classify

Type Members

trait Classifier[L, -T] extends (T) ⇒ L

Represents a classifier from observations of type T to labels of type L.
Represents a classifier from observations of type T to labels of type L. Implementers should only need to implement score.
class LFMatrix[L, TF] extends NumericOps[LFMatrix[L, TF]] with Serializable

This stupidly named class is a Label-Feature Matrix, which is to say that it's a the weight matrix used by most of the classifier trainers.
This stupidly named class is a Label-Feature Matrix, which is to say that it's a the weight matrix used by most of the classifier trainers. It's basically a matrix with one row per label, and the rows are some Tensor type (TF). TF is a mnemonic for Feature Tensor.
L
label type
TF
feature tensor type

Annotations
@SerialVersionUID()
class LinearClassifier[L, TW, TL, TF] extends Classifier[L, TF] with Serializable

A LinearClassifier is a multi-class classifier with decision function: \hat y_i = \arg\max_y w_y^T x_i + b_y
A LinearClassifier is a multi-class classifier with decision function: \hat y_i = \arg\max_y w_y^T x_i + b_y

Annotations
@SerialVersionUID()
class NCA[L, T, M] extends Classifier[L, T]

dialogue 6/19/14
class NNetClassifier[L, T] extends Classifier[L, T]

A NeuralNetwork classifier uses a neural network to get unnormalize log probabilities for the scores of the classifier.
A NeuralNetwork classifier uses a neural network to get unnormalize log probabilities for the scores of the classifier. These are used to predict terms.
class NaiveBayes[L, W] extends Classifier[L, Counter[W, Double]] with Serializable

Implements a Naive-Bayes Classifer over bags of words.
Implements a Naive-Bayes Classifer over bags of words. It automatically trains itself given the collection c of learning examples.

Annotations
@SerialVersionUID()
case class TrainerParams(type: String = "Logistic", input: File = new java.io.File("train"), help: Boolean = false) extends Product with Serializable
class UnindexedLFMatrix[L, TF] extends NumericOps[UnindexedLFMatrix[L, TF]] with Serializable

This is the unindexed weights matrix: it acts as a tensor over the label types, rather than their indexed components
This is the unindexed weights matrix: it acts as a tensor over the label types, rather than their indexed components

Annotations
@SerialVersionUID()
class kNearestNeighbor[L, T, D] extends Classifier[L, T]

kNearestNeighbor 6/8/14

Value Members

object Classifier
object Initializers

nak 7/7/14
object LFMatrix extends Serializable
object LinearClassifier extends Serializable
object LogisticClassifier

A multi-class logistic/softmax/maxent classifier.
object LogisticClassifierFromCsv

This is an example app for creating a logistic classifier from data that is stored as string valued features and string valued labels, e.
This is an example app for creating a logistic classifier from data that is stored as string valued features and string valued labels, e.g.
verb=join,noun=board,prep=as,prep_obj=director,V verb=isIs,noun=chairman,prep=of,prep_obj=N.V.,N verb=named,noun=director,prep=of,prep_obj=conglomerate,N
These are examples from Ratnarparkhi's classic prepositional phrase attachment dataset, discussed in the following homework:
http://ata-s12.utcompling.com/assignments/classification
The homework includes pointers to the data and to Scala code for generating said features.
This example handles creating a feature index and getting the examples into the right data structures for training with the logistic regression classifier, which should serve as a useful example for creating features and classifiers using the API.
object NCA
object NNetClassifier
object NaiveBayes extends Serializable
object Perceptron
object SVM

Object for creating SupportVectorMachines
object Trainer extends App

Main program that builds a classifier from a breeze.data.SparseFeatureDataset.
Main program that builds a classifier from a breeze.data.SparseFeatureDataset. You can build a logistic classifier or an SVM, at the moment.
object UnindexedLFMatrix extends Serializable
object kNearestNeighbor

package classify

Type Members

trait Classifier[L, -T] extends (T) ⇒ L

class LFMatrix[L, TF] extends NumericOps[LFMatrix[L, TF]] with Serializable

class LinearClassifier[L, TW, TL, TF] extends Classifier[L, TF] with Serializable

class NCA[L, T, M] extends Classifier[L, T]

class NNetClassifier[L, T] extends Classifier[L, T]

class NaiveBayes[L, W] extends Classifier[L, Counter[W, Double]] with Serializable

case class TrainerParams(type: String = "Logistic", input: File = new java.io.File("train"), help: Boolean = false) extends Product with Serializable

class UnindexedLFMatrix[L, TF] extends NumericOps[UnindexedLFMatrix[L, TF]] with Serializable

class kNearestNeighbor[L, T, D] extends Classifier[L, T]

Value Members

object Classifier

object Initializers

object LFMatrix extends Serializable

object LinearClassifier extends Serializable

object LogisticClassifier

object LogisticClassifierFromCsv

object NCA

object NNetClassifier

object NaiveBayes extends Serializable

object Perceptron

object SVM

object Trainer extends App

object UnindexedLFMatrix extends Serializable

object kNearestNeighbor

Ungrouped