.. _l-onnx-docai.onnx.ml-SVMClassifier: ========================== ai.onnx.ml - SVMClassifier ========================== .. contents:: :local: .. _l-onnx-opai-onnx-ml-svmclassifier-1: SVMClassifier - 1 (ai.onnx.ml) ============================== **Version** * **name**: `SVMClassifier (GitHub) `_ * **domain**: **ai.onnx.ml** * **since_version**: **1** * **function**: False * **support_level**: SupportType.COMMON * **shape inference**: True This version of the operator has been available **since version 1 of domain ai.onnx.ml**. **Summary** Support Vector Machine classifier **Attributes** * **classlabels_ints**: Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined. * **classlabels_strings**: Class labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined. * **coefficients**: * **kernel_params**: List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel. * **kernel_type**: The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'. Default value is ``'LINEAR'``. * **post_transform**: Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT' Default value is ``'NONE'``. * **prob_a**: First set of probability coefficients. * **prob_b**: Second set of probability coefficients. This array must be same size as prob_a.
If these are provided then output Z are probability estimates, otherwise they are raw scores. * **rho**: * **support_vectors**: * **vectors_per_class**: **Inputs** * **X** (heterogeneous) - **T1**: Data to be classified. **Outputs** * **Y** (heterogeneous) - **T2**: Classification outputs (one class per example). * **Z** (heterogeneous) - **tensor(float)**: Class scores (one per class per example), if prob_a and prob_b are provided they are probabilities for each class, otherwise they are raw scores. **Type Constraints** * **T1** in ( tensor(double), tensor(float), tensor(int32), tensor(int64) ): The input must be a tensor of a numeric type, either [C] or [N,C]. * **T2** in ( tensor(int64), tensor(string) ): The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used. Its size will match the bactch size of the input. **Examples**