| Package | Description |
|---|---|
| com.aliasi.classify |
Classes for classifying data and evaluation.
|
| com.aliasi.crf |
Classes and interfaces for conditional random fields.
|
| com.aliasi.dca |
Classes for fitting and running discrete choice analysis (DCA) models.
|
| com.aliasi.features |
Classes for extracting feature vectors from objects and parsing
objects for feature handlers.
|
| com.aliasi.matrix |
Classes for matrices and vectors.
|
| com.aliasi.stats |
Classes for handling basic statical distributions and estimators.
|
| com.aliasi.test.unit.matrix |
| Modifier and Type | Method and Description |
|---|---|
Proximity<Vector> |
KnnClassifier.proximity()
Returns the proximity measure for this KNN classifier.
|
| Modifier and Type | Method and Description |
|---|---|
ScoredClassification |
BigVectorClassifier.classify(Vector x)
Return a scored classification consisting of the top results
for the specified vector input.
|
ConditionalClassification |
LogisticRegressionClassifier.classifyVector(Vector v)
Returns the classification of the specified vector using the
logistic regression model underlying this classifier.
|
| Constructor and Description |
|---|
BigVectorClassifier(Vector[] termVectors,
int maxResults)
Construct a big vector classifier with the specified term
vectors, maximum number of results, and categories equal to the
string representations of the category identifiers.
|
BigVectorClassifier(Vector[] termVectors,
String[] categories,
int maxResults)
Construct a big vector classifier with the specified term
vectors, categories, and maximum number of results.
|
| Constructor and Description |
|---|
KnnClassifier(FeatureExtractor<? super E> featureExtractor,
int k,
Distance<Vector> distance)
Construct a k-nearest-neighbor classifier based on the
specified feature extractor, specified maximum number of
neighbors, and specified distance function.
|
KnnClassifier(FeatureExtractor<? super E> extractor,
int k,
Proximity<Vector> proximity,
boolean weightByProximity)
Construct a k-nearest-neighbor classifier based on the
specified feature extractor, specified maximum number of
neighbors, specified proximity function, and boolean flag
indicating whether or not to weight nearest neighbors by proximity
during classification.
|
| Modifier and Type | Method and Description |
|---|---|
Vector[] |
ChainCrf.coefficients()
Return the coefficient vectors for this CRF.
|
| Constructor and Description |
|---|
ChainCrf(String[] tags,
boolean[] legalTagStarts,
boolean[] legalTagEnds,
boolean[][] legalTagTransitions,
Vector[] coefficients,
SymbolTable featureSymbolTable,
ChainCrfFeatureExtractor<E> featureExtractor,
boolean addInterceptFeature)
Construct a conditional random field from the specified tags,
feature vector coefficients, symbol table for feature, feature
extractors and flag indicating whether to add intercepts or
not.
|
ChainCrf(String[] tags,
Vector[] coefficients,
SymbolTable featureSymbolTable,
ChainCrfFeatureExtractor<E> featureExtractor,
boolean addInterceptFeature)
Construct a conditional random field from the specified tags,
feature vector coefficients, symbol table for feature, feature
extractors and flag indicating whether to add intercepts or
not.
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
DiscreteChooser.coefficients()
Return an unmodifiable view of the coefficients
underlying this discrete chooser.
|
| Modifier and Type | Method and Description |
|---|---|
double[] |
DiscreteChooser.choiceLogProbs(Vector[] choices)
Return an array of (natural) log choice probabilities
corresponding to the input array of choices.
|
double[] |
DiscreteChooser.choiceProbs(Vector[] choices)
Return an array of choice probabilities corresponding to the
input array of choices.
|
int |
DiscreteChooser.choose(Vector[] choices)
Returns the most likely choice among the choices in the
specified array of vectors.
|
static DiscreteChooser |
DiscreteChooser.estimate(Vector[][] alternativess,
int[] choices,
RegressionPrior prior,
int priorBlockSize,
AnnealingSchedule annealingSchedule,
double minImprovement,
int minEpochs,
int maxEpochs,
Reporter reporter)
Returns a discrete choice model estimated from the specified
training data, prior, and learning parameters.
