Serialized Form

Package smile.base.cart
- Class smile.base.cart.DecisionNode extends LeafNode implements Serializable
  
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  - Serialized Fields
    - output
```
int output
```
      The predicted output.
    - count
```
int[] count
```
      The number of node samples in each class.
- Class smile.base.cart.InternalNode extends java.lang.Object implements Serializable
  - Serialized Fields
    - trueChild
```
Node trueChild
```
      Children node.
    - falseChild
```
Node falseChild
```
      Children node.
    - feature
```
int feature
```
      The split feature for this node.
    - score
```
double score
```
      Reduction in impurity compared to parent.
    - deviance
```
double deviance
```
      The deviance of node.
- Class smile.base.cart.LeafNode extends java.lang.Object implements Serializable
  - Serialized Fields
    - size
```
int size
```
      The number of samples in the node.
- Class smile.base.cart.NominalNode extends InternalNode implements Serializable
  
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  - Serialized Fields
    - value
```
int value
```
      The split value.
- Class smile.base.cart.OrdinalNode extends InternalNode implements Serializable
  
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  - Serialized Fields
    - value
```
double value
```
      The split value.
- Class smile.base.cart.RegressionNode extends LeafNode implements Serializable
  
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  - Serialized Fields
    - mean
```
double mean
```
      The mean of response variable.
    - output
```
double output
```
      The predicted output. In standard regression tree, this is same as the mean. However, in gradient tree boosting, this may be different.
    - rss
```
double rss
```
      The residual sum of squares.
Package smile.base.mlp
- Class smile.base.mlp.HiddenLayer extends Layer implements Serializable
  
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  - Serialized Fields
    - f
```
ActivationFunction f
```
      The activation function.
- Class smile.base.mlp.Layer extends java.lang.Object implements Serializable
  
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  - Serialized Fields
    - n
```
int n
```
      The number of neurons in this layer
    - p
```
int p
```
      The number of input variables.
    - output
```
double[] output
```
      The output vector.
    - gradient
```
double[] gradient
```
      The gradient vector.
    - weight
```
smile.math.matrix.DenseMatrix weight
```
      The affine transformation matrix.
    - delta
```
smile.math.matrix.DenseMatrix delta
```
      The weight delta of current sample.
    - update
```
smile.math.matrix.DenseMatrix update
```
      The weight update of mini batch or momentum.
    - activation
```
java.util.function.Consumer<T> activation
```
      The activation/output function.
- Class smile.base.mlp.OutputLayer extends Layer implements Serializable
  
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  - Serialized Fields
    - cost
```
Cost cost
```
      The cost function.
    - f
```
OutputFunction f
```
      The output function.
Package smile.base.rbf
- Class smile.base.rbf.RBF extends java.lang.Object implements Serializable
  
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  - Serialized Fields
    - center
```
java.lang.Object center
```
      The center of neuron.
    - rbf
```
smile.math.rbf.RadialBasisFunction rbf
```
      Radial basis function.
    - distance
```
smile.math.distance.Metric<T> distance
```
      Metric distance.
Package smile.base.svm
- Class smile.base.svm.KernelMachine extends java.lang.Object implements Serializable
  
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  - Serialized Fields
    - kernel
```
smile.math.kernel.MercerKernel<T> kernel
```
      The kernel function.
    - instances
```
java.lang.Object[] instances
```
      The support vectors (or control points).
    - w
```
double[] w
```
      The linear weights.
    - b
```
double b
```
      The intercept.
- Class smile.base.svm.LASVM extends java.lang.Object implements Serializable
  
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  - Serialized Fields
    - kernel
```
smile.math.kernel.MercerKernel<T> kernel
```
      The kernel function.
    - Cp
```
double Cp
```
      The soft margin penalty parameter for positive samples.
    - Cn
```
double Cn
```
      The soft margin penalty parameter for negative samples.
    - tol
```
double tol
```
      The tolerance of convergence test.
    - sv
```
java.util.LinkedList<E> sv
```
      Support vectors.
    - b
```
double b
```
      Threshold of decision function.
    - minmaxflag
```
boolean minmaxflag
```
      True if minmax() is already called after update.
    - svmin
```
SupportVector<T> svmin
```
      Most violating pair. argmin gi of m_i < alpha_i argmax gi of alpha_i < M_i where m_i = min{0, y_i * C} and M_i = max{0, y_i * C}
    - svmax
```
SupportVector<T> svmax
```
    - gmin
```
double gmin
```
    - gmax
```
double gmax
```
    - x
```
java.lang.Object[] x
```
      The training samples.
    - K
```
double[][] K
```
      The kernel matrix.
- Class smile.base.svm.LinearKernelMachine extends java.lang.Object implements Serializable
  
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  - Serialized Fields
    - w
```
double[] w
```
      The weight vector.
    - b
```
double b
```
      The intercept.
- Class smile.base.svm.SupportVector extends java.lang.Object implements Serializable
  
