or more times. Then, call doJobs. More...

class GMath

Provides some useful math functions. More...

class GMatrix

Represents a matrix or a database table. More...

class GMatrixFactorization

This factors the sparse matrix of ratings, M, such that M = PQ^T where each row in P gives the principal preferences for the corresponding user, and each row in Q gives the linear combination of those preferences that map to a rating for an item. (Actually, P and Q also contain an extra column added for a bias.) This class is implemented according to the specification on page 631 in Takacs, G., Pilaszy, I., Nemeth, B., and Tikk, D. Scalable collaborative filtering approaches for large recommender systems. The Journal of Machine Learning Research, 10:623–656, 2009. ISSN 1532-4435., except with the addition of learning-rate decay and a different stopping criteria. More...

class GMaxPooling2D

class GMeanMarginsTree

A GMeanMarginsTree is an oblique decision tree specified in Gashler, Michael S. and Giraud-Carrier, Christophe and Martinez, Tony. Decision Tree Ensemble: Small Heterogeneous Is Better Than Large Homogeneous. In The Seventh International Conference on Machine Learning and Applications, Pages 900 - 905, ICMLA '08. 2008. It divides features as follows: It finds the mean and principle component of the output vectors. It divides all the vectors into two groups, one that has a positive dot-product with the principle component (after subtracting the mean) and one that has a negative dot-product with the principle component (after subtracting the mean). Next it finds the average input vector for each of the two groups. Then it finds the mean and principle component of those two vectors. The dividing criteria for this node is to subtract the mean and then see whether the dot-product with the principle component is positive or negative. More...

class GMergeDataHolder

This class guarantees that the rows in b are merged vertically back into a when this object goes out of scope. More...

class GMinBinSearch

This is a hill climber for semi-linear error surfaces that minimizes testing with an approach like binary-search. It only searches approximately within the unit cube (although it may stray a little outside of it). It is the target function's responsibility to map this into an appropriate space. More...

class GMixedRelation

class GMixtureOfGaussians

This class uses Expectency Maximization to find the mixture of Gaussians that best approximates the data in a specified real attribute of a data set. More...

class GMomentumGreedySearch

At each iteration this algorithm moves in only one dimension. If the situation doesn't improve it tries the opposite direction. If both directions are worse, it decreases the step size for that dimension, otherwise it increases the step size for that dimension. More...

class GMultivariateNormalDistribution

A multivariate Normal distribution. It can compute the likelihood of a specified vector, and can also generate random vectors from the distribution. More...

class GNaiveBayes

A naive Bayes classifier. More...

class GNaiveInstance

This is an instance-based learner. Instead of finding the k-nearest neighbors of a feature vector, it finds the k-nearst neighbors in each dimension. That is, it finds n*k neighbors, considering each dimension independently. It then combines the label from all of these neighbors to make a prediction. Finding neighbors in this way makes it more robust to high-dimensional datasets. It tends to perform worse than k-nn in low-dimensional space, and better than k-nn in high-dimensional space. (It may be thought of as a cross between a k-nn instance learner and a Naive Bayes learner. It only supports continuous features and labels (so it is common to wrap it in a Categorize filter which will convert nominal features to a categorical distribution of continuous values). More...

class GNeighborFinder

Finds the k-nearest neighbors of any vector in a dataset. More...

class GNeighborFinderGeneralizing

Finds the k-nearest neighbors (in a dataset) of an arbitrary vector (which may or may not be in the dataset). More...

class GNeighborGraph

This wraps a neighbor finding algorithm. It caches the queries for neighbors for the purpose of improving runtime performance. More...

class GNeighborTransducer

An instance-based transduction algorithm. More...

class GNeuralDecomposition

class GNeuralNet

GNeuralNet contains GLayers stacked upon each other. GLayer contains GBlocks concatenated beside each other. (GNeuralNet is a type of GBlock, so you can nest.) Each GBlock is an array of differentiable network units (artificial neurons). The user must add at least one GBlock to each GLayer. More...

class GNeuralNetLearner

A thin wrapper around a GNeuralNet that implements the GIncrementalLearner interface. More...

class GNeuralNetOptimizer

Optimizes the parameters of a differentiable function using an objective function. More...

class GNeuralNetTargetFunction

A class that facilitates training a neural network with an arbitrary optimization algorithm. More...

class GNodeHashTable

This is a hash table that uses any object which inherits from HashTableNode as the key. More...

class GNoiseGenerator

Just generates Gaussian noise. More...

class GNominalToCat

This is sort-of the opposite of discretize. It converts each nominal attribute to a categorical distribution by representing each value using the corresponding row of the identity matrix. For example, if a certain nominal attribute has 4 possible values, then a value of 3 would be encoded as the vector 0 0 1 0. When predictions are converted back to nominal values, the mode of the categorical distribution is used as the predicted value. (This is similar to Weka's NominalToBinaryFilter.) More...

class GNormalDistribution

This is the Normal (a.k.a. Gaussian) distribution. More...

class GNormalize

This transform scales and shifts continuous values to make them fall within a specified range. More...

class GNurbs

NURBS = Non Uniform Rational B-Spline Periodic = closed loop. More...

class GObjective

A loss function used to train a differentiable function. More...

class GOptimizer

This is the base class of all search algorithms that can jump to any vector in the search space seek the vector that minimizes error. More...

class GOptimizerBasicTestTargetFunction

class GOverrunSentinel

Placing these on the stack can help catch buffer overruns. More...

class GPackageClient

This class abstracts a client that speaks a home-made protocol that guarantees packages will arrive in the same order and size as when they were sent. This protocol is a simple layer on top of TCP. More...

class GPackageConnection

This is a helper class used by GPackageServer. If you implement a custom connection object for a sub-class of GPackageServer, then it should inherrit from this class. More...

class GPackageConnectionBuf

This is a helper class used by GPackageConnection. More...

class GPackageServer

This class abstracts a server that speaks a home-made protocol that guarantees packages will arrive in the same order and size as when they were sent. This protocol is a simple layer on top of TCP. More...

class GPairProduct

Generates data by computing the product of each pair of attributes. This is useful for augmenting data. More...

class GParallelOptimizers

This class simplifies simultaneously solving several optimization problems. More...

class GParticleSwarm

An optimization algorithm inspired by flocking birds. More...

class GPassiveConsole

This class provides a non-blocking method for reading characters from stdin. (If there are no characters ready in stdin, it immediately returns '\0'.) The constructor sets flags on the console so that it passes characters to the stream immediately (instead of when Enter is pressed), and so that it doesn't echo the keys (if desired), and it makes stdin non-blocking. The destructor puts all those things back the way they were. More...

