builtin Package

builtin Package

binica Module

scot.builtin.binica.binica(data)

Simple wrapper for the binica program.

BINICA is bundled with EEGLAB, or can be downloaded from here:

http://sccn.ucsd.edu/eeglab/binica/

scot.builtin.binica.check_binary(binary)

check if binary is available, and try to obtain it if not

pca Module

principal component analysis (PCA) implementation

scot.builtin.pca.pca(x, subtract_mean=False, normalize=False, sort_components=True, reducedim=None, algorithm=<function pca_eig at 0x7fd35c0d8d40>)

pca( x, subtract_mean=False,

normalize=False, sort_components=True, retain_variance=None, algorithm=pcaEIG ):

calculate principal component analysis (PCA).

scot.builtin.pca.pca_eig(x)

calculate PCA as eigenvalues of the covariance (observations in rows)

scot.builtin.pca.pca_svd(data)

calculate PCA from SVD (observations in rows)

utils Module

scot.builtin.utils.cartesian(arrays, out=None)

Generate a cartesian product of input arrays.

Parameters:

arrays : list of array-like

1-D arrays to form the cartesian product of.

out : ndarray

Array to place the cartesian product in.

Returns:

out : ndarray

2-D array of shape (M, len(arrays)) containing cartesian products formed of input arrays.

References

http://stackoverflow.com/a/1235363/3005167

Examples

>>> cartesian(([1, 2, 3], [4, 5], [6, 7]))
array([[1, 4, 6],
       [1, 4, 7],
       [1, 5, 6],
       [1, 5, 7],
       [2, 4, 6],
       [2, 4, 7],
       [2, 5, 6],
       [2, 5, 7],
       [3, 4, 6],
       [3, 4, 7],
       [3, 5, 6],
       [3, 5, 7]])

var Module

vector autoregressive (VAR) model implementation

class scot.builtin.var.VAR(model_order, delta=0, xvschema=<function multitrial at 0x7fd35c0cc5f0>)

Bases: scot.var.VARBase

Represents a vector autoregressive (VAR) model.

Note on the arrangement of model coefficients:

b is of shape m, m*p, with sub matrices arranged as follows:

b_00 b_01 ... b_0m b_10 b_11 ... b_1m .... .... .... b_m0 b_m1 ... b_mm

Each sub matrix b_ij is a column vector of length p that contains the filter coefficients from channel j (source) to channel i (sink).

fit(data)

Fit the model to data.

optimize(data)

Optimize the var model’s hyperparameters (such as regularization).

optimize_delta_bisection(data, skipstep=1)

Use the bisection method to find optimal regularization parameter delta.

Behavior of this function depends on the xvschema attribute.