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  - Serialized Fields
    - i
```
int i
```
      The index of support vector in training samples.
    - x
```
java.lang.Object x
```
      Support vector.
    - alpha
```
double alpha
```
      Lagrangian multiplier of support vector.
    - g
```
double g
```
      Gradient y - Kα.
    - cmin
```
double cmin
```
      Lower bound of alpha.
    - cmax
```
double cmax
```
      Upper bound of alpha.
    - k
```
double k
```
      Kernel value k(x, x)
Package smile.classification
- Class smile.classification.AdaBoost extends java.lang.Object implements Serializable
  
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  - Serialized Fields
    - formula
```
smile.data.formula.Formula formula
```
      Design matrix formula
    - k
```
int k
```
      The number of classes.
    - trees
```
DecisionTree[] trees
```
      Forest of decision trees.
    - alpha
```
double[] alpha
```
      The weight of each decision tree.
    - error
```
double[] error
```
      The weighted error of each decision tree during training.
    - importance
```
double[] importance
```
      Variable importance. Every time a split of a node is made on variable the (GINI, information gain, etc.) impurity criterion for the two descendent nodes is less than the parent node. Adding up the decreases for each individual variable over all trees in the forest gives a fast variable importance that is often very consistent with the permutation importance measure.
    - labels
```
smile.util.IntSet labels
```
      The class label encoder.
- Class smile.classification.ClassLabels extends java.lang.Object implements Serializable
  
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  - Serialized Fields
    - k
```
int k
```
      The number of classes.
    - labels
```
smile.util.IntSet labels
```
      The class labels. If the class labels are already in [0, k), this is empty.
    - y
```
int[] y
```
      The sample class id in [0, k).
    - ni
```
int[] ni
```
      The number of samples per classes.
    - priori
```
double[] priori
```
      The estimated priori probabilities.
    - field
```
java.util.Optional<T> field
```
      The optional meta data of response variable.
- Class smile.classification.DecisionTree extends CART implements Serializable
  
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  - Serialized Fields
    - rule
```
SplitRule rule
```
      The splitting rule.
    - k
```
int k
```
      The number of classes.
    - labels
```
java.util.Optional<T> labels
```
      The class label encoder.
- Class smile.classification.DiscreteNaiveBayes extends java.lang.Object implements Serializable
  
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  - Serialized Fields
    - model
```
DiscreteNaiveBayes.Model model
```
      The generation model of naive Bayes.
    - k
```
int k
```
      The number of classes.
    - p
```
int p
```
      The number of independent variables.
    - priori
```
double[] priori
```
      The priori probability of each class.
    - sigma
```
double sigma
```
      Amount of add-k smoothing of evidence. By default, we use add-one or Laplace smoothing, which simply adds one to each count to eliminate zeros. Add-one smoothing can be interpreted as a uniform prior (each term occurs once for each class) that is then updated as evidence from the training data comes in.
    - fixedPriori
```
boolean fixedPriori
```
      If true, don't update the priori during learning.
    - n
```
int n
```
      The total number of documents.
    - nc
```
int[] nc
```
      The number of documents in each class.
    - nt
```
int[] nt
```
      The number of terms per class.
    - ntc
```
int[][] ntc
```
      The number of each term per class.
    - logcondprob
```
double[][] logcondprob
```
      The log conditional probabilities for document classification.
    - labels
```
smile.util.IntSet labels
```
      The class label encoder.
- Class smile.classification.FLD extends java.lang.Object implements Serializable
  
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  - Serialized Fields
    - p
```
int p
```
      The dimensionality of data.
    - k
```
int k
```
      The number of classes.
    - scaling
```
smile.math.matrix.DenseMatrix scaling
```
      Project matrix.
    - mean
```
double[] mean
```
      Projected mean vector.
    - mu
```
double[][] mu
```
      Projected class mean vectors.
    - labels
```
smile.util.IntSet labels
```
      The class label encoder.
- Class smile.classification.GradientTreeBoost extends java.lang.Object implements Serializable
  
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  - Serialized Fields
    - formula
```
smile.data.formula.Formula formula
```
      Design matrix formula
    - k
```
int k
```
      The number of classes.
    - trees
```
RegressionTree[] trees
```
      Forest of regression trees for binary classification.
    - forest
```
RegressionTree[][] forest
```
      Forest of regression trees for multi-class classification.
    - importance
```
double[] importance
```
      Variable importance. Every time a split of a node is made on variable the impurity criterion for the two descendent nodes is less than the parent node. Adding up the decreases for each individual variable over all trees in the forest gives a simple variable importance.
    - b
```
double b
```
      The intercept for binary classification.
    - shrinkage
```
double shrinkage
```
      The shrinkage parameter in (0, 1] controls the learning rate of procedure.
    - labels
```
smile.util.IntSet labels
```
      The class label encoder.
- Class smile.classification.IsotonicRegressionScaling extends java.lang.Object implements Serializable
  
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  - Serialized Fields
    - buckets
```
double[] buckets
```
      The step-wise buckets of function values in ascending order.
    - prob
```
double[] prob
```
      The probability of instances falling into the corresponding buckets.
- Class smile.classification.KNN extends java.lang.Object implements Serializable
  
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  - Serialized Fields
    - knn
```
KNNSearch<K,V> knn
```
      The data structure for nearest neighbor search.
    - y
```
int[] y
```
      The labels of training samples.
    - k
```
int k
```
      The number of neighbors for decision.
    - labels
```
smile.util.IntSet labels
```
      The class labels.
- Class smile.classification.LDA extends java.lang.Object implements Serializable
  
