ai.onnx.ml - SVMRegressor#

SVMRegressor - 1 (ai.onnx.ml)#

Version#

  • name: SVMRegressor (GitHub)

  • domain: ai.onnx.ml

  • since_version: 1

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: False

This version of the operator has been available since version 1 of domain ai.onnx.ml.

Summary#

Support Vector Machine regression prediction and one-class SVM anomaly detection.

Attributes#

  • coefficients - FLOATS :

    Support vector coefficients.

  • kernel_params - FLOATS :

    List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.

  • kernel_type - STRING (default is 'LINEAR'):

    The kernel type, one of ‘LINEAR,’ ‘POLY,’ ‘RBF,’ ‘SIGMOID’.

  • n_supports - INT (default is '0'):

    The number of support vectors.

  • one_class - INT (default is '0'):

    Flag indicating whether the regression is a one-class SVM or not.

  • post_transform - STRING (default is 'NONE'):

    Indicates the transform to apply to the score.
    One of ‘NONE,’ ‘SOFTMAX,’ ‘LOGISTIC,’ ‘SOFTMAX_ZERO,’ or ‘PROBIT.’

  • rho - FLOATS :

  • support_vectors - FLOATS :

    Chosen support vectors

Inputs#

  • X (heterogeneous) - T:

    Data to be regressed.

Outputs#

  • Y (heterogeneous) - tensor(float):

    Regression outputs (one score per target per example).

Type Constraints#

  • T in ( tensor(double), tensor(float), tensor(int32), tensor(int64) ):

    The input type must be a tensor of a numeric type, either [C] or [N,C].