LinearRegressor#

Domain: ai.onnx.ml
Since version: 1

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.

Inputs

X (T): Data to be regressed.

Outputs

Y (tensor(float)): Regression outputs (one per target, per example).

Type Constraints

T: The input must be a tensor of a numeric type. Allowed types: tensor(double), tensor(float), tensor(int32), tensor(int64).

Examples#

test_cc_linearregressor_single_target

Node:
  ai.onnx.ml.LinearRegressor(x) -> (y)
  Attributes:
    coefficients = [0.5, -1.0]
    intercepts = [0.25]
    targets = 1
    post_transform = "NONE"

Inputs:
  x: shape=(2, 2), dtype=float32
    [[2., 1.],
     [0., 3.]]

Outputs:
  y: shape=(2, 1), dtype=float32
    [[ 0.25],
     [-2.75]]