- CART - Class in smile.base.cart
-
Classification and regression tree.
- CART(Formula, StructType, StructField, Node, double[]) - Constructor for class smile.base.cart.CART
-
Constructor.
- CART(DataFrame, StructField, int, int, int, int, int[], int[][]) - Constructor for class smile.base.cart.CART
-
Constructor.
- CentroidClustering<T,U> - Class in smile.clustering
-
In centroid-based clustering, clusters are represented by a central vector,
which may not necessarily be a member of the data set.
- CentroidClustering(double, T[], int[]) - Constructor for class smile.clustering.CentroidClustering
-
Constructor.
- centroids - Variable in class smile.clustering.CentroidClustering
-
The centroids of each cluster.
- centroids() - Method in class smile.vq.BIRCH
-
Returns the cluster centroids of leaf nodes.
- CLARANS<T> - Class in smile.clustering
-
Clustering Large Applications based upon RANdomized Search.
- CLARANS(double, T[], int[], Distance<T>) - Constructor for class smile.clustering.CLARANS
-
Constructor.
- classification(int, T[], int[], BiFunction<T[], int[], M>) - Static method in interface smile.validation.Bootstrap
-
Runs classification bootstrap validation.
- classification(int, Formula, DataFrame, BiFunction<Formula, DataFrame, M>) - Static method in interface smile.validation.Bootstrap
-
Runs classification bootstrap validation.
- classification(int, T[], int[], BiFunction<T[], int[], M>) - Static method in interface smile.validation.CrossValidation
-
Runs classification cross validation.
- classification(int, Formula, DataFrame, BiFunction<Formula, DataFrame, M>) - Static method in interface smile.validation.CrossValidation
-
Runs classification cross validation.
- classification(T[], int[], BiFunction<T[], int[], M>) - Static method in interface smile.validation.LOOCV
-
Runs leave-one-out cross validation tests.
- classification(Formula, DataFrame, BiFunction<Formula, DataFrame, DataFrameClassifier>) - Static method in interface smile.validation.LOOCV
-
Runs leave-one-out cross validation tests.
- ClassificationMetric - Interface in smile.validation.metric
-
An abstract interface to measure the classification performance.
- ClassificationMetrics - Class in smile.validation
-
The classification validation metrics.
- ClassificationMetrics(double, double, int, int, double) - Constructor for class smile.validation.ClassificationMetrics
-
Constructor.
- ClassificationMetrics(double, double, int, int, double, double) - Constructor for class smile.validation.ClassificationMetrics
-
Constructor of multiclass soft classifier validation.
- ClassificationMetrics(double, double, int, int, double, double, double, double, double, double) - Constructor for class smile.validation.ClassificationMetrics
-
Constructor of binary classifier validation.
- ClassificationMetrics(double, double, int, int, double, double, double, double, double, double, double, double) - Constructor for class smile.validation.ClassificationMetrics
-
Constructor of binary soft classifier validation.
- ClassificationMetrics(double, double, int, int, double, double, double, double, double, double, double, double, double) - Constructor for class smile.validation.ClassificationMetrics
-
Constructor.
- ClassificationValidation<M> - Class in smile.validation
-
Classification model validation results.
- ClassificationValidation(M, int[], int[], double, double) - Constructor for class smile.validation.ClassificationValidation
-
Constructor.
- ClassificationValidation(M, int[], int[], double[][], double, double) - Constructor for class smile.validation.ClassificationValidation
-
Constructor of soft classifier validation.
- ClassificationValidations<M> - Class in smile.validation
-
Classification model validation results.
- ClassificationValidations(List<ClassificationValidation<M>>) - Constructor for class smile.validation.ClassificationValidations
-
Constructor.
- Classifier<T> - Interface in smile.classification
-
A classifier assigns an input object into one of a given number of categories.
- ClassLabels - Class in smile.classification
-
Map arbitrary class labels to [0, k), where k is the number of classes.
- ClassLabels(int, int[], IntSet) - Constructor for class smile.classification.ClassLabels
-
Constructor.
- clear() - Method in class smile.base.cart.CART
-
Clear the workspace of building tree.
- clear(double) - Method in class smile.vq.NeuralMap
-
Removes staled neurons and the edges beyond lifetime.
- clone() - Method in class smile.neighbor.lsh.Probe
-
- clustering(double[][], double[][], int[], int[]) - Method in class smile.clustering.BBDTree
-
Given k cluster centroids, this method assigns data to nearest centroids.
- ClusteringMetric - Interface in smile.validation.metric
-
An abstract interface to measure the clustering performance.
- coefficients() - Method in class smile.classification.LogisticRegression.Binomial
-
Returns an array of size (p+1) containing the linear weights
of binary logistic regression, where p is the dimension of
feature vectors.
- coefficients() - Method in class smile.classification.LogisticRegression.Multinomial
-
Returns a 2d-array of size (k-1) x (p+1), containing the linear weights
of multi-class logistic regression, where k is the number of classes
and p is the dimension of feature vectors.
- coefficients() - Method in class smile.classification.Maxent.Binomial
-
Returns an array of size (p+1) containing the linear weights
of binary logistic regression, where p is the dimension of
feature vectors.
- coefficients() - Method in class smile.classification.Maxent.Multinomial
-
Returns a 2d-array of size (k-1) x (p+1), containing the linear weights
of multi-class logistic regression, where k is the number of classes
and p is the dimension of feature vectors.
- coefficients() - Method in class smile.classification.SparseLogisticRegression.Binomial
-
Returns an array of size (p+1) containing the linear weights
of binary logistic regression, where p is the dimension of
feature vectors.
- coefficients() - Method in class smile.classification.SparseLogisticRegression.Multinomial
-
Returns a 2d-array of size (k-1) x (p+1), containing the linear weights
of multi-class logistic regression, where k is the number of classes
and p is the dimension of feature vectors.
- coefficients() - Method in class smile.glm.GLM
-
Returns an array of size (p+1) containing the linear weights
of binary logistic regression, where p is the dimension of
feature vectors.
- coefficients() - Method in class smile.regression.LinearModel
-
Returns the linear coefficients (without intercept).
- comparator - Static variable in class smile.base.cart.Split
-
- compareTo(CentroidClustering<T, U>) - Method in class smile.clustering.CentroidClustering
-
- compareTo(MEC<T>) - Method in class smile.clustering.MEC
-
- compareTo(PrH) - Method in class smile.neighbor.lsh.PrH
-
- compareTo(Probe) - Method in class smile.neighbor.lsh.Probe
-
- compareTo(PrZ) - Method in class smile.neighbor.lsh.PrZ
-
- compareTo(Neighbor<K, V>) - Method in class smile.neighbor.Neighbor
-
- compareTo(Neuron) - Method in class smile.vq.hebb.Neuron
-
- CompleteLinkage - Class in smile.clustering.linkage
-
Complete linkage.
- CompleteLinkage(double[][]) - Constructor for class smile.clustering.linkage.CompleteLinkage
-
Constructor.
- CompleteLinkage(int, float[]) - Constructor for class smile.clustering.linkage.CompleteLinkage
-
Constructor.
- components - Variable in class smile.projection.ICA
-
The independent components (row-wise).
- computeGradient(double[]) - Method in class smile.base.mlp.Layer
-
Computes the parameter gradient for a sample of (mini-)batch.
- computeGradientUpdate(double[], double, double, double) - Method in class smile.base.mlp.Layer
-
Computes the parameter gradient and update the weights.
- computeOutputGradient(double[], double) - Method in class smile.base.mlp.OutputLayer
-
Compute the network output gradient.
- confidence - Variable in class smile.association.AssociationRule
-
The confidence value.
- confusion - Variable in class smile.validation.ClassificationValidation
-
The confusion matrix.
- ConfusionMatrix - Class in smile.validation.metric
-
The confusion matrix of truth and predictions.
- ConfusionMatrix(int[][]) - Constructor for class smile.validation.metric.ConfusionMatrix
-
Constructor.
- consequent - Variable in class smile.association.AssociationRule
-
Consequent itemset.
- coordinates - Variable in class smile.manifold.IsoMap
-
The coordinate matrix in embedding space.
- coordinates - Variable in class smile.manifold.LaplacianEigenmap
-
The coordinate matrix in embedding space.
- coordinates - Variable in class smile.manifold.LLE
-
The coordinate matrix in embedding space.
- coordinates - Variable in class smile.manifold.TSNE
-
The coordinate matrix in embedding space.
- coordinates - Variable in class smile.manifold.UMAP
-
The coordinate matrix in embedding space.
- coordinates - Variable in class smile.mds.IsotonicMDS
-
The coordinates.
- coordinates - Variable in class smile.mds.MDS
-
The principal coordinates.
- coordinates - Variable in class smile.mds.SammonMapping
-
The coordinates.
- cor(double[][]) - Static method in class smile.projection.PCA
-
Fits principal component analysis with correlation matrix.
- Cost - Enum in smile.base.mlp
-
Neural network cost function.
- cost() - Method in class smile.base.mlp.OutputLayer
-
Returns the cost function of neural network.
- count() - Method in class smile.base.cart.DecisionNode
-
Returns the number of node samples in each class.
- counter - Variable in class smile.vq.hebb.Neuron
-
The local counter variable (e.g.
- cov - Variable in class smile.regression.GaussianProcessRegression.JointPrediction
-
The covariance matrix of joint predictive distribution at query points.
- cov(double[], int) - Static method in interface smile.timeseries.TimeSeries
-
Autocovariance function.
- CoverTree<E> - Class in smile.neighbor
-
Cover tree is a data structure for generic nearest neighbor search, which
is especially efficient in spaces with small intrinsic dimension.
- CoverTree(E[], Metric<E>) - Constructor for class smile.neighbor.CoverTree
-
Constructor.
- CoverTree(E[], Metric<E>, double) - Constructor for class smile.neighbor.CoverTree
-
Constructor.
- CRF - Class in smile.sequence
-
First-order linear conditional random field.
- CRF(StructType, RegressionTree[][], double) - Constructor for class smile.sequence.CRF
-
Constructor.
- CRFLabeler<T> - Class in smile.sequence
-
First-order CRF sequence labeler.
- CRFLabeler(CRF, Function<T, Tuple>) - Constructor for class smile.sequence.CRFLabeler
-
Constructor.
- crossentropy - Variable in class smile.validation.ClassificationMetrics
-
The cross entropy on validation data.
- CrossEntropy - Interface in smile.validation.metric
-
Cross entropy generalizes the log loss metric to multiclass problems.
- CrossValidation - Interface in smile.validation
-
Cross-validation is a technique for assessing how the results of a
statistical analysis will generalize to an independent data set.
- f(double[]) - Method in interface smile.base.mlp.ActivationFunction
-
The output function.
- f(double[]) - Method in class smile.base.mlp.HiddenLayer
-
- f(double[]) - Method in class smile.base.mlp.Layer
-
The activation or output function.
- f(double[]) - Method in enum smile.base.mlp.OutputFunction
-
The output function.
- f(double[]) - Method in class smile.base.mlp.OutputLayer
-
- f(T) - Method in class smile.base.rbf.RBF
-
The activation function.
- f(double[]) - Method in class smile.base.svm.LinearKernelMachine
-
Returns the value of decision function.
- f(int[]) - Method in class smile.base.svm.LinearKernelMachine
-
Returns the value of decision function.
- f(SparseArray) - Method in class smile.base.svm.LinearKernelMachine
-
Returns the value of decision function.
- f(double) - Method in class smile.projection.ica.Exp
-
- f(double) - Method in class smile.projection.ica.Kurtosis
-
- f(double) - Method in class smile.projection.ica.LogCosh
-
- f1 - Variable in class smile.validation.ClassificationMetrics
-
The F-1 score on validation data.
- F1 - Static variable in class smile.validation.metric.FScore
-
The F_1 score, the harmonic mean of precision and recall.
- F2 - Static variable in class smile.validation.metric.FScore
-
The F_2 score, which weighs recall higher than precision.
- Fallout - Class in smile.validation.metric
-
Fall-out, false alarm rate, or false positive rate (FPR)
- Fallout() - Constructor for class smile.validation.metric.Fallout
-
- falseChild() - Method in class smile.base.cart.InternalNode
-
Returns the false branch child.
- FDR - Class in smile.validation.metric
-
The false discovery rate (FDR) is ratio of false positives
to combined true and false positives, which is actually 1 - precision.
- FDR() - Constructor for class smile.validation.metric.FDR
-
- feature() - Method in class smile.base.cart.InternalNode
-
Returns the split feature.
- FeatureRanking - Interface in smile.feature
-
Univariate feature ranking metric.
- features - Variable in class smile.sequence.CRFLabeler
-
The feature function.
- FeatureTransform - Interface in smile.feature
-
Feature transformation.
- FHalf - Static variable in class smile.validation.metric.FScore
-
The F_0.5 score, which weighs recall lower than precision.
- findBestSplit(LeafNode, int, int, boolean[]) - Method in class smile.base.cart.CART
-
Finds the best attribute to split on a set of samples.
- findBestSplit(LeafNode, int, double, int, int) - Method in class smile.base.cart.CART
-
Finds the best split for given column.
- findBestSplit(LeafNode, int, double, int, int) - Method in class smile.classification.DecisionTree
-
- findBestSplit(LeafNode, int, double, int, int) - Method in class smile.regression.RegressionTree
-
- fit(double[][], int) - Static method in class smile.base.rbf.RBF
-
Learns Gaussian RBF function and centers from data.
- fit(double[][], int, int) - Static method in class smile.base.rbf.RBF
-
Learns Gaussian RBF function and centers from data.
- fit(double[][], int, double) - Static method in class smile.base.rbf.RBF
-
Learns Gaussian RBF function and centers from data.
- fit(T[], Metric<T>, int) - Static method in class smile.base.rbf.RBF
-
Learns Gaussian RBF function and centers from data.
- fit(T[], Metric<T>, int, int) - Static method in class smile.base.rbf.RBF
-
Learns Gaussian RBF function and centers from data.
- fit(T[], Metric<T>, int, double) - Static method in class smile.base.rbf.RBF
-
Learns Gaussian RBF function and centers from data.
- fit(T[], int[]) - Method in class smile.base.svm.LASVM
-
Trains the model.
- fit(T[], int[], int) - Method in class smile.base.svm.LASVM
-
Trains the model.
- fit(T[], double[]) - Method in class smile.base.svm.SVR
-
Fits a epsilon support vector regression model.
- fit(Formula, DataFrame) - Static method in class smile.classification.AdaBoost
-
Fits a AdaBoost model.
- fit(Formula, DataFrame, Properties) - Static method in class smile.classification.AdaBoost
-
Fits a AdaBoost model.
- fit(Formula, DataFrame, int, int, int, int) - Static method in class smile.classification.AdaBoost
-
Fits a AdaBoost model.
- fit(int[]) - Static method in class smile.classification.ClassLabels
-
Learns the class label mapping from samples.
- fit(BaseVector) - Static method in class smile.classification.ClassLabels
-
Learns the class label mapping from samples.
- fit(Formula, DataFrame) - Static method in class smile.classification.DecisionTree
-
Learns a classification tree.
- fit(Formula, DataFrame, Properties) - Static method in class smile.classification.DecisionTree
-
Learns a classification tree.
- fit(Formula, DataFrame, SplitRule, int, int, int) - Static method in class smile.classification.DecisionTree
-
Learns a classification tree.
- fit(Formula, DataFrame) - Static method in class smile.classification.FLD
-
Learn Fisher's linear discriminant.
- fit(Formula, DataFrame, Properties) - Static method in class smile.classification.FLD
-
Learn Fisher's linear discriminant.
- fit(double[][], int[]) - Static method in class smile.classification.FLD
-
Learn Fisher's linear discriminant.
- fit(double[][], int[], int, double) - Static method in class smile.classification.FLD
-
Learn Fisher's linear discriminant.
- fit(Formula, DataFrame) - Static method in class smile.classification.GradientTreeBoost
-
Fits a gradient tree boosting for classification.
- fit(Formula, DataFrame, Properties) - Static method in class smile.classification.GradientTreeBoost
-
Fits a gradient tree boosting for classification.
- fit(Formula, DataFrame, int, int, int, int, double, double) - Static method in class smile.classification.GradientTreeBoost
-
Fits a gradient tree boosting for classification.
- fit(double[], int[]) - Static method in class smile.classification.IsotonicRegressionScaling
-
Trains the Isotonic Regression scaling.
