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cleaning on warping functions

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beckdaniel 2016-02-24 11:17:22 +00:00
parent 03e59b764d
commit 8deac1d058
1 changed files with 18 additions and 24 deletions
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42
GPy/util/warping_functions.py
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@ -174,20 +174,14 @@ class TanhWarpingFunction_d(WarpingFunction):
Transform y with f using parameter vector psi
psi = [[a,b,c]]
:math:`f = \\sum_{terms} a * tanh(b*(y+c))`
:math:`f = (y * d) + \\sum_{terms} a * tanh(b *(y + c))`
"""
#1. check that number of params is consistent
# assert psi.shape[0] == self.n_terms, 'inconsistent parameter dimensions'
# assert psi.shape[1] == 4, 'inconsistent parameter dimensions'
d = self.d
mpsi = self.psi
#3. transform data
z = d*y.copy()
z = d * y.copy()
for i in range(len(mpsi)):
a,b,c = mpsi[i]
z += a*np.tanh(b*(y+c))
a, b, c = mpsi[i]
z += a * np.tanh(b * (y + c))
return z
def f_inv(self, z, max_iterations=100, y=None):
@ -214,7 +208,7 @@ class TanhWarpingFunction_d(WarpingFunction):
while it == 0 or (np.abs(update).sum() > 1e-10 and it < max_iterations):
fy = self.f(y)
fgrady = self.fgrad_y(y)
update = (fy - z)/fgrady
update = (fy - z) / fgrady
y -= update
it += 1
if it == max_iterations:
@ -226,17 +220,18 @@ class TanhWarpingFunction_d(WarpingFunction):
"""
gradient of f w.r.t to y ([N x 1])
:returns: Nx1 vector of derivatives, unless return_precalc is true, then it also returns the precomputed stuff
:returns: Nx1 vector of derivatives, unless return_precalc is true,
then it also returns the precomputed stuff
"""
d = self.d
mpsi = self.psi
# vectorized version
S = (mpsi[:,1]*(y[:,:,None] + mpsi[:,2])).T
S = (mpsi[:,1] * (y[:,:,None] + mpsi[:,2])).T
R = np.tanh(S)
D = 1-R**2
D = 1 - (R ** 2)
GRAD = (d + (mpsi[:,0:1][:,:,None]*mpsi[:,1:2][:,:,None]*D).sum(axis=0)).T
GRAD = (d + (mpsi[:,0:1][:,:,None] * mpsi[:,1:2][:,:,None] * D).sum(axis=0)).T
if return_precalc:
return GRAD, S, R, D
@ -255,28 +250,27 @@ class TanhWarpingFunction_d(WarpingFunction):
gradients = np.zeros((y.shape[0], y.shape[1], len(mpsi), 4))
for i in range(len(mpsi)):
a,b,c = mpsi[i]
gradients[:,:,i,0] = (b*(1.0/np.cosh(s[i]))**2).T
gradients[:,:,i,1] = a*(d[i] - 2.0*s[i]*r[i]*(1.0/np.cosh(s[i]))**2).T
gradients[:,:,i,2] = (-2.0*a*(b**2)*r[i]*((1.0/np.cosh(s[i]))**2)).T
gradients[:,:,0,3] = 1.0
gradients[:, :, i, 0] = (b * (1.0/np.cosh(s[i])) ** 2).T
gradients[:, :, i, 1] = a * (d[i] - 2.0 * s[i] * r[i] * (1.0/np.cosh(s[i])) ** 2).T
gradients[:, :, i, 2] = (-2.0 * a * (b ** 2) * r[i] * ((1.0 / np.cosh(s[i])) ** 2)).T
gradients[:, :, 0, 3] = 1.0
if return_covar_chain:
covar_grad_chain = np.zeros((y.shape[0], y.shape[1], len(mpsi), 4))
for i in range(len(mpsi)):
a,b,c = mpsi[i]
covar_grad_chain[:, :, i, 0] = (r[i]).T
covar_grad_chain[:, :, i, 1] = (a*(y + c) * ((1.0/np.cosh(s[i]))**2).T)
covar_grad_chain[:, :, i, 2] = a*b*((1.0/np.cosh(s[i]))**2).T
covar_grad_chain[:, :, i, 1] = (a * (y + c) * ((1.0 / np.cosh(s[i])) ** 2).T)
covar_grad_chain[:, :, i, 2] = a * b * ((1.0 / np.cosh(s[i])) ** 2).T
covar_grad_chain[:, :, 0, 3] = y
return gradients, covar_grad_chain
return gradients
def _get_param_names(self):
variables = ['a', 'b', 'c', 'd']
names = sum([['warp_tanh_%s_t%i' % (variables[n],q) for n in range(3)] for q in range(self.n_terms)],[])
names = sum([['warp_tanh_%s_t%i' % (variables[n],q) for n in range(3)]
for q in range(self.n_terms)],[])
names.append('warp_tanh_d')
return names