class GPCA

Principal Component Analysis. (Computes the principal components about the mean of the data when you call train. The transformed (reduced-dimensional) data will have a mean about the origin.) More...

class GPearsonCorrelation

This is a similarity metric that computes the Pearson correlation between two sparse vectors. More...

class GPipe

This class wraps the handle of a pipe. It closes the pipe when it is destroyed. This class is useful in conjunction with GApp::systemExecute for reading from, or writing to, the standard i/o streams of a child process. More...

class GPlotLabelSpacer

If you need to place grid lines or labels at regular intervals (like 1000, 2000, 3000, 4000... or 20, 25, 30, 35... or 0, 2, 4, 6, 8, 10...) this class will help you pick where to place the labels so that there are a reasonable number of them, and they all land on nice label values. More...

class GPlotLabelSpacerLogarithmic

Similar to GPlotLabelSpacer, except for logarithmic grids. To plot in logarithmic space, set your plot window to have a range from log_e(min) to log_e(max). When you actually plot things, plot them at log_e(x), where x is the position of the thing you want to plot. More...

class GPlotWindow

This class makes it easy to plot points and functions on 2D cartesian coordinates. More...

class GPoissonDistribution

The Poisson distribution. More...

class GPolicyLearner

This is the base class for algorithms that learn a policy. More...

class GPolynomial

This regresses a multi-dimensional polynomial to fit the data. More...

class GPrediction

This class is used to represent the predicted distribution made by a supervised learning algorithm. (It is just a shallow wrapper around GDistribution.) It is used in conjunction with calls to GSupervisedLearner::predictDistribution. The predicted distributions will be either categorical distributions (for nominal values) or Normal distributions (for continuous values). More...

class GPriorityQueue

An implementation of a double-ended heap-based priority queue. (Note that the multimap STL class can also be used to implement a double-ended priority queue, but the STL does not currently provide a heap-based double-ended priority queue, which is asymptotically more efficient for insertions.) More...

class GPriorityQueueEntry

An internal class used by GPriorityQueue. You should not use this class directly. More...

class GProbeSearch

This is somewhat of a multi-dimensional version of binary-search. It greedily probes the best choices first, but then starts trying the opposite choices at the higher divisions so that it can also handle non-monotonic target functions. Each iteration performs a binary (divide-and-conquer) search within the unit hypercube. (Your target function should scale the candidate vectors as necessary to cover the desired space.) Because the high-level divisions are typically less correlated with the quality of the final result than the low-level divisions, it searches through the space of possible "probes" by toggling choices in the order from high level to low level. In low-dimensional space, this algorithm tends to quickly find good solutions, especially if the target function is somewhat smooth. In high-dimensional space, the number of iterations to find a good solution seems to grow exponentially. More...

class GProgressEstimator

This class is used with big loops to estimate the wall-clock time until completion. It works by computing the running median of the duration of recent iterations, and projecting that duration across the remaining iterations. More...

class GQLearner

The base class of a Q-Learner. To use this class, there are four abstract methods you'll need to implement. See also the comment for GPolicyLearner. More...

class GRand

This is a 64-bit pseudo-random number generator. More...

class GRandomDirectionBinarySearch

This algorithm picks a random direction, then uses binary search to determine how far to step, and repeats. More...

class GRandomForest

class GRandomIndexIterator

This class iterates over all the integer values from 0 to length-1 in random order. More...

class GRandomSearch

At each iteration, this tries a random vector from the unit hypercube. (Your target function should scale the candidate vectors as necessary to cover the desired space.) More...

class GRayTraceAreaLight

Represents a light source with area. More...

class GRayTraceBoundingBoxBase

A class used for making ray-tracing faster. More...

class GRayTraceBoundingBoxInterior

A class used for making ray-tracing faster. More...

class GRayTraceBoundingBoxLeaf

A class used for making ray-tracing faster. More...

class GRayTraceCamera

Represents the camera for a ray tracing scene. More...

class GRayTraceColor

This class represents a color. It's more precise than GColor, but takes up more memory. Note that the ray tracer ignores the alpha channel because the material specifies a unique transmission color. More...

class GRayTraceDirectionalLight

Represents directional light in a ray-tracing scene. More...

class GRayTraceImageTexture

class GRayTraceLight

Represents a source of light in a ray-tracing scene. More...

class GRayTraceMaterial

class GRayTraceObject

An object in a ray-tracing scene. More...

class GRayTracePhysicalMaterial

Represents the material of which an object is made in a ray-tracing scene. More...

class GRayTracePointLight

Represents a point light in a ray-tracing scene. More...

class GRayTraceScene

Represents a scene that you can ray-trace. More...

class GRayTraceSphere

A sphere in a ray-tracing scene. More...

class GRayTraceTriangle

A single triangle in a ray-tracing scene. More...

class GRayTraceTriMesh

Represents a triangle mesh in a ray-tracing scene. More...

class GRecommenderLib

class GRect

Represents a rectangular region with integers. More...

class GRegionAjacencyGraph

The base class for region ajacency graphs. These are useful for breaking down an image into patches of similar color. More...

class GRegionAreaIterator

Iterates over all the pixels in an image that have the same color and are transitively adjacent. In other words, if you were to flood-fill a the specified point, this returns all the pixels that would be changed. More...

class GRegionBorderIterator

Iterates the border of a 2D region by running around the border and reporting the coordinates of each interior border pixel and the direction to the edge. It goes in a counter-clockwise direction. More...

class GRelation

Holds the metadata for a dataset. More...

class GRelationalElement

class GRelationalRow

class GRelationalTable

See GTree.cpp for an example of how to use this class. More...

class GReleaseDataHolder

This is a special holder that guarantees the data set will release all of its data before it is deleted. More...

class GResamplingAdaBoost

This is an implementation of AdaBoost, except instead of using weighted samples, it resamples the training set by giving each sample a probability proportional to its weight. This difference enables it to work with algorithms that do not support weighted samples. More...

class GReservoir

This transforms data by passing it through a multi-layer perceptron with randomely-initialized weights. (This transform automatically converts nominal attributes to categorical as necessary, but it does not normalize. In other words, it assumes that all continuous attributes fall approximately within a 0-1 range.) More...

class GReservoirNet

This model uses a randomely-initialized network to map the inputs into a higher-dimensional space, and it uses a layer of perceptrons to learn in this augmented space. More...

class GRMSPropOptimizer

Trains a neural network with RMS-prop. More...

class GRowDistance

This uses a combination of Euclidean distance for continuous attributes, and Hamming distance for nominal attributes. In particular, for each attribute, it calculates pA[i]-pB[i], squares it and takes the square root of that sum. For nominal attributes pA[i]-pB[i] is 0 if they are the same and 1 if they are different. More...