- fit(T[], int[], Distance<T>) - Static method in class smile.classification.KNN
-
Learn the 1-NN classifier.
- fit(T[], int[], int, Distance<T>) - Static method in class smile.classification.KNN
-
Learn the K-NN classifier.
- fit(double[][], int[]) - Static method in class smile.classification.KNN
-
Learn the 1-NN classifier.
- fit(double[][], int[], int) - Static method in class smile.classification.KNN
-
Learn the K-NN classifier.
- fit(Formula, DataFrame) - Static method in class smile.classification.LDA
-
Learns linear discriminant analysis.
- fit(Formula, DataFrame, Properties) - Static method in class smile.classification.LDA
-
Learns linear discriminant analysis.
- fit(double[][], int[]) - Static method in class smile.classification.LDA
-
Learns linear discriminant analysis.
- fit(double[][], int[], Properties) - Static method in class smile.classification.LDA
-
Learns linear discriminant analysis.
- fit(double[][], int[], double[], double) - Static method in class smile.classification.LDA
-
Learns linear discriminant analysis.
- fit(Formula, DataFrame) - Static method in class smile.classification.LogisticRegression
-
Fits logistic regression.
- fit(Formula, DataFrame, Properties) - Static method in class smile.classification.LogisticRegression
-
Fits logistic regression.
- fit(double[][], int[]) - Static method in class smile.classification.LogisticRegression
-
Fits logistic regression.
- fit(double[][], int[], Properties) - Static method in class smile.classification.LogisticRegression
-
Fits logistic regression.
- fit(double[][], int[], double, double, int) - Static method in class smile.classification.LogisticRegression
-
Fits logistic regression.
- fit(int, int[][], int[]) - Static method in class smile.classification.Maxent
-
Learn maximum entropy classifier.
- fit(int, int[][], int[], Properties) - Static method in class smile.classification.Maxent
-
Learn maximum entropy classifier.
- fit(int, int[][], int[], double, double, int) - Static method in class smile.classification.Maxent
-
Learn maximum entropy classifier.
- fit(T[], int[], BiFunction<T[], int[], Classifier<T>>) - Static method in class smile.classification.OneVersusOne
-
Fits a multi-class model with binary classifiers.
- fit(T[], int[], int, int, BiFunction<T[], int[], Classifier<T>>) - Static method in class smile.classification.OneVersusOne
-
Fits a multi-class model with binary classifiers.
- fit(Formula, DataFrame, BiFunction<Formula, DataFrame, DataFrameClassifier>) - Static method in class smile.classification.OneVersusOne
-
Fits a multi-class model with binary data frame classifiers.
- fit(T[], int[], BiFunction<T[], int[], Classifier<T>>) - Static method in class smile.classification.OneVersusRest
-
Fits a multi-class model with binary classifiers.
- fit(T[], int[], int, int, BiFunction<T[], int[], Classifier<T>>) - Static method in class smile.classification.OneVersusRest
-
Fits a multi-class model with binary classifiers.
- fit(Formula, DataFrame, BiFunction<Formula, DataFrame, DataFrameClassifier>) - Static method in class smile.classification.OneVersusRest
-
Fits a multi-class model with binary data frame classifiers.
- fit(double[], int[]) - Static method in class smile.classification.PlattScaling
-
Trains the Platt scaling.
- fit(double[], int[], int) - Static method in class smile.classification.PlattScaling
-
Trains the Platt scaling.
- fit(Classifier<T>, T[], int[]) - Static method in class smile.classification.PlattScaling
-
Fits Platt Scaling to estimate posteriori probabilities.
- fit(Formula, DataFrame) - Static method in class smile.classification.QDA
-
Learns quadratic discriminant analysis.
- fit(Formula, DataFrame, Properties) - Static method in class smile.classification.QDA
-
Learns quadratic discriminant analysis.
- fit(double[][], int[]) - Static method in class smile.classification.QDA
-
Learn quadratic discriminant analysis.
- fit(double[][], int[], Properties) - Static method in class smile.classification.QDA
-
Learns quadratic discriminant analysis.
- fit(double[][], int[], double[], double) - Static method in class smile.classification.QDA
-
Learn quadratic discriminant analysis.
- fit(Formula, DataFrame) - Static method in class smile.classification.RandomForest
-
Fits a random forest for classification.
- fit(Formula, DataFrame, Properties) - Static method in class smile.classification.RandomForest
-
Fits a random forest for classification.
- fit(Formula, DataFrame, int, int, SplitRule, int, int, int, double) - Static method in class smile.classification.RandomForest
-
Fits a random forest for classification.
- fit(Formula, DataFrame, int, int, SplitRule, int, int, int, double, int[]) - Static method in class smile.classification.RandomForest
-
Fits a random forest for regression.
- fit(Formula, DataFrame, int, int, SplitRule, int, int, int, double, int[], LongStream) - Static method in class smile.classification.RandomForest
-
Fits a random forest for classification.
- fit(T[], int[], RBF<T>[]) - Static method in class smile.classification.RBFNetwork
-
Fits a RBF network.
- fit(T[], int[], RBF<T>[], boolean) - Static method in class smile.classification.RBFNetwork
-
Fits a RBF network.
- fit(Formula, DataFrame) - Static method in class smile.classification.RDA
-
Learns regularized discriminant analysis.
- fit(Formula, DataFrame, Properties) - Static method in class smile.classification.RDA
-
Learns regularized discriminant analysis.
- fit(double[][], int[], Properties) - Static method in class smile.classification.RDA
-
Learns regularized discriminant analysis.
- fit(double[][], int[], double) - Static method in class smile.classification.RDA
-
Learn regularized discriminant analysis.
- fit(double[][], int[], double, double[], double) - Static method in class smile.classification.RDA
-
Learn regularized discriminant analysis.
- fit(SparseDataset, int[]) - Static method in class smile.classification.SparseLogisticRegression
-
Fits logistic regression.
- fit(SparseDataset, int[], Properties) - Static method in class smile.classification.SparseLogisticRegression
-
Fits logistic regression.
- fit(SparseDataset, int[], double, double, int) - Static method in class smile.classification.SparseLogisticRegression
-
Fits logistic regression.
- fit(double[][], int[], double, double) - Static method in class smile.classification.SVM
-
Fits a binary-class linear SVM.
- fit(int[][], int[], int, double, double) - Static method in class smile.classification.SVM
-
Fits a binary-class linear SVM of binary sparse data.
- fit(SparseArray[], int[], int, double, double) - Static method in class smile.classification.SVM
-
Fits a binary-class linear SVM.
- fit(T[], int[], MercerKernel<T>, double, double) - Static method in class smile.classification.SVM
-
Fits a binary-class SVM.
- fit(T[], Distance<T>, int) - Static method in class smile.clustering.CLARANS
-
Clustering data into k clusters.
- fit(T[], Distance<T>, int, int) - Static method in class smile.clustering.CLARANS
-
Constructor.
- fit(double[][], int, double) - Static method in class smile.clustering.DBSCAN
-
Clustering the data with KD-tree.
- fit(T[], Distance<T>, int, double) - Static method in class smile.clustering.DBSCAN
-
Clustering the data.
- fit(T[], RNNSearch<T, T>, int, double) - Static method in class smile.clustering.DBSCAN
-
Clustering the data.
- fit(double[][], double, int) - Static method in class smile.clustering.DENCLUE
-
Clustering data.
- fit(double[][], double, int, double, int) - Static method in class smile.clustering.DENCLUE
-
Clustering data.
- fit(double[][], int) - Static method in class smile.clustering.DeterministicAnnealing
-
Clustering data into k clusters.
- fit(double[][], int, double, int, double, double) - Static method in class smile.clustering.DeterministicAnnealing
-
Clustering data into k clusters.
- fit(double[][], int) - Static method in class smile.clustering.GMeans
-
Clustering data with the number of clusters
determined by G-Means algorithm automatically.
- fit(double[][], int, int, double) - Static method in class smile.clustering.GMeans
-
Clustering data with the number of clusters
determined by G-Means algorithm automatically.
- fit(Linkage) - Static method in class smile.clustering.HierarchicalClustering
-
Fits the Agglomerative Hierarchical Clustering with given linkage
method, which includes proximity matrix.
- fit(double[][], int) - Static method in class smile.clustering.KMeans
-
Partitions data into k clusters up to 100 iterations.
- fit(double[][], int, int, double) - Static method in class smile.clustering.KMeans
-
Partitions data into k clusters up to 100 iterations.
- fit(BBDTree, double[][], int, int, double) - Static method in class smile.clustering.KMeans
-
Partitions data into k clusters.
- fit(int[][], int) - Static method in class smile.clustering.KModes
-
Fits k-modes clustering.
- fit(int[][], int, int) - Static method in class smile.clustering.KModes
-
Fits k-modes clustering.
- fit(T[], Distance<T>, int, double) - Static method in class smile.clustering.MEC
-
Clustering the data.
- fit(T[], RNNSearch<T, T>, int, double, int[], double) - Static method in class smile.clustering.MEC
-
Clustering the data.
- fit(SparseArray[], int) - Static method in class smile.clustering.SIB
-
Clustering data into k clusters up to 100 iterations.
- fit(SparseArray[], int, int) - Static method in class smile.clustering.SIB
-
Clustering data into k clusters.
- fit(Matrix, int) - Static method in class smile.clustering.SpectralClustering
-
Spectral graph clustering.
- fit(Matrix, int, int, double) - Static method in class smile.clustering.SpectralClustering
-
Spectral graph clustering.
- fit(double[][], int, double) - Static method in class smile.clustering.SpectralClustering
-
Spectral clustering the data.
- fit(double[][], int, double, int, double) - Static method in class smile.clustering.SpectralClustering
-
Spectral clustering the data.
- fit(double[][], int, int, double) - Static method in class smile.clustering.SpectralClustering
-
Spectral clustering with Nystrom approximation.
- fit(double[][], int, int, double, int, double) - Static method in class smile.clustering.SpectralClustering
-
Spectral clustering with Nystrom approximation.
- fit(double[][], int) - Static method in class smile.clustering.XMeans
-
Clustering data with the number of clusters
determined by X-Means algorithm automatically.
- fit(double[][], int, int, double) - Static method in class smile.clustering.XMeans
-
Clustering data with the number of clusters
determined by X-Means algorithm automatically.
- fit(DataFrame) - Static method in class smile.feature.MaxAbsScaler
-
Learns transformation parameters from a dataset.
- fit(double[][]) - Static method in class smile.feature.MaxAbsScaler
-
Learns transformation parameters from a dataset.
- fit(DataFrame) - Static method in class smile.feature.RobustStandardizer
-
Learns transformation parameters from a dataset.
- fit(double[][]) - Static method in class smile.feature.RobustStandardizer
-
Learns transformation parameters from a dataset.
- fit(DataFrame) - Static method in class smile.feature.Scaler
-
Learns transformation parameters from a dataset.
- fit(double[][]) - Static method in class smile.feature.Scaler
-
Learns transformation parameters from a dataset.
- fit(DataFrame) - Static method in class smile.feature.Standardizer
-
Learns transformation parameters from a dataset.
- fit(double[][]) - Static method in class smile.feature.Standardizer
-
Learns transformation parameters from a dataset.
- fit(DataFrame) - Static method in class smile.feature.WinsorScaler
-
Learns transformation parameters from a dataset with 5% lower limit
and 95% upper limit.
- fit(DataFrame, double, double) - Static method in class smile.feature.WinsorScaler
-
Learns transformation parameters from a dataset.
- fit(double[][]) - Static method in class smile.feature.WinsorScaler
-
Learns transformation parameters from a dataset.
- fit(double[][], double, double) - Static method in class smile.feature.WinsorScaler
-
Learns transformation parameters from a dataset.
- fit(Formula, DataFrame, Model) - Static method in class smile.glm.GLM
-
Fits the generalized linear model with IWLS (iteratively reweighted least squares).
- fit(Formula, DataFrame, Model, Properties) - Static method in class smile.glm.GLM
-
Fits the generalized linear model with IWLS (iteratively reweighted least squares).
- fit(Formula, DataFrame, Model, double, int) - Static method in class smile.glm.GLM
-
Fits the generalized linear model with IWLS (iteratively reweighted least squares).
- fit(RNNSearch<double[], double[]>, double[][], double) - Method in class smile.neighbor.MPLSH
-
Fits the posteriori multiple probe algorithm.
- fit(RNNSearch<double[], double[]>, double[][], double, int) - Method in class smile.neighbor.MPLSH
-
Fits the posteriori multiple probe algorithm.
- fit(RNNSearch<double[], double[]>, double[][], double, int, double) - Method in class smile.neighbor.MPLSH
-
Train the posteriori multiple probe algorithm.
- fit(double[][], int) - Static method in class smile.projection.ICA
-
Fits independent component analysis.
- fit(double[][], int, Properties) - Static method in class smile.projection.ICA
-
Fits independent component analysis.
- fit(double[][], int, DifferentiableFunction, double, int) - Static method in class smile.projection.ICA
-
Fits independent component analysis.
- fit(T[], MercerKernel<T>, int) - Static method in class smile.projection.KPCA
-
Fits kernel principal component analysis.
- fit(T[], MercerKernel<T>, int, double) - Static method in class smile.projection.KPCA
-
Fits kernel principal component analysis.
- fit(double[][]) - Static method in class smile.projection.PCA
-
Fits principal component analysis with covariance matrix.
- fit(double[][], int) - Static method in class smile.projection.ProbabilisticPCA
-
Fits probabilistic principal component analysis.
- fit(Formula, DataFrame, Properties) - Static method in class smile.regression.ElasticNet
-
Fit an Elastic Net model.
- fit(Formula, DataFrame, double, double) - Static method in class smile.regression.ElasticNet
-
Fit an Elastic Net model.
- fit(Formula, DataFrame, double, double, double, int) - Static method in class smile.regression.ElasticNet
-
Fit an Elastic Net model.
- fit(T[], double[], MercerKernel<T>, Properties) - Static method in class smile.regression.GaussianProcessRegression
-
Fits a regular Gaussian process model.
- fit(T[], double[], MercerKernel<T>, double) - Static method in class smile.regression.GaussianProcessRegression
-
Fits a regular Gaussian process model by the method of subset of regressors.
- fit(T[], double[], MercerKernel<T>, double, boolean, double, int) - Static method in class smile.regression.GaussianProcessRegression
-
Fits a regular Gaussian process model.
- fit(T[], double[], T[], MercerKernel<T>, Properties) - Static method in class smile.regression.GaussianProcessRegression
-
Fits an approximate Gaussian process model by the method of subset of regressors.
- fit(T[], double[], T[], MercerKernel<T>, double) - Static method in class smile.regression.GaussianProcessRegression
-
Fits an approximate Gaussian process model by the method of subset of regressors.
- fit(T[], double[], T[], MercerKernel<T>, double, boolean) - Static method in class smile.regression.GaussianProcessRegression
-
Fits an approximate Gaussian process model by the method of subset of regressors.
- fit(Formula, DataFrame) - Static method in class smile.regression.GradientTreeBoost
-
Fits a gradient tree boosting for regression.
- fit(Formula, DataFrame, Properties) - Static method in class smile.regression.GradientTreeBoost
-
Fits a gradient tree boosting for regression.
- fit(Formula, DataFrame, Loss, int, int, int, int, double, double) - Static method in class smile.regression.GradientTreeBoost
-
Fits a gradient tree boosting for regression.
- fit(Formula, DataFrame) - Static method in class smile.regression.LASSO
-
Fits a L1-regularized least squares model.
- fit(Formula, DataFrame, Properties) - Static method in class smile.regression.LASSO
-
Fits a L1-regularized least squares model.
- fit(Formula, DataFrame, double) - Static method in class smile.regression.LASSO
-
Fits a L1-regularized least squares model.
- fit(Formula, DataFrame, double, double, int) - Static method in class smile.regression.LASSO
-
Fits a L1-regularized least squares model.
- fit(Formula, DataFrame) - Static method in class smile.regression.OLS
-
Fits an ordinary least squares model.
- fit(Formula, DataFrame, Properties) - Static method in class smile.regression.OLS
-
Fits an ordinary least squares model.
- fit(Formula, DataFrame, String, boolean, boolean) - Static method in class smile.regression.OLS
-
Fits an ordinary least squares model.
- fit(Formula, DataFrame) - Static method in class smile.regression.RandomForest
-
Learns a random forest for regression.
