braintools.analysis.population.PCAResult

class braintools.analysis.population.PCAResult(components, explained_variance, explained_variance_ratio, mean, projection)

Result of pca() (a pytree, so it passes through jax.jit).

Variables:

• components (jax.Array) – Principal axes, shape (k, D), unit-norm rows ordered by decreasing explained variance (sklearn’s components_).

• explained_variance (jax.Array or brainunit.Quantity) – Variance along each axis, shape (k,), with ddof=1. Carries the squared input unit for Quantity input.

• explained_variance_ratio (jax.Array) – Fraction of the total variance explained by each axis, shape (k,).

• mean (jax.Array or brainunit.Quantity) – Per-feature mean that was subtracted, shape (D,).

• projection (jax.Array or brainunit.Quantity) – Mean-centred data projected onto the axes, shape (T, k).

__init__()

Methods

__init__()
count(value, /)	Return number of occurrences of value.
index(value[, start, stop])	Return first index of value.

Attributes

components	Alias for field number 0
explained_variance	Alias for field number 1
explained_variance_ratio	Alias for field number 2
mean	Alias for field number 3
projection	Alias for field number 4