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better f_inv

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This commit is contained in:
Nicolò Fusi 2013-05-08 15:47:06 +02:00
parent 323545f2d1
commit b17cc60182
2 changed files with 25 additions and 10 deletions
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11
GPy/models/warped_GP.py
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@ -23,6 +23,7 @@ class warpedGP(GP):
self.warping_function = TanhWarpingFunction_d(warping_terms) self.warping_function = TanhWarpingFunction_d(warping_terms)
self.warping_params = (np.random.randn(self.warping_function.n_terms*3+1,) * 1) self.warping_params = (np.random.randn(self.warping_function.n_terms*3+1,) * 1)
Y = self._scale_data(Y)
self.has_uncertain_inputs = False self.has_uncertain_inputs = False
self.Y_untransformed = Y.copy() self.Y_untransformed = Y.copy()
self.predict_in_warped_space = False self.predict_in_warped_space = False
@ -30,6 +31,14 @@ class warpedGP(GP):
GP.__init__(self, X, likelihood, kernel, normalize_X=normalize_X, Xslices=Xslices) GP.__init__(self, X, likelihood, kernel, normalize_X=normalize_X, Xslices=Xslices)
def _scale_data(self, Y):
self._Ymax = Y.max()
self._Ymin = Y.min()
return (Y-self._Ymin)/(self._Ymax-self._Ymin) - 0.5
def _unscale_data(self, Y):
return (Y + 0.5)*(self._Ymax - self._Ymin) + self._Ymin
def _set_params(self, x): def _set_params(self, x):
self.warping_params = x[:self.warping_function.num_parameters] self.warping_params = x[:self.warping_function.num_parameters]
Y = self.transform_data() Y = self.transform_data()
@ -79,5 +88,5 @@ class warpedGP(GP):
if self.predict_in_warped_space: if self.predict_in_warped_space:
mu = self.warping_function.f_inv(mu, self.warping_params) mu = self.warping_function.f_inv(mu, self.warping_params)
var = self.warping_function.f_inv(var, self.warping_params) var = self.warping_function.f_inv(var, self.warping_params)
mu = self._unscale_data(mu)
return mu, var return mu, var

24
GPy/util/warping_functions.py
View file

@ -81,7 +81,7 @@ class TanhWarpingFunction(WarpingFunction):
iterations: number of N.R. iterations iterations: number of N.R. iterations
""" """
y = y.copy() y = y.copy()
z = np.ones_like(y) z = np.ones_like(y)
@ -176,7 +176,7 @@ class TanhWarpingFunction_d(WarpingFunction):
mpsi = psi.copy() mpsi = psi.copy()
d = psi[-1] d = psi[-1]
mpsi = mpsi[:self.num_parameters-1].reshape(self.n_terms, 3) mpsi = mpsi[:self.num_parameters-1].reshape(self.n_terms, 3)
#3. transform data #3. transform data
z = d*y.copy() z = d*y.copy()
for i in range(len(mpsi)): for i in range(len(mpsi)):
@ -185,7 +185,7 @@ class TanhWarpingFunction_d(WarpingFunction):
return z return z
def f_inv(self, y, psi, iterations = 30): def f_inv(self, z, psi, max_iterations = 1000):
""" """
calculate the numerical inverse of f calculate the numerical inverse of f
@ -194,13 +194,19 @@ class TanhWarpingFunction_d(WarpingFunction):
""" """
y = y.copy() z = z.copy()
z = np.ones_like(y) y = np.ones_like(z)
it = 0
update = np.inf
for i in range(iterations): while it == 0 or (np.abs(update).sum() > 1e-10 and it < max_iterations):
z -= (self.f(z, psi) - y)/self.fgrad_y(z,psi) update = (self.f(y, psi) - z)/self.fgrad_y(y, psi)
y -= update
return z it += 1
if it == max_iterations:
print "WARNING!!! Maximum number of iterations reached in f_inv "
return y
def fgrad_y(self, y, psi, return_precalc = False): def fgrad_y(self, y, psi, return_precalc = False):