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  - Serialized Fields
    - p
```
int p
```
      The dimensionality of data.
    - k
```
int k
```
      The number of classes.
    - logppriori
```
double[] logppriori
```
      The constant term of discriminant function of each class.
    - priori
```
double[] priori
```
      The a priori probabilities of each class.
    - mu
```
double[][] mu
```
      THe mean vectors of each class.
    - eigen
```
double[] eigen
```
      The eigen values of common variance matrix.
    - scaling
```
smile.math.matrix.DenseMatrix scaling
```
      The eigen vectors of common covariance matrix, which transforms observations to discriminant functions, normalized so that common covariance matrix is spherical.
    - labels
```
smile.util.IntSet labels
```
      The class label encoder.
- Class smile.classification.LogisticRegression extends java.lang.Object implements Serializable
  
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  - Serialized Fields
    - p
```
int p
```
      The dimension of input space.
    - k
```
int k
```
      The number of classes.
    - L
```
double L
```
      The log-likelihood of learned model.
    - w
```
double[] w
```
      The linear weights for binary logistic regression.
    - W
```
double[][] W
```
      The linear weights for multi-class logistic regression.
    - lambda
```
double lambda
```
      Regularization factor.
    - eta
```
double eta
```
      learning rate for stochastic gradient descent.
    - labels
```
smile.util.IntSet labels
```
      The class label encoder.
- Class smile.classification.Maxent extends java.lang.Object implements Serializable
  
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  - Serialized Fields
    - p
```
int p
```
      The dimension of input space.
    - k
```
int k
```
      The number of classes.
    - L
```
double L
```
      The log-likelihood of learned model.
    - w
```
double[] w
```
      The linear weights for binary logistic regression.
    - W
```
double[][] W
```
      The linear weights for multi-class logistic regression.
    - eta
```
double eta
```
      learning rate for stochastic gradient descent.
    - labels
```
smile.util.IntSet labels
```
      The class label encoder.
- Class smile.classification.MLP extends MultilayerPerceptron implements Serializable
  
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  - Serialized Fields
    - k
```
int k
```
      The number of classes.
    - labels
```
smile.util.IntSet labels
```
      The class label encoder.
- Class smile.classification.NaiveBayes extends java.lang.Object implements Serializable
  
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  - Serialized Fields
    - k
```
int k
```
      The number of classes.
    - p
```
int p
```
      The number of independent variables.
    - priori
```
double[] priori
```
      The priori probability of each class.
    - prob
```
smile.stat.distribution.Distribution[][] prob
```
      The conditional distribution for general purpose naive Bayes classifier.
    - labels
```
smile.util.IntSet labels
```
      The class label encoder.
- Class smile.classification.OneVersusOne extends java.lang.Object implements Serializable
  
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  - Serialized Fields
    - k
```
int k
```
      The number of classes.
    - classifiers
```
Classifier<T>[][] classifiers
```
      The binary classifier.
    - platts
```
PlattScaling[][] platts
```
      The binary classifier.
    - labels
```
smile.util.IntSet labels
```
      The class label encoder.
- Class smile.classification.OneVersusRest extends java.lang.Object implements Serializable
  
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  - Serialized Fields
    - k
```
int k
```
      The number of classes.
    - classifiers
```
Classifier<T>[] classifiers
```
      The binary classifier.
    - platts
```
PlattScaling[] platts
```
      The probability estimation by Platt scaling.
    - labels
```
smile.util.IntSet labels
```
      The class label encoder.
- Class smile.classification.PlattScaling extends java.lang.Object implements Serializable
  
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  - Serialized Fields
    - alpha
```
double alpha
```
      The scaling parameter.
    - beta
```
double beta
```
      The scaling parameter.
- Class smile.classification.QDA extends java.lang.Object implements Serializable
  
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  - Serialized Fields
    - p
```
int p
```
      The dimensionality of data.
    - k
```
int k
```
      The number of classes.
    - logppriori
```
double[] logppriori
```
      Constant term of discriminant function of each class.
    - priori
```
double[] priori
```
      A priori probabilities of each class.
    - mu
```
double[][] mu
```
      Mean vectors of each class.
    - eigen
```
double[][] eigen
```
      Eigen values of each covariance matrix.
    - scaling
```
smile.math.matrix.DenseMatrix[] scaling
```
      Eigen vectors of each covariance matrix, which transforms observations to discriminant functions, normalized so that within groups covariance matrix is spherical.
    - labels
```
smile.util.IntSet labels
```
      The class label encoder.
- Class smile.classification.RandomForest extends java.lang.Object implements Serializable
  
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  - Serialized Fields
    - formula
```
smile.data.formula.Formula formula
```
      Design matrix formula
    - trees
```
java.util.List<E> trees
```
      Forest of decision trees. The second value is the accuracy of tree on the OOB samples, which can be used a weight when aggregating tree votes.
    - k
```
int k
```
      The number of classes.
    - error
```
double error
```
      Out-of-bag estimation of error rate, which is quite accurate given that enough trees have been grown (otherwise the OOB estimate can bias upward).
    - importance
```
double[] importance
```
      Variable importance. Every time a split of a node is made on variable the (GINI, information gain, etc.) impurity criterion for the two descendent nodes is less than the parent node. Adding up the decreases for each individual variable over all trees in the forest gives a fast variable importance that is often very consistent with the permutation importance measure.
    - labels
```
smile.util.IntSet labels
```
      The class label encoder.
- Class smile.classification.RBFNetwork extends java.lang.Object implements Serializable
  