class GRunningCovariance

Computes a running covariance matrix about the origin. More...

class GSampleClimber

This is a variant of empirical gradient descent that tries to estimate the gradient using a minimal number of samples. It is more efficient than empirical gradient descent, but it only works well if the optimization surface is quite locally linear. More...

class GScalingUnfolder

This is a nonlinear dimensionality reduction algorithm loosely inspired by Maximum Variance Unfolding. It iteratively scales up the data, then restores distances between neighbors. More...

class GSDL

A collection of routines that are useful when interfacing with SDL. More...

class GSelfOrganizingMap

An implementation of a Kohonen self-organizing map. More...

class GSGDOptimizer

Trains a neural network by stochastic gradient descent. More...

class GShortcutPruner

This uses "betweeenness centrality" to find the shortcuts in a table of neighbors and replaces them with INVALID_INDEX. More...

class GSignalHandler

Temporarily handles certain signals. (When this object is destroyed, it puts all the signal handlers back the way they were.) Periodically call "check" to see if a signal has occurred. More...

class GSimpleAssignment

A simple concrete implementation of the GAssignment protocol using std::vector<int> More...

class GSimplePriorityQueue

Implements a simple priority queue of objects sorted by a double-precision value. More...

class GSmtp

For sending email to an SMTP server. More...

class GSoftImpulseDistribution

class GSparseClusterer

This is a base class for clustering algorithms that operate on sparse matrices. More...

class GSparseClusterRecommender

This class clusters the rows according to a sparse similarity metric, then uses the baseline vector in each cluster to make predictions. More...

class GSparseInstance

An experimental instance-based learner that prunes attribute-values (or predicates) instead of entire instances. This may be viewed as a form of rule-learning that beings with instance-based learning and then prunes. More...

class GSparseMatrix

This class stores a row-compressed sparse matrix. That is, each row consists of a map from a column-index to a value. More...

class GSparseNeighborFinder

Finds neighbors by measuring the distance to all points using a sparse distance metric. More...

class GSparseSimilarity

The base class for similarity metrics that operate on sparse vectors. More...

class GSparseVec

Provides static methods for operating on sparse vectors. More...

class GSpinLock

On Windows, this implements a spin-lock. On Linux, this wraps pthread_mutex. More...

class GSpinLockHolder

This holder takes a lock (if it is non-NULL) when you construct it. It guarantees to release the lock when it is destroyed. More...

class GSquaredError

The default loss function is squared error. More...

class GStemmer

This class just wraps the Porter Stemmer. It finds the stems of words. Examples: "cats"->"cat" "dogs"->"dog" "fries"->"fri" "fishes"->"fish" "pies"->"pi" "lovingly"->"lovingli" "candy"->"candi" "babies"->"babi" "bus"->"bu" "busses"->"buss" "women"->"women" "hasty"->"hasti" "hastily"->"hastili" "fly"->"fly" "kisses"->"kiss" "goes"->"goe" "brought"->"brought" As you can see the stems aren't always real words, but that's okay as long as it produces the same stem for words that have the same etymological roots. Even then it still isn't perfect (notice it got "bus" wrong), but it should still improve analysis somewhat in many cases. More...

class GStringChopper

This class chops a big string at word breaks so you can display it intelligently on multiple lines. More...

class GSubImageFinder

This class uses Fourier phase correlation to efficiently find sub-images within a larger image. More...

class GSubImageFinder2

This class uses heuristics to find sub-images within a larger image. It is slower, but more stable than GSubImageFinder. More...

class GSupervisedLearner

This is the base class of algorithms that learn with supervision and have an internal hypothesis model that allows them to generalize rows that were not available at training time. More...

class GSVG

This class simplifies plotting data to an SVG file. More...

class GTargetFunction

The optimizer seeks to find values that minimize this target function. More...

class GTCPClient

This class is an abstraction of a TCP client socket connection. More...

class GTCPConnection

This class is used by GTCPServer to represent a connection with one of the clients. (If you want to associate some additional objects with each connection, you can inherrit from this class, and overload GTCPServer::makeConnection to return your own custom object.) More...

class GTCPServer

This class is an abstraction of a TCP server, which maintains a set of socket connections. More...

class GTempBufHelper

A helper class used by the GTEMPBUF macro. More...

class GThread

A wrapper for PThreads on Linux and for some corresponding WIN32 api on Windows. More...

class GTime

Provides some time-related functions. More...

class GTokenizer

This is a simple tokenizer that reads a file, one token at-a-time. To use it, you should make a child class that defines several character sets. Example: More...

class GTransducer

This is the base class of supervised learning algorithms (that may or may not have an internal model allowing them to generalize rows that were not available at training time). Note that the literature typically refers to supervised learning algorithms that can't generalize (because they lack an internal hypothesis model) as "Semi-supervised". (You cannot generalize with a semi-supervised algorithm–you have to train again with the new rows.) More...

class GTransform

This is the base class of algorithms that transform data without supervision. More...

class GTreeNode

This is a helper class used by GIndexedMultiSet. More...

class GTriMeshBuilder

class GUniformDistribution

This is a continuous uniform distribution. More...

class GUniformRelation

A relation with a minimal memory footprint that assumes all attributes are continuous, or all of them are nominal and have the same number of possible values. More...

class GUnivariateDistribution

This is the base class for univariate distributions. More...

class GVec

Represents a mathematical vector of doubles. More...

class GVecWrapper

This class temporarily wraps a GVec around an array of doubles. You should take care to ensure this object is destroyed before the array it wraps. More...

class GVocabulary

This is a helper class which is useful for text-mining. It collects words, stems them, filters them through a list of stop-words, and assigns a discrete number to each word. More...

class GWag

This model trains several multi-layer perceptrons, then averages their weights together in an intelligent manner. More...

class GWave

Currently only supports PCM wave format. More...

class GWaveIterator

This class iterates over the samples in a WAVE file. Regardless of the bits-per-sample, this iterator will convert all samples to doubles with a range from -1 to 1. More...

class GWavelet

Wavelet transform. More...

class GWebSocketClient

class GWeightedModel

This is a helper-class used by GBag. More...

class GWidget

The base class of all GUI widgets. More...

class GWidgetAnimation

An image with multiple frames. More...

class GWidgetAtomic

The base class of all atomic widgets (widgets that are not composed of other widgets). More...

class GWidgetBulletGroup

This creates a whole group of bullets arranged either horizontally or vertically at regular intervals. More...

