rita
Class RiTravesty

java.lang.Object
  rita.RiObject
      rita.RiTravesty

All Implemented Interfaces:

processing.core.PConstants, RiConstants

public class RiTravesty
extends RiObject

Represents a Markov chain (or n-Gram) model that treats each character as a separate token (a la the original Travesty program). Provides a range of methods (see examples below) to query the model for probabilites and/or completions.

    RiTravesty rm = new RiTravesty(this, 5);

    rm.setEndOfSequenceChar(' ');                // treats each word as separate input

    rm.loadFile("myTestFile.txt");               // load the file (w' no multiplier)

    rm.printTree();                              // prints the model as a tree

                            ---------------------------

    println("p(g)="+rm.getProbability("g"));     // the probability of 'g'

    println("p(X|g)="+rm.getProbabilities("g")); // probabilities for next letters(X) after g, p(X|g) 

    String comp = rm.getCompletion("gr");        // returns a prob. random completion 

    String[] comps = rm.getCompletions("gr");    // returns all possible completions
 

Invisible:

Field Summary

static char

NULL_CHAR

Fields inherited from interface rita.support.RiConstants

BEHAVIOR_COMPLETED, BOUNDING_BOX_ALPHA, BRILL_POS_TAGGER, EASE_IN, EASE_IN_CUBIC, EASE_IN_EXPO, EASE_IN_OUT, EASE_IN_OUT_CUBIC, EASE_IN_OUT_EXPO, EASE_IN_OUT_QUARTIC, EASE_IN_OUT_SINE, EASE_IN_QUARTIC, EASE_IN_SINE, EASE_OUT, EASE_OUT_CUBIC, EASE_OUT_EXPO, EASE_OUT_QUARTIC, EASE_OUT_SINE, ESS, FADE_COLOR, FADE_IN, FADE_OUT, FADE_TO_TEXT, FIRST_PERSON, FUTURE_TENSE, ID, LERP, LINEAR, MAXENT_POS_TAGGER, MINIM, MOVE, MUTABLE, PAST_TENSE, PHONEME_BOUNDARY, PHONEMES, PLING_STEMMER, PLURAL, PORTER_STEMMER, POS, PRESENT_TENSE, SCALE_TO, SECOND_PERSON, SENTENCE_BOUNDARY, SINGULAR, SONIA, SPEECH_COMPLETED, STRESSES, SYLLABLE_BOUNDARY, SYLLABLES, TEXT, TEXT_ENTERED, THIRD_PERSON, TIMER, TIMER_COMPLETED, TIMER_TICK, TOKENS, UNKNOWN, WORD_BOUNDARY

Fields inherited from interface processing.core.PConstants

A, AB, ADD, AG, ALPHA, ALPHA_MASK, ALT, AMBIENT, AR, ARC, ARGB, ARROW, B, BACKSPACE, BASELINE, BEEN_LIT, BEVEL, BLEND, BLUE_MASK, BLUR, BOTTOM, BOX, BURN, CENTER, CENTER_DIAMETER, CENTER_RADIUS, CHATTER, CLOSE, CMYK, CODED, COMPLAINT, CONTROL, CORNER, CORNERS, CROSS, CUSTOM, DA, DARKEST, DB, DEG_TO_RAD, DELETE, DG, DIAMETER, DIFFERENCE, DILATE, DIRECTIONAL, DISABLE_ACCURATE_TEXTURES, DISABLE_DEPTH_SORT, DISABLE_DEPTH_TEST, DISABLE_OPENGL_2X_SMOOTH, DISABLE_OPENGL_ERROR_REPORT, DODGE, DOWN, DR, DXF, EB, EDGE, EG, ELLIPSE, ENABLE_ACCURATE_TEXTURES, ENABLE_DEPTH_SORT, ENABLE_DEPTH_TEST, ENABLE_NATIVE_FONTS, ENABLE_OPENGL_2X_SMOOTH, ENABLE_OPENGL_4X_SMOOTH, ENABLE_OPENGL_ERROR_REPORT, ENTER, EPSILON, ER, ERODE, ERROR_BACKGROUND_IMAGE_FORMAT, ERROR_BACKGROUND_IMAGE_SIZE, ERROR_PUSHMATRIX_OVERFLOW, ERROR_PUSHMATRIX_UNDERFLOW, ERROR_TEXTFONT_NULL_PFONT, ESC, EXCLUSION, G, GIF, GRAY, GREEN_MASK, HALF_PI, HAND, HARD_LIGHT, HINT_COUNT, HSB, IMAGE, INVERT, JAVA2D, JPEG, LEFT, LIGHTEST, LINE, LINES, LINUX, MACOSX, MAX_FLOAT, MAX_INT, MIN_FLOAT, MIN_INT, MITER, MODEL, MULTIPLY, NORMAL, NORMALIZED, NX, NY, NZ, OPAQUE, OPEN, OPENGL, ORTHOGRAPHIC, OTHER, OVERLAY, P2D, P3D, PATH, PDF, PERSPECTIVE, PI, platformNames, POINT, POINTS, POLYGON, POSTERIZE, PROBLEM, PROJECT, QUAD, QUAD_STRIP, QUADS, QUARTER_PI, R, RAD_TO_DEG, RADIUS, RECT, RED_MASK, REPLACE, RETURN, RGB, RIGHT, ROUND, SA, SB, SCREEN, SG, SHAPE, SHIFT, SHINE, SOFT_LIGHT, SPB, SPG, SPHERE, SPOT, SPR, SQUARE, SR, SUBTRACT, SW, TAB, TARGA, THIRD_PI, THRESHOLD, TIFF, TOP, TRIANGLE, TRIANGLE_FAN, TRIANGLE_STRIP, TRIANGLES, TWO_PI, TX, TY, TZ, U, UP, V, VERTEX_FIELD_COUNT, VW, VX, VY, VZ, WAIT, WHITESPACE, WINDOWS, X, Y, Z

