ai.onnx.ml - LinearRegressor#
LinearRegressor - 1 (ai.onnx.ml)#
Version
name: LinearRegressor (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
Generalized linear regression evaluation.
If targets is set to 1 (default) then univariate regression is performed.
If targets is set to M then M sets of coefficients must be passed in as a sequence and M results will be output for each input n in N.
The coefficients array is of length n, and the coefficients for each target are contiguous. Intercepts are optional but if provided must match the number of targets.
Attributes
coefficients: Weights of the model(s).
intercepts: Weights of the intercepts, if used.
post_transform: Indicates the transform to apply to the regression output vector.<br>One of ‘NONE,’ ‘SOFTMAX,’ ‘LOGISTIC,’ ‘SOFTMAX_ZERO,’ or ‘PROBIT’ Default value is
'NONE'.targets: The total number of regression targets, 1 if not defined. Default value is
1.
Inputs
X (heterogeneous) - T: Data to be regressed.
Outputs
Y (heterogeneous) - tensor(float): Regression outputs (one per target, per example).
Type Constraints
T in ( tensor(double), tensor(float), tensor(int32), tensor(int64) ): The input must be a tensor of a numeric type.
Examples