types
Class StaticUtils

java.lang.Object
  types.StaticUtils

public class StaticUtils
extends Object

Some methods that are useful to have in linear models.

Author:

kuzman

Constructor Summary

StaticUtils()

Method Summary

static void	add(double[] u, double[] v, double[] w, double d) u = v + d*w
static int	argmax(double[] v) computes arg max_i v[i]
static double	computeAccuracy(LinearClassifier h, ArrayList<ClassificationInstance> data) The accuracy of classifier h on the data set data.
static double	computeAccuracyS(LinearTagger h, ArrayList<SequenceInstance> data) The accuracy of classifier h on the data set data.
static double	dotProduct(double[] v, double[] w)
static double	dotProduct(SparseVector v, double[] w) the dot product between a sparse vector v and a dense vector w.
static double[]	exp(double[] vec) res = exp(vec)
static double[][]	exp(double[][] vec) res = exp(vec)
static double[][][]	exp(double[][][] vec) res = exp(vec)
static LinearTagger	loadTagger(String fname)
static SparseVector	lookupCollection(Collection<String> in, Alphabet a)
static void	plusEquals(double[] w, double[] v, double d) w <- w + d*v
static void	plusEquals(double[] w, SparseVector v) update: w = w + v
static void	plusEquals(double[] w, SparseVector v, double d) w <- w + d*v
static void	plusEquals(SparseVector w, SparseVector v) w <- w + v
static void	saveTagger(LinearTagger h, String fname)
static void	shuffle(ArrayList list, long seed) shuffle a list (intended as a list of instances) in place using seed as the random seed.
static ArrayList<ClassificationInstance>[]	split(ArrayList<ClassificationInstance> l, int splitAt)
static ArrayList<SequenceInstance>[]	splitS(ArrayList<SequenceInstance> l, int splitAt)
static double	sum(double[] probs)
static double	twoNormSquared(double[] w)

Methods inherited from class java.lang.Object

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

Constructor Detail

StaticUtils

public StaticUtils()

Method Detail

dotProduct

public static double dotProduct(SparseVector v,
                                double[] w)

the dot product between a sparse vector v and a dense vector w.

dotProduct

public static double dotProduct(double[] v,
                                double[] w)

shuffle

public static void shuffle(ArrayList list,
                           long seed)

shuffle a list (intended as a list of instances) in place using seed as the random seed.

computeAccuracyS

public static double computeAccuracyS(LinearTagger h,
                                      ArrayList<SequenceInstance> data)

The accuracy of classifier h on the data set data. This is just the number of correct tokens divided by the number of tokens.

computeAccuracy

public static double computeAccuracy(LinearClassifier h,
                                     ArrayList<ClassificationInstance> data)

The accuracy of classifier h on the data set data. This is just the number of correct instance divided by the number of instances.

split

public static ArrayList<ClassificationInstance>[] split(ArrayList<ClassificationInstance> l,
                                                        int splitAt)

splitS

public static ArrayList<SequenceInstance>[] splitS(ArrayList<SequenceInstance> l,
                                                   int splitAt)

plusEquals

public static void plusEquals(double[] w,
                              SparseVector v)

update: w = w + v

exp

public static double[] exp(double[] vec)

res = exp(vec)

exp

public static double[][] exp(double[][] vec)

res = exp(vec)

exp

public static double[][][] exp(double[][][] vec)

res = exp(vec)

twoNormSquared

public static double twoNormSquared(double[] w)

Parameters:

w -

Returns:

sum_i w[i]^2

sum

public static double sum(double[] probs)

Parameters:

probs -

Returns:

sum_i probs[i]

plusEquals

public static void plusEquals(SparseVector w,
                              SparseVector v)

w <- w + v

plusEquals

public static void plusEquals(double[] w,
                              SparseVector v,
                              double d)

w <- w + d*v

plusEquals

public static void plusEquals(double[] w,
                              double[] v,
                              double d)

w <- w + d*v

add

public static void add(double[] u,
                       double[] v,
                       double[] w,
                       double d)

u = v + d*w

argmax

public static int argmax(double[] v)

computes arg max_i v[i]

lookupCollection

public static SparseVector lookupCollection(Collection<String> in,
                                            Alphabet a)

saveTagger

public static void saveTagger(LinearTagger h,
                              String fname)
                       throws IOException

Throws:

IOException

loadTagger

public static LinearTagger loadTagger(String fname)
                               throws IOException,
                                      ClassNotFoundException

Throws:

IOException

ClassNotFoundException