def test_linear_regress(self):
data = self.sc.parallelize([(1, array([1.5, 2.3, 6.2, 5.1, 3.4, 2.1]))])
x = array([array([1, 0, 0, 0, 0, 0]), array([0, 1, 0, 0, 0, 0])])
model = RegressionModel.load(x, "linear")
betas, stats, resid = model.fit(data)
assert allclose(betas.map(lambda (_, v): v).collect()[0], array([-2.7, -1.9]))
assert allclose(stats.map(lambda (_, v): v).collect()[0], array([0.42785299]))
assert allclose(resid.map(lambda (_, v): v).collect()[0], array([0, 0, 2, 0.9, -0.8, -2.1]))
stats, betas = regress(data, x, "linear")
stats.collect()
betas.collect()
stats, comps, latent, scores, traj = regresswithpca(data, x, "linear")
stats.collect()
scores.collect()
def test_blinear_regress(self):
data = self.sc.parallelize([(1, array([1.5, 2.3, 6.2, 5.1, 3.4, 2.1]))])
x1 = array([array([1, 0, 1, 0, 1, 0]), array([0, 1, 0, 1, 0, 1])])
x2 = array([array([1, 1, 0, 0, 0, 0]), array([0, 0, 1, 1, 0, 0]), array([0, 0, 0, 0, 1, 1])])
model = RegressionModel.load((x1, x2), "bilinear")
betas, stats, resid = model.fit(data)
tol = 1e-4 # to handle rounding errors
assert allclose(betas.map(lambda (_, v): v).collect()[0], array([-3.1249, 5.6875, 0.4375]), atol=tol)
assert allclose(stats.map(lambda (_, v): v).collect()[0], array([0.6735]), tol)
assert allclose(resid.map(lambda (_, v): v).collect()[0], array([0, -0.8666, 0, 1.9333, 0, -1.0666]), atol=tol)
stats, betas = regress(data, (x1, x2), "bilinear")
stats.collect()
betas.collect()
stats, comps, latent, scores, traj = regresswithpca(data, (x1, x2), "bilinear")
stats.collect()
scores.collect()
def test_linear_regress(self):
data = self.sc.parallelize([(1, array([1.5, 2.3, 6.2, 5.1, 3.4,
2.1]))])
x = array([array([1, 0, 0, 0, 0, 0]), array([0, 1, 0, 0, 0, 0])])
model = RegressionModel.load(x, "linear")
betas, stats, resid = model.fit(data)
assert (allclose(
betas.map(lambda (_, v): v).collect()[0], array([-2.7, -1.9])))
assert (allclose(
stats.map(lambda (_, v): v).collect()[0], array([0.42785299])))
assert (allclose(
resid.map(lambda (_, v): v).collect()[0],
array([0, 0, 2, 0.9, -0.8, -2.1])))
stats, betas = regress(data, x, "linear")
stats.collect()
betas.collect()
stats, comps, latent, scores, traj = regresswithpca(data, x, "linear")
stats.collect()
scores.collect()
data.cache()
# compute mean map
vals = stats(data,"mean")
save(vals,outputdir,"mean_vals","matlab")
# compute local cor
if args.neighbourhood != 0:
cor = localcorr(data,args.neighbourhood)
save(cor,outputdir,"local_corr","matlab")
# if stim argument is not default
if args.stim != '-':
# parse into different stim names
p = re.compile('-')
stims = p.split(args.stim)
# compute regression
for i in range(len(stims)):
modelfile = os.path.join(args.datafolder, args.basename + stims[i])
stats, betas = regress(data, modelfile, args.regressmode)
tune = tuning(betas,modelfile, args.tuningmode)
out_name = "stats_" + stims[i]
save(stats, outputdir, out_name, "matlab")
out_name = "tune_" + stims[i]
save(tune, outputdir, out_name, "matlab")
def test_mean_regression(self):
data = get_data_regression(self)
betas, stats, comps, latent, scores, traj, r = regress(data, FISH_LINEAR_MODEL, "mean")
stats.collect()
scores.collect()
r.collect()
def test_bilinear_regression(self):
data = get_data_regression(self)
betas, stats, comps, latent, scores, traj, r = regress(data, FISH_BILINEAR_MODEL, "bilinear")
stats.collect()
scores.collect()
r.collect()
def test_linear_shuffle_regression(self):
data = get_data_regression(self)
betas, stats, comps, latent, scores, traj, r = regress(data, FISH_LINEAR_MODEL, "linear-shuffle")
stats.collect()
scores.collect()
r.collect()
array([0, 0, 1, 1, 0, 0]),
array([0, 0, 0, 0, 1, 1])
])
model = RegressionModel.load((x1, x2), "bilinear")
betas, stats, resid = model.fit(data)
tol = 1E-4 # to handle rounding errors
assert (allclose(betas.map(lambda (_, v): v).collect()[0],
array([-3.1249, 5.6875, 0.4375]),
atol=tol))
assert (allclose(
stats.map(lambda (_, v): v).collect()[0], array([0.6735]), tol))
assert (allclose(resid.map(lambda (_, v): v).collect()[0],
array([0, -0.8666, 0, 1.9333, 0, -1.0666]),
atol=tol))
stats, betas = regress(data, (x1, x2), "bilinear")
stats.collect()
betas.collect()
stats, comps, latent, scores, traj = regresswithpca(
data, (x1, x2), "bilinear")
stats.collect()
scores.collect()
class TestTuning(RegressionTestCase):
"""Test accuracy of gaussian and circular tuning
by building small stimulus arrays and testing
on small data against ground truth
(ground truth for gaussian tuning
derived by doing the algebra in MATLAB,
