Analysis

dca.analysis.linear_decode_r2(X_train, Y_train, X_test, Y_test, decoding_window=1, offset=0)

Train a linear model on the training set and test on the test set.

This will work with batched training data and/or batched test data.

X_trainndarray (time, channels) or (batches, time, channels)

Feature training data for regression.

Y_trainndarray (time, channels) or (batches, time, channels)

Target training data for regression.

X_testndarray (time, channels) or (batches, time, channels)

Feature test data for regression.

Y_testndarray (time, channels) or (batches, time, channels)

Target test data for regression.

decoding_windowint

Number of time samples of X to use for predicting Y (should be odd). Centered around offset value.

offsetint

Temporal offset for prediction (0 is same-time prediction).

dca.analysis.random_complement(proj, size=1, random_state=None)

Computes a random vector in the orthogonal complement to proj.

Parameters:

• proj (ndarray (full dim, low dim)) – Projection matrix.

• random_state (NumPy random state (optional)) – Random state for rng.

Returns:

comp_vec – Random vector in the complement space.

Return type:

ndarray (full dim, 1)