class GWidgetBulletHole

The easiest way to do bullets is to use the GWidgetBulletGroup class, but if you really want to manage individual bullets yourself, you can use this class to do it. More...

class GWidgetCanvas

A painting canvas. More...

class GWidgetCheckBox

class GWidgetCommon

class GWidgetDialog

A form or dialog. More...

class GWidgetFileSystemBrowser

class GWidgetGrid

class GWidgetGroup

The base class of all widgets that are composed of other widgets. More...

class GWidgetGroupBox

This just draws a rectangular box. More...

class GWidgetHorizScrollBar

Makes a horizontal scroll bar. More...

class GWidgetHorizSlider

class GWidgetImageButton

A button with an image on it. The left half of the image is the unpressed image and the right half is the pressed image. More...

class GWidgetProgressBar

Automatically determines wether to be horizontal or vertical based on dimensions. Progress ranges from 0 to 1, or from 0 to -1 if you want it to go the other way. More...

class GWidgetSliderTab

This widget is not meant to be used by itself. It creates one of the parts of a scroll bar or slider bar. More...

class GWidgetTextBox

This is a box in which the user can enter text. More...

class GWidgetTextButton

A button with text on it. More...

class GWidgetTextLabel

A text label. More...

class GWidgetTextTab

Represents a tab (like for tabbed menus, etc.) More...

class GWidgetVCRButton

A button with a common icon on it. More...

class GWidgetVertScrollBar

Makes a vertical scroll bar. More...

class GWidgetVertSlider

class GWidgetWave

class GWordIterator

This iterates over the words in a block of text. More...

class GWordStats

Stores statistics about each word in a GVocabulary. More...

class GWorkerThread

An abstract class for performing jobs. The idea is that you should be able to write the code to perform the jobs, then use it in either a serial or parallel manner. The class you write, that inherits from this one, will typically have additional constructor parameters that pass in any values or data necessary to define the jobs. More...

struct HashBucket

This is an internal structure used by GHashTable. More...

class HashTableNode

Objects used with GNodeHashTable should inherit from this class. They must implement two methods (to hash and compare the nodes). More...

class Holder

This class is very similar to the standard C++ class auto_ptr, except it throws an exception if you try to make a copy of it. This way, it will fail early if you use it in a manner that could result in non-deterministic behavior. (For example, if you create a vector of auto_ptrs, wierd things happen if an oom exception is thrown while resizing the buffer–part of the data will be lost when it reverts back to the original buffer. But if you make a vector of these, it will fail quickly, thus alerting you to the issue.) More...

class OptimizerTargetFunc

struct PathData

Helper struct to hold the results from GFile::ParsePath. More...

class ShouldMaximize

Tag class to indicate that the linearAssignment routine should maximize the cost of the assignment. More...

class ShouldMinimize

Tag class to indicate that the linearAssignment routine should minimize the cost of the assignment. More...

struct strCmp

struct strComp

class VectorOfPointersHolder

Deletes all of the pointers in a vector when this object goes out of scope. More...

Typedefs
typedef void(*	DaemonMainFunc) (void *pArg)

typedef void(*	EnsembleProgressCallback) (void *pThis, size_t i, size_t n)

typedef double	G3DReal

typedef double(*	MathFunc) (void *pThis, double x)

typedef int(*	PointerComparer) (void pThis, void pA, void *pB)

typedef void(*	RepValidateCallback) (void *pThis, size_t nRep, size_t nFold, double foldSSE, size_t rows)

typedef void(*	sighandler_t) (int)

typedef std::map< size_t, double >	SparseVec

typedef unsigned long int	THREAD_HANDLE

Functions
int	_stricmp (const char szA, const char szB)

int	_strnicmp (const char szA, const char szB, int len)

unsigned int	abgrToArgb (unsigned int rgba)

class GClasses::GRand	beta (double alpha, double beta)

virtual std::size_t	binomial (std::size_t n, double p)
	Returns a random value from a binomial distribution This method draws n samples from a uniform distribution, so it is very slow for large values of n. binomial_approx is generally much faster. More...

virtual std::size_t	binomial_approx (std::size_t n, double p)
	Returns a random value approximately from a binomial distribution. This method uses a normal distribution to approximate the binomial distribution. It is O(1), and is generally quite accurate when n is large and p is not too close to 0 or 1. More...

virtual std::size_t	categorical (std::vector< double > &probabilities)
	Returns a random value from a categorical distribution with the specified vector of category probabilities. (Note: If you need to draw many values from a categorical distribution, the GCategoricalSampler and GCategoricalSamplerBatch classes are designed to do this more efficiently.) More...

virtual double	cauchy ()
	Returns a random value from a standard Cauchy distribution. More...

virtual double	chiSquare (double t)
	Returns a random value from a chi-squared distribution. More...

int	ClipChan (int n)

double	cost (const GSimpleAssignment &assign, const GMatrix &costs)
	Return the cost of the assignment assign for the matrix costs. More...

virtual void	dirichlet (double pOutVec, const double pParams, int dims)
	Returns a random vector from a dirichlet distribution with the specified parameters and dimensionality. (Both pOutVec and pParams should be an array of dims values.) More...

virtual double	exponential ()
	Returns a random value from a standard exponential distribution. (To convert it to a random value from an arbitrary exponential distribution, just divide the value this returns by the rate (usually lambda), or if you use the scale parameterization, just multiply the value this returns by the scale (usually beta)). More...

virtual double	f (double t, double u)
	Returns a random value from an f-distribution. More...

long	filelength (int filedes)

unsigned int	gAHSV (int alpha, float hue, float saturation, float value)

virtual double	gamma (double alpha)
	Returns a random value from a gamma distribution with beta=theta=1. To convert to a value from an arbitrary gamma distribution, just divide the value this returns by beta (or use alpha=k, and multiply the value this returns by theta). More...

unsigned int	gARGB (int a, int r, int g, int b)

void	GAssertFailed (const char *filename, int line)

void	GAssertFailed (const char filename, int line, const char message)

virtual int	geometric (double p)
	Returns a random value from a geometric distribution with support for {0, 1, 2, ...}. More...

unsigned int	gFromGray (int gray)

void	GRelationalTable_test ()

unsigned int	gRGB (int r, int g, int b)

void	GSimplePriorityQueue_test ()

unsigned int	hexToRgb (const char *szHex)

void	LAPVJRCT (GMatrix cost, std::vector< int > &rowAssign, std::vector< int > &colAssign, std::vector< double > &rowPotential, std::vector< double > &colPotential, double &totalCost, const double epsilon=1e-8)
	Rectangular linear assignment problem solver by Volgenant and Jonker. More...

