classify_class_svmT_classify_class_svmClassifyClassSvmClassifyClassSvmclassify_class_svm (算子)
名称
classify_class_svmT_classify_class_svmClassifyClassSvmClassifyClassSvmclassify_class_svm — 通过支持向量机对特征向量进行分类。
签名
def classify_class_svm(svmhandle: HHandle, features: Sequence[float], num: Sequence[int]) -> Sequence[int]
def classify_class_svm_s(svmhandle: HHandle, features: Sequence[float], num: Sequence[int]) -> int
描述
classify_class_svmclassify_class_svmClassifyClassSvmClassifyClassSvmClassifyClassSvmclassify_class_svm computes the best NumNumNumNumnumnum classes of
the feature vector FeaturesFeaturesFeaturesFeaturesfeaturesfeatures with the SVM SVMHandleSVMHandleSVMHandleSVMHandleSVMHandlesvmhandle
and returns them in ClassClassClassClassclassValclass. If the classifier was
created in the ModeModeModeModemodemode = 'one-versus-one'"one-versus-one""one-versus-one""one-versus-one""one-versus-one""one-versus-one", the
classes are ordered by the number of votes of the sub-classifiers. If
ModeModeModeModemodemode = 'one-versus-all'"one-versus-all""one-versus-all""one-versus-all""one-versus-all""one-versus-all" was used, the classes are ordered
by the value of each sub-classifier (see create_class_svmcreate_class_svmCreateClassSvmCreateClassSvmCreateClassSvmcreate_class_svm for more
details). If the classifier was created in the ModeModeModeModemodemode =
'novelty-detection'"novelty-detection""novelty-detection""novelty-detection""novelty-detection""novelty-detection", it determines whether the feature vector
belongs to the same class as the training data (ClassClassClassClassclassValclass = 1) or is
regarded as outlier (ClassClassClassClassclassValclass = 0). In this case NumNumNumNumnumnum must be
set to 1 as the classifier only determines membership.
Before calling classify_class_svmclassify_class_svmClassifyClassSvmClassifyClassSvmClassifyClassSvmclassify_class_svm, the SVM must be trained
with train_class_svmtrain_class_svmTrainClassSvmTrainClassSvmTrainClassSvmtrain_class_svm。
执行信息
- 多线程类型：可重入（与非独占算子并行运行）。
- 多线程作用域：全局（可从任何线程调用）。
- 未采用并行化处理。
参数
SVMHandleSVMHandleSVMHandleSVMHandleSVMHandlesvmhandle (输入控制) class_svm → HClassSvm, HTupleHHandleHTupleHtuple (handle) (IntPtr) (HHandle) (handle)
SVM 句柄。
FeaturesFeaturesFeaturesFeaturesfeaturesfeatures (输入控制) real-array → HTupleSequence[float]HTupleHtuple (real) (double) (double) (double)
Feature vector.
NumNumNumNumnumnum (输入控制) integer-array → HTupleSequence[int]HTupleHtuple (integer) (int / long) (Hlong) (Hlong)
Number of best classes to determine.
默认值：
1
建议值：
1, 2, 3, 4, 5
ClassClassClassClassclassValclass (输出控制) integer(-array) → HTupleSequence[int]HTupleHtuple (integer) (int / long) (Hlong) (Hlong)
Result of classifying the feature vector with
the SVM.
结果
如果参数有效，算子 classify_class_svmclassify_class_svmClassifyClassSvmClassifyClassSvmClassifyClassSvmclassify_class_svm 返回值 2 ( H_MSG_TRUE )。如有必要，则抛出异常。
可能的前趋
train_class_svmtrain_class_svmTrainClassSvmTrainClassSvmTrainClassSvmtrain_class_svm,
read_class_svmread_class_svmReadClassSvmReadClassSvmReadClassSvmread_class_svm
替代
apply_dl_classifierapply_dl_classifierApplyDlClassifierApplyDlClassifierApplyDlClassifierapply_dl_classifier
另见
create_class_svmcreate_class_svmCreateClassSvmCreateClassSvmCreateClassSvmcreate_class_svm
参考文献
John Shawe-Taylor, Nello Cristianini: “Kernel Methods for Pattern
Analysis”; Cambridge University Press, Cambridge; 2004.
Bernhard Schölkopf, Alexander J.Smola: “Learning with Kernels”;
MIT Press, London; 1999.
模块
基础