def make_simple_trajectories(tasks, npt=201,
speed_mid=0.5, speed_width=0.2):
'''
Make linear trajectories from start to finish
with a gaussian speed profile.
Parameters
----------
tasks : ndarray
shape (n, 6)
n tasks with start position :3 and target position 3:
'''
ndim = tasks.shape[1] / 2
time = np.linspace(0, 1, npt)
spd = curves.gauss1d(time, 1., speed_width, speed_mid)
spd /= np.sum(spd)
start, stop = tasks[:,:ndim], tasks[:,ndim:]
drn = stop - start
vel = spd[None,:,None] * drn[:,None,:] # shape (task, time, space)
disp = np.cumsum(vel/2. * time[None,:,None], axis=1) # kinematic eqn
pos = disp + start[:,None]
return time, pos, spd
def test_gam_predict_cv():
dsname = 'frank-osmd'
align = 'hold'
lag = 0.1 # s
b0 = 20 # Hz
noise_sd = 1. # Hz
b_scale = 10.
nbin = 10
ds = datasets[dsname]
dc = DataCollection(ds.get_files()[:5])
unit = ds.get_units()[0]
dc.add_unit(unit, lag)
bnd = dc.make_binned(nbin=nbin, align=align, do_count=True)
ntask, nrep, nedge, ndim = bnd.pos.shape
pos = bnd.pos.reshape(ntask * nrep, nedge, -1)
drn = kin.get_dir(pos)
# simplest model
# y = b0 + Bd.D
B = np.array([.2, .6, .4]) * b_scale
y = b0 + np.dot(drn, B)
noise = np.random.normal(0, noise_sd, size=y.shape)
y += noise
bnd.set_count_from_flat(y[:,None])
out = gam_predict_cv(bnd, ['kd'], [dsname, unit, lag, align], \
family='gaussian')
have = np.mean(out.coef[0], axis=0)[:4]
want = np.array([b0, B[0], B[1], B[2]])
np.testing.assert_array_almost_equal(have, want, decimal=1)
# changing Bdt model, gaussian noise model
# y = b0 + Bdt.D
gc_type = [('A', float), ('fwhm', float), ('c', float)]
gcoeff = np.array([[(1., 1.5, 3.), (.75, 2.5, 6.)],
[(.4, 4., 2.), (.1, 2., 5.)],
[(.8, 3., 5.), (.6, 4.5, 6.5)]], dtype=gc_type)
gcoeff = gcoeff.view(np.recarray)
x = np.linspace(0, 10, nbin)
Bt = np.zeros((3, nbin))
for Bkt, gc in zip(Bt, gcoeff):
g0 = gauss1d(x, gc.A[0], gc.fwhm[0], gc.c[0])
g1 = gauss1d(x, gc.A[1], gc.fwhm[1], gc.c[1])
Bkt[:] = g0 + g1
Bt *= b_scale
y = b0 + np.sum(Bt.T[None] * drn, axis=-1)
y = np.random.normal(y, noise_sd)
bnd.set_count_from_flat(y[:,None])
out = gam_predict_cv(bnd, ['kdX'], [dsname, unit, lag, align], \
family='gaussian')
# check that actual and predicted are correlated
x = out.actual.flatten()
y = out.pred[8].flatten()
mc, rp = pearsonr(x,y)
np.testing.assert_approx_equal(mc, 1, significant=1)