GSimpleAssignment	linearAssignment (GMatrix costs, ShouldMinimize sm=ShouldMinimize(), const double epsilon=1e-8)
	Return a GSimpleAssignment that minimizes the cost of the assignment. More...

GSimpleAssignment	linearAssignment (GMatrix benefits, ShouldMaximize sm, const double epsilon=1e-8)
	Return a GSimpleAssignment that maximizes the benefit of the assignment. More...

std::vector< GSimpleAssignment >	linearAssignmentBruteForce (GMatrix costs, ShouldMinimize sm=ShouldMinimize())
	Solve a linear assignment minimization problem by the VERY SLOW brute force method. More...

std::vector< GSimpleAssignment >	linearAssignmentBruteForce (GMatrix benefits, ShouldMaximize sm)
	Solve a linear assignment maximization problem by the VERY SLOW brute force method. More...

virtual double	logistic ()
	Returns a random value from a standard logistic distribution. To convert to a random value from an arbitrary logistic distribution, just multiply the value this returns by the scale (or divide by the steepness), and then add the mean. More...

virtual double	logNormal (double mean, double dev)
	Returns a random value from a log-Normal distribution. More...

unsigned int	MixColors (unsigned int a, unsigned int b, int nRatio)

unsigned int	MultiplyBrightness (unsigned int c, float f)

virtual double	normal ()
	Returns a random value from a standard normal distribution. (To convert it to a random value from an arbitrary normal distribution, just multiply the value this returns by the deviation (usually lowercase-sigma), then add the mean (usually mu).) More...

std::ostream &	operator<< (std::ostream &out, const GSimpleAssignment &gsa)
	Print assignment gsa to out in a human-readable form. More...

virtual int	poisson (double mu)
	Returns a random value from a Poisson distribution. More...

std::string	pre_pad (size_t final_length, char pad_char, const std::string &src_string)
	Prepends however many pad_char characters are needed to make src_string have a length of final_length, and returns the results. More...

template<class T >
T	reverseBits (T value)
	Reverses the bits of value given that T is an unsigned integral type with binary representation and a number of bits that are a power of 2. More...

void	reverseBitsTest ()

const char *	rgbToHex (char *pOutHex, unsigned int c)

void	rgbToHsv (unsigned int c, float pHue, float pSaturation, float *pValue)

size_t	safe_strcpy (char szDest, const char szSrc, size_t nDestBufferSize)
	This is similar to strncpy, but it always makes sure that there is a null-terminating '\0' at the end of the new string. Returns the length of the new string. More...

virtual void	simplex (double *pOutVec, std::size_t dims)
	Draws uniformly from a unit simplex. (This is a special case of drawing from a dirichlet distribution with uniform parameters.) More...

virtual double	softImpulse (double s)
	Returns a random value from a soft-impulse distribution with support from 0 to 1. (The cdf of the soft-impulse distribution is the soft-step function: (1/(pow(1/x-1,s)+1)). The mean is always at 0.5, where the probability-density is s. More...

virtual double	student (double t)
	Returns a random value from Student's t-distribution. More...

void	test_to_str ()
	Run unit tests for the to_str functions. Throws an exception if it detects an error. More...

void	TestContains (std::string expectedSubstring, std::string got, std::string desc)
	Verify that expectedSubstring is a substring of got for test code. Unlike Assert, this check does not disappear in optimized builds. More...

template<class T1 , class T2 >
void	TestEqual (const T1 &expected, const T2 &got, std::string test_descr)
	Verify that expected and got are equal for test code. Unlike Assert, this check does not disappear in optimized builds. More...

void	TestEqual (char const expected, char const got, std::string desc)
	"Specialization" of TestEqual for c-strings done using overloading More...

void	TestEqual (char const expected, char got, std::string desc)
	"Specialization" of TestEqual for c-strings done using overloading More...

void	TestEqual (char expected, char got, std::string desc)
	"Specialization" of TestEqual for c-strings done using overloading More...

void	testLinearAssignment ()
	Runs unit tests on the linear assignment code and supporting code. Throws an exception if it detects an error. More...

std::string	to_str (const GDomNode &node)
	Converts a GDomNode to a string. More...

std::string	to_str (const GDom &doc)
	Converts a GDom to a string. More...

template<typename T >
std::string	to_str (const T &n)
	Convert another type that has a stream-insertion operator << to a string. More...

std::string	to_str (const GMatrix &m)
	Allow GMatrix objects to easily be converted into a string for debugging. More...

template<typename T >
std::string	to_str (const std::vector< T > &v)
	Convert a vector to a string (should allow converting nested stl containers like vector<list ...> to strings) More...

template<typename T >
std::string	to_str (const std::list< T > &v)
	Convert a list to a string (should allow converting nested stl containers like vector<list ...> to strings) More...

template<typename T >
std::string	to_str (const std::set< T > &v)
	Convert a set to a string (should allow converting nested stl containers like set<vector..> to string) More...

template<typename T >
std::string	to_str (const std::deque< T > &v)
	Convert a deque to a string (should allow converting nested stl containers like deque<list ...> to strings) More...

template<typename T >
std::string	to_str (const std::multiset< T > &v)
	Convert a multiset to a string (should allow converting nested stl containers like multiset<list ...> to strings) More...

template<typename Key , typename T >
std::string	to_str (const std::multimap< Key, T > &v)
	Convert a multimap to a string (should allow converting nested stl containers like multimap<list< ...> , vector< ... > > to strings) More...

template<typename Key , typename T >
std::string	to_str (const std::map< Key, T > &v)
	Convert a map to a string (should allow converting nested stl containers like map<list< ...> , vector< ... > > to strings) More...

std::string	to_str (const std::vector< bool > &vv)
	Convert a vector<bool> to a string (should allow converting nested stl containers like vector<list ...> to strings) More...

template<typename T , typename U >
std::string	to_str (std::pair< T, U > v)
	Convert a pair to a string (should allow converting nested stl containers like pair<list<vector...>, multiset<foo> > to strings) More...

template<typename T >
std::string	to_str (T begin, T end, std::string spec="")
	Convert an stl container-like object to a string. More...

std::string	to_str (const GVec &v)
	Allow GVec objects to easily be converted into a string for debugging. More...

std::string	to_str (const GSupervisedLearner &learner)
	Converts a GSupervisedLearner to a string. More...

virtual double	uniform ()
	Returns a pseudo-random double from 0 (inclusive) to 1 (exclusive). This uses 52 random bits for the mantissa, and discards the extra 12 random bits. More...

virtual double	uniform (double min, double max)
	Returns a pseudo-random double from min (inclusive) to max (exclusive). More...

void	verboten ()

virtual double	weibull (double gamma)
	Returns a random value from a Weibull distribution with lambda=1. More...

