| PiecewiseLinearTransform takes inputs
| – predictions, a 2-D or 1-D tensor (Tensor)
| of size (batch_size x prediction_dimensions).
|
| The piecewise linear functions are
| stored in bounds, slopes and intercepts.
| The output tensor has the same shape
| of input predictions and contains
| the predictions transformed by the
| piecewise linear functions.
|
| Each column of predictions has its own
| piecewise linear transformation functions.
|
| Therefore the size of piecewise function
| parameters are pieces x prediction_dimensions,
| except for binary predictions where
| only the positive prediction needs
| them.
|
| ———–
| @note
|
| in each piece, low bound is excluded
| while high bound is included. Also the
| piecewise linear function must be continuous.
|
| Notes
|
| - If the input is binary predictions
| (Nx2 or Nx1 tensor), set the binary arg
| to true so that one group of piecewise
| linear functions is needed (see details
| below).
|
| - The transform parameters (bounds,
| slopes, intercepts) can be passed either
| through args or through input blobs.
|
| - If we have multiple groups of piecewise
| linear functions, each group has the
| same number of pieces.
|
| - If a prediction is out of the bounds,
| it is capped to the smallest or largest
| bound.
|