|
| Constructor and Description |
|---|
DiscreteChooser(Vector coefficients)
Construct a discrete chooser with the specified
coefficient vector.
|
| Modifier and Type | Method and Description |
|---|---|
static Vector |
Features.toVector(Map<String,? extends Number> featureVector,
SymbolTable table,
int numDimensions,
boolean addIntercept)
Convert the specified feature vector into a sparse float vector using
the specified symbol table to encode features as integers.
|
static Vector |
Features.toVectorAddSymbols(Map<String,? extends Number> featureVector,
SymbolTable table,
int numDimensions,
boolean addIntercept)
Convert the specified feature vector into a sparse float vector
using the specified symbol table to encode features as
integers, adding features to the symbol table if necessary.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractVector
An
AbstractVector implements most of a vector's
functionality in terms of methods for dimensionality and values. |
class |
DenseVector
A
DenseVector is a vector implementation suitable for
vectors with primarily non-zero values. |
class |
SparseFloatVector
A
SparseFloatVector implements an immutable sparse
vector with values represented as single-precision floating point
numbers. |
| Modifier and Type | Method and Description |
|---|---|
Vector |
SparseFloatVector.add(Vector v) |
Vector |
Vector.add(Vector v)
Returns a new vector that is the reuslt of adding this vector
to the specified vector.
|
Vector |
AbstractVector.add(Vector v)
Returns the result of adding the specified vector to this
vector.
|
Vector |
DenseVector.add(Vector v) |
Vector |
Matrix.columnVector(int column)
Returns the vector of values in the specified column.
|
Vector |
AbstractMatrix.columnVector(int column)
Returns the specified column of this matrix.
|
Vector |
Matrix.rowVector(int row)
Returns the vector of values in the specified row.
|
Vector |
AbstractMatrix.rowVector(int row)
Returns the specified row of this matrix.
|
static Vector |
Matrices.unmodifiableVector(Vector v)
Returns an unmodifiable view of the specified vector.
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
SparseFloatVector.add(Vector v) |
Vector |
Vector.add(Vector v)
Returns a new vector that is the reuslt of adding this vector
to the specified vector.
|
Vector |
AbstractVector.add(Vector v)
Returns the result of adding the specified vector to this
vector.
|
Vector |
DenseVector.add(Vector v) |
double |
SparseFloatVector.cosine(Vector v) |
double |
Vector.cosine(Vector v)
Returns the cosine product of this vector with the specified
vector.
|
double |
AbstractVector.cosine(Vector v)
Returns the cosine of this vector and the specified vector.
|
double |
TaxicabDistance.distance(Vector v1,
Vector v2)
Returns the taxicab distance between the specified pair
of vectors.
|
double |
CosineDistance.distance(Vector v1,
Vector v2)
Returns one minus the proximity of the vectors.
|
double |
EuclideanDistance.distance(Vector v1,
Vector v2)
Returns the Euclidean distance between the specified pair
of vectors.
|
double |
MinkowskiDistance.distance(Vector v1,
Vector v2)
Returns the Minkowski distance between the specified pair
of vectors.
|
double |
SparseFloatVector.dotProduct(Vector v) |
double |
Vector.dotProduct(Vector v)
Returns the dot product of this vector with the specified
vector.
|
double |
AbstractVector.dotProduct(Vector v)
Returns the dot product (inner product) of this vector with the
specified vector.
|
double |
DenseVector.dotProduct(Vector v) |
void |
SparseFloatVector.increment(double scale,
Vector v)
This operation is not supported for sparse vectors.
|
void |
Vector.increment(double scale,
Vector v)
Adds the specified vector multiplied by the specified scalar to
this vector.
|
void |
AbstractVector.increment(double scale,
Vector v)
Adds the specified vector multiplied by the specified scalar to
this vector.
