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()
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,
ground truth for circular tuning
derived from MATLAB's circular statistics toolbox
circ_mean and circ_kappa functions)
Also tests that main analysis script runs without crashing