Variables
class GClasses::GRand	B
	where More...

class GClasses::GRayTraceColor	beta [X\sim\textrm{Be}(\alpha,\beta)\f]

Typedef Documentation

typedef void(* GClasses::DaemonMainFunc) (void *pArg)

typedef void(* GClasses::EnsembleProgressCallback) (void *pThis, size_t i, size_t n)

typedef double GClasses::G3DReal

typedef double(* GClasses::MathFunc) (void *pThis, double x)

typedef int(* GClasses::PointerComparer) (void *pThis, void *pA, void *pB)

typedef void(* GClasses::RepValidateCallback) (void *pThis, size_t nRep, size_t nFold, double foldSSE, size_t rows)

typedef void(* GClasses::sighandler_t) (int)

typedef std::map<size_t,double> GClasses::SparseVec

typedef unsigned long int GClasses::THREAD_HANDLE

Function Documentation

int GClasses::_stricmp	(	const char *	szA,
		const char *	szB
	)

int GClasses::_strnicmp	(	const char *	szA,
		const char *	szB,
		int	len
	)

unsigned int GClasses::abgrToArgb ( unsigned int rgba )

inline

class GClasses::GRand GClasses::beta	(	double	alpha,
		double	beta
	)

Parameters

alpha	The α parameter (see above)
beta	The β parameter (see above)

virtual std::size_t GClasses::binomial	(	std::size_t	n,
		double	p
	)

virtual

Returns a random value from a binomial distribution This method draws n samples from a uniform distribution, so it is very slow for large values of n. binomial_approx is generally much faster.

virtual std::size_t GClasses::binomial_approx	(	std::size_t	n,
		double	p
	)

virtual

Returns a random value approximately from a binomial distribution. This method uses a normal distribution to approximate the binomial distribution. It is O(1), and is generally quite accurate when n is large and p is not too close to 0 or 1.

virtual std::size_t GClasses::categorical ( std::vector< double > & probabilities )

virtual

Returns a random value from a categorical distribution with the specified vector of category probabilities. (Note: If you need to draw many values from a categorical distribution, the GCategoricalSampler and GCategoricalSamplerBatch classes are designed to do this more efficiently.)

virtual double GClasses::cauchy ( )

virtual

Returns a random value from a standard Cauchy distribution.

virtual double GClasses::chiSquare ( double t )

virtual

Returns a random value from a chi-squared distribution.

int GClasses::ClipChan ( int n )

inline

double GClasses::cost	(	const GSimpleAssignment &	assign,
		const GMatrix &	costs
	)

Return the cost of the assignment assign for the matrix costs.

costs[i][j] is the cost of assigning element i from set A to element j from set B. This retuns the sum of the costs of making the assignments given in assign. Non-assigned members have zero costs.

Parameters

assign	The assignment whose cost will be computed
costs	costs[i][j] is the cost of assigning element i from set A to element j from set B.

virtual void GClasses::dirichlet	(	double *	pOutVec,
		const double *	pParams,
		int	dims
	)

virtual

Returns a random vector from a dirichlet distribution with the specified parameters and dimensionality. (Both pOutVec and pParams should be an array of dims values.)

virtual double GClasses::exponential ( )

virtual

Returns a random value from a standard exponential distribution. (To convert it to a random value from an arbitrary exponential distribution, just divide the value this returns by the rate (usually lambda), or if you use the scale parameterization, just multiply the value this returns by the scale (usually beta)).

virtual double GClasses::f	(	double	t,
		double	u
	)

virtual

Returns a random value from an f-distribution.

long GClasses::filelength ( int filedes )

unsigned int GClasses::gAHSV	(	int	alpha,
		float	hue,
		float	saturation,
		float	value
	)

virtual double GClasses::gamma ( double alpha )

virtual

Returns a random value from a gamma distribution with beta=theta=1. To convert to a value from an arbitrary gamma distribution, just divide the value this returns by beta (or use alpha=k, and multiply the value this returns by theta).

unsigned int GClasses::gARGB	(	int	a,
		int	r,
		int	g,
		int	b
	)

inline

void GClasses::GAssertFailed	(	const char *	filename,
		int	line
	)

void GClasses::GAssertFailed	(	const char *	filename,
		int	line,
		const char *	message
	)

virtual int GClasses::geometric ( double p )

virtual

Returns a random value from a geometric distribution with support for {0, 1, 2, ...}.

Parameters

p	the probability of success in the underlying bernoulli trials. 0 < p < 1

unsigned int GClasses::gFromGray ( int gray )

inline

void GClasses::GRelationalTable_test ( )

unsigned int GClasses::gRGB	(	int	r,
		int	g,
		int	b
	)

inline

void GClasses::GSimplePriorityQueue_test ( )

unsigned int GClasses::hexToRgb ( const char * szHex )

void GClasses::LAPVJRCT	(	GMatrix	cost,
		std::vector< int > &	rowAssign,
		std::vector< int > &	colAssign,
		std::vector< double > &	rowPotential,
		std::vector< double > &	colPotential,
		double &	totalCost,
		const double	epsilon = `1e-8`
	)

Rectangular linear assignment problem solver by Volgenant and Jonker.

Solves the rectangular linar assignment problem:

Given a non-negative cost matrix cost, create a binary assignment matrix x to minimize the cost of the assignments, that is, minimize sum cost[i][j]*x[i][j]. numRows <= numColumns

The assignments are 1 if assigned, and zero if unassigned. At most one row can be assigned to each column and vice versa. That fixing k, sum x[i][k] <= 1 and sum x[k][j] == 1.

The x matrix is encoded in rowAssign (and for convenience) colAssign. rowAssign[row] is the column assigned to that row. colAssign[column] is the row assigned to that column or -1 if no row was assigned.

rowPotential[row] and colPotential[column] give the dual potentials after the assignment. The dual problem is:

maximize ( sum rowPotential[row] ) + ( sum colPotential[column] )

subject to:

cost[i][j]-rowPotential[i]-colPotential[j] >= 0 for all i,j

rowPotential[row] >= 0

colPotential[column] >= 0

totalCost is the cost of the generated assignment

For more information, see:

"Linear and semi-assignment problems: a core-oriented approach", Computers in Operations Research, Vol. 23, No. 10, pp. 917-932, 1996, Elsevier Science Ltd

Parameters

cost	The cost matrix - must be non-negative and have at least as many columns as rows
rowAssign	Output vector. rowAssign[row] is the index of the column that was assigned to that row
colAssign	Output vector. colAssign[column] is the index of the row that was assigned to that column, or -1 if no row was assigned.
rowPotential	Output vector. The potential assigned to a given row in the solution to the dual problem (see main description).
colPotential	Output vector. The potential assigned to a given column in the solution to the dual problem (see main description).
totalCost	Output double. The total cost of the optimal assignment
epsilon	Threshold to use in floating point comparisons. If two values differ by epsilon, they are considered to be equal

GSimpleAssignment GClasses::linearAssignment	(	GMatrix	costs,
		ShouldMinimize	sm = `ShouldMinimize()`,
		const double	epsilon = `1e-8`
	)

Return a GSimpleAssignment that minimizes the cost of the assignment.

costs[i][j] is the cost of assigning element i in set A to element j in set B. The GSimpleAssignment object returned minimizes the sum of the costs for all assignments made. All elements in the smaller set will be assigned a corresponding element in the larger set.

