Serialized Form

Package smile.classification
- Class smile.classification.AdaBoost extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  1L
  - Serialized Fields
    - 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.
- Class smile.classification.DecisionTree extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  1L
  - Serialized Fields
    - attributes
```
smile.data.Attribute[] attributes
```
      The attributes of independent variable.
    - 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 the tree gives a simple measure of variable importance.
    - root
```
smile.classification.DecisionTree.Node root
```
      The root of the regression tree
    - rule
```
DecisionTree.SplitRule rule
```
      The splitting rule.
    - k
```
int k
```
      The number of classes.
    - nodeSize
```
int nodeSize
```
      The minimum size of leaf nodes.
    - maxNodes
```
int maxNodes
```
      The maximum number of leaf nodes in the tree.
    - mtry
```
int mtry
```
      The number of input variables to be used to determine the decision at a node of the tree.
- Class smile.classification.FLD extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  1L
  - Serialized Fields
    - p
```
int p
```
      The dimensionality of data.
    - k
```
int k
```
      The number of classes.
    - mean
```
double[] mean
```
      Original common mean vector.
    - mu
```
double[][] mu
```
      Original class mean vectors.
    - scaling
```
double[][] scaling
```
      Project matrix.
    - smean
```
double[] smean
```
      Projected common mean vector.
    - smu
```
double[][] smu
```
      Projected class mean vectors.
- Class smile.classification.GradientTreeBoost extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  1L
  - Serialized Fields
    - 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.
    - shrinkage
```
double shrinkage
```
      The shrinkage parameter in (0, 1] controls the learning rate of procedure.
    - maxNodes
```
int maxNodes
```
      The number of leaves in each tree.
    - ntrees
```
int ntrees
```
      The number of trees.
    - subsample
```
double subsample
```
      The sampling rate for stochastic tree boosting.
- Class smile.classification.KNN extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  1L
  - 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.
    - c
```
int c
```
      The number of classes.
- Class smile.classification.LDA extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  1L
  - Serialized Fields
    - p
```
int p
```
      The dimensionality of data.
    - k
```
int k
```
      The number of classes.
    - ct
```
double[] ct
```
      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.
    - scaling
```
double[][] scaling
```
      Eigen vectors of common covariance matrix, which transforms observations to discriminant functions, normalized so that common covariance matrix is spherical.
    - eigen
```
double[] eigen
```
      Eigen values of common variance matrix.
- Class smile.classification.LogisticRegression extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  1L
  - 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.
- Class smile.classification.Maxent extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  1L
  - 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.
- Class smile.classification.NaiveBayes extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  1L
  - Serialized Fields
    - model
```
NaiveBayes.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.
    - prob
```
smile.stat.distribution.Distribution[][] prob
```
      The conditional distribution for general purpose naive Bayes classifier.
    - 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.
    - predefinedPriori
```
boolean predefinedPriori
```
      True if the priori probabilities are pre defined by user.
    - n
```
int n
```
      The total number of instances.
    - nc
```
int[] nc
```
      The number of instances in each class.
    - nt
```
int[] nt
```
      The number of terms in each class.
    - ntc
```
int[][] ntc
```
      The number of terms in each class.
    - condprob
```
double[][] condprob
```
      The log conditional probabilities for document classification.
- Class smile.classification.NeuralNetwork extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  1L
  - Serialized Fields
    - errorFunction
```
NeuralNetwork.ErrorFunction errorFunction
```
      The type of error function of network.
    - activationFunction
```
NeuralNetwork.ActivationFunction activationFunction
```
      The type of activation function in output layer.
    - p
```
int p
```
      The dimensionality of data.
    - k
```
int k
```
      The number of classes.
    - net
```
smile.classification.NeuralNetwork.Layer[] net
```
      layers of this net
    - inputLayer
```
smile.classification.NeuralNetwork.Layer inputLayer
```
      input layer
    - outputLayer
```
smile.classification.NeuralNetwork.Layer outputLayer
```
      output layer
    - eta
```
double eta
```
      learning rate
    - alpha
```
double alpha
```
      momentum factor
    - lambda
```
double lambda
```
      weight decay factor, which is also a regularization term.