- fit(Formula, DataFrame, Properties) - Static method in class smile.regression.RandomForest
-
Learns a random forest for regression.
- fit(Formula, DataFrame, int, int, int, int, int, double) - Static method in class smile.regression.RandomForest
-
Learns a random forest for regression.
- fit(Formula, DataFrame, int, int, int, int, int, double, LongStream) - Static method in class smile.regression.RandomForest
-
Learns a random forest for regression.
- fit(T[], double[], RBF<T>[]) - Static method in class smile.regression.RBFNetwork
-
Fits a RBF network.
- fit(T[], double[], RBF<T>[], boolean) - Static method in class smile.regression.RBFNetwork
-
Fits a RBF network.
- fit(Formula, DataFrame) - Static method in class smile.regression.RegressionTree
-
Learns a regression tree.
- fit(Formula, DataFrame, Properties) - Static method in class smile.regression.RegressionTree
-
Learns a regression tree.
- fit(Formula, DataFrame, int, int, int) - Static method in class smile.regression.RegressionTree
-
Learns a regression tree.
- fit(Formula, DataFrame) - Static method in class smile.regression.RidgeRegression
-
Fits a ridge regression model.
- fit(Formula, DataFrame, Properties) - Static method in class smile.regression.RidgeRegression
-
Fits a ridge regression model.
- fit(Formula, DataFrame, double) - Static method in class smile.regression.RidgeRegression
-
Fits a ridge regression model.
- fit(Formula, DataFrame, double[], double[], double[]) - Static method in class smile.regression.RidgeRegression
-
Fits a generalized ridge regression model that minimizes a
weighted least squares criterion augmented with a
generalized ridge penalty:
- fit(double[][], double[], double, double, double) - Static method in class smile.regression.SVR
-
Fits a linear epsilon-SVR.
- fit(int[][], double[], int, double, double, double) - Static method in class smile.regression.SVR
-
Fits a linear epsilon-SVR of binary sparse data.
- fit(SparseArray[], double[], int, double, double, double) - Static method in class smile.regression.SVR
-
Fits a linear epsilon-SVR of sparse data.
- fit(T[], double[], MercerKernel<T>, double, double, double) - Static method in class smile.regression.SVR
-
Fits a epsilon-SVR.
- fit(Tuple[][], int[][]) - Static method in class smile.sequence.CRF
-
Fits a CRF model.
- fit(Tuple[][], int[][], Properties) - Static method in class smile.sequence.CRF
-
Fits a CRF model.
- fit(Tuple[][], int[][], int, int, int, int, double) - Static method in class smile.sequence.CRF
-
Fits a CRF model.
- fit(T[][], int[][], Function<T, Tuple>) - Static method in class smile.sequence.CRFLabeler
-
Fits a CRF model.
- fit(T[][], int[][], Function<T, Tuple>, Properties) - Static method in class smile.sequence.CRFLabeler
-
Fits a CRF model.
- fit(T[][], int[][], Function<T, Tuple>, int, int, int, int, double) - Static method in class smile.sequence.CRFLabeler
-
Fits a CRF.
- fit(int[][], int[][]) - Static method in class smile.sequence.HMM
-
Fits an HMM by maximum likelihood estimation.
- fit(T[][], int[][], ToIntFunction<T>) - Static method in class smile.sequence.HMM
-
Fits an HMM by maximum likelihood estimation.
- fit(T[][], int[][], ToIntFunction<T>) - Static method in class smile.sequence.HMMLabeler
-
Fits an HMM by maximum likelihood estimation.
- fit(double[], int) - Static method in class smile.timeseries.AR
-
Fits an autoregressive model with Yule-Walker procedure.
- fit(double[], int, int) - Static method in class smile.timeseries.ARMA
-
Fits an ARMA model with Hannan-Rissanen algorithm.
- fitness(double[][], int[], double[][], int[], ClassificationMetric, BiFunction<double[][], int[], Classifier<double[]>>) - Static method in class smile.feature.GAFE
-
Returns a classification fitness measure.
- fitness(double[][], double[], double[][], double[], RegressionMetric, BiFunction<double[][], double[], Regression<double[]>>) - Static method in class smile.feature.GAFE
-
Returns a regression fitness function.
- fitness(String, DataFrame, DataFrame, ClassificationMetric, BiFunction<Formula, DataFrame, DataFrameClassifier>) - Static method in class smile.feature.GAFE
-
Returns a classification fitness function.
- fitness(String, DataFrame, DataFrame, RegressionMetric, BiFunction<Formula, DataFrame, DataFrameRegression>) - Static method in class smile.feature.GAFE
-
Returns a regression fitness function.
- fittedValues() - Method in class smile.glm.GLM
-
Returns the fitted mean values.
- fittedValues() - Method in class smile.regression.LinearModel
-
Returns the fitted values.
- fittedValues - Variable in class smile.regression.Regression.Metric
-
The fitted values.
- fittedValues() - Method in class smile.timeseries.AR
-
Returns the fitted values.
- fittedValues() - Method in class smile.timeseries.ARMA
-
Returns the fitted values.
- fitTime - Variable in class smile.validation.ClassificationMetrics
-
The time in milliseconds of fitting the model.
- fitTime - Variable in class smile.validation.RegressionMetrics
-
The time in milliseconds of fitting the model.
- FLD - Class in smile.classification
-
Fisher's linear discriminant.
- FLD(double[], double[][], Matrix) - Constructor for class smile.classification.FLD
-
Constructor.
- FLD(double[], double[][], Matrix, IntSet) - Constructor for class smile.classification.FLD
-
Constructor.
- forecast() - Method in class smile.timeseries.AR
-
Returns 1-step ahead forecast.
- forecast(int) - Method in class smile.timeseries.AR
-
Returns l-step ahead forecast.
- forecast() - Method in class smile.timeseries.ARMA
-
Returns 1-step ahead forecast.
- forecast(int) - Method in class smile.timeseries.ARMA
-
Returns l-step ahead forecast.
- formula - Variable in class smile.base.cart.CART
-
The model formula.
- formula() - Method in class smile.classification.AdaBoost
-
- formula() - Method in interface smile.classification.DataFrameClassifier
-
Returns the formula associated with the model.
- formula() - Method in class smile.classification.DecisionTree
-
Returns null if the tree is part of ensemble algorithm.
- formula() - Method in class smile.classification.GradientTreeBoost
-
- formula() - Method in class smile.classification.RandomForest
-
- formula() - Method in interface smile.feature.TreeSHAP
-
Returns the formula associated with the model.
- formula - Variable in class smile.glm.GLM
-
The symbolic description of the model to be fitted.
- formula() - Method in interface smile.regression.DataFrameRegression
-
Returns the formula associated with the model.
- formula() - Method in class smile.regression.GradientTreeBoost
-
- formula() - Method in class smile.regression.LinearModel
-
- formula() - Method in class smile.regression.RandomForest
-
- formula() - Method in class smile.regression.RegressionTree
-
Returns null if the tree is part of ensemble algorithm.
- FPGrowth - Class in smile.association
-
Frequent item set mining based on the FP-growth (frequent pattern growth)
algorithm, which employs an extended prefix-tree (FP-tree) structure to
store the database in a compressed form.
- FPTree - Class in smile.association
-
FP-tree data structure used in FP-growth (frequent pattern growth)
algorithm for frequent item set mining.
- FScore - Class in smile.validation.metric
-
The F-score (or F-measure) considers both the precision and the recall of the test
to compute the score.
- FScore() - Constructor for class smile.validation.metric.FScore
-
Constructor of F1 score.
- FScore(double) - Constructor for class smile.validation.metric.FScore
-
Constructor of general F-score.
- ftest() - Method in class smile.regression.LinearModel
-
Returns the F-statistic of goodness-of-fit.
- g(double[], double[]) - Method in interface smile.base.mlp.ActivationFunction
-
The gradient function.
- g(Cost, double[], double[]) - Method in enum smile.base.mlp.OutputFunction
-
The gradient function.
- g(double) - Method in class smile.projection.ica.Exp
-
- g(double) - Method in class smile.projection.ica.Kurtosis
-
- g(double) - Method in class smile.projection.ica.LogCosh
-
- g2(double) - Method in class smile.projection.ica.Exp
-
- g2(double) - Method in class smile.projection.ica.Kurtosis
-
- g2(double) - Method in class smile.projection.ica.LogCosh
-
- GAFE - Class in smile.feature
-
Genetic algorithm based feature selection.
- GAFE() - Constructor for class smile.feature.GAFE
-
Constructor.
- GAFE(Selection, int, Crossover, double, double) - Constructor for class smile.feature.GAFE
-
Constructor.
- Gaussian(double, double) - Static method in interface smile.vq.Neighborhood
-
Returns Gaussian neighborhood function.
- GaussianProcessRegression<T> - Class in smile.regression
-
Gaussian Process for Regression.
- GaussianProcessRegression(MercerKernel<T>, T[], double[], double) - Constructor for class smile.regression.GaussianProcessRegression
-
Constructor.
- GaussianProcessRegression(MercerKernel<T>, T[], double[], double, double, double) - Constructor for class smile.regression.GaussianProcessRegression
-
Constructor.
- GaussianProcessRegression(MercerKernel<T>, T[], double[], double, double, double, Matrix.Cholesky, double) - Constructor for class smile.regression.GaussianProcessRegression
-
Constructor.
- GaussianProcessRegression.JointPrediction - Class in smile.regression
-
The joint prediction of multiple data points.
- get(int) - Method in class smile.neighbor.lsh.Hash
-
Returns the bucket entry for the given hash value.
- get(double[]) - Method in class smile.neighbor.lsh.Hash
-
Returns the bucket entry for the given point.
- getCenter() - Method in class smile.projection.PCA
-
Returns the center of data.
- getCenter() - Method in class smile.projection.ProbabilisticPCA
-
Returns the center of data.
- getCoordinates() - Method in class smile.projection.KPCA
-
Returns the nonlinear principal component scores, i.e., the representation
of learning data in the nonlinear principal component space.
- getCumulativeVarianceProportion() - Method in class smile.projection.PCA
-
Returns the cumulative proportion of variance contained in principal components,
ordered from largest to smallest.
- getHeight() - Method in class smile.clustering.HierarchicalClustering
-
Returns a set of n-1 non-decreasing real values, which are the clustering height,
i.e., the value of the criterion associated with the clustering method
for the particular agglomeration.
- getInitialStateProbabilities() - Method in class smile.sequence.HMM
-
Returns the initial state probabilities.
- getInputSize() - Method in class smile.base.mlp.Layer
-
Returns the dimension of input vector (not including bias value).
- getLearningRate() - Method in class smile.base.mlp.MultilayerPerceptron
-
Returns the learning rate.
- getLearningRate() - Method in class smile.classification.LogisticRegression
-
Returns the learning rate of stochastic gradient descent.
- getLearningRate() - Method in class smile.classification.Maxent
-
Returns the learning rate of stochastic gradient descent.
- getLearningRate() - Method in class smile.classification.SparseLogisticRegression
-
Returns the learning rate of stochastic gradient descent.
- getLearningRate() - Method in class smile.projection.GHA
-
Returns the learning rate.
- getLoadings() - Method in class smile.projection.PCA
-
Returns the variable loading matrix, ordered from largest to smallest
by corresponding eigenvalues.
- getLoadings() - Method in class smile.projection.ProbabilisticPCA
-
Returns the variable loading matrix, ordered from largest to smallest
by corresponding eigenvalues.
- getMomentum() - Method in class smile.base.mlp.MultilayerPerceptron
-
Returns the momentum factor.
- getNoiseVariance() - Method in class smile.projection.ProbabilisticPCA
-
Returns the variance of noise.
- getOutputSize() - Method in class smile.base.mlp.Layer
-
Returns the dimension of output vector.
- getProbeSequence(double[], double, int) - Method in class smile.neighbor.lsh.PosterioriModel
-
Generate query-directed probes.
- getProjection() - Method in class smile.classification.FLD
-
Returns the projection matrix W.
- getProjection() - Method in class smile.projection.GHA
-
Returns the projection matrix.
- getProjection() - Method in class smile.projection.KPCA
-
Returns the projection matrix.
- getProjection() - Method in interface smile.projection.LinearProjection
-
Returns the projection matrix.
- getProjection() - Method in class smile.projection.PCA
-
- getProjection() - Method in class smile.projection.ProbabilisticPCA
-
Returns the projection matrix.
- getProjection() - Method in class smile.projection.RandomProjection
-
- getStateTransitionProbabilities() - Method in class smile.sequence.HMM
-
Returns the state transition probabilities.
- getSymbolEmissionProbabilities() - Method in class smile.sequence.HMM
-
Returns the symbol emission probabilities.
- getTree() - Method in class smile.clustering.HierarchicalClustering
-
Returns an n-1 by 2 matrix of which row i describes the merging of clusters at
step i of the clustering.
- getVariance() - Method in class smile.projection.PCA
-
Returns the principal component variances, ordered from largest to smallest,
which are the eigenvalues of the covariance or correlation matrix of learning data.
- getVarianceProportion() - Method in class smile.projection.PCA
-
Returns the proportion of variance contained in each principal component,
ordered from largest to smallest.
- getVariances() - Method in class smile.projection.KPCA
-
Returns the eigenvalues of kernel principal components, ordered from largest to smallest.
- getWeightDecay() - Method in class smile.base.mlp.MultilayerPerceptron
-
Returns the weight decay factor.
- GHA - Class in smile.projection
-
Generalized Hebbian Algorithm.
- GHA(int, int, double) - Constructor for class smile.projection.GHA
-
Constructor.
- GHA(double[][], double) - Constructor for class smile.projection.GHA
-
Constructor.
- GLM - Class in smile.glm
-
Generalized linear models.
- GLM(Formula, String[], Model, double[], double, double, double, double[], double[], double[][]) - Constructor for class smile.glm.GLM
-
Constructor.
- GMeans - Class in smile.clustering
-
G-Means clustering algorithm, an extended K-Means which tries to
automatically determine the number of clusters by normality test.
- GMeans(double, double[][], int[]) - Constructor for class smile.clustering.GMeans
-
Constructor.
- gradient() - Method in class smile.base.mlp.Layer
-
Returns the output gradient vector.
- GradientTreeBoost - Class in smile.classification
-
Gradient boosting for classification.
- GradientTreeBoost(Formula, RegressionTree[], double, double, double[]) - Constructor for class smile.classification.GradientTreeBoost
-
Constructor of binary class.
- GradientTreeBoost(Formula, RegressionTree[], double, double, double[], IntSet) - Constructor for class smile.classification.GradientTreeBoost
-
Constructor of binary class.
- GradientTreeBoost(Formula, RegressionTree[][], double, double[]) - Constructor for class smile.classification.GradientTreeBoost
-
Constructor of multi-class.
- GradientTreeBoost(Formula, RegressionTree[][], double, double[], IntSet) - Constructor for class smile.classification.GradientTreeBoost
-
Constructor of multi-class.
- GradientTreeBoost - Class in smile.regression
-
Gradient boosting for regression.
- GradientTreeBoost(Formula, RegressionTree[], double, double, double[]) - Constructor for class smile.regression.GradientTreeBoost
-
Constructor.
- graph - Variable in class smile.manifold.IsoMap
-
The nearest neighbor graph.
- graph - Variable in class smile.manifold.LaplacianEigenmap
-
Nearest neighbor graph.
- graph - Variable in class smile.manifold.LLE
-
Nearest neighbor graph.
- graph - Variable in class smile.manifold.UMAP
-
The nearest neighbor graph.
- grid() - Method in class smile.validation.Hyperparameters
-
Generates a stream of hyperparameters for grid search.
- GrowingNeuralGas - Class in smile.vq
-
Growing Neural Gas.
- GrowingNeuralGas(int) - Constructor for class smile.vq.GrowingNeuralGas
-
Constructor.
- GrowingNeuralGas(int, double, double, int, int, double, double) - Constructor for class smile.vq.GrowingNeuralGas
-
Constructor.
- L - Variable in class smile.regression.GaussianProcessRegression
-
The log marginal likelihood, which may be not available (NaN) when the model
is fit with approximate methods.
- L - Variable in class smile.vq.BIRCH
-
The number of CF entries in the leaf nodes.
- labels - Variable in class smile.classification.ClassLabels
-
The class labels.
- lad() - Static method in interface smile.base.cart.Loss
-
Least absolute deviation regression.