Parameters

costs	costs[i][j] is the cost of assigning element i in set A to element j in set B
sm	a tag parameter to differentiate this linearAssignment function from the version that maximizes the benefit of the assignment. Just pass ShouldMinimize()
epsilon	fudge factor for floating-point equality comparisons after multiple additions and subtractions. If two values lie within this distance of one another, they are considered equal. Make sure that the costs in your cost matrix that are truely non-equal have distances much greater than epsilon.

Returns: a Simple assignment object that minimizes the cost of the assignment.

See also: linearAssignment(GMatrix, ShouldMaximize, const double); LAPVJRCT

GSimpleAssignment GClasses::linearAssignment	(	GMatrix	benefits,
		ShouldMaximize	sm,
		const double	epsilon = `1e-8`
	)

Return a GSimpleAssignment that maximizes the benefit of the assignment.

benefits[i][j] is the benefit of assigning element i in set A to element j in set B. The GSimpleAssignment object returned maximizes the sum of the benefits for all assignments made. All elements in the smaller set will be assigned a corresponding element in the larger set.

Parameters

benefits	benefits[i][j] is the benefit of assigning element i in set A to element j in set B
sm	a tag parameter to differentiate this linearAssignment function from the version that minimizes the cost of the assignment. Just pass ShouldMaximize()
epsilon	fudge factor for floating-point equality comparisons after multiple additions and subtractions. If two values lie within this distance of one another, they are considered equal. Make sure that the costs in your cost matrix that are truely non-equal have distances much greater than epsilon.

Returns: a Simple assignment object that maximizes the cost of the assignment.

See also: linearAssignment(GMatrix, ShouldMinimize, const double); LAPVJRCT

std::vector<GSimpleAssignment> GClasses::linearAssignmentBruteForce	(	GMatrix	costs,
		ShouldMinimize	sm = `ShouldMinimize()`
	)

Solve a linear assignment minimization problem by the VERY SLOW brute force method.

This method is mainly here for testing.

Finds all simple assignments that minimize the cost matrix. (For certain degenerate cases (like the matrix where all costs are the same), this can be a large number of assignments, be careful). Takes time n choose m where n is the larger matrix dimension and m is the smaller. Always assigns one element to each of the smaller dimensions of the matrix.

The rows are the A set and the columns are the B set.

Parameters

costs	costs[i][j] is the cost of assigning element i from set A to element j from set B
sm	A tag class to distinguish this from the code which maximizes the benefits. Just pass ShouldMinimize

std::vector<GSimpleAssignment> GClasses::linearAssignmentBruteForce	(	GMatrix	benefits,
		ShouldMaximize	sm
	)

Solve a linear assignment maximization problem by the VERY SLOW brute force method.

This method is mainly here for testing.

Finds all simple assignments that maximize the benefit matrix. (For certain degenerate cases (like the matrix where all benefits are the same), this can be a large number of assignments, be careful). Takes time n choose m where n is the larger matrix dimension and m is the smaller. Always assigns one element to each of the smaller dimensions of the matrix.

The rows are the A set and the columns are the B set.

Parameters

benefits	benefits[i][j] is the benefit of assigning element i from set A to element j from set B
sm	A tag class to distinguish this from the code which maximizes the benefits. Just pass ShouldMaximize

virtual double GClasses::logistic ( )

virtual

Returns a random value from a standard logistic distribution. To convert to a random value from an arbitrary logistic distribution, just multiply the value this returns by the scale (or divide by the steepness), and then add the mean.

virtual double GClasses::logNormal	(	double	mean,
		double	dev
	)

virtual

Returns a random value from a log-Normal distribution.

unsigned int GClasses::MixColors	(	unsigned int	a,
		unsigned int	b,
		int	nRatio
	)

inline

unsigned int GClasses::MultiplyBrightness	(	unsigned int	c,
		float	f
	)

inline

virtual double GClasses::normal ( )

virtual

Returns a random value from a standard normal distribution. (To convert it to a random value from an arbitrary normal distribution, just multiply the value this returns by the deviation (usually lowercase-sigma), then add the mean (usually mu).)

std::ostream& GClasses::operator<<	(	std::ostream &	out,
		const GSimpleAssignment &	gsa
	)

Print assignment gsa to out in a human-readable form.

The GSimpleAssignment gsa is written to out as a sequence of pairs {a,b} where a is an index in set A and b is an index into set B. Any indices that are not listed are unassigned.

Thus the assignment from the bForA array [3,2,-1,9] would be output as [{0,3},{1,2},{3,9}]

Parameters

out	the stream on which the assignment will be printed
gsa	the GSimpleAssignment object to print

Returns: out after gsa has been written to it

virtual int GClasses::poisson ( double mu )

virtual

Returns a random value from a Poisson distribution.

std::string GClasses::pre_pad	(	size_t	final_length,
		char	pad_char,
		const std::string &	src_string
	)

Prepends however many pad_char characters are needed to make src_string have a length of final_length, and returns the results.

template<class T >

T GClasses::reverseBits ( T value )

Reverses the bits of value given that T is an unsigned integral type with binary representation and a number of bits that are a power of 2.

