Table of Contents

Mojo struct

PCA

@memory_only
struct PCA

Principal component analysis (PCA). Linear dimensionality reduction using Singular Value Decomposition of the data to project it to a lower dimensional space.

Aliases

  • __del__is_trivial = False

Fields

  • n_components (Int): Number of components to keep.
  • components (Matrix)
  • components_T (Matrix)
  • explained_variance (Matrix): The amount of variance explained by each of the selected components.
  • explained_variance_ratio (Matrix): Percentage of variance explained by each of the selected components.
  • mean (Matrix)
  • whiten (Bool): To transform data to have zero mean, unit variance, and no correlation between features.
  • whiten_ (Matrix)
  • lapack (Bool): Use LAPACK to calculate svd.

Implemented traits

AnyType, ImplicitlyDestructible

Methods

__init__

fn __init__(out self, n_components: Int, whiten: Bool = False, lapack: Bool = False)

Args:

  • n_components (Int)
  • whiten (Bool)
  • lapack (Bool)
  • self (Self)

Returns:

Self

fit

fn fit(mut self, X: Matrix)

Fit the model.

Args:

  • self (Self)
  • X (Matrix)

Raises:

transform

fn transform(self, X: Matrix) -> Matrix

Apply dimensionality reduction to X. X is projected on the first principal components previously extracted from a training set.

Args:

  • self (Self)
  • X (Matrix)

Returns:

Matrix: Projection of X in the first principal components.

Raises:

inverse_transform

fn inverse_transform(self, X_transformed: Matrix) -> Matrix

Transform data back to its original space.

Args:

  • self (Self)
  • X_transformed (Matrix)

Returns:

Matrix: Original data.

Raises:

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