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Classes |
class | AbstractNeuron |
| Implementation helper for common types of Neurons. More...
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class | ArgParser |
class | CenterNeuron |
| A center-neuron, the building block of a Radial basis function network. More...
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class | DynamicKohonenParameters |
class | EucledianTopology |
| Standard eucledian topology OPT: zounds. More...
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class | Exception |
| A common exception class used by all classes in the annie library. More...
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class | ExKohonenDraw |
class | File |
| The idea is that this class will be used to read in information from text files created by the "save" function in various annie classes. More...
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class | GABNeuron |
| Neuron for Gain Adaptive BP. More...
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class | HopfieldBase |
| A Hopfield network - content addressable memory and energy. More...
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struct | HopfieldBase::NData |
class | HopfieldNetwork |
| A "standard" incarnation of the Hopfield network usable form most purposes. More...
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class | Image |
| represents one image (actually a SDL_Context wrapper ..) More...
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struct | Image::Color |
| the color components should be in [0, 1] More...
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class | ImageCompressor |
| (usually lossy) image compressor. More...
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class | ImageCompressor::PartialListener |
class | InputLayer |
| A layer of input neurons. More...
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class | InputNeuron |
| Input neurons are slightly special. More...
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class | KohonenAnalyzer |
struct | KohonenAnalyzer::Outs |
class | KohonenDraw |
| Draws examples and progress of the Kohonen clustering using OpenGL --> Should be called from the redraw thread. More...
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class | KohonenNetwork |
| Todo: getOutput (&& getWin*) doesn't give anything sensible in this impl. More...
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class | KohonenNeuron |
class | KohonenParameters |
| Note: it is closely tied to Topology - the meaning NeighborhoodSize depends on the Topology used. More...
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class | Layer |
| Abstraction for a "layer" of neurons, i.e., a group of neurons not connected to each other. More...
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class | Link |
| Abstraction of a connection between two neurons. More...
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class | Matrix |
| A class for 2-dimensional matrices. More...
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class | MLPCompressor |
| yeah, yeah - ValueUpdateListener should have been inner class More...
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class | MLPKohonenAnalyzer |
| Analyzes MLP using Kohonen. More...
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class | MultiLayerNetwork |
| Abstraction of a multi-layer perceptron network. More...
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struct | MultiLayerNetwork::Error |
| Error summator will be moved up for general use.. More...
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class | Network |
| Another core class of the annie library, a generic template for a neural network. More...
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class | Neuron |
| One of the fundamental annie classes - the basic Neuron. More...
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struct | NumberParameter |
class | ParametrizedNeuron |
| Neuron with a parametrized activation function. More...
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class | RadialBasisNetwork |
| A Radial Basis Function Neural Network. More...
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class | RecurrentNetwork |
| RN constructed from a genotype. More...
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class | RecurrentNeuron |
| A neuron used for recurrent networks. More...
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struct | Redrawer |
struct | RigidVisualiser |
| Simple text-mode visualiser, which prints updated value after each change (synchronous). More...
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struct | Selector |
| Select features from the TS vectors. More...
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struct | Shrinker |
struct | Shuffler |
| Shuffles the given training set every nth epoch. More...
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class | SimpleNeuron |
| A simple perceptron - i.e., it takes as input the weighted sum of the outputs of the neurons connected to it. More...
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struct | SimpleVisualiser |
| Simple text-mode visualiser, which prints updated value after each change (synchronous), it it didn't occur too soon. More...
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class | StandardKohonenParameters |
| learningParam = e ^ (- slope * ( ln (time) ^ 2 ) ) neighborHoodSize = (MAX_NB_SIZE) * learningParam More...
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class | StaticKohonenParameters |
| Static kohonen parameters - don't change unless changed by the user. More...
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struct | Stepper |
| Waits for keypress on each "epoch" if "stepped" is true. More...
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struct | StringParameter |
| also holds the destination More...
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class | TerminatedException |
class | TileCompressor |
| Compress image by dividing it to squares. More...
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class | TLayer |
| nice getters... More...
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class | Topology |
| Class used as a template to the KohonenNetwork network. More...
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class | TrainingSet |
| This is an abstraction for the set of patterns which are used to "train" a network. More...
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struct | TSTransformer |
| 2 vectors -> 2 vectors More...
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class | TwoLayerNetwork |
| Two layered networks are very commonly used. More...
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class | Vector |
class | Video |
| Video (and input) system maintaince. More...
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class | Video::Redrawer |
| User redraw class. draw() will be called from the video thread. More...
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Typedefs |
typedef double | real |
| Use this instead of double/float for real numbers pertaining to annie.
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typedef real(* | TimeSeriesCreatingFunction )(unsigned time) |
| Function that can be sampled in discrete time steps.
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typedef std::vector< real > | SamplesContainer |
| Store for samples.
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typedef real(* | SamplingFunction )() |
| generates one sample at each call see...
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typedef real(* | ActivationFunction )(real) |
| These functions are used as activation functions of the neurons, i.e., the function applied to the raw output of the neurons.
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typedef std::vector< Link * > | LINKS |
| A set of Links, ie, weighted connections between two neurons.
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typedef real(* | ParametrizedActivationFunction )(real, real parameter) |
typedef TLayer< RecurrentNeuron > | RecurrentLayer |
| implementation detail, don't bother :)
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typedef Vector(* | XformFunction )(const Vector &in) |
| transforms one vector to another.