    - target
```
double[] target
```
      The buffer to store target value of training instance.
- Class smile.classification.QDA extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  1L
  - Serialized Fields
    - p
```
int p
```
      The dimensionality of data.
    - k
```
int k
```
      The number of classes.
    - ct
```
double[] ct
```
      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.
    - scaling
```
double[][][] scaling
```
      Eigen vectors of each covariance matrix, which transforms observations to discriminant functions, normalized so that within groups covariance matrix is spherical.
    - ev
```
double[][] ev
```
      Eigen values of each covariance matrix.
- Class smile.classification.RandomForest extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  1L
  - Serialized Fields
    - 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.
- Class smile.classification.RBFNetwork extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  1L
  - Serialized Fields
    - k
```
int k
```
      The number of classes.
    - centers
```
java.lang.Object[] centers
```
      The centers of RBF functions.
    - w
```
double[][] w
```
      The linear weights.
    - distance
```
smile.math.distance.Metric<T> distance
```
      The distance metric functor.
    - rbf
```
smile.math.rbf.RadialBasisFunction[] rbf
```
      The radial basis function.
    - normalized
```
boolean normalized
```
      True to fit a normalized RBF network.
- Class smile.classification.RDA extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  1L
  - Serialized Fields
    - p
```
int p
```
      The dimensionality of data.
    - k
```
int k
```
      The number of classes.
    - ct
```
double[] ct
```
      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.
    - scaling
```
double[][][] scaling
```
      Eigen vectors of each covariance matrix, which transforms observations to discriminant functions, normalized so that within groups covariance matrix is spherical.
    - ev
```
double[][] ev
```
      Eigen values of each covariance matrix.
- Class smile.classification.SVM extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  1L
  - Serialized Fields
    - svm
```
smile.classification.SVM.LASVM svm
```
      Learned two-class support vector machine.
    - svms
```
java.util.List<E> svms
```
      Learned multi-class support vector machines.
    - kernel
```
smile.math.kernel.MercerKernel<T> kernel
```
      The kernel function.
    - p
```
int p
```
      The dimensionality of instances. Useful for sparse arrays.
    - k
```
int k
```
      The number of classes;
    - strategy
```
SVM.Multiclass strategy
```
      The strategy for multi-class classification.
    - wi
```
double[] wi
```
      The class weight.
    - tol
```
double tol
```
      The tolerance of convergence test.
Package smile.imputation
- Class smile.imputation.MissingValueImputationException extends java.lang.Exception implements Serializable
  
  serialVersionUID:
  
  -4517157687017284522L

Serialized Form

Package smile.classification

Class smile.classification.AdaBoost extends java.lang.Object implements Serializable

Serialized Fields

k

trees

alpha

error

importance

Class smile.classification.DecisionTree extends java.lang.Object implements Serializable

Serialized Fields

attributes

importance

root

rule

k

nodeSize

maxNodes

mtry

Class smile.classification.FLD extends java.lang.Object implements Serializable

Serialized Fields

p

k

mean

mu

scaling

smean

smu

Class smile.classification.GradientTreeBoost extends java.lang.Object implements Serializable

Serialized Fields

k

trees

forest

importance

b

shrinkage

maxNodes

ntrees

subsample

Class smile.classification.KNN extends java.lang.Object implements Serializable

Serialized Fields

knn

y

k

c

Class smile.classification.LDA extends java.lang.Object implements Serializable

Serialized Fields

p

k

ct

priori

mu

scaling

eigen

Class smile.classification.LogisticRegression extends java.lang.Object implements Serializable

Serialized Fields

p

k

L

w

W

Class smile.classification.Maxent extends java.lang.Object implements Serializable

Serialized Fields

p

k

L

w

W

Class smile.classification.NaiveBayes extends java.lang.Object implements Serializable

Serialized Fields

model

k

p

priori

prob

sigma

predefinedPriori

n

nc

nt