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  - Serialized Fields
    - k
```
int k
```
      The number of classes.
    - w
```
smile.math.matrix.DenseMatrix w
```
      The linear weights.
    - rbf
```
RBF<T>[] rbf
```
      The radial basis function.
    - normalized
```
boolean normalized
```
      True to fit a normalized RBF network.
    - labels
```
smile.util.IntSet labels
```
      The class label encoder.
- Class smile.classification.RDA extends QDA implements Serializable
  
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- Class smile.classification.SVM extends KernelMachine<T> implements Serializable
Package smile.clustering
- Class smile.clustering.CentroidClustering extends PartitionClustering implements Serializable
  
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  - Serialized Fields
    - distortion
```
double distortion
```
      The total distortion.
    - centroids
```
java.lang.Object[] centroids
```
      The centroids of each cluster.
    - distance
```
java.util.function.ToDoubleBiFunction<T,U> distance
```
      The lambda of distance measure.
- Class smile.clustering.CLARANS extends CentroidClustering<T,T> implements Serializable
  
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- Class smile.clustering.DBSCAN extends PartitionClustering implements Serializable
  
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  - Serialized Fields
    - minPts
```
double minPts
```
      The minimum number of points required to form a cluster
    - radius
```
double radius
```
      The neighborhood radius.
    - nns
```
RNNSearch<K,V> nns
```
      Data structure for neighborhood search.
- Class smile.clustering.DENCLUE extends PartitionClustering implements Serializable
  
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  - Serialized Fields
    - tol
```
double tol
```
      The tolerance of hill-climbing procedure.
    - sigma
```
double sigma
```
      The smooth parameter in the Gaussian kernel.
    - attractors
```
double[][] attractors
```
      The density attractor of each observation.
    - radius
```
double[] radius
```
      The radius of density attractor.
    - samples
```
double[][] samples
```
      The samples decided by k-means used in the iterations of hill climbing.
- Class smile.clustering.DeterministicAnnealing extends CentroidClustering<double[],double[]> implements Serializable
  
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- Class smile.clustering.GMeans extends CentroidClustering<double[],double[]> implements Serializable
  
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- Class smile.clustering.HierarchicalClustering extends java.lang.Object implements Serializable
  
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  - Serialized Fields
    - merge
```
int[][] merge
```
      An n-1 by 2 matrix of which row i describes the merging of clusters at step i of the clustering. If an element j in the row is less than n, then observation j was merged at this stage. If j ≥ n then the merge was with the cluster formed at the (earlier) stage j-n of the algorithm.
    - height
```
double[] height
```
      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.
- Class smile.clustering.KMeans extends CentroidClustering<double[],double[]> implements Serializable
  
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- Class smile.clustering.KModes extends CentroidClustering<int[],int[]> implements Serializable
  
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- Class smile.clustering.MEC extends PartitionClustering implements Serializable
  
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  - Serialized Fields
    - entropy
```
double entropy
```
      The conditional entropy as the objective function.
    - radius
```
double radius
```
      The range of neighborhood.
    - nns
```
RNNSearch<K,V> nns
```
      The neighborhood search data structure.
- Class smile.clustering.PartitionClustering extends java.lang.Object implements Serializable
  - Serialized Fields
    - k
```
int k
```
      The number of clusters.
    - y
```
int[] y
```
      The cluster labels of data.
    - size
```
int[] size
```
      The number of observations in each cluster.
- Class smile.clustering.SIB extends CentroidClustering<double[],smile.util.SparseArray> implements Serializable
  
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- Class smile.clustering.SpectralClustering extends PartitionClustering implements Serializable
  
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  - Serialized Fields
    - distortion
```
double distortion
```
      The distortion in feature space.
- Class smile.clustering.XMeans extends CentroidClustering<double[],double[]> implements Serializable
  
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Package smile.feature
- Class smile.feature.MaxAbsScaler extends java.lang.Object implements Serializable
  
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  - Serialized Fields
    - schema
```
smile.data.type.StructType schema
```
      The schema of data.
    - scale
```
double[] scale
```
      Scaling factor.
- Class smile.feature.Normalizer extends java.lang.Object implements Serializable
  
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  - Serialized Fields
    - norm
```
Normalizer.Norm norm
```
      The type of norm .
- Class smile.feature.RobustStandardizer extends Standardizer implements Serializable
  
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- Class smile.feature.Scaler extends java.lang.Object implements Serializable
  
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  - Serialized Fields
    - schema
```
smile.data.type.StructType schema
```
      The schema of data.
    - lo
```
double[] lo
```
      Lower bound.
    - hi
```
double[] hi
```
      Upper bound.
- Class smile.feature.Standardizer extends java.lang.Object implements Serializable
  