- lambda - Variable in class smile.base.mlp.MultilayerPerceptron
-
The L2 regularization factor, which is also the weight decay factor.
- LaplacianEigenmap - Class in smile.manifold
-
Laplacian Eigenmap.
- LaplacianEigenmap(int[], double[][], AdjacencyList) - Constructor for class smile.manifold.LaplacianEigenmap
-
Constructor with discrete weights.
- LaplacianEigenmap(double, int[], double[][], AdjacencyList) - Constructor for class smile.manifold.LaplacianEigenmap
-
Constructor with Gaussian kernel.
- LASSO - Class in smile.regression
-
Lasso (least absolute shrinkage and selection operator) regression.
- LASSO() - Constructor for class smile.regression.LASSO
-
- LASVM<T> - Class in smile.base.svm
-
LASVM is an approximate SVM solver that uses online approximation.
- LASVM(MercerKernel<T>, double, double) - Constructor for class smile.base.svm.LASVM
-
Constructor.
- LASVM(MercerKernel<T>, double, double, double) - Constructor for class smile.base.svm.LASVM
-
Constructor.
- lattice(int, int, double[][]) - Static method in class smile.vq.SOM
-
Creates a lattice of which the weight vectors are randomly selected from samples.
- Layer - Class in smile.base.mlp
-
A layer in the neural network.
- Layer(int, int) - Constructor for class smile.base.mlp.Layer
-
Constructor.
- Layer(Matrix, double[]) - Constructor for class smile.base.mlp.Layer
-
Constructor.
- LayerBuilder - Class in smile.base.mlp
-
The builder of layers.
- LayerBuilder(int) - Constructor for class smile.base.mlp.LayerBuilder
-
Constructor.
- LDA - Class in smile.classification
-
Linear discriminant analysis.
- LDA(double[], double[][], double[], Matrix) - Constructor for class smile.classification.LDA
-
Constructor.
- LDA(double[], double[][], double[], Matrix, IntSet) - Constructor for class smile.classification.LDA
-
Constructor.
- LeafNode - Class in smile.base.cart
-
A leaf node in decision tree.
- LeafNode(int) - Constructor for class smile.base.cart.LeafNode
-
Constructor.
- leafs() - Method in class smile.base.cart.InternalNode
-
- leafs() - Method in class smile.base.cart.LeafNode
-
- leafs() - Method in interface smile.base.cart.Node
-
Returns the number of leaf nodes in the subtree.
- learningRate - Variable in class smile.base.mlp.MultilayerPerceptron
-
The learning rate.
- leverage - Variable in class smile.association.AssociationRule
-
The difference between the probability of the rule and the expected
probability if the items were statistically independent.
- lift - Variable in class smile.association.AssociationRule
-
How many times more often antecedent and consequent occur together
than expected if they were statistically independent.
- linear() - Static method in interface smile.base.mlp.ActivationFunction
-
Linear/Identity function.
- linear(int) - Static method in class smile.base.mlp.Layer
-
Returns a hidden layer with linear activation function.
- LinearKernelMachine - Class in smile.base.svm
-
Linear kernel machine.
- LinearKernelMachine(double[], double) - Constructor for class smile.base.svm.LinearKernelMachine
-
Constructor.
- LinearModel - Class in smile.regression
-
Linear model.
- LinearModel(Formula, StructType, Matrix, double[], double[], double) - Constructor for class smile.regression.LinearModel
-
Constructor.
- LinearProjection - Interface in smile.projection
-
Linear projection.
- LinearSearch<T> - Class in smile.neighbor
-
Brute force linear nearest neighbor search.
- LinearSearch(T[], Distance<T>) - Constructor for class smile.neighbor.LinearSearch
-
Constructor.
- link(double) - Method in interface smile.glm.model.Model
-
The link function.
- Linkage - Class in smile.clustering.linkage
-
A measure of dissimilarity between clusters (i.e.
- Linkage(double[][]) - Constructor for class smile.clustering.linkage.Linkage
-
Initialize the linkage with the lower triangular proximity matrix.
- Linkage(int, float[]) - Constructor for class smile.clustering.linkage.Linkage
-
Initialize the linkage with the lower triangular proximity matrix.
- ljung(double[], int) - Static method in class smile.timeseries.BoxTest
-
Box-Pierce test.
- LLE - Class in smile.manifold
-
Locally Linear Embedding.
- LLE(int[], double[][], AdjacencyList) - Constructor for class smile.manifold.LLE
-
Constructor.
- lloyd(double[][], int) - Static method in class smile.clustering.KMeans
-
The implementation of Lloyd algorithm as a benchmark.
- lloyd(double[][], int, int, double) - Static method in class smile.clustering.KMeans
-
The implementation of Lloyd algorithm as a benchmark.
- LLSImputation - Class in smile.imputation
-
Local least squares missing value imputation.
- LLSImputation(int) - Constructor for class smile.imputation.LLSImputation
-
Constructor.
- log() - Static method in interface smile.glm.model.Poisson
-
log link function.
- LogCosh - Class in smile.projection.ica
-
A good general-purpose contrast function for ICA.
- LogCosh() - Constructor for class smile.projection.ica.LogCosh
-
- logistic(int[]) - Static method in interface smile.base.cart.Loss
-
Logistic regression loss for binary classification.
- logistic(int, int, int[], double[][]) - Static method in interface smile.base.cart.Loss
-
Logistic regression loss for multi-class classification.
- LogisticRegression - Class in smile.classification
-
Logistic regression.
- LogisticRegression(int, double, double, IntSet) - Constructor for class smile.classification.LogisticRegression
-
Constructor.
- LogisticRegression.Binomial - Class in smile.classification
-
Binomial logistic regression.
- LogisticRegression.Multinomial - Class in smile.classification
-
Multinomial logistic regression.
- logit() - Static method in interface smile.glm.model.Bernoulli
-
logit link function.
- logit(int[]) - Static method in interface smile.glm.model.Binomial
-
logit link function.
- loglikelihood() - Method in class smile.classification.LogisticRegression
-
Returns the log-likelihood of model.
- loglikelihood() - Method in class smile.classification.Maxent
-
Returns the log-likelihood of model.
- loglikelihood() - Method in class smile.classification.SparseLogisticRegression
-
Returns the log-likelihood of model.
- loglikelihood - Variable in class smile.glm.GLM
-
Log-likelihood.
- loglikelihood() - Method in class smile.glm.GLM
-
Returns the log-likelihood of model.
- loglikelihood(double[], double[]) - Method in interface smile.glm.model.Model
-
The log-likelihood function.
- logloss - Variable in class smile.validation.ClassificationMetrics
-
The log loss on validation data.
- LogLoss - Class in smile.validation.metric
-
Log loss is a evaluation metric for binary classifiers and it is sometimes
the optimization objective as well in case of logistic regression and neural
networks.
- LogLoss() - Constructor for class smile.validation.metric.LogLoss
-
- logp(int[], int[]) - Method in class smile.sequence.HMM
-
Returns the log joint probability of an observation sequence along a
state sequence given this HMM.
- logp(int[]) - Method in class smile.sequence.HMM
-
Returns the logarithm probability of an observation sequence given this
HMM.
- logp(T[], int[]) - Method in class smile.sequence.HMMLabeler
-
Returns the log joint probability of an observation sequence along a
state sequence.
- logp(T[]) - Method in class smile.sequence.HMMLabeler
-
Returns the logarithm probability of an observation sequence.
- LOOCV - Interface in smile.validation
-
Leave-one-out cross validation.
- Loss - Interface in smile.base.cart
-
Regression loss function.
- Loss.Type - Enum in smile.base.cart
-
The type of loss.
- ls() - Static method in interface smile.base.cart.Loss
-
Least squares regression.
- ls(double[]) - Static method in interface smile.base.cart.Loss
-
Least squares regression.
- LSH<E> - Class in smile.neighbor
-
Locality-Sensitive Hashing.
- LSH(double[][], E[], double) - Constructor for class smile.neighbor.LSH
-
Constructor.
- LSH(double[][], E[], double, int) - Constructor for class smile.neighbor.LSH
-
Constructor.
- LSH(int, int, int, double) - Constructor for class smile.neighbor.LSH
-
Constructor.
- LSH(int, int, int, double, int) - Constructor for class smile.neighbor.LSH
-
Constructor.
- m - Variable in class smile.neighbor.lsh.PrZ
-
The index of hash function.
- ma() - Method in class smile.timeseries.ARMA
-
Returns the linear coefficients of MA(q).
- MAD - Variable in class smile.regression.Regression.Metric
-
Mean absolute deviation error.
- MAD - Class in smile.validation.metric
-
Mean absolute deviation error.
- MAD() - Constructor for class smile.validation.metric.MAD
-
- mad - Variable in class smile.validation.RegressionMetrics
-
The mean absolute deviation on validation data.
- matrix - Variable in class smile.validation.metric.ConfusionMatrix
-
Confusion matrix.
- MatthewsCorrelation - Class in smile.validation.metric
-
Matthews correlation coefficient.
- MatthewsCorrelation() - Constructor for class smile.validation.metric.MatthewsCorrelation
-
- MAX - Static variable in class smile.validation.metric.AdjustedMutualInformation
-
Default instance with max normalization.
- max(int[], int[]) - Static method in class smile.validation.metric.AdjustedMutualInformation
-
Calculates the adjusted mutual information of (I(y1, y2) - E(MI)) / (max(H(y1), H(y2)) - E(MI)).
- MAX - Static variable in class smile.validation.metric.NormalizedMutualInformation
-
Default instance with max normalization.
- max(int[], int[]) - Static method in class smile.validation.metric.NormalizedMutualInformation
-
Calculates the normalized mutual information of I(y1, y2) / max(H(y1), H(y2)).
- MaxAbsScaler - Class in smile.feature
-
Scales each feature by its maximum absolute value.
- MaxAbsScaler(StructType, double[]) - Constructor for class smile.feature.MaxAbsScaler
-
Constructor.
- maxDepth - Variable in class smile.base.cart.CART
-
The maximum depth of the tree.
- Maxent - Class in smile.classification
-
Maximum Entropy Classifier.
- Maxent(int, double, double, IntSet) - Constructor for class smile.classification.Maxent
-
Constructor.
- Maxent.Binomial - Class in smile.classification
-
Binomial maximum entropy classifier.
- Maxent.Multinomial - Class in smile.classification
-
Multinomial maximum entropy classifier.
- maxNodes - Variable in class smile.base.cart.CART
-
The maximum number of leaf nodes in the tree.
- mcc - Variable in class smile.validation.ClassificationMetrics
-
The Matthews correlation coefficient on validation data.
- MDS - Class in smile.mds
-
Classical multidimensional scaling, also known as principal coordinates
analysis.
- MDS(double[], double[], double[][]) - Constructor for class smile.mds.MDS
-
Constructor.
- mean() - Method in class smile.base.cart.RegressionNode
-
Returns the mean of response variable.
- mean() - Method in class smile.neighbor.lsh.HashValueParzenModel
-
Returns the mean.
- mean - Variable in class smile.neighbor.lsh.NeighborHashValueModel
-
Mean of hash values of neighbors.
- mean - Variable in class smile.regression.GaussianProcessRegression
-
The mean of responsible variable.
- mean() - Method in class smile.timeseries.AR
-
Returns the mean of time series.
- mean() - Method in class smile.timeseries.ARMA
-
Returns the mean of time series.
- MEC<T> - Class in smile.clustering
-
Non-parametric Minimum Conditional Entropy Clustering.
- MEC(double, double, RNNSearch<T, T>, int, int[]) - Constructor for class smile.clustering.MEC
-
Constructor.
- merge() - Method in class smile.base.cart.InternalNode
-
- merge() - Method in class smile.base.cart.LeafNode
-
- merge() - Method in interface smile.base.cart.Node
-
Try to merge the children nodes and return a leaf node.
- merge(RandomForest) - Method in class smile.classification.RandomForest
-
Merges two random forests.
- merge(int, int) - Method in class smile.clustering.linkage.CompleteLinkage
-
- merge(int, int) - Method in class smile.clustering.linkage.Linkage
-
Merge two clusters into one and update the proximity matrix.
- merge(int, int) - Method in class smile.clustering.linkage.SingleLinkage
-
- merge(int, int) - Method in class smile.clustering.linkage.UPGMALinkage
-
- merge(int, int) - Method in class smile.clustering.linkage.UPGMCLinkage
-
- merge(int, int) - Method in class smile.clustering.linkage.WardLinkage
-
- merge(int, int) - Method in class smile.clustering.linkage.WPGMALinkage
-
- merge(int, int) - Method in class smile.clustering.linkage.WPGMCLinkage
-
- merge(RandomForest) - Method in class smile.regression.RandomForest
-
Merges two random forests.
- metric(T[], double[]) - Method in interface smile.regression.Regression
-
Returns the regression metrics.
- metric(double[], double[]) - Method in interface smile.regression.Regression
-
Returns the regression metrics.
- metrics() - Method in class smile.classification.RandomForest
-
Returns the overall out-of-bag metric estimations.
- metrics - Variable in class smile.classification.RandomForest.Model
-
The performance metrics on out-of-bag samples.
- metrics() - Method in class smile.regression.RandomForest
-
Returns the overall out-of-bag metric estimations.
- metrics - Variable in class smile.regression.RandomForest.Model
-
The performance metrics on out-of-bag samples.
- metrics - Variable in class smile.validation.ClassificationValidation
-
The classification metrics.
- metrics - Variable in class smile.validation.RegressionValidation
-
The regression metrics.
- MIN - Static variable in class smile.validation.metric.AdjustedMutualInformation
-
Default instance with min normalization.
- min(int[], int[]) - Static method in class smile.validation.metric.AdjustedMutualInformation
-
Calculates the adjusted mutual information of (I(y1, y2) - E(MI)) / (min(H(y1), H(y2)) - E(MI)).
- MIN - Static variable in class smile.validation.metric.NormalizedMutualInformation
-
Default instance with min normalization.
- min(int[], int[]) - Static method in class smile.validation.metric.NormalizedMutualInformation
-
Calculates the normalized mutual information of I(y1, y2) / min(H(y1), H(y2)).
- minPts - Variable in class smile.clustering.DBSCAN
-
The minimum number of points required to form a cluster
- minSupport() - Method in class smile.association.FPTree
-
Returns the required minimum support of item sets in terms
of frequency.
- MissingValueImputation - Interface in smile.imputation
-
Interface to impute missing values in the data.
- MissingValueImputationException - Exception in smile.imputation
-
Exception of missing value imputation.
- MissingValueImputationException() - Constructor for exception smile.imputation.MissingValueImputationException
-
Constructor.
- MissingValueImputationException(String) - Constructor for exception smile.imputation.MissingValueImputationException
-
Constructor.
- mle(int, OutputFunction) - Static method in class smile.base.mlp.Layer
-
Returns an output layer with (log-)likelihood cost function.
- MLP - Class in smile.classification
-
Fully connected multilayer perceptron neural network for classification.
- MLP(int, LayerBuilder...) - Constructor for class smile.classification.MLP
-
Constructor.
- MLP(IntSet, int, LayerBuilder...) - Constructor for class smile.classification.MLP
-
Constructor.
- MLP - Class in smile.regression
-
Fully connected multilayer perceptron neural network for regression.
- MLP(int, LayerBuilder...) - Constructor for class smile.regression.MLP
-
Constructor.
- model - Variable in class smile.glm.GLM
-
The model specifications (link function, deviance, etc.).
- Model - Interface in smile.glm.model
-
The GLM model specification.
- model - Variable in class smile.sequence.CRFLabeler
-
The CRF model.
- model - Variable in class smile.sequence.HMMLabeler
-
The HMM model.
- model - Variable in class smile.validation.ClassificationValidation
-
The model.
- model - Variable in class smile.validation.RegressionValidation
-
The model.
- models() - Method in class smile.classification.RandomForest
-
Returns the base models.
- models() - Method in class smile.regression.RandomForest
-
Returns the base models.
- ModelSelection - Interface in smile.validation
-
Model selection criteria.
- momentum - Variable in class smile.base.mlp.MultilayerPerceptron
-
The momentum factor.
- MPLSH<E> - Class in smile.neighbor
-
Multi-Probe Locality-Sensitive Hashing.
- MPLSH(int, int, int, double) - Constructor for class smile.neighbor.MPLSH
-
Constructor.
- MPLSH(int, int, int, double, int) - Constructor for class smile.neighbor.MPLSH
-
Constructor.
