def average_predictive_info(data, settings):
x, y = drop_nan_rows(split3D(data, settings['max_lag']))
nSample, nProcess = x.shape
if nSample < 5 + 5 * nProcess:
# If there are too few samples, there is no point to calculate anything
return np.array(np.nan)
else:
return ee.mi(x, y) / nProcess
def _preprocess_mar_inp(data, inp, nHist):
x, y = splitter.split3D(data, nHist)
assert inp.ndim == 3, "Input matrix must be a 3D matrix"
assert np.prod(inp.shape) != 0, "Input matrix is degenerate"
nTr, nCh, nT = data.shape
nTrInp, nChInp, nTInp = inp.shape
assert nTr == nTrInp, "Input shape must be consistent with data shape"
assert nT == nTInp, "Input shape must be consistent with data shape"
# Convert input into the form (rps) -> (r*s, p)
inpCanon = numpy_transpose_byorder(inp, 'rps', 'rsp')
u = numpy_merge_dimensions(inpCanon[:, nHist:], 0, 2)
# Drop any nan rows that are present in the data or input
return drop_nan_rows([x, y, u])
def average_predictive_info_non_uniform(dataLst, settings):
# Test that all trials have sufficient timesteps for lag estimation
nSampleMin = np.min(set_list_shapes(dataLst, axis=1))
if nSampleMin <= settings['max_lag']:
raise ValueError('lag', settings['max_lag'], 'cannot be estimated for number of timesteps', nSampleMin)
xLst = []
yLst = []
for dataTrial in dataLst:
x, y = drop_nan_rows(split3D(dataTrial, settings['max_lag']))
xLst += [x]
yLst += [y]
xArr = np.vstack(xLst)
yArr = np.vstack(yLst)
nSample, nProcess = xArr.shape
if nSample < 4 * nProcess:
# If there are too few samples, there is no point to calculate anything
return np.array(np.nan)
else:
return ee.mi(xArr, yArr) / nProcess
def mar_testerr(data, settings):
testFrac = settings['testfrac'] if 'testfrac' in settings.keys() else 0.1
x, y = drop_nan_rows(splitter.split3D(data, settings['hist']))
return _mar3D_testerr(x, y, testFrac)
def mar1_coeff(data, settings):
x, y = drop_nan_rows(splitter.split3D(data, 1))
return _mar3D_alpha(x, y)