func (trainer *MaxEntClassifierTrainer) Train(set data.Dataset) Model {
// 检查训练数据是否是分类问题
if !set.GetOptions().IsSupervisedLearning {
log.Fatal("训练数据不是分类问题数据")
}
// 建立新的优化器
optimizer := optimizer.NewOptimizer(trainer.options.Optimizer)
// 建立特征权重向量
featureDimension := set.GetOptions().FeatureDimension
numLabels := set.GetOptions().NumLabels
var weights *util.Matrix
if set.GetOptions().FeatureIsSparse {
weights = util.NewSparseMatrix(numLabels)
} else {
weights = util.NewMatrix(numLabels, featureDimension)
}
// 得到优化的特征权重向量
optimizer.OptimizeWeights(weights, MaxEntComputeInstanceDerivative, set)
classifier := new(MaxEntClassifier)
classifier.Weights = weights
classifier.NumLabels = numLabels
classifier.FeatureDimension = featureDimension
classifier.FeatureDictionary = set.GetFeatureDictionary()
classifier.LabelDictionary = set.GetLabelDictionary()
return classifier
}
// 从options中创建训练器
func NewOnlineSGDClassifier(options OnlineSGDClassifierOptions) *OnlineSGDClassifier {
classifier := new(OnlineSGDClassifier)
classifier.options = options
if classifier.options.BatchSize <= 1 {
classifier.options.BatchSize = 1
}
classifier.weights = util.NewSparseMatrix(options.NumLabels - 1)
classifier.derivative = util.NewSparseMatrix(options.NumLabels - 1)
classifier.instanceDerivative = util.NewSparseMatrix(options.NumLabels - 1)
classifier.evaluator = new(FrapEvaluator)
classifier.evaluator.Init(options.NumInstancesForEvaluation)
classifier.featureDictionary = dictionary.NewDictionary(1)
classifier.labelDictionary = dictionary.NewDictionary(0)
return classifier
}
// 初始化优化结构体
// 为结构体中的向量分配新的内存，向量的长度可能发生变化。
func (opt *lbfgsOptimizer) initStruct(labels, features int, isSparse bool) {
opt.labels = labels
opt.x = make([]*util.Matrix, *lbfgs_history_size)
opt.g = make([]*util.Matrix, *lbfgs_history_size)
opt.s = make([]*util.Matrix, *lbfgs_history_size)
opt.y = make([]*util.Matrix, *lbfgs_history_size)
opt.ro = util.NewVector(*lbfgs_history_size)
opt.alpha = util.NewVector(*lbfgs_history_size)
opt.beta = util.NewVector(*lbfgs_history_size)
if !isSparse {
opt.q = util.NewMatrix(labels, features)
opt.z = util.NewMatrix(labels, features)
for i := 0; i < *lbfgs_history_size; i++ {
opt.x[i] = util.NewMatrix(labels, features)
opt.g[i] = util.NewMatrix(labels, features)
opt.s[i] = util.NewMatrix(labels, features)
opt.y[i] = util.NewMatrix(labels, features)
}
} else {
opt.q = util.NewSparseMatrix(labels)
opt.z = util.NewSparseMatrix(labels)
for i := 0; i < *lbfgs_history_size; i++ {
opt.x[i] = util.NewSparseMatrix(labels)
opt.g[i] = util.NewSparseMatrix(labels)
opt.s[i] = util.NewSparseMatrix(labels)
opt.y[i] = util.NewSparseMatrix(labels)
}
}
}