Modified (added checks and some consts for readability) from the post at http://www.velocityreviews.com/forums/t457514-reverse-bit-order.html by Pete Becker, Author of "The Standard C++ Library Extensions: a Tutorial and Reference"

void GClasses::reverseBitsTest ( )

const char* GClasses::rgbToHex	(	char *	pOutHex,
		unsigned int	c
	)

void GClasses::rgbToHsv	(	unsigned int	c,
		float *	pHue,
		float *	pSaturation,
		float *	pValue
	)

size_t GClasses::safe_strcpy	(	char *	szDest,
		const char *	szSrc,
		size_t	nDestBufferSize
	)

This is similar to strncpy, but it always makes sure that there is a null-terminating '\0' at the end of the new string. Returns the length of the new string.

virtual void GClasses::simplex	(	double *	pOutVec,
		std::size_t	dims
	)

virtual

Draws uniformly from a unit simplex. (This is a special case of drawing from a dirichlet distribution with uniform parameters.)

virtual double GClasses::softImpulse ( double s )

virtual

Returns a random value from a soft-impulse distribution with support from 0 to 1. (The cdf of the soft-impulse distribution is the soft-step function: (1/(pow(1/x-1,s)+1)). The mean is always at 0.5, where the probability-density is s.

virtual double GClasses::student ( double t )

virtual

Returns a random value from Student's t-distribution.

void GClasses::test_to_str ( )

Run unit tests for the to_str functions. Throws an exception if it detects an error.

void GClasses::TestContains	(	std::string	expectedSubstring,
		std::string	got,
		std::string	desc
	)

Verify that expectedSubstring is a substring of got for test code. Unlike Assert, this check does not disappear in optimized builds.

If got contains expectedSubstring then does nothing. Otherwise prints to stderr:

Substring match failed: ---------test_descr goes here ---------------

Expected substring: ------------expectedSubstring goes here---------------
Got               : ------------got goes here     ---------------

Then it throws an exception using ThrowError

Parameters

expectedSubstring	The value expected as a substring from specifications
got	The value actually produced by the code
test_descr	A short test description to allow a human to easily find the failing test in the code and understand why it was written and have some help in diagnosing the bug.

template<class T1 , class T2 >

void GClasses::TestEqual	(	const T1 &	expected,
		const T2 &	got,
		std::string	test_descr
	)

Verify that expected and got are equal for test code. Unlike Assert, this check does not disappear in optimized builds.

If expected==got then does nothing. Otherwise prints to stderr:

Test for equality failed: ---------test_descr goes here ---------------

Expected: ------------expected goes here---------------
Got     : ------------got goes here     ---------------

Then it throws an exception using ThrowError

Calls operator==(const T1&,const T2&) to determine equality.

Calls GClasses::to_str to form the string representation of expected and got

Parameters

expected	The value expected from specifications
got	The value actually produced by the code
test_descr	A short test description to allow a human to easily find the failing test in the code and understand why it was written and have some help in diagnosing the bug.

void GClasses::TestEqual	(	char const *	expected,
		char const *	got,
		std::string	desc
	)

"Specialization" of TestEqual for c-strings done using overloading

void GClasses::TestEqual	(	char const *	expected,
		char *	got,
		std::string	desc
	)

"Specialization" of TestEqual for c-strings done using overloading

void GClasses::TestEqual	(	char *	expected,
		char *	got,
		std::string	desc
	)

"Specialization" of TestEqual for c-strings done using overloading

void GClasses::testLinearAssignment ( )

Runs unit tests on the linear assignment code and supporting code. Throws an exception if it detects an error.

std::string GClasses::to_str ( const GDomNode & node )

Converts a GDomNode to a string.

std::string GClasses::to_str ( const GDom & doc )

Converts a GDom to a string.

template<typename T >

std::string GClasses::to_str ( const T & n )

Convert another type that has a stream-insertion operator << to a string.

std::string GClasses::to_str ( const GMatrix & m )

Allow GMatrix objects to easily be converted into a string for debugging.

Note: this is defined with the rest of the GMatrix code in GMatrix.cpp. It is only declared in GError.h

Parameters

m	the matrix that will be converted to a string

Returns: a string representing the matrix m

See also: template<class T> to_str(const T& n)

template<typename T >

std::string GClasses::to_str ( const std::vector< T > & v )

Convert a vector to a string (should allow converting nested stl containers like vector<list ...> to strings)

template<typename T >

std::string GClasses::to_str ( const std::list< T > & v )

Convert a list to a string (should allow converting nested stl containers like vector<list ...> to strings)

template<typename T >

std::string GClasses::to_str ( const std::set< T > & v )

Convert a set to a string (should allow converting nested stl containers like set<vector..> to string)

template<typename T >

std::string GClasses::to_str ( const std::deque< T > & v )

Convert a deque to a string (should allow converting nested stl containers like deque<list ...> to strings)

template<typename T >

std::string GClasses::to_str ( const std::multiset< T > & v )

Convert a multiset to a string (should allow converting nested stl containers like multiset<list ...> to strings)

template<typename Key , typename T >

std::string GClasses::to_str ( const std::multimap< Key, T > & v )

Convert a multimap to a string (should allow converting nested stl containers like multimap<list< ...> , vector< ... > > to strings)

template<typename Key , typename T >

std::string GClasses::to_str ( const std::map< Key, T > & v )

Convert a map to a string (should allow converting nested stl containers like map<list< ...> , vector< ... > > to strings)

std::string GClasses::to_str ( const std::vector< bool > & vv )

Convert a vector<bool> to a string (should allow converting nested stl containers like vector<list ...> to strings)

template<typename T , typename U >

std::string GClasses::to_str ( std::pair< T, U > v )

Convert a pair to a string (should allow converting nested stl containers like pair<list<vector...>, multiset<foo> > to strings)

template<typename T >

std::string GClasses::to_str	(	T	begin,
		T	end,
		std::string	spec = `""`
	)

Convert an stl container-like object to a string.

std::string GClasses::to_str ( const GVec & v )

Allow GVec objects to easily be converted into a string for debugging.

Parameters

v	the GVec that will be converted to a string

Returns: a string representing the GVec v

See also: template<class T> to_str(const T& n)

std::string GClasses::to_str ( const GSupervisedLearner & learner )

Converts a GSupervisedLearner to a string.

virtual double GClasses::uniform ( )

virtual

Returns a pseudo-random double from 0 (inclusive) to 1 (exclusive). This uses 52 random bits for the mantissa, and discards the extra 12 random bits.

virtual double GClasses::uniform	(	double	min,
		double	max
	)

virtual

Returns a pseudo-random double from min (inclusive) to max (exclusive).

Parameters

min	the minimum of the interval on which the samples returned are distributed
max	the supremum of the interval on which the samples returned are distributed. max will not be generated unless max == min or unless the interval is otherwise pathological

void GClasses::verboten ( )

virtual double GClasses::weibull ( double gamma )

virtual

Returns a random value from a Weibull distribution with lambda=1.

Variable Documentation

class GClasses::GRand GClasses::B

where

class GClasses::GRayTraceColor GClasses::beta[ X \sim \textrm{Be}(\alpha, \beta)\f]

Namespaces

Classes

Typedefs

Functions

Variables

Typedef Documentation

Function Documentation

Variable Documentation