# `Scholar.Preprocessing.MinMaxScaler`
[🔗](https://github.com/elixir-nx/scholar/blob/main/lib/scholar/preprocessing/min_max_scaler.ex#L1)

Scales a tensor by dividing each sample in batch by maximum absolute value in the batch

Centering and scaling happen independently on each feature by computing the relevant
statistics on the samples in the training set. Maximum absolute value then is
stored to be used on new samples.

# `fit`
[🔗](https://github.com/elixir-nx/scholar/blob/main/lib/scholar/preprocessing/min_max_scaler.ex#L83)

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.

* `:min_bound` - The lower boundary of the desired range of transformed data. The default value is `0`.

* `:max_bound` - The upper boundary of the desired range of transformed data. The default value is `1`.

## Return values

  Returns a struct with the following parameters:

  * `min_data`: the calculated minimum value of samples.

  * `max_data`: the calculated maximum value of samples.

  * `min_bound`: The lower boundary of the desired range of transformed data.

  * `max_bound`: The upper boundary of the desired range of transformed data.

## Examples

    iex> t = Nx.tensor([[1, -1, 2], [2, 0, 0], [0, 1, -1]])
    iex> Scholar.Preprocessing.MinMaxScaler.fit(t)
    %Scholar.Preprocessing.MinMaxScaler{
      min_data: Nx.tensor(
        [
          [-1]
        ]
      ),
      max_data: Nx.tensor(
        [
          [2]
        ]
      ),
      min_bound: Nx.tensor(
        0
      ),
      max_bound: Nx.tensor(
        1
      )
    }

# `fit_transform`
[🔗](https://github.com/elixir-nx/scholar/blob/main/lib/scholar/preprocessing/min_max_scaler.ex#L164)

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.MinMaxScaler.fit_transform(t)
    #Nx.Tensor<
      f32[3][3]
      [
        [0.6666666865348816, 0.0, 1.0],
        [1.0, 0.3333333432674408, 0.3333333432674408],
        [0.3333333432674408, 0.6666666865348816, 0.0]
      ]
    >

# `transform`
[🔗](https://github.com/elixir-nx/scholar/blob/main/lib/scholar/preprocessing/min_max_scaler.ex#L133)

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.MinMaxScaler.fit(t)
    iex> Scholar.Preprocessing.MinMaxScaler.transform(scaler, t)
    #Nx.Tensor<
      f32[3][3]
      [
        [0.6666666865348816, 0.0, 1.0],
        [1.0, 0.3333333432674408, 0.3333333432674408],
        [0.3333333432674408, 0.6666666865348816, 0.0]
      ]
    >

    iex> t = Nx.tensor([[1, -1, 2], [2, 0, 0], [0, 1, -1]])
    iex> scaler = Scholar.Preprocessing.MinMaxScaler.fit(t)
    iex> new_tensor = Nx.tensor([[0.5, 1, -1], [0.3, 0.8, -1.6]])
    iex> Scholar.Preprocessing.MinMaxScaler.transform(scaler, new_tensor)
    #Nx.Tensor<
      f32[2][3]
      [
        [0.5, 0.6666666865348816, 0.0],
        [0.43333330750465393, 0.5999999642372131, -0.20000000298023224]
      ]
    >

---

*Consult [api-reference.md](api-reference.md) for complete listing*