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  - Serialized Fields
    - schema
```
smile.data.type.StructType schema
```
      The schema of data.
    - mu
```
double[] mu
```
      Mean or median.
    - std
```
double[] std
```
      Standard deviation or IQR.
- Class smile.feature.WinsorScaler extends Scaler implements Serializable
  
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Package smile.imputation
- Class smile.imputation.MissingValueImputationException extends java.lang.Exception implements Serializable
  
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Package smile.manifold
- Class smile.manifold.IsoMap extends java.lang.Object implements Serializable
  
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  - Serialized Fields
    - index
```
int[] index
```
      The original sample index.
    - coordinates
```
double[][] coordinates
```
      The coordinates.
    - graph
```
smile.graph.Graph graph
```
      The nearest neighbor graph.
- Class smile.manifold.LaplacianEigenmap extends java.lang.Object implements Serializable
  
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  - Serialized Fields
    - width
```
double width
```
      The width of heat kernel.
    - index
```
int[] index
```
      The original sample index.
    - coordinates
```
double[][] coordinates
```
      Coordinate matrix.
    - graph
```
smile.graph.Graph graph
```
      Nearest neighbor graph.
- Class smile.manifold.LLE extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
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  - Serialized Fields
    - index
```
int[] index
```
      The original sample index.
    - coordinates
```
double[][] coordinates
```
      Coordinate matrix.
    - graph
```
smile.graph.Graph graph
```
      Nearest neighbor graph.
Package smile.neighbor
- Class smile.neighbor.BKTree extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
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  - Serialized Fields
    - root
```
smile.neighbor.BKTree.Node root
```
      The root of BK-tree.
    - distance
```
smile.math.distance.Metric<T> distance
```
      The distance metric. Note that the metric must be a discrete distance, e.g. edit distance, Hamming distance, Lee distance, Jaccard distance, and taxonomic distance, etc.
    - count
```
int count
```
      The number of nodes in the tree.
- Class smile.neighbor.CoverTree extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
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  - Serialized Fields
    - data
```
java.lang.Object[] data
```
      The dataset to build the cover tree.
    - distance
```
smile.math.distance.Metric<T> distance
```
      The distance/metric function for nearest neighbor search.
    - root
```
smile.neighbor.CoverTree.Node root
```
      The root node.
    - base
```
double base
```
      The base of our expansion constant. In other words the 2 in 2^i used in covering tree and separation invariants of a cover tree. In paper it's suggested the separation invariant is relaxed in batch construction.
    - invLogBase
```
double invLogBase
```
      If we have base 2 then this can be viewed as 1/ln(2), which can be used later on to do invLogBase*ln(d) instead of ln(d)/ln(2), to get log2(d), in getScale method.
- Class smile.neighbor.KDTree extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
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  - Serialized Fields
    - keys
```
double[][] keys
```
      The keys of data objects.
    - data
```
java.lang.Object[] data
```
      The data objects.
    - root
```
smile.neighbor.KDTree.Node root
```
      The root node of KD-Tree.
    - index
```
int[] index
```
      The index of objects in each nodes.
- Class smile.neighbor.LSH extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
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  - Serialized Fields
    - keys
```
java.util.ArrayList<E> keys
```
      The keys of data objects.
    - data
```
java.util.ArrayList<E> data
```
      The data objects.
    - hash
```
java.util.List<E> hash
```
      Hash functions.
    - H
```
int H
```
      The size of hash table.
    - k
```
int k
```
      The number of random projections per hash value.
    - w
```
double w
```
      The width of projection. The hash function is defined as floor((a * x + b) / w). The value of w determines the bucket interval.
- Class smile.neighbor.MPLSH extends LSH<E> implements Serializable
  
  serialVersionUID:
  
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  - Serialized Fields
    - model
```
java.util.List<E> model
```
      Pre-computed posteriori lookup table to generate multiple probes.
- Class smile.neighbor.MutableLSH extends LSH<E> implements Serializable
- Class smile.neighbor.SNLSH extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
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  - Serialized Fields
    - mask
```
long mask
```
      Hash function mask.
    - bands
```
java.util.LinkedHashMap<K,V>[] bands
```
      Signature fractions
    - data
```
java.util.List<E> data
```
      The data objects.
    - keys
```
java.util.List<E> keys
```
      The keys of data objects.
    - signatures
```
java.util.List<E> signatures
```
      signatures generated by simhash
    - simhash
```
SimHash<T> simhash
```
Package smile.neighbor.lsh
- Class smile.neighbor.lsh.Bucket extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
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  - Serialized Fields
    - bucket
```
int bucket
```
      The bucket id is given by the universal bucket hashing. The id is used instead of the full k-vector (value of the hash function g) describing the bucket. With a high probability all buckets will have different pairs of id's.
    - entry
```
smile.util.IntArrayList entry
```
      The indices of points that all have the same value for hash function g.
- Class smile.neighbor.lsh.Hash extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
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  - Serialized Fields
    - MAX_HASH_RND
```
int MAX_HASH_RND
```
      The range of universal hashing random integers [0, 2^29).
    - P
```
int P
```
      The prime number in universal bucket hashing.
    - H
```
int H
```
      The size of hash table.
    - d
```
int d
```
      The dimensionality of data.
    - k
```
int k
```
      The number of random projections per hash value.
    - w
```
double w
```
      The width of projection. The hash function is defined as floor((a * x + b) / w). The value of w determines the bucket interval.
    - c
```
int[] c
```
      The random integer used for universal bucket hashing.
    - a
```
smile.math.matrix.DenseMatrix a
```
      The random vectors with entries chosen independently from a Gaussian distribution.
    - b
```
double[] b
```
      Real numbers chosen uniformly from the range [0, w].
    - table
```
Bucket[] table
```
      Hash table.
- Class smile.neighbor.lsh.MultiProbeHash extends Hash implements Serializable
  
  serialVersionUID:
  