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typedef std::vector< annie::Image * > | Images |
| container for images
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typedef std::vector< real > | VFather |
| A vector of real numbers.
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Functions |
void | parseArgs (int argc, char *argv[], const NumberParameter *numeric, StringParameter *strin) |
| shortcut - you don't have to use the ArgParser yourself
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char | getLastKeyPressed () |
char | waitForKey () |
void | initControlThread (uint winW=600, uint winH=600) |
| also initializes video
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void | goodbyeControlThread () |
| nicely terminates CT Must be called even if you don't want to terminate the thread prematurely
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void | changeWindowSize (uint winW, uint winH, bool wait=true) |
| change the screen size
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void | forceRedraw (bool wait=true) |
| redraw the screen
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Video * | getVideo () |
void | assert_failed (const char *cond, const char *function, const char *file, int line) |
void | sampleFunction (TimeSeriesCreatingFunction f, unsigned from, unsigned to, SamplesContainer &out) |
| Sample the given function.
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TrainingSet * | makeExamples (unsigned inputs, unsigned outputs, const SamplesContainer &samples, int from=0, int to=-1) |
TrainingSet * | randomSamples (uint samples, uint dim, SamplingFunction *samplingFunctions) |
| Produce set of points randomly drawn from the N-d cube.
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TrainingSet * | randomSamples (uint samples, uint dim, SamplingFunction samplingFunction) |
| sugar for randomSamples - apply the same sampling function to all dimensions
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TrainingSet * | transformInputs (TrainingSet &source, XformFunction f, uint res_ins) throw () |
| Transform the input component of training examples by given function.
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TrainingSet * | transformInputs (TrainingSet &source, XformFunction f) throw () |
| sugar
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Vector | toPolar (const Vector &in) |
| map input to sphere [0] = r [1] = angle ( <- [0, 1], not radians!!!)
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Vector | normalize (const Vector &in) |
Vector | vectorIdentity (const Vector &in) |
template<int T_multiplier1000> Vector | Xscale (const Vector &in) |
| Heck, float cannot be used as a template parameter...
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template<int T_add1000> Vector | Xadd (const Vector &in) |
TrainingSet * | uniformCube (uint samples, uint dim) |
| some shortcuts these are good examples of samplers usage
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TrainingSet * | uniformSphere (uint samples, uint dim) |
| each spike is equally covered => center is denser
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real | isPositive (real x) |
| Similar to the signum function, but used for non-bipolar Hopfield networks.
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std::ostream & | operator<< (std::ostream &o, const Image &i) |
std::ostream & | operator<< (std::ostream &o, const Image::Color &c) |
void | runKohonenAnalyzer (KohonenAnalyzer &ka) |
real | identity (real x) |
| The identity function, ie, f(x) = x.
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real | didentity (real x) |
| Derivative of the identity function, thus always returns 1.
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real | sigmoid (real x) |
| Sigmoidal activation f(x).
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real | dsigmoid (real x) |
| Derivative of the sigmoidal function.
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real | gaussian (real x) |
| The guassian function. Sigma is taken to be 1.0.
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real | dgaussian (real x) |
| Derivative of the gaussian function (sigma is taken to be 1.0).
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real | signum (real x) |
| Signum function Returns real instead of int because this function can be used as an activation function too, so wanted to keep the signature the same as other activation functions.
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real | tansig (real x) |
| The tansig activation function. Courtesy Jose Ramos (warta@users.sourceforge.net).
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real | dtansig (real x) |
| Derivative of the tansign activation function. Courtesy Jose Ramos (warta@users.sourceforge.net).
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real | psigmoid (real x, real lambda) |
| Sigmoidal activation with slope modifier lambda f(x) = 1/(1+e^(lambda*x)).
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real | dpsigmoid (real x, real lambda) |
| Derivative of the sigmoidal function.
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real | uniformRandom (real min, real max) |
| returns a number drawn uniformly from min, max
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Vector | uniformRandomVector (real min, real max, uint size) |
real | random () |
| Generates a random real number between -1.0 and 1.0.
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real | random01 () |
| Generates a random real number between 0.0 and 1.0.
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int | randomInt (int low, int high) |
| Generates a random integer between given bounds.
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void | sysInit () |
| system dependent.
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Images | makeSubimages (const annie::Image &i, unsigned xSize, unsigned ySize) |
TrainingSet * | makeSubImageExamples (const Images &outImages, std::vector< Vector > &outExamples) |
| make vector from each tile.
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void | runCompressor (ImageCompressor &ic, const char *image, bool graay=false) |
| generic ImageCompressor utilization uses ["input"] as the input file
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std::ostream & | operator<< (std::ostream &os, const annie::Vector &v) |
bool | operator== (const Vector &o1, const Vector &o2) |
Variables |
const real | REAL_MAX = std::numeric_limits<double>::max() REAL_MIN=std::numeric_limits<double>::min() |
const int | BINARY_FILE = 0 |
| The TrainingSet can be saved as a binary file or a text file, the latter allowing users to create a training set without using annie.
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const int | TEXT_FILE = 1 |
const real | SIGMOID_APROX_THRESHOLD = 30 |
SDL_PixelFormat | defaultRGBformat |
| the only format of surface we create
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