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logistic seems working but more tests are needed

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beckdaniel 2016-02-24 13:27:19 +00:00
parent 4dc6f0ec6c
commit 91e625a9bd
2 changed files with 5 additions and 3 deletions
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4
GPy/testing/model_tests.py
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@ -316,10 +316,12 @@ class MiscTests(unittest.TestCase):
X = (2 * np.pi) * np.random.random(151) - np.pi
Y = np.sin(X) + np.random.normal(0,0.2,151)
Y = np.array([np.power(abs(y),float(1)/3) * (1,-1)[y<0] for y in Y])
Y = np.abs(Y)
import matplotlib.pyplot as plt
warp_k = GPy.kern.RBF(1)
warp_f = GPy.util.warping_functions.TanhFunction(n_terms=2)
#warp_f = GPy.util.warping_functions.TanhFunction(n_terms=2)
warp_f = GPy.util.warping_functions.LogisticFunction(n_terms=2)
warp_m = GPy.models.WarpedGP(X[:, None], Y[:, None], kernel=warp_k, warping_function=warp_f)
m = GPy.models.GPRegression(X[:, None], Y[:, None])

4
GPy/util/warping_functions.py
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@ -338,7 +338,7 @@ class LogisticFunction(WarpingFunction):
return grad, logistic_term, logistic_grad
return grad
def fgrad_y_psi(self, y, psi):
def fgrad_y_psi(self, y, return_covar_chain=False):
"""
gradient of f w.r.t to y and psi
@ -351,7 +351,7 @@ class LogisticFunction(WarpingFunction):
for i in xrange(self.n_terms):
a, b, c = mpsi[i]
gradients[:, :, i, 0] = b * l_grad[i].T
b2l_term = b - (2 * l_term[i])
b2l_term = b - (2 * l_term[i].T)
al_grad = a * l_grad[i].T
#gradients[:, :, i, 1] = a * (d[i] - 2.0 * s[i] * r[i] * (1.0/np.cosh(s[i])) ** 2).T
gradients[:, :, i, 1] = (1 + ((y + c) * b2l_term)) * al_grad