- mse(int, OutputFunction) - Static method in class smile.base.mlp.Layer
-
Returns an output layer with mean squared error cost function.
- MSE - Variable in class smile.regression.Regression.Metric
-
Mean squared error.
- MSE - Class in smile.validation.metric
-
Mean squared error.
- MSE() - Constructor for class smile.validation.metric.MSE
-
- mse - Variable in class smile.validation.RegressionMetrics
-
The mean squared error on validation data.
- mtry - Variable in class smile.base.cart.CART
-
The number of input variables to be used to determine the decision
at a node of the tree.
- mu - Variable in class smile.glm.GLM
-
The fitted mean values.
- mu - Variable in class smile.regression.GaussianProcessRegression.JointPrediction
-
The mean of predictive distribution at query points.
- MultilayerPerceptron - Class in smile.base.mlp
-
Fully connected multilayer perceptron neural network.
- MultilayerPerceptron(Layer...) - Constructor for class smile.base.mlp.MultilayerPerceptron
-
Constructor.
- multinomial(Formula, DataFrame) - Static method in class smile.classification.LogisticRegression
-
Fits multinomial logistic regression.
- multinomial(Formula, DataFrame, Properties) - Static method in class smile.classification.LogisticRegression
-
Fits multinomial logistic regression.
- multinomial(double[][], int[]) - Static method in class smile.classification.LogisticRegression
-
Fits multinomial logistic regression.
- multinomial(double[][], int[], Properties) - Static method in class smile.classification.LogisticRegression
-
Fits multinomial logistic regression.
- multinomial(double[][], int[], double, double, int) - Static method in class smile.classification.LogisticRegression
-
Fits multinomial logistic regression.
- Multinomial(double[][], double, double, IntSet) - Constructor for class smile.classification.LogisticRegression.Multinomial
-
Constructor.
- multinomial(int, int[][], int[]) - Static method in class smile.classification.Maxent
-
Learn maximum entropy classifier.
- multinomial(int, int[][], int[], Properties) - Static method in class smile.classification.Maxent
-
Learn maximum entropy classifier.
- multinomial(int, int[][], int[], double, double, int) - Static method in class smile.classification.Maxent
-
Learn maximum entropy classifier.
- Multinomial(double[][], double, double, IntSet) - Constructor for class smile.classification.Maxent.Multinomial
-
Constructor.
- multinomial(SparseDataset, int[]) - Static method in class smile.classification.SparseLogisticRegression
-
Fits multinomial logistic regression.
- multinomial(SparseDataset, int[], Properties) - Static method in class smile.classification.SparseLogisticRegression
-
Fits multinomial logistic regression.
- multinomial(SparseDataset, int[], double, double, int) - Static method in class smile.classification.SparseLogisticRegression
-
Fits multinomial logistic regression.
- Multinomial(double[][], double, double, IntSet) - Constructor for class smile.classification.SparseLogisticRegression.Multinomial
-
Constructor.
- MultiProbeHash - Class in smile.neighbor.lsh
-
The hash function for data in Euclidean spaces.
- MultiProbeHash(int, int, double, int) - Constructor for class smile.neighbor.lsh.MultiProbeHash
-
Constructor.
- MultiProbeSample - Class in smile.neighbor.lsh
-
Training sample for MPLSH.
- MultiProbeSample(double[], List<double[]>) - Constructor for class smile.neighbor.lsh.MultiProbeSample
-
Constructor.
- mustart(double) - Method in interface smile.glm.model.Model
-
The function to estimates the tarting values of means given y.
- MutableLSH<E> - Class in smile.neighbor
-
Mutable LSH.
- MutableLSH(int, int, int, double) - Constructor for class smile.neighbor.MutableLSH
-
Constructor.
- MutualInformation - Class in smile.validation.metric
-
Mutual Information for comparing clustering.
- MutualInformation() - Constructor for class smile.validation.metric.MutualInformation
-
- of(int, Supplier<Stream<int[]>>) - Static method in class smile.association.FPTree
-
One-step construction of FP-tree if the database is available as stream.
- of(double, Supplier<Stream<int[]>>) - Static method in class smile.association.FPTree
-
One-step construction of FP-tree if the database is available as stream.
- of(int, int[][]) - Static method in class smile.association.FPTree
-
One-step construction of FP-tree if the database is available in main memory.
- of(double, int[][]) - Static method in class smile.association.FPTree
-
One-step construction of FP-tree if the database is available in main memory.
- of(T[], RadialBasisFunction, Metric<T>) - Static method in class smile.base.rbf.RBF
-
Makes a set of RBF neurons.
- of(T[], RadialBasisFunction[], Metric<T>) - Static method in class smile.base.rbf.RBF
-
Makes a set of RBF neurons.
- of(KernelMachine<double[]>) - Static method in class smile.base.svm.LinearKernelMachine
-
Creates a linear kernel machine.
- of(double[][]) - Static method in class smile.clustering.linkage.CompleteLinkage
-
Given a set of data, computes the proximity and then the linkage.
- of(T[], Distance<T>) - Static method in class smile.clustering.linkage.CompleteLinkage
-
Given a set of data, computes the proximity and then the linkage.
- of(double[][]) - Static method in class smile.clustering.linkage.SingleLinkage
-
Given a set of data, computes the proximity and then the linkage.
- of(T[], Distance<T>) - Static method in class smile.clustering.linkage.SingleLinkage
-
Given a set of data, computes the proximity and then the linkage.
- of(double[][]) - Static method in class smile.clustering.linkage.UPGMALinkage
-
Given a set of data, computes the proximity and then the linkage.
- of(T[], Distance<T>) - Static method in class smile.clustering.linkage.UPGMALinkage
-
Given a set of data, computes the proximity and then the linkage.
- of(double[][]) - Static method in class smile.clustering.linkage.UPGMCLinkage
-
Given a set of data, computes the proximity and then the linkage.
- of(T[], Distance<T>) - Static method in class smile.clustering.linkage.UPGMCLinkage
-
Given a set of data, computes the proximity and then the linkage.
- of(double[][]) - Static method in class smile.clustering.linkage.WardLinkage
-
Given a set of data, computes the proximity and then the linkage.
- of(T[], Distance<T>) - Static method in class smile.clustering.linkage.WardLinkage
-
Given a set of data, computes the proximity and then the linkage.
- of(double[][]) - Static method in class smile.clustering.linkage.WPGMALinkage
-
Given a set of data, computes the proximity and then the linkage.
- of(T[], Distance<T>) - Static method in class smile.clustering.linkage.WPGMALinkage
-
Given a set of data, computes the proximity and then the linkage.
- of(double[][]) - Static method in class smile.clustering.linkage.WPGMCLinkage
-
Given a set of data, computes the proximity and then the linkage.
- of(T[], Distance<T>) - Static method in class smile.clustering.linkage.WPGMCLinkage
-
Given a set of data, computes the proximity and then the linkage.
- of(double[][], int[]) - Static method in class smile.feature.SignalNoiseRatio
-
Univariate feature ranking.
- of(double[][], int[]) - Static method in class smile.feature.SumSquaresRatio
-
Univariate feature ranking.
- of(double[][], int) - Static method in class smile.manifold.IsoMap
-
Runs the C-Isomap algorithm with Euclidean distance.
- of(double[][], int, int, boolean) - Static method in class smile.manifold.IsoMap
-
Runs the Isomap algorithm.
- of(T[], Distance<T>, int) - Static method in class smile.manifold.IsoMap
-
Runs the C-Isomap algorithm.
- of(T[], Distance<T>, int, int, boolean) - Static method in class smile.manifold.IsoMap
-
Runs the Isomap algorithm.
- of(double[][], int) - Static method in class smile.manifold.LaplacianEigenmap
-
Laplacian Eigenmaps with discrete weights.
- of(double[][], int, int, double) - Static method in class smile.manifold.LaplacianEigenmap
-
Laplacian Eigenmap with Gaussian kernel.
- of(T[], Distance<T>, int) - Static method in class smile.manifold.LaplacianEigenmap
-
Laplacian Eigenmaps with discrete weights.
- of(T[], Distance<T>, int, int, double) - Static method in class smile.manifold.LaplacianEigenmap
-
Laplacian Eigenmap with Gaussian kernel.
- of(double[][], int) - Static method in class smile.manifold.LLE
-
Runs the LLE algorithm.
- of(double[][], int, int) - Static method in class smile.manifold.LLE
-
Runs the LLE algorithm.
- of(double[][]) - Static method in class smile.manifold.UMAP
-
Runs the UMAP algorithm.
- of(T[], Distance<T>) - Static method in class smile.manifold.UMAP
-
Runs the UMAP algorithm.
- of(double[][], int) - Static method in class smile.manifold.UMAP
-
Runs the UMAP algorithm.
- of(T[], Distance<T>, int) - Static method in class smile.manifold.UMAP
-
Runs the UMAP algorithm.
- of(double[][], int, int, int, double, double, double, int, double) - Static method in class smile.manifold.UMAP
-
Runs the UMAP algorithm.
- of(T[], Distance<T>, int, int, int, double, double, double, int, double) - Static method in class smile.manifold.UMAP
-
Runs the UMAP algorithm.
- of(double[][]) - Static method in class smile.mds.IsotonicMDS
-
Fits Kruskal's non-metric MDS with default k = 2, tolerance = 1E-4 and maxIter = 200.
- of(double[][], int) - Static method in class smile.mds.IsotonicMDS
-
Fits Kruskal's non-metric MDS.
- of(double[][], Properties) - Static method in class smile.mds.IsotonicMDS
-
Fits Kruskal's non-metric MDS.
- of(double[][], int, double, int) - Static method in class smile.mds.IsotonicMDS
-
Fits Kruskal's non-metric MDS.
- of(double[][], double[][], double, int) - Static method in class smile.mds.IsotonicMDS
-
Fits Kruskal's non-metric MDS.
- of(double[][]) - Static method in class smile.mds.MDS
-
Fits the classical multidimensional scaling.
- of(double[][], int) - Static method in class smile.mds.MDS
-
Fits the classical multidimensional scaling.
- of(double[][], Properties) - Static method in class smile.mds.MDS
-
Fits the classical multidimensional scaling.
- of(double[][], int, boolean) - Static method in class smile.mds.MDS
-
Fits the classical multidimensional scaling.
- of(double[][]) - Static method in class smile.mds.SammonMapping
-
Fits Sammon's mapping with default k = 2, lambda = 0.2, tolerance = 1E-4 and maxIter = 100.
- of(double[][], int) - Static method in class smile.mds.SammonMapping
-
Fits Sammon's mapping.
- of(double[][], Properties) - Static method in class smile.mds.SammonMapping
-
Fits Sammon's mapping.
- of(double[][], int, double, double, double, int) - Static method in class smile.mds.SammonMapping
-
Fits Sammon's mapping.
- of(double[][], double[][], double, double, double, int) - Static method in class smile.mds.SammonMapping
-
Fits Sammon's mapping.
- of(T, int, double) - Static method in class smile.neighbor.Neighbor
-
Creates a neighbor object, of which key and object are the same.
- of(int, int) - Static method in class smile.projection.RandomProjection
-
Generates a non-sparse random projection.
- of(int, int) - Static method in interface smile.validation.Bootstrap
-
Bootstrap sampling.
- of(int[], int) - Static method in interface smile.validation.Bootstrap
-
Stratified bootstrap sampling.
- of(T[], int[], T[], int[], BiFunction<T[], int[], M>) - Static method in class smile.validation.ClassificationValidation
-
Trains and validates a model on a train/validation split.
- of(Bag[], T[], int[], BiFunction<T[], int[], M>) - Static method in class smile.validation.ClassificationValidation
-
Trains and validates a model on multiple train/validation split.
- of(Formula, DataFrame, DataFrame, BiFunction<Formula, DataFrame, M>) - Static method in class smile.validation.ClassificationValidation
-
Trains and validates a model on a train/validation split.
- of(Bag[], Formula, DataFrame, BiFunction<Formula, DataFrame, M>) - Static method in class smile.validation.ClassificationValidation
-
Trains and validates a model on multiple train/validation split.
- of(int, int) - Static method in interface smile.validation.CrossValidation
-
Creates a k-fold cross validation.
- of(int, int, boolean) - Static method in interface smile.validation.CrossValidation
-
Creates a k-fold cross validation.
- of(int[], int) - Static method in interface smile.validation.CrossValidation
-
Cross validation with stratified folds.
- of(int) - Static method in interface smile.validation.LOOCV
-
Returns the training sample index for each round.
- of(int[], int[]) - Static method in class smile.validation.metric.Accuracy
-
Calculates the classification accuracy.
- of(int[], int[]) - Static method in class smile.validation.metric.AdjustedRandIndex
-
Calculates the adjusted rand index.
- of(int[], double[]) - Static method in class smile.validation.metric.AUC
-
Calculates AUC for binary classifier.
- of(int[], int[]) - Static method in class smile.validation.metric.ConfusionMatrix
-
Creates the confusion matrix.
- of(int[], double[][]) - Static method in interface smile.validation.metric.CrossEntropy
-
Calculates the cross entropy for multiclass classifier.
- of(int[], int[]) - Static method in class smile.validation.metric.Error
-
Calculates the number of errors.
- of(int[], int[]) - Static method in class smile.validation.metric.Fallout
-
Calculates the false alarm rate.
- of(int[], int[]) - Static method in class smile.validation.metric.FDR
-
Calculates the false discovery rate.
- of(double, int[], int[]) - Static method in class smile.validation.metric.FScore
-
Calculates the F1 score.
- of(int[], double[]) - Static method in class smile.validation.metric.LogLoss
-
Calculates the Log Loss for binary classifier.
- of(double[], double[]) - Static method in class smile.validation.metric.MAD
-
Calculates the mean absolute deviation error.
- of(int[], int[]) - Static method in class smile.validation.metric.MatthewsCorrelation
-
Calculates Matthews correlation coefficient.
- of(double[], double[]) - Static method in class smile.validation.metric.MSE
-
Calculates the mean squared error.
- of(int[], int[]) - Static method in class smile.validation.metric.MutualInformation
-
Calculates the mutual information.
- of(int[], int[]) - Static method in class smile.validation.metric.Precision
-
Calculates the precision.
- of(double[], double[]) - Static method in class smile.validation.metric.R2
-
Calculates the R squared coefficient.
- of(int[], int[]) - Static method in class smile.validation.metric.RandIndex
-
Calculates the rand index.
- of(int[], int[]) - Static method in class smile.validation.metric.Recall
-
Calculates the recall/sensitivity.
- of(double[], double[]) - Static method in class smile.validation.metric.RMSE
-
Calculates the root mean squared error.
- of(double[], double[]) - Static method in class smile.validation.metric.RSS
-
Calculates the residual sum of squares.
- of(int[], int[]) - Static method in class smile.validation.metric.Sensitivity
-
Calculates the sensitivity.
- of(int[], int[]) - Static method in class smile.validation.metric.Specificity
-
Calculates the specificity.
- of(T[], double[], T[], double[], BiFunction<T[], double[], M>) - Static method in class smile.validation.RegressionValidation
-
Trains and validates a model on a train/validation split.
- of(Bag[], T[], double[], BiFunction<T[], double[], M>) - Static method in class smile.validation.RegressionValidation
-
Trains and validates a model on multiple train/validation split.
- of(Formula, DataFrame, DataFrame, BiFunction<Formula, DataFrame, M>) - Static method in class smile.validation.RegressionValidation
-
Trains and validates a model on a train/validation split.
- of(Bag[], Formula, DataFrame, BiFunction<Formula, DataFrame, M>) - Static method in class smile.validation.RegressionValidation
-
Trains and validates a model on multiple train/validation split.
- of(int, int, int) - Method in interface smile.vq.Neighborhood
-
Returns the changing rate of neighborhood at a given iteration.
- OLS - Class in smile.regression
-
Ordinary least squares.
- OLS() - Constructor for class smile.regression.OLS
-
- ols(double[], int) - Static method in class smile.timeseries.AR
-
Fits an autoregressive model with least squares method.
- ols(double[], int, boolean) - Static method in class smile.timeseries.AR
-
Fits an autoregressive model with least squares method.
- OneVersusOne<T> - Class in smile.classification
-
One-vs-one strategy for reducing the problem of
multiclass classification to multiple binary classification problems.
- OneVersusOne(Classifier<T>[][], PlattScaling[][]) - Constructor for class smile.classification.OneVersusOne
-
Constructor.