|
void |
DenseVector.increment(double scale,
Vector v)
Adds the specified vector multiplied by the specified scalar to
this vector.
|
double |
CosineDistance.proximity(Vector v1,
Vector v2)
Returns the cosine between the specified vectors.
|
double |
KernelFunction.proximity(Vector v1,
Vector v2)
Return the result of applying the kernel function to the
specified pair of vectors.
|
double |
GaussianRadialBasisKernel.proximity(Vector v1,
Vector v2)
Returns the result of applying this Guassian radial basis
kernel to the specified vectors.
|
double |
HyperbolicTangentKernel.proximity(Vector v1,
Vector v2)
Returns the result of applying the hyperbolic tangent kernel
function to to the specified vectors.
|
double |
DotProductKernel.proximity(Vector v1,
Vector v2)
Returns the dot product of the input vectors.
|
double |
PolynomialKernel.proximity(Vector v1,
Vector v2)
Returns the result of applying the polynomial kernel of
this class's degree to the specified vectors.
|
static double[] |
Matrices.toArray(Vector v)
Returns the content of the specified vector as an array.
|
static Vector |
Matrices.unmodifiableVector(Vector v)
Returns an unmodifiable view of the specified vector.
|
| Constructor and Description |
|---|
DenseVector(Vector v)
Cosntruct a deep copy of the specified vector as a dense vector.
|
| Modifier and Type | Method and Description |
|---|---|
Vector[] |
LogisticRegression.weightVectors()
Returns an array of views of the weight vectors used for this
regression model.
|
| Modifier and Type | Method and Description |
|---|---|
double[] |
LogisticRegression.classify(Vector x)
Returns an array of conditional probabilities indexed by
outcomes for the specified input vector.
|
void |
LogisticRegression.classify(Vector x,
double[] ysHat)
Fills the specified array with the conditional probabilities
indexed by outcomes for the specified input vector.
|
static LogisticRegression |
LogisticRegression.estimate(Vector[] xs,
int[] cs,
RegressionPrior prior,
AnnealingSchedule annealingSchedule,
Reporter reporter,
double minImprovement,
int minEpochs,
int maxEpochs)
Estimate a logistic regression model from the specified input
data using the specified Gaussian prior, initial learning rate
and annealing rate, the minimum improvement per epoch, the
minimum and maximum number of estimation epochs, and a
reporter.
|
static LogisticRegression |
LogisticRegression.estimate(Vector[] xs,
int[] cs,
RegressionPrior prior,
int blockSize,
LogisticRegression hotStart,
AnnealingSchedule annealingSchedule,
double minImprovement,
int rollingAverageSize,
int minEpochs,
int maxEpochs,
ObjectHandler<LogisticRegression> handler,
Reporter reporter)
Estimate a logistic regression model from the specified input
data using the specified Gaussian prior, initial learning rate
and annealing rate, the minimum improvement per epoch, the
minimum and maximum number of estimation epochs, and a
reporter.
|
static double |
LogisticRegression.log2Likelihood(Vector[] inputs,
int[] cats,
LogisticRegression regression)
Returns the log (base 2) likelihood of the specified inputs
with the specified categories using the specified regression
model.
|
double |
RegressionPrior.log2Prior(Vector beta)
Returns the log (base 2) prior density for a specified
coefficient vector (up to an additive constant).
|
double |
RegressionPrior.log2Prior(Vector[] betas)
Returns the log (base 2) prior density for the specified
array of coefficient vectors (up to an additive constant).
|
| Constructor and Description |
|---|
LogisticRegression(Vector weightVector)
Construct a binomial logistic regression model with the
specified parameter vector.
|
LogisticRegression(Vector[] weightVectors)
Construct a multinomial logistic regression model with
the specified weight vectors.
|
| Modifier and Type | Class and Description |
|---|---|
static class |
AbstractVectorTest.MutVect |
static class |
AbstractVectorTest.Vector1 |
static class |
AbstractVectorTest.Vector2 |
Copyright © 2016 Alias-i, Inc.. All rights reserved.