Compute the maximum absolute value of samples to be used for later scaling.

Options

• :axes - Axes to calculate the max absolute value over. By default the absolute values are calculated between the whole tensors.

Return values

Returns a struct with the following parameters:

• max_abs: the calculated maximum absolute value of samples.

Examples

iex> t = Nx.tensor([[1, -1, 2], [2, 0, 0], [0, 1, -1]])
iex> Scholar.Preprocessing.MaxAbsScaler.fit(t)
%Scholar.Preprocessing.MaxAbsScaler{
  max_abs: Nx.tensor(
    [
      [2]
    ]
  )
}copy

Standardizes the tensor by removing the mean and scaling to unit variance.

Examples

iex> t = Nx.tensor([[1, -1, 2], [2, 0, 0], [0, 1, -1]])
iex> Scholar.Preprocessing.MaxAbsScaler.fit_transform(t)
#Nx.Tensor<
  f32[3][3]
  [
    [0.5, -0.5, 1.0],
    [1.0, 0.0, 0.0],
    [0.0, 0.5, -0.5]
  ]
>copy

Performs the standardization of the tensor using a fitted scaler.

Examples

iex> t = Nx.tensor([[1, -1, 2], [2, 0, 0], [0, 1, -1]])
iex> scaler = Scholar.Preprocessing.MaxAbsScaler.fit(t)
iex> Scholar.Preprocessing.MaxAbsScaler.transform(scaler, t)
#Nx.Tensor<
  f32[3][3]
  [
    [0.5, -0.5, 1.0],
    [1.0, 0.0, 0.0],
    [0.0, 0.5, -0.5]
  ]
>
iex> t = Nx.tensor([[1, -1, 2], [2, 0, 0], [0, 1, -1]])
iex> scaler = Scholar.Preprocessing.MaxAbsScaler.fit(t)
iex> new_tensor = Nx.tensor([[0.5, 1, -1], [0.3, 0.8, -1.6]])
iex> Scholar.Preprocessing.MaxAbsScaler.transform(scaler, new_tensor)
#Nx.Tensor<
  f32[2][3]
  [
    [0.25, 0.5, -0.5],
    [0.15000000596046448, 0.4000000059604645, -0.800000011920929]
  ]
>copy