- OneVersusOne(Classifier<T>[][], PlattScaling[][], IntSet) - Constructor for class smile.classification.OneVersusOne
-
Constructor.
- OneVersusRest<T> - Class in smile.classification
-
One-vs-rest (or one-vs-all) strategy for reducing the problem of
multiclass classification to multiple binary classification problems.
- OneVersusRest(Classifier<T>[], PlattScaling[]) - Constructor for class smile.classification.OneVersusRest
-
Constructor.
- OneVersusRest(Classifier<T>[], PlattScaling[], IntSet) - Constructor for class smile.classification.OneVersusRest
-
Constructor.
- OnlineClassifier<T> - Interface in smile.classification
-
Classifier with online learning capability.
- OnlineRegression<T> - Interface in smile.regression
-
Regression model with online learning capability.
- oob - Variable in class smile.validation.Bag
-
The index of testing instances.
- order - Variable in class smile.base.cart.CART
-
An index of training values.
- order(DataFrame) - Static method in class smile.base.cart.CART
-
Returns the index of ordered samples for each ordinal column.
- ordinal - Variable in class smile.sequence.HMMLabeler
-
The lambda returns the ordinal numbers of symbols.
- OrdinalNode - Class in smile.base.cart
-
A node with a ordinal split variable (real-valued or ordinal categorical value).
- OrdinalNode(int, double, double, double, Node, Node) - Constructor for class smile.base.cart.OrdinalNode
-
Constructor.
- OrdinalSplit - Class in smile.base.cart
-
The data about of a potential split for a leaf node.
- OrdinalSplit(LeafNode, int, double, double, int, int, int, int, IntPredicate) - Constructor for class smile.base.cart.OrdinalSplit
-
Constructor.
- OUTLIER - Static variable in class smile.clustering.PartitionClustering
-
Cluster label for outliers or noises.
- OUTLIER - Static variable in interface smile.vq.VectorQuantizer
-
The label for outliers or noises.
- output() - Method in class smile.base.cart.DecisionNode
-
Returns the predicted value.
- output(int[], int[]) - Method in interface smile.base.cart.Loss
-
Calculate the node output.
- output() - Method in class smile.base.cart.RegressionNode
-
Returns the predicted value.
- output - Variable in class smile.base.mlp.Layer
-
The output vector.
- output() - Method in class smile.base.mlp.Layer
-
Returns the output vector.
- output - Variable in class smile.base.mlp.MultilayerPerceptron
-
The output layer.
- OutputFunction - Enum in smile.base.mlp
-
The output function of neural networks.
- outputGradient - Variable in class smile.base.mlp.Layer
-
The output gradient.
- OutputLayer - Class in smile.base.mlp
-
The output layer in the neural network.
- OutputLayer(int, int, OutputFunction, Cost) - Constructor for class smile.base.mlp.OutputLayer
-
Constructor.
- OutputLayerBuilder - Class in smile.base.mlp
-
The builder of output layers.
- OutputLayerBuilder(int, OutputFunction, Cost) - Constructor for class smile.base.mlp.OutputLayerBuilder
-
Constructor.
- p - Variable in class smile.base.mlp.Layer
-
The number of input variables.
- p - Variable in class smile.base.mlp.MultilayerPerceptron
-
The dimensionality of input data.
- p(int[], int[]) - Method in class smile.sequence.HMM
-
Returns the joint probability of an observation sequence along a state
sequence given this HMM.
- p(int[]) - Method in class smile.sequence.HMM
-
Returns the probability of an observation sequence given this HMM.
- p(T[], int[]) - Method in class smile.sequence.HMMLabeler
-
Returns the joint probability of an observation sequence along a state
sequence.
- p(T[]) - Method in class smile.sequence.HMMLabeler
-
Returns the probability of an observation sequence.
- p() - Method in class smile.timeseries.AR
-
Returns the order of AR.
- p() - Method in class smile.timeseries.ARMA
-
Returns the order of AR.
- pacf(double[], int) - Static method in interface smile.timeseries.TimeSeries
-
Partial autocorrelation function.
- partition(int) - Method in class smile.clustering.HierarchicalClustering
-
Cuts a tree into several groups by specifying the desired number.
- partition(double) - Method in class smile.clustering.HierarchicalClustering
-
Cuts a tree into several groups by specifying the cut height.
- PartitionClustering - Class in smile.clustering
-
Partition clustering.
- PartitionClustering(int, int[]) - Constructor for class smile.clustering.PartitionClustering
-
Constructor.
- PCA - Class in smile.projection
-
Principal component analysis.
- PCA(double[], double[], Matrix) - Constructor for class smile.projection.PCA
-
Constructor.
- pierce(double[], int) - Static method in class smile.timeseries.BoxTest
-
Box-Pierce test.
- PlattScaling - Class in smile.classification
-
Platt scaling or Platt calibration is a way of transforming the outputs
of a classification model into a probability distribution over classes.
- PlattScaling(double, double) - Constructor for class smile.classification.PlattScaling
-
Constructor.
- points() - Method in class smile.neighbor.lsh.Bucket
-
Returns the points in the bucket.
- Poisson - Interface in smile.glm.model
-
The response variable is of Poisson distribution.
- posteriori(double[]) - Method in class smile.base.cart.DecisionNode
-
Returns the class probability.
- posteriori(int[], double[]) - Static method in class smile.base.cart.DecisionNode
-
Returns the class probability.
- posteriori - Variable in class smile.validation.ClassificationValidation
-
The posteriori probability of prediction if the model is a soft classifier.
- PosterioriModel - Class in smile.neighbor.lsh
-
Pre-computed posteriori probabilities for generating multiple probes.
- PosterioriModel(MultiProbeHash, MultiProbeSample[], int, double) - Constructor for class smile.neighbor.lsh.PosterioriModel
-
Constructor.
- pr - Variable in class smile.neighbor.lsh.PrH
-
The probability
- precision - Variable in class smile.validation.ClassificationMetrics
-
The precision on validation data.
- Precision - Class in smile.validation.metric
-
The precision or positive predictive value (PPV) is ratio of true positives
to combined true and false positives, which is different from sensitivity.
- Precision() - Constructor for class smile.validation.metric.Precision
-
- predicate() - Method in class smile.base.cart.NominalSplit
-
- predicate() - Method in class smile.base.cart.OrdinalSplit
-
- predicate() - Method in class smile.base.cart.Split
-
Returns the lambda that tests on the split feature.
- predict(Tuple) - Method in class smile.base.cart.InternalNode
-
Evaluates the tree over an instance.
- predict(Tuple) - Method in class smile.base.cart.LeafNode
-
- predict(Tuple) - Method in interface smile.base.cart.Node
-
Evaluate the tree over an instance.
- predict(Tuple) - Method in class smile.base.cart.NominalNode
-
- predict(Tuple) - Method in class smile.base.cart.OrdinalNode
-
- predict(Tuple) - Method in class smile.classification.AdaBoost
-
- predict(Tuple, double[]) - Method in class smile.classification.AdaBoost
-
Predicts the class label of an instance and also calculate a posteriori
probabilities.
- predict(T) - Method in interface smile.classification.Classifier
-
Predicts the class label of an instance.
- predict(T[]) - Method in interface smile.classification.Classifier
-
Predicts the class labels of an array of instances.
- predict(Tuple) - Method in interface smile.classification.DataFrameClassifier
-
Predicts the class label of an instance.
- predict(DataFrame) - Method in interface smile.classification.DataFrameClassifier
-
Predicts the class labels of a data frame.
- predict(Tuple) - Method in class smile.classification.DecisionTree
-
- predict(Tuple, double[]) - Method in class smile.classification.DecisionTree
-
Predicts the class label of an instance and also calculate a posteriori
probabilities.
- predict(int[]) - Method in class smile.classification.DiscreteNaiveBayes
-
Predict the class of an instance.
- predict(int[], double[]) - Method in class smile.classification.DiscreteNaiveBayes
-
Predict the class of an instance.
- predict(SparseArray) - Method in class smile.classification.DiscreteNaiveBayes
-
Predict the class of an instance.
- predict(SparseArray, double[]) - Method in class smile.classification.DiscreteNaiveBayes
-
Predict the class of an instance.
- predict(double[]) - Method in class smile.classification.FLD
-
- predict(Tuple) - Method in class smile.classification.GradientTreeBoost
-
- predict(Tuple, double[]) - Method in class smile.classification.GradientTreeBoost
-
- predict(double) - Method in class smile.classification.IsotonicRegressionScaling
-
Returns the posterior probability estimate P(y = 1 | x).
- predict(T) - Method in class smile.classification.KNN
-
- predict(T, double[]) - Method in class smile.classification.KNN
-
- predict(double[]) - Method in class smile.classification.LDA
-
- predict(double[], double[]) - Method in class smile.classification.LDA
-
- predict(double[]) - Method in class smile.classification.LogisticRegression.Binomial
-
- predict(double[], double[]) - Method in class smile.classification.LogisticRegression.Binomial
-
- predict(double[]) - Method in class smile.classification.LogisticRegression.Multinomial
-
- predict(double[], double[]) - Method in class smile.classification.LogisticRegression.Multinomial
-
- predict(int[]) - Method in class smile.classification.Maxent.Binomial
-
- predict(int[], double[]) - Method in class smile.classification.Maxent.Binomial
-
- predict(int[]) - Method in class smile.classification.Maxent.Multinomial
-
- predict(int[], double[]) - Method in class smile.classification.Maxent.Multinomial
-
- predict(double[], double[]) - Method in class smile.classification.MLP
-
- predict(double[]) - Method in class smile.classification.MLP
-
- predict(double[]) - Method in class smile.classification.NaiveBayes
-
Predict the class of an instance.
- predict(double[], double[]) - Method in class smile.classification.NaiveBayes
-
Predict the class of an instance.
- predict(T) - Method in class smile.classification.OneVersusOne
-
Prediction is based on voting.
- predict(T, double[]) - Method in class smile.classification.OneVersusOne
-
Prediction is based posteriori probability estimation.
- predict(T) - Method in class smile.classification.OneVersusRest
-
- predict(T, double[]) - Method in class smile.classification.OneVersusRest
-
- predict(double[]) - Method in class smile.classification.QDA
-
- predict(double[], double[]) - Method in class smile.classification.QDA
-
- predict(Tuple) - Method in class smile.classification.RandomForest
-
- predict(Tuple, double[]) - Method in class smile.classification.RandomForest
-
- predict(T) - Method in class smile.classification.RBFNetwork
-
- predict(T, double[]) - Method in interface smile.classification.SoftClassifier
-
Predicts the class label of an instance and also calculate a posteriori
probabilities.
- predict(T[], double[][]) - Method in interface smile.classification.SoftClassifier
-
Predicts the class labels of an array of instances.
- predict(SparseArray) - Method in class smile.classification.SparseLogisticRegression.Binomial
-
- predict(SparseArray, double[]) - Method in class smile.classification.SparseLogisticRegression.Binomial
-
- predict(SparseArray) - Method in class smile.classification.SparseLogisticRegression.Multinomial
-
- predict(SparseArray, double[]) - Method in class smile.classification.SparseLogisticRegression.Multinomial
-
- predict(T) - Method in class smile.classification.SVM
-
- predict(U) - Method in class smile.clustering.CentroidClustering
-
Classifies a new observation.
- predict(T) - Method in class smile.clustering.DBSCAN
-
Classifies a new observation.
- predict(double[]) - Method in class smile.clustering.DENCLUE
-
Classifies a new observation.
- predict(T) - Method in class smile.clustering.MEC
-
Cluster a new instance.
- predict(Tuple) - Method in class smile.glm.GLM
-
Predicts the mean response.
- predict(DataFrame) - Method in class smile.glm.GLM
-
Predicts the mean response.
- predict(Tuple) - Method in interface smile.regression.DataFrameRegression
-
Predicts the dependent variable of a tuple instance.
- predict(DataFrame) - Method in interface smile.regression.DataFrameRegression
-
Predicts the dependent variables of a data frame.
- predict(T) - Method in class smile.regression.GaussianProcessRegression
-
- predict(T, double[]) - Method in class smile.regression.GaussianProcessRegression
-
Predicts the mean and standard deviation of an instance.
- predict(Tuple) - Method in class smile.regression.GradientTreeBoost
-
- predict(T) - Method in class smile.regression.KernelMachine
-
- predict(double[]) - Method in class smile.regression.LinearModel
-
- predict(Tuple) - Method in class smile.regression.LinearModel
-
- predict(DataFrame) - Method in class smile.regression.LinearModel
-
- predict(double[]) - Method in class smile.regression.MLP
-
- predict(Tuple) - Method in class smile.regression.RandomForest
-
- predict(T) - Method in class smile.regression.RBFNetwork
-
- predict(T) - Method in interface smile.regression.Regression
-
Predicts the dependent variable of an instance.
- predict(T[]) - Method in interface smile.regression.Regression
-
Predicts the dependent variables of an array of instances.
- predict(Tuple) - Method in class smile.regression.RegressionTree
-
- predict(Tuple[]) - Method in class smile.sequence.CRF
-
Returns the most likely label sequence given the feature sequence by the
forward-backward algorithm.
- predict(T[]) - Method in class smile.sequence.CRFLabeler
-
Returns the most likely label sequence given the feature sequence by the
forward-backward algorithm.
- predict(int[]) - Method in class smile.sequence.HMM
-
Returns the most likely state sequence given the observation sequence by
the Viterbi algorithm, which maximizes the probability of
P(I | O, HMM).
- predict(T[]) - Method in class smile.sequence.HMMLabeler
-
Returns the most likely state sequence given the observation sequence by
the Viterbi algorithm, which maximizes the probability of
P(I | O, HMM).
- predict(T[]) - Method in interface smile.sequence.SequenceLabeler
-
Predicts the sequence labels.
- prediction - Variable in class smile.validation.ClassificationValidation
-
The model prediction.
- prediction - Variable in class smile.validation.RegressionValidation
-
The model prediction.
- predictors(Tuple) - Method in class smile.base.cart.CART
-
Returns the predictors by the model formula if it is not null.
- PrH - Class in smile.neighbor.lsh
-
Probability for given query object and hash function.
- PrH(int, double) - Constructor for class smile.neighbor.lsh.PrH
-
Constructor.
- prh - Variable in class smile.neighbor.lsh.PrZ
-
The n_i probabilities for h_m hash function,
where n_i = u_i_max - u_i_min + 1.
- priori - Variable in class smile.classification.ClassLabels
-
The estimated priori probabilities.
- priori() - Method in class smile.classification.DiscreteNaiveBayes
-
Returns a priori probabilities.
- priori() - Method in class smile.classification.LDA
-
Returns a priori probabilities.
- priori() - Method in class smile.classification.NaiveBayes
-
Returns a priori probabilities.
- priori() - Method in class smile.classification.QDA
-
Returns a priori probabilities.
- ProbabilisticClassificationMetric - Interface in smile.validation.metric
-
An abstract interface to measure the probabilistic classification performance.
- ProbabilisticPCA - Class in smile.projection
-
Probabilistic principal component analysis.
- ProbabilisticPCA(double, double[], Matrix, Matrix) - Constructor for class smile.projection.ProbabilisticPCA
-
Constructor.
- Probe - Class in smile.neighbor.lsh
-
Probe to check for nearest neighbors.
- Probe(int[]) - Constructor for class smile.neighbor.lsh.Probe
-
Constructor.
- project(double[]) - Method in class smile.classification.FLD
-
- project(double[][]) - Method in class smile.classification.FLD
-
- project(T) - Method in class smile.projection.KPCA
-
- project(T[]) - Method in class smile.projection.KPCA
-
- project(double[]) - Method in interface smile.projection.LinearProjection
-
- project(double[][]) - Method in interface smile.projection.LinearProjection
-
- project(double[]) - Method in class smile.projection.PCA
-
- project(double[][]) - Method in class smile.projection.PCA
-
- project(double[]) - Method in class smile.projection.ProbabilisticPCA
-
- project(double[][]) - Method in class smile.projection.ProbabilisticPCA
-
- project(T) - Method in interface smile.projection.Projection
-
Project a data point to the feature space.
- project(T[]) - Method in interface smile.projection.Projection
-
Project a set of data to the feature space.
- Projection<T> - Interface in smile.projection
-
A projection is a kind of feature extraction technique that transforms data
from the input space to a feature space, linearly or nonlinearly.
