def test_kldiv(self):
arr = scipy.random.random((21, 13))
fm = fixmat.TestFixmatFactory(categories=[1, 2, 3],
filenumbers=[1, 2, 3, 4, 5, 6],
subjectindices=[1, 2, 3, 4, 5, 6],
params={
'pixels_per_degree': 10,
'image_size': [100, 500]
})
kl = measures.kldiv(arr, arr)
self.assertEqual(
kl, 0, "KL Divergence between same distribution should be 0")
kl = measures.kldiv(None, None, distp=fm, distq=fm, scale_factor=0.25)
self.assertEqual(
kl, 0, "KL Divergence between same distribution should be 0")
fdm = fixmat.compute_fdm(fm)
kl = measures.kldiv_model(fdm, fm)
self.assertTrue(
kl < 10**-13,
"KL Divergence between same distribution should be almost 0")
fm.x = np.array([])
fm.y = np.array([])
kl = measures.kldiv(None, None, distp=fm, distq=fm, scale_factor=0.25)
self.assertTrue(np.isnan(kl))
def eval_fits(self):
print('Fit Splines')
spline_fit, hist = sb.fit2d(self.train_samples,
np.linspace(0,self.shape[0], self.shape[0]+1),
np.linspace(0,self.shape[1], self.shape[1]+1))
spline_kl = measures.kldiv(spline_fit, self.target)
hist_kl = measures.kldiv(hist, self.target)
try:
print('Fit KDE')
kde_est = gaussian_kde(self.train_samples)
x,y = np.mgrid[0:self.shape[0], self.shape[1]]
kde_fit = kde_est.evaluate((np.array(x), np.array(y)))
kde_kl = measures.kldiv(kde_fit, self.target)
except:
kde_kl = np.nan
print('LinAlgError')
print(spline_kl, hist_kl, kde_kl)
return spline_kl, hist_kl, kde_kl
def eval_fits(self):
print 'Fit Splines'
spline_fit, hist = sb.fit2d(self.train_samples,
np.linspace(0,self.shape[0], self.shape[0]+1),
np.linspace(0,self.shape[1], self.shape[1]+1))
spline_kl = measures.kldiv(spline_fit, self.target)
hist_kl = measures.kldiv(hist, self.target)
try:
print 'Fit KDE'
kde_est = gaussian_kde(self.train_samples)
x,y = np.mgrid[0:self.shape[0], self.shape[1]]
kde_fit = kde_est.evaluate((np.array(x), np.array(y)))
kde_kl = measures.kldiv(kde_fit, self.target)
except:
kde_kl = np.nan
print 'LinAlgError'
print spline_kl, hist_kl, kde_kl
return spline_kl, hist_kl, kde_kl
def test_kldiv(self):
arr = scipy.random.random((21,13))
fm = fixmat.TestFixmatFactory(categories = [1,2,3],
filenumbers = [1,2,3,4,5,6],
subjectindices = [1, 2, 3, 4, 5, 6],
params = {'pixels_per_degree':10, 'image_size':[100,500]})
kl = measures.kldiv(arr, arr)
self.assertEqual(kl, 0,
"KL Divergence between same distribution should be 0")
kl = measures.kldiv(None, None, distp = fm, distq = fm, scale_factor = 0.25)
self.assertEqual(kl, 0,
"KL Divergence between same distribution should be 0")
fdm = fixmat.compute_fdm(fm)
kl = measures.kldiv_model(fdm, fm)
self.assertTrue(kl < 10**-13,
"KL Divergence between same distribution should be almost 0")
fm.x = np.array([])
fm.y = np.array([])
kl = measures.kldiv(None, None, distp = fm, distq = fm, scale_factor = 0.25)
self.assertTrue(np.isnan(kl))
