StandardScaler

#include <Skigen/Preprocessing>

template <typename Scalar = double>
class Skigen::StandardScaler(with_mean=true, with_std=true)

Standardize features by removing the mean and scaling to unit variance.

The standard score of a sample x is calculated as:

$$z = \frac{x - \mu}{\sigma}$$

where $\mu$ is the mean of the training samples and $\sigma$ is the standard deviation. Centering and scaling happen independently on each feature by computing the relevant statistics on the samples in the training set.

Mirrors sklearn.preprocessing.StandardScaler.

---

Parameters:

• with_mean : bool, default=true If true, center the data before scaling (bool, default true).

• with_std : bool, default=true If true, scale the data to unit variance (bool, default true).

---

Attributes:

• with_mean : bool Whether centering is enabled.

• with_std : bool Whether scaling to unit variance is enabled.

• mean : RowVectorType Per-feature mean (1 × n_features).

• var : RowVectorType Per-feature variance (1 × n_features).

• scale : RowVectorType Per-feature standard deviation (1 × n_features).

• n_samples_seen : IndexType Number of samples processed during fit().

---

Methods

fit(X)

Compute the mean and std to be used for later scaling.

Parameters:

• X : MatrixType Training data of shape (n_samples, n_features).

Returns:

• result : StandardScaler Reference to the fitted transformer (*this).

---

transform(X)

Perform standardization by centering and scaling.

Parameters:

• X : MatrixType Data matrix of shape (n_samples, n_features).

Returns:

• result : MatrixType Transformed data of same shape.

Throws:

• std::runtime_error — if the model has not been fitted.

---

inverse_transform(X)

Scale back the data to the original representation.

Parameters:

• X : MatrixType Transformed data of shape (n_samples, n_features).

Returns:

• result : MatrixType Un-transformed data of same shape.

Throws:

• std::runtime_error — if the model has not been fitted.

---

transform_inplace(X)

Transform features in-place by standardizing.

Parameters:

• X : Eigen::Ref< MatrixType > Data matrix of shape (n_samples, n_features), modified in place.

Throws:

• std::runtime_error — if the model has not been fitted.

---

inverse_transform_inplace(X)

Inverse-transform features in-place.

Parameters:

• X : Eigen::Ref< MatrixType > Standardized data matrix, modified in place to original scale.

Throws:

• std::runtime_error — if the model has not been fitted.

---

Example

// Fit and transform
Skigen::StandardScaler<double> scaler;
Eigen::MatrixXd Z = scaler.fit_transform(X);

std::cout << "Standardized:\n" << Z << "\n\n";
std::cout << "Mean:  " << scaler.mean()  << "\n";
std::cout << "Scale: " << scaler.scale() << "\n\n";

// Round-trip: inverse_transform recovers the original data
Eigen::MatrixXd X_back = scaler.inverse_transform(Z);
std::cout << "Recovered:\n" << X_back << "\n\n";

---