- propagate(double[]) - Method in class smile.base.mlp.Layer
-
Propagates signals from a lower layer to this layer.
- propagate(double[]) - Method in class smile.base.mlp.MultilayerPerceptron
-
Propagates the signals through the neural network.
- proportion - Variable in class smile.mds.MDS
-
The proportion of variance contained in each principal component.
- proximity(double[][]) - Static method in class smile.clustering.linkage.Linkage
-
Calculate the proximity matrix (linearized in column major) with Euclidean distance.
- proximity(T[], Distance<T>) - Static method in class smile.clustering.linkage.Linkage
-
Calculate the proximity matrix (linearized in column major).
- prune(DataFrame) - Method in class smile.classification.DecisionTree
-
Returns a new decision tree by reduced error pruning.
- prune(DataFrame) - Method in class smile.classification.RandomForest
-
Returns a new random forest by reduced error pruning.
- PrZ - Class in smile.neighbor.lsh
-
Probability list of all buckets for given query object.
- PrZ(int, PrH[]) - Constructor for class smile.neighbor.lsh.PrZ
-
Constructor.
- put(double[], E) - Method in class smile.neighbor.LSH
-
Insert an item into the hash table.
- put(double[], E) - Method in class smile.neighbor.MutableLSH
-
- put(K, V) - Method in class smile.neighbor.SNLSH
-
Adds a new item.
- pvalue() - Method in class smile.regression.LinearModel
-
Returns the p-value of goodness-of-fit test.
- pvalue - Variable in class smile.timeseries.BoxTest
-
p-value
- R2 - Class in smile.validation.metric
-
R2.
- R2() - Constructor for class smile.validation.metric.R2
-
- r2 - Variable in class smile.validation.RegressionMetrics
-
The R-squared score on validation data.
- radius - Variable in class smile.clustering.DBSCAN
-
The neighborhood radius.
- radius - Variable in class smile.clustering.MEC
-
The range of neighborhood.
- RandIndex - Class in smile.validation.metric
-
Rand Index.
- RandIndex() - Constructor for class smile.validation.metric.RandIndex
-
- random() - Method in class smile.validation.Hyperparameters
-
Generates a stream of hyperparameters for random search.
- RandomForest - Class in smile.classification
-
Random forest for classification.
- RandomForest(Formula, int, RandomForest.Model[], ClassificationMetrics, double[]) - Constructor for class smile.classification.RandomForest
-
Constructor.
- RandomForest(Formula, int, RandomForest.Model[], ClassificationMetrics, double[], IntSet) - Constructor for class smile.classification.RandomForest
-
Constructor.
- RandomForest - Class in smile.regression
-
Random forest for regression.
- RandomForest(Formula, RandomForest.Model[], RegressionMetrics, double[]) - Constructor for class smile.regression.RandomForest
-
Constructor.
- RandomForest.Model - Class in smile.classification
-
The base model.
- RandomForest.Model - Class in smile.regression
-
The base model.
- RandomProjection - Class in smile.projection
-
Random projection is a promising dimensionality reduction technique for
learning mixtures of Gaussians.
- RandomProjection(Matrix) - Constructor for class smile.projection.RandomProjection
-
Constructor.
- range(E, double, List<Neighbor<E, E>>) - Method in class smile.neighbor.BKTree
-
- range(E, int, List<Neighbor<E, E>>) - Method in class smile.neighbor.BKTree
-
Search the neighbors in the given radius of query object, i.e.
- range(E, double, List<Neighbor<E, E>>) - Method in class smile.neighbor.CoverTree
-
- range(double[], double, List<Neighbor<double[], E>>) - Method in class smile.neighbor.KDTree
-
- range(T, double, List<Neighbor<T, T>>) - Method in class smile.neighbor.LinearSearch
-
- range(double[], double, List<Neighbor<double[], E>>) - Method in class smile.neighbor.LSH
-
- range(double[], double, List<Neighbor<double[], E>>) - Method in class smile.neighbor.MPLSH
-
- range(double[], double, List<Neighbor<double[], E>>, double, int) - Method in class smile.neighbor.MPLSH
-
Search the neighbors in the given radius of query object, i.e.
- range(K, double, List<Neighbor<K, V>>) - Method in interface smile.neighbor.RNNSearch
-
Search the neighbors in the given radius of query object, i.e.
- range(K, double, List<Neighbor<K, V>>) - Method in class smile.neighbor.SNLSH
-
- rank(double[][], int[]) - Method in interface smile.feature.FeatureRanking
-
Univariate feature ranking.
- rank(double[][], int[]) - Method in class smile.feature.SignalNoiseRatio
-
- rank(double[][], int[]) - Method in class smile.feature.SumSquaresRatio
-
- RBF<T> - Class in smile.base.rbf
-
A neuron in radial basis function network.
- RBF(T, RadialBasisFunction, Metric<T>) - Constructor for class smile.base.rbf.RBF
-
Constructor.
- RBFNetwork<T> - Class in smile.classification
-
Radial basis function networks.
- RBFNetwork(int, RBF<T>[], Matrix, boolean) - Constructor for class smile.classification.RBFNetwork
-
Constructor.
- RBFNetwork(int, RBF<T>[], Matrix, boolean, IntSet) - Constructor for class smile.classification.RBFNetwork
-
Constructor.
- RBFNetwork<T> - Class in smile.regression
-
Radial basis function network.
- RBFNetwork(RBF<T>[], double[], boolean) - Constructor for class smile.regression.RBFNetwork
-
Constructor.
- RDA - Class in smile.classification
-
Regularized discriminant analysis.
- RDA(double[], double[][], double[][], Matrix[]) - Constructor for class smile.classification.RDA
-
Constructor.
- RDA(double[], double[][], double[][], Matrix[], IntSet) - Constructor for class smile.classification.RDA
-
Constructor.
- Recall - Class in smile.validation.metric
-
In information retrieval area, sensitivity is called recall.
- Recall() - Constructor for class smile.validation.metric.Recall
-
- rectifier() - Static method in interface smile.base.mlp.ActivationFunction
-
The rectifier activation function max(0, x).
- rectifier(int) - Static method in class smile.base.mlp.Layer
-
Returns a hidden layer with rectified linear activation function.
- Regression<T> - Interface in smile.regression
-
Regression analysis includes any techniques for modeling and analyzing
the relationship between a dependent variable and one or more independent
variables.
- regression(int, T[], double[], BiFunction<T[], double[], M>) - Static method in interface smile.validation.Bootstrap
-
Runs regression bootstrap validation.
- regression(int, Formula, DataFrame, BiFunction<Formula, DataFrame, M>) - Static method in interface smile.validation.Bootstrap
-
Runs regression bootstrap validation.
- regression(int, T[], double[], BiFunction<T[], double[], M>) - Static method in interface smile.validation.CrossValidation
-
Runs regression cross validation.
- regression(int, Formula, DataFrame, BiFunction<Formula, DataFrame, M>) - Static method in interface smile.validation.CrossValidation
-
Runs regression cross validation.
- regression(T[], double[], BiFunction<T[], double[], M>) - Static method in interface smile.validation.LOOCV
-
Runs leave-one-out cross validation tests.
- regression(Formula, DataFrame, BiFunction<Formula, DataFrame, DataFrameRegression>) - Static method in interface smile.validation.LOOCV
-
Runs leave-one-out cross validation tests.
- Regression.Metric - Class in smile.regression
-
Regression metrics.
- RegressionMetric - Interface in smile.validation.metric
-
An abstract interface to measure the regression performance.
- RegressionMetrics - Class in smile.validation
-
The regression validation metrics.
- RegressionMetrics(double, double, int, double, double, double, double, double) - Constructor for class smile.validation.RegressionMetrics
-
Constructor.
- RegressionNode - Class in smile.base.cart
-
A leaf node in regression tree.
- RegressionNode(int, double, double, double) - Constructor for class smile.base.cart.RegressionNode
-
Constructor.
- RegressionTree - Class in smile.regression
-
Regression tree.
- RegressionTree(DataFrame, Loss, StructField, int, int, int, int, int[], int[][]) - Constructor for class smile.regression.RegressionTree
-
Constructor.
- RegressionValidation<M> - Class in smile.validation
-
Regression model validation results.
- RegressionValidation(M, double[], double[], RegressionMetrics) - Constructor for class smile.validation.RegressionValidation
-
Constructor.
- RegressionValidations<M> - Class in smile.validation
-
Regression model validation results.
- RegressionValidations(List<RegressionValidation<M>>) - Constructor for class smile.validation.RegressionValidations
-
Constructor.
- regressors - Variable in class smile.regression.GaussianProcessRegression
-
The regressors.
- remove(int) - Method in class smile.neighbor.lsh.Bucket
-
Removes a point from bucket.
- remove(double[], E) - Method in class smile.neighbor.MutableLSH
-
Remove an entry from the hash table.
- removeEdge(Neuron) - Method in class smile.vq.hebb.Neuron
-
Removes an edge.
- replace(Node, Node) - Method in class smile.base.cart.InternalNode
-
Returns a new internal node with children replaced.
- replace(Node, Node) - Method in class smile.base.cart.NominalNode
-
- replace(Node, Node) - Method in class smile.base.cart.OrdinalNode
-
- residual() - Method in interface smile.base.cart.Loss
-
Returns the residual vector.
- residuals() - Method in class smile.regression.LinearModel
-
Returns the residuals, that is response minus fitted values.
- residuals - Variable in class smile.regression.Regression.Metric
-
The residuals, that is response minus fitted values.
- residuals() - Method in class smile.timeseries.AR
-
Returns the residuals, that is response minus fitted values.
- residuals() - Method in class smile.timeseries.ARMA
-
Returns the residuals, that is response minus fitted values.
- response - Variable in class smile.base.cart.CART
-
The schema of response variable.
- response() - Method in interface smile.base.cart.Loss
-
Returns the response variable for next iteration.
- rho - Variable in class smile.base.mlp.MultilayerPerceptron
-
The discounting factor for the history/coming gradient in RMSProp.
- RidgeRegression - Class in smile.regression
-
Ridge Regression.
- RidgeRegression() - Constructor for class smile.regression.RidgeRegression
-
- rmsBiasGradient - Variable in class smile.base.mlp.Layer
-
The accumulate bias gradient.
- RMSE - Variable in class smile.regression.Regression.Metric
-
Root mean squared error.
- RMSE - Class in smile.validation.metric
-
Root mean squared error.
- RMSE() - Constructor for class smile.validation.metric.RMSE
-
- rmse - Variable in class smile.validation.RegressionMetrics
-
The root mean squared error on validation data.
- rmsWeightGradient - Variable in class smile.base.mlp.Layer
-
The accumulate weight gradient.
- RNNSearch<K,V> - Interface in smile.neighbor
-
A range nearest neighbor search retrieves the nearest neighbors to a query
in a range.
- RobustStandardizer - Class in smile.feature
-
Robustly standardizes numeric feature by subtracting
the median and dividing by the IQR.
- RobustStandardizer(StructType, double[], double[]) - Constructor for class smile.feature.RobustStandardizer
-
Constructor.
- root - Variable in class smile.base.cart.CART
-
The root of decision tree.
- root() - Method in class smile.base.cart.CART
-
Returs the root node.
- rounds - Variable in class smile.validation.ClassificationValidations
-
The multiple round validations.
- rounds - Variable in class smile.validation.RegressionValidations
-
The multiple round validations.
- RSquared() - Method in class smile.regression.LinearModel
-
Returns R2 statistic.
- RSquared - Variable in class smile.regression.Regression.Metric
-
R2 coefficient of determination.
- RSquared() - Method in class smile.timeseries.AR
-
Returns R2 statistic.
- RSquared() - Method in class smile.timeseries.ARMA
-
Returns R2 statistic.
- RSS() - Method in class smile.regression.LinearModel
-
Returns the residual sum of squares.
- RSS - Variable in class smile.regression.Regression.Metric
-
Residual sum of squares.
- RSS() - Method in class smile.timeseries.AR
-
Returns the residual sum of squares.
- RSS() - Method in class smile.timeseries.ARMA
-
Returns the residual sum of squares.
- RSS - Class in smile.validation.metric
-
Residual sum of squares.
- RSS() - Constructor for class smile.validation.metric.RSS
-
- rss - Variable in class smile.validation.RegressionMetrics
-
The residual sum of squares.
- run(int, Supplier<T>) - Static method in class smile.clustering.PartitionClustering
-
Runs a clustering algorithm multiple times and return the best one
(e.g.
- SammonMapping - Class in smile.mds
-
The Sammon's mapping is an iterative technique for making interpoint
distances in the low-dimensional projection as close as possible to the
interpoint distances in the high-dimensional object.
- SammonMapping(double, double[][]) - Constructor for class smile.mds.SammonMapping
-
Constructor.
- sample(int) - Method in class smile.regression.GaussianProcessRegression.JointPrediction
-
Draw samples from Gaussian process.
- samples - Variable in class smile.base.cart.CART
-
The samples for training this node.
- samples - Variable in class smile.validation.Bag
-
The random samples.
- scale() - Method in class smile.classification.ClassLabels
-
Returns the nominal scale for the class labels.
- scale(double) - Method in class smile.classification.PlattScaling
-
Returns the posterior probability estimate P(y = 1 | x).
- Scaler - Class in smile.feature
-
Scales all numeric variables into the range [0, 1].
- Scaler(StructType, double[], double[]) - Constructor for class smile.feature.Scaler
-
Constructor.
- schema - Variable in class smile.base.cart.CART
-
The schema of predictors.
- schema() - Method in class smile.classification.AdaBoost
-
- schema() - Method in interface smile.classification.DataFrameClassifier
-
Returns the design matrix schema.
- schema() - Method in class smile.classification.DecisionTree
-
- schema() - Method in class smile.classification.GradientTreeBoost
-
- schema() - Method in class smile.classification.RandomForest
-
- schema - Variable in class smile.feature.MaxAbsScaler
-
The schema of data.
- schema() - Method in interface smile.regression.DataFrameRegression
-
Returns the schema of predictors.
- schema() - Method in class smile.regression.GradientTreeBoost
-
- schema() - Method in class smile.regression.LinearModel
-
- schema() - Method in class smile.regression.RandomForest
-
- schema() - Method in class smile.regression.RegressionTree
-
- score() - Method in class smile.base.cart.InternalNode
-
Returns the split score (reduction of impurity).
- score(T) - Method in class smile.base.svm.KernelMachine
-
Returns the decision function value.
- score(T) - Method in interface smile.classification.Classifier
-
The classification score function.
- score(int[], int[]) - Method in class smile.validation.metric.Accuracy
-
- score(int[], int[]) - Method in class smile.validation.metric.AdjustedMutualInformation
-
- score(int[], int[]) - Method in class smile.validation.metric.AdjustedRandIndex
-
- score(int[], double[]) - Method in class smile.validation.metric.AUC
-
- score(int[], int[]) - Method in interface smile.validation.metric.ClassificationMetric
-
Returns a score to measure the quality of classification.
- score(int[], int[]) - Method in interface smile.validation.metric.ClusteringMetric
-
Returns a score to measure the quality of clustering.
- score(int[], int[]) - Method in class smile.validation.metric.Error
-
- score(int[], int[]) - Method in class smile.validation.metric.Fallout
-
- score(int[], int[]) - Method in class smile.validation.metric.FDR
-
- score(int[], int[]) - Method in class smile.validation.metric.FScore
-
- score(int[], double[]) - Method in class smile.validation.metric.LogLoss
-
- score(double[], double[]) - Method in class smile.validation.metric.MAD
-
- score(int[], int[]) - Method in class smile.validation.metric.MatthewsCorrelation
-
- score(double[], double[]) - Method in class smile.validation.metric.MSE
-
- score(int[], int[]) - Method in class smile.validation.metric.MutualInformation
-
- score(int[], int[]) - Method in class smile.validation.metric.NormalizedMutualInformation
-
- score(int[], int[]) - Method in class smile.validation.metric.Precision
-
- score(int[], double[]) - Method in interface smile.validation.metric.ProbabilisticClassificationMetric
-
Returns a score to measure the quality of classification.
- score(double[], double[]) - Method in class smile.validation.metric.R2
-
- score(int[], int[]) - Method in class smile.validation.metric.RandIndex
-
- score(int[], int[]) - Method in class smile.validation.metric.Recall
-
- score(double[], double[]) - Method in interface smile.validation.metric.RegressionMetric
-
Returns a score to measure the quality of regression.
