Converts a tensor into binary values based on the given threshold.

Options

• :type - Type of the resultant tensor. The default value is :f32.

• :threshold - Feature values below or equal to this are replaced by 0, above it by 1. The default value is 0.

Examples

iex> Scholar.Preprocessing.binarize(Nx.tensor([[1.0, -1.0, 2.0], [2.0, 0.0, 0.0], [0.0, 1.0, -1.0]]))
#Nx.Tensor<
  f32[3][3]
  [
    [1.0, 0.0, 1.0],
    [1.0, 0.0, 0.0],
    [0.0, 1.0, 0.0]
  ]
>

iex> Scholar.Preprocessing.binarize(Nx.tensor([[1.0, -1.0, 2.0], [2.0, 0.0, 0.0], [0.0, 1.0, -1.0]]), threshold: 1.3, type: {:u, 8})
#Nx.Tensor<
  u8[3][3]
  [
    [0, 0, 1],
    [1, 0, 0],
    [0, 0, 0]
  ]
>

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

It is a shortcut for Scholar.Preprocessing.MaxAbsScaler.fit_transform/2. See Scholar.Preprocessing.MaxAbsScaler for more information.

Examples

iex> Scholar.Preprocessing.max_abs_scale(Nx.tensor([1, 2, 3]))
#Nx.Tensor<
  f32[3]
  [0.3333333432674408, 0.6666666865348816, 1.0]
>

iex> Scholar.Preprocessing.max_abs_scale(Nx.tensor([[1, -1, 2], [3, 0, 0], [0, 1, -1], [2, 3, 1]]), axes: [0])
#Nx.Tensor<
  f32[4][3]
  [
    [0.3333333432674408, -0.3333333432674408, 1.0],
    [1.0, 0.0, 0.0],
    [0.0, 0.3333333432674408, -0.5],
    [0.6666666865348816, 1.0, 0.5]
  ]
>

iex> Scholar.Preprocessing.max_abs_scale(42)
#Nx.Tensor<
  f32
  1.0
>

Scales a tensor by a given range.

It is a shortcut for Scholar.Preprocessing.MinMaxScaler.fit_transform/2. See Scholar.Preprocessing.MinMaxScaler for more information.

Examples

iex> Scholar.Preprocessing.min_max_scale(Nx.tensor([1, 2, 3]))
#Nx.Tensor<
  f32[3]
  [0.0, 0.5, 1.0]
>

iex> Scholar.Preprocessing.min_max_scale(42)
#Nx.Tensor<
  f32
  0.0
>

Normalize samples individually to unit norm.

The zero-tensors cannot be normalized and they stay the same after normalization.

It is a shortcut for Scholar.Preprocessing.Normalizer.fit_transform/2. See Scholar.Preprocessing.Normalizer for more information.

Examples

iex> Scholar.Preprocessing.normalize(Nx.tensor([[0, 0, 0], [3, 4, 5], [-2, 4, 3]]), axes: [1])
#Nx.Tensor<
  f32[3][3]
  [
    [0.0, 0.0, 0.0],
    [0.4242640733718872, 0.5656854510307312, 0.7071067690849304],
    [-0.3713906705379486, 0.7427813410758972, 0.5570860505104065]
  ]
>

iex> Scholar.Preprocessing.normalize(Nx.tensor([[0, 0, 0], [3, 4, 5], [-2, 4, 3]]))
#Nx.Tensor<
  f32[3][3]
  [
    [0.0, 0.0, 0.0],
    [0.3375263810157776, 0.4500351846218109, 0.5625439882278442],
    [-0.22501759231090546, 0.4500351846218109, 0.3375263810157776]
  ]
>

It is a shortcut for Scholar.Preprocessing.OneHotEncoder.fit_transform/2. See Scholar.Preprocessing.OneHotEncoder for more information.

Examples

iex> Scholar.Preprocessing.one_hot_encode(Nx.tensor([2, 0, 3, 2, 1, 1, 0]), num_classes: 4)
#Nx.Tensor<
  u8[7][4]
  [
    [0, 0, 1, 0],
    [1, 0, 0, 0],
    [0, 0, 0, 1],
    [0, 0, 1, 0],
    [0, 1, 0, 0],
    [0, 1, 0, 0],
    [1, 0, 0, 0]
  ]
>

It is a shortcut for Scholar.Preprocessing.OrdinalEncoder.fit_transform/2. See Scholar.Preprocessing.OrdinalEncoder for more information.

Examples

iex> Scholar.Preprocessing.ordinal_encode(Nx.tensor([3, 2, 4, 56, 2, 4, 2]), num_classes: 4)
#Nx.Tensor<
  s64[7]
  [1, 0, 2, 3, 0, 2, 0]
>

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

It is a shortcut for Scholar.Preprocessing.StandardScale.fit_transform/3. See Scholar.Preprocessing.StandardScale for more information.

Examples

iex> Scholar.Preprocessing.standard_scale(Nx.tensor([1,2,3]))
#Nx.Tensor<
  f32[3]
  [-1.2247447967529297, 0.0, 1.2247447967529297]
>