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  - Serialized Fields
    - umin
```
double[] umin
```
      The minimum values of hashing functions for given dataset.
    - umax
```
double[] umax
```
      The maximum values of hashing functions for given dataset.
- Class smile.neighbor.lsh.PosterioriModel extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
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  - Serialized Fields
    - hash
```
MultiProbeHash hash
```
      The hash function to model.
    - lookup
```
PrH[][][] lookup
```
      The posteriori probabilities lookup table.
- Class smile.neighbor.lsh.PrH extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
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  - Serialized Fields
    - u
```
int u
```
      The index of bucket.
    - pr
```
double pr
```
      The probability
Package smile.projection
- Class smile.projection.GHA extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
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  - Serialized Fields
    - p
```
int p
```
      The dimension of feature space.
    - n
```
int n
```
      The dimension of input space.
    - r
```
double r
```
      The learning rate;
    - projection
```
smile.math.matrix.DenseMatrix projection
```
      Projection matrix.
    - y
```
double[] y
```
      Workspace for W * x.
    - wy
```
double[] wy
```
      Workspace for W' * y.
- Class smile.projection.ICA extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
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  - Serialized Fields
    - components
```
double[][] components
```
      The independent components (row-wise).
- Class smile.projection.KPCA extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  2L
  - Serialized Fields
    - p
```
int p
```
      The dimension of feature space.
    - data
```
java.lang.Object[] data
```
      Learning data.
    - kernel
```
smile.math.kernel.MercerKernel<T> kernel
```
      Mercer kernel.
    - mean
```
double[] mean
```
      The row mean of kernel matrix.
    - mu
```
double mu
```
      The mean of kernel matrix.
    - latent
```
double[] latent
```
      The eigenvalues of kernel principal components.
    - projection
```
smile.math.matrix.DenseMatrix projection
```
      The projection matrix.
    - coordinates
```
double[][] coordinates
```
      The coordinates of projected training data.
- Class smile.projection.PCA extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  2L
  - Serialized Fields
    - p
```
int p
```
      The dimension of feature space.
    - n
```
int n
```
      The dimension of input space.
    - mu
```
double[] mu
```
      The sample mean.
    - pmu
```
double[] pmu
```
      The projected sample mean.
    - eigvectors
```
smile.math.matrix.DenseMatrix eigvectors
```
      The matrix of variable loadings, whose columns contain the eigenvectors.
    - eigvalues
```
double[] eigvalues
```
      Eigenvalues of principal components.
    - proportion
```
double[] proportion
```
      The proportion of variance contained in each principal component.
    - cumulativeProportion
```
double[] cumulativeProportion
```
      The cumulative proportion of variance contained in principal components.
    - projection
```
smile.math.matrix.DenseMatrix projection
```
      Projection matrix.
- Class smile.projection.PPCA extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
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  - Serialized Fields
    - mu
```
double[] mu
```
      The sample mean.
    - pmu
```
double[] pmu
```
      The projected sample mean.
    - noise
```
double noise
```
      The variance of noise part.
    - loading
```
smile.math.matrix.DenseMatrix loading
```
      The loading matrix.
    - projection
```
smile.math.matrix.DenseMatrix projection
```
      The projection matrix.
- Class smile.projection.RandomProjection extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
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  - Serialized Fields
    - projection
```
smile.math.matrix.DenseMatrix projection
```
      Projection matrix.
Package smile.projection.ica
- Class smile.projection.ica.Gaussian extends java.lang.Object implements Serializable
- Class smile.projection.ica.Kurtosis extends java.lang.Object implements Serializable
- Class smile.projection.ica.LogCosh extends java.lang.Object implements Serializable
Package smile.regression
- Class smile.regression.GradientTreeBoost extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
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  - Serialized Fields
    - formula
```
smile.data.formula.Formula formula
```
      Design matrix formula
    - trees
```
RegressionTree[] trees
```
      Forest of regression trees.
    - b
```
double b
```
      The intercept.
    - importance
```
double[] importance
```
      Variable importance. Every time a split of a node is made on variable the impurity criterion for the two descendent nodes is less than the parent node. Adding up the decreases for each individual variable over all trees in the forest gives a simple variable importance.
    - shrinkage
```
double shrinkage
```
      The shrinkage parameter in (0, 1] controls the learning rate of procedure.
- Class smile.regression.KernelMachine extends KernelMachine<T> implements Serializable
  
  serialVersionUID:
  