- score(double[], double[]) - Method in class smile.validation.metric.RMSE
-
- score(double[], double[]) - Method in class smile.validation.metric.RSS
-
- score(int[], int[]) - Method in class smile.validation.metric.Sensitivity
-
- score(int[], int[]) - Method in class smile.validation.metric.Specificity
-
- scores - Variable in class smile.mds.MDS
-
The component scores.
- scoreTime - Variable in class smile.validation.ClassificationMetrics
-
The time in milliseconds of scoring the validation data.
- scoreTime - Variable in class smile.validation.RegressionMetrics
-
The time in milliseconds of scoring the validation data.
- sd() - Method in class smile.neighbor.lsh.HashValueParzenModel
-
Returns the standard deviation.
- sd - Variable in class smile.regression.GaussianProcessRegression.JointPrediction
-
The standard deviation of predictive distribution at query points.
- sd - Variable in class smile.regression.GaussianProcessRegression
-
The standard deviation of responsible variable.
- sd - Variable in class smile.validation.ClassificationValidations
-
The standard deviation of metrics.
- sd - Variable in class smile.validation.RegressionValidations
-
The standard deviation of metrics.
- seed(T[], T[], int[], ToDoubleBiFunction<T, T>) - Static method in class smile.clustering.PartitionClustering
-
Initialize cluster membership of input objects with K-Means++ algorithm.
- seed(int, double[][]) - Static method in class smile.vq.NeuralGas
-
Selects random samples as initial neurons of Neural Gas.
- sensitivity - Variable in class smile.validation.ClassificationMetrics
-
The sensitivity on validation data.
- Sensitivity - Class in smile.validation.metric
-
Sensitivity or true positive rate (TPR) (also called hit rate, recall) is a
statistical measures of the performance of a binary classification test.
- Sensitivity() - Constructor for class smile.validation.metric.Sensitivity
-
- SequenceLabeler<T> - Interface in smile.sequence
-
A sequence labeler assigns a class label to each position of the sequence.
- setEdgeAge(Neuron, int) - Method in class smile.vq.hebb.Neuron
-
Sets the age of edge.
- setLearningRate(TimeFunction) - Method in class smile.base.mlp.MultilayerPerceptron
-
Sets the learning rate.
- setLearningRate(double) - Method in class smile.classification.LogisticRegression
-
Sets the learning rate of stochastic gradient descent.
- setLearningRate(double) - Method in class smile.classification.Maxent
-
Sets the learning rate of stochastic gradient descent.
- setLearningRate(double) - Method in class smile.classification.SparseLogisticRegression
-
Sets the learning rate of stochastic gradient descent.
- setLearningRate(double) - Method in class smile.projection.GHA
-
Set the learning rate.
- setMomentum(TimeFunction) - Method in class smile.base.mlp.MultilayerPerceptron
-
Sets the momentum factor.
- setProb(PrZ[]) - Method in class smile.neighbor.lsh.Probe
-
Calculate the probability of the probe.
- setProjection(int) - Method in class smile.projection.PCA
-
Set the projection matrix with given number of principal components.
- setProjection(double) - Method in class smile.projection.PCA
-
Set the projection matrix with top principal components that contain
(more than) the given percentage of variance.
- setRMSProp(double, double) - Method in class smile.base.mlp.MultilayerPerceptron
-
Sets RMSProp parameters.
- setWeightDecay(double) - Method in class smile.base.mlp.MultilayerPerceptron
-
Sets the weight decay factor.
- shap(DataFrame) - Method in class smile.base.cart.CART
-
Returns the average of absolute SHAP values over a data frame.
- shap(Tuple) - Method in class smile.base.cart.CART
-
- shap(DataFrame) - Method in class smile.classification.GradientTreeBoost
-
Returns the average of absolute SHAP values over a data frame.
- shap(Tuple) - Method in class smile.classification.GradientTreeBoost
-
- SHAP<T> - Interface in smile.feature
-
SHAP (SHapley Additive exPlanations) is a game theoretic approach to
explain the output of any machine learning model.
- shap(T) - Method in interface smile.feature.SHAP
-
Returns the SHAP values.
- shap(Stream<T>) - Method in interface smile.feature.SHAP
-
Returns the average of absolute SHAP values over a data set.
- shap(Tuple) - Method in interface smile.feature.TreeSHAP
-
- shap(DataFrame) - Method in interface smile.feature.TreeSHAP
-
Returns the average of absolute SHAP values over a data frame.
- shift() - Method in class smile.neighbor.lsh.Probe
-
This operation shifts to the right the last nonzero component if
it is equal to one and if it is not the last one.
- SIB - Class in smile.clustering
-
The Sequential Information Bottleneck algorithm.
- SIB(double, double[][], int[]) - Constructor for class smile.clustering.SIB
-
Constructor.
- sigmoid() - Static method in interface smile.base.mlp.ActivationFunction
-
Logistic sigmoid function: sigmoid(v)=1/(1+exp(-v)).
- sigmoid(int) - Static method in class smile.base.mlp.Layer
-
Returns a hidden layer with sigmoid activation function.
- SignalNoiseRatio - Class in smile.feature
-
The signal-to-noise (S2N) metric ratio is a univariate feature ranking metric,
which can be used as a feature selection criterion for binary classification
problems.
- SignalNoiseRatio() - Constructor for class smile.feature.SignalNoiseRatio
-
- SingleLinkage - Class in smile.clustering.linkage
-
Single linkage.
- SingleLinkage(double[][]) - Constructor for class smile.clustering.linkage.SingleLinkage
-
Constructor.
- SingleLinkage(int, float[]) - Constructor for class smile.clustering.linkage.SingleLinkage
-
Constructor.
- size() - Method in class smile.association.FPGrowth
-
Returns the number transactions in the database.
- size() - Method in class smile.association.FPTree
-
Returns the number transactions in the database.
- size() - Method in class smile.base.cart.CART
-
Returns the number of nodes in the tree.
- size() - Method in class smile.base.cart.InternalNode
-
- size - Variable in class smile.base.cart.LeafNode
-
The number of samples in the node.
- size() - Method in class smile.base.cart.LeafNode
-
- size() - Method in interface smile.base.cart.Node
-
Returns the number of samples in the node.
- size() - Method in class smile.classification.AdaBoost
-
Returns the number of trees in the model.
- size() - Method in class smile.classification.GradientTreeBoost
-
Returns the number of trees in the model.
- size() - Method in class smile.classification.RandomForest
-
Returns the number of trees in the model.
- size() - Method in class smile.clustering.linkage.Linkage
-
Returns the proximity matrix size.
- size - Variable in class smile.clustering.PartitionClustering
-
The number of observations in each cluster.
- size() - Method in class smile.regression.GradientTreeBoost
-
Returns the number of trees in the model.
- size() - Method in class smile.regression.RandomForest
-
Returns the number of trees in the model.
- size - Variable in class smile.validation.ClassificationMetrics
-
The validation data size.
- size - Variable in class smile.validation.RegressionMetrics
-
The validation data size.
- smile.association - package smile.association
-
Frequent item set mining and association rule mining.
- smile.base.cart - package smile.base.cart
-
Classification and regression tree base package.
- smile.base.mlp - package smile.base.mlp
-
Multilayer perceptron neural network base package.
- smile.base.rbf - package smile.base.rbf
-
RBF network base package.
- smile.base.svm - package smile.base.svm
-
Support vector machine base package.
- smile.classification - package smile.classification
-
Classification algorithms.
- smile.clustering - package smile.clustering
-
Clustering analysis.
- smile.clustering.linkage - package smile.clustering.linkage
-
Cluster dissimilarity measures.
- smile.feature - package smile.feature
-
Feature generation, normalization and selection.
- smile.glm - package smile.glm
-
Generalized linear models.
- smile.glm.model - package smile.glm.model
-
The error distribution models.
- smile.imputation - package smile.imputation
-
Missing value imputation.
- smile.manifold - package smile.manifold
-
Manifold learning finds a low-dimensional basis for describing
high-dimensional data.
- smile.mds - package smile.mds
-
Multidimensional scaling.
- smile.neighbor - package smile.neighbor
-
Nearest neighbor search.
- smile.neighbor.lsh - package smile.neighbor.lsh
-
LSH internal classes.
- smile.projection - package smile.projection
-
Feature extraction.
- smile.projection.ica - package smile.projection.ica
-
The contrast functions in FastICA.
- smile.regression - package smile.regression
-
Regression analysis.
- smile.sequence - package smile.sequence
-
Learning algorithms for sequence data.
- smile.timeseries - package smile.timeseries
-
Time series analysis.
- smile.validation - package smile.validation
-
Model validation and selection.
- smile.validation.metric - package smile.validation.metric
-
- smile.vq - package smile.vq
-
Vector quantization is a lossy compression technique used in speech
and image coding.
- smile.vq.hebb - package smile.vq.hebb
-
Hebbian theory is a neuroscientific theory claiming that an increase in
synaptic efficacy arises from a presynaptic cell's repeated and persistent
stimulation of a postsynaptic cell.
- SNLSH<K,V> - Class in smile.neighbor
-
Locality-Sensitive Hashing for Signatures.
- SNLSH(int, SimHash<K>) - Constructor for class smile.neighbor.SNLSH
-
Constructor.
- SoftClassifier<T> - Interface in smile.classification
-
Soft classifiers calculate a posteriori probabilities besides the class
label of an instance.
- SOM - Class in smile.vq
-
Self-Organizing Map.
- SOM(double[][][], TimeFunction, Neighborhood) - Constructor for class smile.vq.SOM
-
Constructor.
- sparse(int, KernelMachine<SparseArray>) - Static method in class smile.base.svm.LinearKernelMachine
-
Creates a linear kernel machine.
- sparse(int, int) - Static method in class smile.projection.RandomProjection
-
Generates a sparse random projection.
- SparseLogisticRegression - Class in smile.classification
-
Logistic regression on sparse data.
- SparseLogisticRegression(int, double, double, IntSet) - Constructor for class smile.classification.SparseLogisticRegression
-
Constructor.
- SparseLogisticRegression.Binomial - Class in smile.classification
-
Binomial logistic regression.
- SparseLogisticRegression.Multinomial - Class in smile.classification
-
Multinomial logistic regression.
- SparseOneHotEncoder - Class in smile.feature
-
Encode categorical integer features using sparse one-hot scheme.
- SparseOneHotEncoder(StructType) - Constructor for class smile.feature.SparseOneHotEncoder
-
Constructor.
- specificity - Variable in class smile.validation.ClassificationMetrics
-
The specificity on validation data.
- Specificity - Class in smile.validation.metric
-
Specificity (SPC) or True Negative Rate is a statistical measures of the
performance of a binary classification test.
- Specificity() - Constructor for class smile.validation.metric.Specificity
-
- SpectralClustering - Class in smile.clustering
-
Spectral Clustering.
- SpectralClustering(double, int, int[]) - Constructor for class smile.clustering.SpectralClustering
-
Constructor.
- split(Split, PriorityQueue<Split>) - Method in class smile.base.cart.CART
-
Split a node into two children nodes.
- Split - Class in smile.base.cart
-
The data about of a potential split for a leaf node.
- Split(LeafNode, int, double, int, int, int, int) - Constructor for class smile.base.cart.Split
-
Constructor.
- SplitRule - Enum in smile.base.cart
-
The criterion to choose variable to split instances.
- SQRT - Static variable in class smile.validation.metric.AdjustedMutualInformation
-
Default instance with sqrt normalization.
- sqrt(int[], int[]) - Static method in class smile.validation.metric.AdjustedMutualInformation
-
Calculates the adjusted mutual information of (I(y1, y2) - E(MI)) / (sqrt(H(y1) * H(y2)) - E(MI)).
- SQRT - Static variable in class smile.validation.metric.NormalizedMutualInformation
-
Default instance with sqrt normalization.
- sqrt(int[], int[]) - Static method in class smile.validation.metric.NormalizedMutualInformation
-
Calculates the normalized mutual information of I(y1, y2) / sqrt(H(y1) * H(y2)).
- Standardizer - Class in smile.feature
-
Standardizes numeric feature to 0 mean and unit variance.
- Standardizer(StructType, double[], double[]) - Constructor for class smile.feature.Standardizer
-
Constructor.
- stress - Variable in class smile.mds.IsotonicMDS
-
The final stress achieved.
- stress - Variable in class smile.mds.SammonMapping
-
The final stress achieved.
- SUM - Static variable in class smile.validation.metric.AdjustedMutualInformation
-
Default instance with sum normalization.
- sum(int[], int[]) - Static method in class smile.validation.metric.AdjustedMutualInformation
-
Calculates the adjusted mutual information of (I(y1, y2) - E(MI)) / (0.5 * (H(y1) + H(y2)) - E(MI)).
- SUM - Static variable in class smile.validation.metric.NormalizedMutualInformation
-
Default instance with sum normalization.
- sum(int[], int[]) - Static method in class smile.validation.metric.NormalizedMutualInformation
-
Calculates the normalized mutual information of 2 * I(y1, y2) / (H(y1) + H(y2)).
- SumSquaresRatio - Class in smile.feature
-
The ratio of between-groups to within-groups sum of squares is a univariate
feature ranking metric, which can be used as a feature selection criterion
for multi-class classification problems.
- SumSquaresRatio() - Constructor for class smile.feature.SumSquaresRatio
-
- support - Variable in class smile.association.AssociationRule
-
The support value.
- support - Variable in class smile.association.ItemSet
-
The associated support of item set.
- SupportVector<T> - Class in smile.base.svm
-
Support vector.
- SupportVector(int, T, int, double, double, double, double, double) - Constructor for class smile.base.svm.SupportVector
-
- SVDImputation - Class in smile.imputation
-
Missing value imputation with singular value decomposition.
- SVDImputation(int) - Constructor for class smile.imputation.SVDImputation
-
Constructor.
- SVM<T> - Class in smile.classification
-
Support vector machines for classification.
- SVM(MercerKernel<T>, T[], double[], double) - Constructor for class smile.classification.SVM
-
Constructor.
- SVR<T> - Class in smile.base.svm
-
Epsilon support vector regression.
- SVR(MercerKernel<T>, double, double, double) - Constructor for class smile.base.svm.SVR
-
Constructor.
- SVR - Class in smile.regression
-
Epsilon support vector regression.
- SVR() - Constructor for class smile.regression.SVR
-
- value - Variable in class smile.neighbor.Neighbor
-
The data object of neighbor.
- valueOf(String) - Static method in enum smile.base.cart.Loss.Type
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in interface smile.base.cart.Loss
-
Parses the loss.
- valueOf(String) - Static method in enum smile.base.cart.SplitRule
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum smile.base.mlp.Cost
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum smile.base.mlp.OutputFunction
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum smile.classification.DiscreteNaiveBayes.Model
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum smile.feature.Normalizer.Norm
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum smile.timeseries.AR.Method
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum smile.timeseries.BoxTest.Type
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum smile.validation.metric.AdjustedMutualInformation.Method
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum smile.validation.metric.NormalizedMutualInformation.Method
-
Returns the enum constant of this type with the specified name.
- values() - Static method in enum smile.base.cart.Loss.Type
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum smile.base.cart.SplitRule
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum smile.base.mlp.Cost
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum smile.base.mlp.OutputFunction
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum smile.classification.DiscreteNaiveBayes.Model
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum smile.feature.Normalizer.Norm
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Method in class smile.neighbor.MutableLSH
-
Returns the values.
- values() - Static method in enum smile.timeseries.AR.Method
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum smile.timeseries.BoxTest.Type
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum smile.validation.metric.AdjustedMutualInformation.Method
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum smile.validation.metric.NormalizedMutualInformation.Method
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- var - Variable in class smile.neighbor.lsh.NeighborHashValueModel
-
Variance of hash values of neighbors.
- variance(double) - Method in interface smile.glm.model.Model
-
The variance function.
- variance() - Method in class smile.timeseries.AR
-
Returns the residual variance.
- variance() - Method in class smile.timeseries.ARMA
-
Returns the residual variance.
- VectorQuantizer - Interface in smile.vq
-
Vector quantizer with competitive learning.
- viterbi(Tuple[]) - Method in class smile.sequence.CRF
-
Labels sequence with Viterbi algorithm.
- viterbi(T[]) - Method in class smile.sequence.CRFLabeler
-
Labels sequence with Viterbi algorithm.
- vote(Tuple, double[]) - Method in class smile.classification.RandomForest
-
Predict and estimate the probability by voting.