package
ml
Type Members
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case class
ClassifierPerformance[N, DATA, F, G](data: F, retrieve: (DATA) ⇒ Boolean, relevant: (DATA) ⇒ Boolean)(implicit functor: Functor[F, DATA, (N, N, N, N), G], agg: Aggregatable[G, (N, N, N, N), (N, N, N, N)], field: Field[N]) extends Product with Serializable
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case class
ConfusionMatrix[T, CLASS, L, F, M, G, H](classifier: (T) ⇒ CLASS, data: F, labelExtractor: (T) ⇒ L, classes: IndexedSeq[CLASS])(implicit evidence$1: Order[CLASS], evidence$2: Order[L], la: LinearAlgebra[M, Int, Int, Double], finite: Finite[F, Int], functorF: Functor[F, T, (L, CLASS), G], functorG: Functor[G, (L, CLASS), L, H], sf: SetFrom[H, L], mr: MapReducible[G, (L, CLASS), Int, (Int, CLASS), Map[(Int, CLASS), Int]], mf: MapFrom[List[(L, Int)], L, Int]) extends Product with Serializable
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case class
GeneticAlgorithm[G <: HList, Z <: HList](populationSize: Int = 1000, numGenerations: Int = 100)(implicit species: Species[G], zipper: Aux[::[G, ::[G, HNil]], Z], mapperMix: Mapper[Mixer.type, Z], mapperMutate: Mapper[Mutator.type, Z]) extends Product with Serializable
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case class
GeneticAlgorithmLog[G](popLog: IndexedSeq[(G, Double)], mins: TreeMap[Int, Double], maxs: TreeMap[Int, Double], aves: TreeMap[Int, Double]) extends Product with Serializable
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class
HMMEdge[N] extends AnyRef
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case class
IdentityFeatureNormalizer[M](X: M)(implicit la: LinearAlgebra[M, Int, Int, Double]) extends Normalize[M] with Product with Serializable
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case class
KMeans[T, F, G, M](data: F, N: Int, featureExtractor: (T) ⇒ Seq[Double], normalizerMaker: (M) ⇒ Normalize[M], constructor: (Seq[Double]) ⇒ T, K: Int, iterations: Int)(implicit evidence$1: Eq[T], space: MetricSpace[M, Double], functor: Functor[F, T, Seq[Double], G], la: LinearAlgebra[M, Int, Int, Double], index: Indexed[G, Int, Seq[Double]], finite: Finite[F, Int]) extends (T) ⇒ Int with Product with Serializable
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case class
LatentSemanticAnalysis() extends Product with Serializable
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case class
LinearFeatureNormalizer[M](X: M)(implicit la: LinearAlgebra[M, Int, Int, Double]) extends Normalize[M] with Product with Serializable
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case class
LinearRegression[D, M](examples: Seq[D], numFeatures: Int, featureExtractor: (D) ⇒ Seq[Double], objectiveExtractor: (D) ⇒ Double, α: Double = 0.1, iterations: Int = 100)(implicit la: LinearAlgebra[M, Int, Int, Double]) extends Product with Serializable
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case class
LogisticRegression[D, M](examples: List[D], numObservations: Int, observationExtractor: (D) ⇒ List[Double], objectiveExtractor: (D) ⇒ Boolean, α: Double = 0.1, numIterations: Int = 100)(implicit la: LinearAlgebra[M, Int, Int, Double]) extends Product with Serializable
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case class
NaiveBayesClassifier[DATA, FEATURE, CLASS, F, G, N](data: F, featureRandomVariables: List[Distribution[FEATURE, N]], classRandomVariable: Distribution[CLASS, N], featureExtractor: (DATA) ⇒ List[FEATURE], classExtractor: (DATA) ⇒ CLASS)(implicit evidence$1: Order[FEATURE], evidence$2: Order[CLASS], evidence$3: Eq[CLASS], evidence$4: Field[N], evidence$5: Order[N], agg: Aggregatable[F, DATA, Map[(CLASS, String, FEATURE), N]], functor: Functor[F, DATA, CLASS, G], tal: Talliable[G, CLASS, N]) extends (DATA) ⇒ CLASS with Product with Serializable
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trait
Normalize[M] extends (Seq[Double]) ⇒ M
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case class
PCAFeatureNormalizer[M](cutoff: Double, X: M)(implicit la: LinearAlgebra[M, Int, Int, Double]) extends Normalize[M] with Product with Serializable
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trait
Species[G] extends AnyRef
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case class
ZScoreFeatureNormalizer[M](X: M)(implicit la: LinearAlgebra[M, Int, Int, Double]) extends Normalize[M] with Product with Serializable
Value Members
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object
KMeans extends Serializable
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object
Mater extends Poly1
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object
Mixer extends Poly1
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object
Mutator extends Poly1
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ClassifierPerformance computes measures of classification performance
They are:
* Precision * Recall * Specificity * Accuracy * F1
The (boolean) "classification task" is defined by two arguments:
1) predict: given a datum, determines whether the value is "in" the retrieved set 2) actual : given a datum, determines whether the value is *actually* "in" the retrieved set
See http://en.wikipedia.org/wiki/Precision_and_recall for more information.
http://en.wikipedia.org/wiki/F1_score