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- Class smile.regression.LinearModel extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
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  - Serialized Fields
    - formula
```
smile.data.formula.Formula formula
```
      Design matrix formula
    - schema
```
smile.data.type.StructType schema
```
      The schema of design matrix.
    - p
```
int p
```
      The dimensionality.
    - b
```
double b
```
      The intercept.
    - w
```
double[] w
```
      The linear weights.
    - ttest
```
double[][] ttest
```
      The coefficients, their standard errors, t-scores, and p-values.
    - fittedValues
```
double[] fittedValues
```
      The fitted values.
    - residuals
```
double[] residuals
```
      The residuals, that is response minus fitted values.
    - RSS
```
double RSS
```
      Residual sum of squares.
    - error
```
double error
```
      Residual standard error.
    - df
```
int df
```
      The degree-of-freedom of residual standard error.
    - RSquared
```
double RSquared
```
      R². R² is a statistic that will give some information about the goodness of fit of a model. In regression, the R² coefficient of determination is a statistical measure of how well the regression line approximates the real data points. An R² of 1.0 indicates that the regression line perfectly fits the data.
      In the case of ordinary least-squares regression, R² increases as we increase the number of variables in the model (R² will not decrease). This illustrates a drawback to one possible use of R², where one might try to include more variables in the model until "there is no more improvement". This leads to the alternative approach of looking at the adjusted R².
    - adjustedRSquared
```
double adjustedRSquared
```
      Adjusted R². The adjusted R² has almost same explanation as R² but it penalizes the statistic as extra variables are included in the model.
    - F
```
double F
```
      The F-statistic of the goodness-of-fit of the model.
    - pvalue
```
double pvalue
```
      The p-value of the goodness-of-fit test of the model.
    - V
```
smile.math.matrix.DenseMatrix V
```
      First initialized to the matrix (X^TX)^-1, it is updated with each new learning instance.
- Class smile.regression.MLP extends MultilayerPerceptron implements Serializable
  
  serialVersionUID:
  
  2L
- Class smile.regression.RandomForest extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
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  - Serialized Fields
    - formula
```
smile.data.formula.Formula formula
```
      Design matrix formula
    - trees
```
RegressionTree[] trees
```
      Forest of regression trees.
    - error
```
double error
```
      Out-of-bag estimation of RMSE, which is quite accurate given that enough trees have been grown (otherwise the OOB estimate can bias upward).
    - importance
```
double[] importance
```
      Variable importance. Every time a split of a node is made on variable the impurity criterion for the two descendent nodes is less than the parent node. Adding up the decreases for each individual variable over all trees in the forest gives a fast variable importance that is often very consistent with the permutation importance measure.
- Class smile.regression.RBFNetwork extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
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  - Serialized Fields
    - w
```
double[] w
```
      The linear weights.
    - rbf
```
RBF<T>[] rbf
```
      The radial basis functions.
    - normalized
```
boolean normalized
```
      True to fit a normalized RBF network.
- Class smile.regression.RegressionTree extends CART implements Serializable
  
  serialVersionUID:
  