Constructor Summary

RiTravesty(processing.core.PApplet parent, int nFactor)
Construct a Markov (or n-gram) model and set its n-factor

Method Summary

boolean	containsChar(char token) Returns true if the model contains the token in any position, else false.
java.lang.String	getCompletion(java.lang.String start) Chooses a single completion (if there are more than 1) based upon probabilistic random choices.
char	getEndOfSequenceChar() Returns the character marking the end of an input sequence;
int	getNFactor() Returns the current n-value for the model
java.util.Map	getProbabilities(java.lang.String path) Returns the full set of possible next tokens (as a HashMap: String -> Float (probability)) given an array of tokens representing the path down the tree (with length less than n).
float	getProbability(char token) Returns the raw (unigram) probability for a token in the model, or 0 if it does not exist
float	getProbability(java.lang.String tokens) Returns the probability of obtaining a sequence of k character tokens were k <= nFactor, e.g., if nFactor = 3, then valid lengths for the String tokens are 1, 2 & 3.
boolean	isSmoothing() Returns whether (add-1) smoothing is enabled for the model
void	loadCharData(java.lang.String rawText, int multiplier) Load a String into the model, treating each character as separate entity
void	loadFile(java.lang.String fileName)
void	loadFile(java.lang.String fileName, int multiplier) Load a text file into the model -- if using Processing, the file should be in the sketch's data folder.
static void	main(java.lang.String[] args)
void	printTree()
void	printTree(boolean sort)
void	printTree(java.io.PrintStream pw)
void	printTree(java.io.PrintStream printStream, boolean sort) Outputs a String representing the models probability tree using the supplied print stream (or System.out).
void	setEndOfSequenceChar(char endOfSequenceChar) Sets the character to mark the end of an input sequence.
void	setUseSmoothing(boolean useSmoothing) Toggles whether (add-1) smoothing is enabled for the model

Methods inherited from class rita.RiObject

dispose, getId, getPApplet, nextId

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

NULL_CHAR

public static final char NULL_CHAR

See Also:

Constant Field Values

Invisible:

Constructor Detail

RiTravesty

public RiTravesty(processing.core.PApplet parent,
                  int nFactor)

Construct a Markov (or n-gram) model and set its n-factor

Method Detail

loadFile

public void loadFile(java.lang.String fileName,
                     int multiplier)

Load a text file into the model -- if using Processing, the file should be in the sketch's data folder.

Parameters:

fileName - name of file to load

multiplier - weighting for tokens in the file;
a weight of 3 is equivalent to loading that file 3 times and gives each token 3x the probability of being chosen on a call to generate().

loadFile

public void loadFile(java.lang.String fileName)

loadCharData

public void loadCharData(java.lang.String rawText,
                         int multiplier)

Load a String into the model, treating each character as separate entity

Parameters:

multiplier - Weighting for tokens in the String
A weight of 3 is equivalent to loading that file 3 times and gives each token 3x the probability of being chosen on a call to generate().

printTree

public void printTree(java.io.PrintStream printStream,
                      boolean sort)

Outputs a String representing the models probability tree using the supplied print stream (or System.out).

NOTE: this method will block for potentially long periods of time on large models.

Parameters:

printStream - where to send the output (default=System.out)

sort - whether the tree is first sorted (by frequency) before being output

printTree

public void printTree(boolean sort)

printTree

public void printTree(java.io.PrintStream pw)

printTree

public void printTree()

getNFactor

public int getNFactor()

Returns the current n-value for the model

isSmoothing

public boolean isSmoothing()

Returns whether (add-1) smoothing is enabled for the model

setUseSmoothing

public void setUseSmoothing(boolean useSmoothing)

Toggles whether (add-1) smoothing is enabled for the model

containsChar

public boolean containsChar(char token)

Returns true if the model contains the token in any position, else false.

getProbability

public float getProbability(char token)

Returns the raw (unigram) probability for a token in the model, or 0 if it does not exist

getProbability

public float getProbability(java.lang.String tokens)

Returns the probability of obtaining a sequence of k character tokens were k <= nFactor, e.g., if nFactor = 3, then valid lengths for the String tokens are 1, 2 & 3.

getProbabilities

public java.util.Map getProbabilities(java.lang.String path)

Returns the full set of possible next tokens (as a HashMap: String -> Float (probability)) given an array of tokens representing the path down the tree (with length less than n). If the input array length is not less than n, or the path cannot be found, or the endnode has no children, null is returned.

As the returned Map represents the full set of possible next tokens, the sum of its probabilities will alwyas equal 1.

See Also:

getProbability(String)

getCompletion

public java.lang.String getCompletion(java.lang.String start)

Chooses a single completion (if there are more than 1) based upon probabilistic random choices.

getEndOfSequenceChar

public char getEndOfSequenceChar()

Returns the character marking the end of an input sequence;

setEndOfSequenceChar

public void setEndOfSequenceChar(char endOfSequenceChar)

Sets the character to mark the end of an input sequence. To parse each word token separately, for example, set this to Character a space - default is Character.EOF.

Parameters:

endOfSequenceChar -

main

public static void main(java.lang.String[] args)