  2L
Package smile.sequence
- Class smile.sequence.CRF extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
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  - Serialized Fields
    - schema
```
smile.data.type.StructType schema
```
      The schema of (x, s_j).
    - potentials
```
RegressionTree[][] potentials
```
      The potential functions for each class.
    - shrinkage
```
double shrinkage
```
      The learning rate.
- Class smile.sequence.CRFLabeler extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
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  - Serialized Fields
    - model
```
CRF model
```
      The CRF model.
    - features
```
java.util.function.Function<T,R> features
```
      The feature function.
- Class smile.sequence.HMM extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
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  - Serialized Fields
    - pi
```
double[] pi
```
      Initial state probabilities.
    - a
```
smile.math.matrix.DenseMatrix a
```
      State transition probabilities.
    - b
```
smile.math.matrix.DenseMatrix b
```
      Symbol emission probabilities.
- Class smile.sequence.HMMLabeler extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  2L
  - Serialized Fields
    - model
```
HMM model
```
      The HMM model.
    - ordinal
```
java.util.function.ToIntFunction<T> ordinal
```
      The lambda returns the ordinal numbers of symbols.
Package smile.taxonomy
- Class smile.taxonomy.TaxonomicDistance extends java.lang.Object implements Serializable
  - Serialized Fields
    - taxonomy
```
Taxonomy taxonomy
```
      The taxonomy basis to calculate the distance.
Package smile.vq
- Class smile.vq.BIRCH extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
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  - Serialized Fields
    - B
```
int B
```
      The branching factor of non-leaf nodes.
    - L
```
int L
```
      The number of CF entries in the leaf nodes.
    - T
```
double T
```
      THe maximum radius of a sub-cluster.
    - d
```
int d
```
      The dimensionality of data.
    - root
```
smile.vq.BIRCH.Node root
```
      The root of CF tree.
- Class smile.vq.GrowingNeuralGas extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
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  - Serialized Fields
    - d
```
int d
```
      The dimensionality of signals.
    - t
```
int t
```
      The number of signals processed so far.
    - epsBest
```
double epsBest
```
      The learning rate to update best matching neuron.
    - epsNeighbor
```
double epsNeighbor
```
      The learning rate to update neighbors of best matching neuron.
    - edgeLifetime
```
int edgeLifetime
```
      The maximum age of edges.
    - lambda
```
int lambda
```
      If the number of input signals so far is an integer multiple of lambda, insert a new neuron.
    - alpha
```
double alpha
```
      Decrease error variables by multiplying them with alpha during inserting a new neuron.
    - beta
```
double beta
```
      Decrease all error variables by multiply them with beta.
    - neurons
```
java.util.ArrayList<E> neurons
```
      Neurons in the neural network.
    - top2
```
Neuron[] top2
```
      The workspace to find nearest neighbors.
- Class smile.vq.NeuralGas extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
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  - Serialized Fields
    - neurons
```
smile.vq.NeuralGas.Neuron[] neurons
```
      The neurons.
    - graph
```
smile.graph.AdjacencyMatrix graph
```
      The network of neurons.
    - alpha
```
smile.math.TimeFunction alpha
```
      The learning rate function.
    - theta
```
smile.math.TimeFunction theta
```
      The neighborhood function.
    - lifetime
```
int lifetime
```
      The lifetime of connections.
    - dist
```
double[] dist
```
      The distance between a new observation to neurons.
    - t
```
int t
```
      The current iteration.
    - eps
```
double eps
```
      The threshold to update neuron if alpha * theta > eps.
- Class smile.vq.NeuralMap extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
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  - Serialized Fields
    - t
```
int t
```
      The number of signals processed so far.
    - r
```
double r
```
      The distance radius to activate a neuron for a given signal.
    - edgeLifetime
```
int edgeLifetime
```
      The maximum age of edges.
    - epsBest
```
double epsBest
```
      The learning rate to update nearest neuron.
    - epsNeighbor
```
double epsNeighbor
```
      The learning to update neighbors of nearest neuron.
    - beta
```
double beta
```
      Decrease the freshness of all neurons by multiply them with beta.
    - neurons
```
java.util.ArrayList<E> neurons
```
      Neurons in the neural network.
    - top2
```
Neuron[] top2
```
      The workspace to find nearest neighbors.
- Class smile.vq.SOM extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
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  - Serialized Fields
    - nrows
```
int nrows
```
      The number of rows in the lattice.
    - ncols
```
int ncols
```
      The number of columns in the lattice.
    - map
```
smile.vq.SOM.Neuron[][] map
```
      The lattice of neurons.
    - neurons
```
smile.vq.SOM.Neuron[] neurons
```
      The neurons in linear array.
    - dist
```
double[] dist
```
      The distance between a new observation to neurons.
    - alpha
```
smile.math.TimeFunction alpha
```
      The learning rate function.
    - theta
```
Neighborhood theta
```
      The neighborhood function.
    - t
```
int t
```
      The current iteration.
    - eps
```
double eps
```
      The threshold to update neuron if alpha * theta > eps.
Package smile.vq.hebb
- Class smile.vq.hebb.Edge extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
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  - Serialized Fields
    - neighbor
```
Neuron neighbor
```
      The neighbor neuron.
    - age
```
int age
```
      The age of this edges.
- Class smile.vq.hebb.Neuron extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
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  - Serialized Fields
    - w
```
double[] w
```
      The reference vector.
    - edges
```
java.util.List<E> edges
```
      The direct connected neighbors.
    - distance
```
double distance
```
      The distance between the neuron and an input signal.
    - counter
```
double counter
```
      The local counter variable (e.g. the accumulated error, freshness, etc.)

Serialized Form

Package smile.base.cart

Class smile.base.cart.DecisionNode extends LeafNode implements Serializable

Serialized Fields

output

count

Class smile.base.cart.InternalNode extends java.lang.Object implements Serializable

Serialized Fields

trueChild

falseChild

feature

score

deviance

Class smile.base.cart.LeafNode extends java.lang.Object implements Serializable

Serialized Fields

size

Class smile.base.cart.NominalNode extends InternalNode implements Serializable

Serialized Fields

value

Class smile.base.cart.OrdinalNode extends InternalNode implements Serializable

Serialized Fields

value

Class smile.base.cart.RegressionNode extends LeafNode implements Serializable

Serialized Fields

mean

output

rss

Package smile.base.mlp

Class smile.base.mlp.HiddenLayer extends Layer implements Serializable

Serialized Fields

f

Class smile.base.mlp.Layer extends java.lang.Object implements Serializable

Serialized Fields

n

p

output

gradient

weight

delta

update

activation

Class smile.base.mlp.OutputLayer extends Layer implements Serializable

Serialized Fields

cost

f

Package smile.base.rbf

Class smile.base.rbf.RBF extends java.lang.Object implements Serializable

Serialized Fields

center

rbf

distance

Package smile.base.svm

Class smile.base.svm.KernelMachine extends java.lang.Object implements Serializable

Serialized Fields

kernel

instances

w

b

Class smile.base.svm.LASVM extends java.lang.Object implements Serializable

Serialized Fields

kernel

Cp

Cn

tol

sv

b

minmaxflag

svmin

svmax

gmin

gmax

x

K

Class smile.base.svm.LinearKernelMachine extends java.lang.Object implements Serializable

Serialized Fields

w

b

Class smile.base.svm.SupportVector extends java.lang.Object implements Serializable

Serialized Fields

i