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first attemot at the new constraint framework

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James Hensman 2013-04-30 11:25:57 +01:00
parent 50a68e1a65
commit 83510e7515
5 changed files with 129 additions and 218 deletions
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52
GPy/kern/kern.py
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@ -71,12 +71,10 @@ class kern(parameterised):
def _transform_gradients(self, g):
x = self._get_params()
g[self.constrained_positive_indices] = g[self.constrained_positive_indices] * x[self.constrained_positive_indices]
g[self.constrained_negative_indices] = g[self.constrained_negative_indices] * x[self.constrained_negative_indices]
[np.put(g, i, g[i] * (x[i] - l) * (h - x[i]) / (h - l)) for i, l, h in zip(self.constrained_bounded_indices, self.constrained_bounded_lowers, self.constrained_bounded_uppers)]
[np.put(x,i,x*t.gradfactor(x[i])) for i,t in zip(self.constrained_indices, self.constraints)]
[np.put(g, i, v) for i, v in [(t[0], np.sum(g[t])) for t in self.tied_indices]]
if len(self.tied_indices) or len(self.constrained_fixed_indices):
to_remove = np.hstack((self.constrained_fixed_indices + [t[1:] for t in self.tied_indices]))
if len(self.tied_indices) or len(self.fixed_indices):
to_remove = np.hstack((self.fixed_indices + [t[1:] for t in self.tied_indices]))
return np.delete(g, to_remove)
else:
return g
@ -93,13 +91,10 @@ class kern(parameterised):
assert self.D == other.D
newkern = kern(self.D, self.parts + other.parts, self.input_slices + other.input_slices)
# transfer constraints:
newkern.constrained_positive_indices = np.hstack((self.constrained_positive_indices, self.Nparam + other.constrained_positive_indices))
newkern.constrained_negative_indices = np.hstack((self.constrained_negative_indices, self.Nparam + other.constrained_negative_indices))
newkern.constrained_bounded_indices = self.constrained_bounded_indices + [self.Nparam + x for x in other.constrained_bounded_indices]
newkern.constrained_bounded_lowers = self.constrained_bounded_lowers + other.constrained_bounded_lowers
newkern.constrained_bounded_uppers = self.constrained_bounded_uppers + other.constrained_bounded_uppers
newkern.constrained_fixed_indices = self.constrained_fixed_indices + [self.Nparam + x for x in other.constrained_fixed_indices]
newkern.constrained_fixed_values = self.constrained_fixed_values + other.constrained_fixed_values
newkern.constrained_indices = self.constrained_indices + [i+self.Nparam for i in other.constrained_indices]
newkern.constraints = self.constraints + other.constraints
newkern.fixed_indices = self.fixed_indices + [self.Nparam + x for x in other.fixed_indices]
newkern.fixed_values = self.fixed_values + other.fixed_values
newkern.tied_indices = self.tied_indices + [self.Nparam + x for x in other.tied_indices]
return newkern
@ -126,13 +121,12 @@ class kern(parameterised):
newkern = kern(D, self.parts + other.parts, self_input_slices + other_input_slices)
# transfer constraints:
newkern.constrained_positive_indices = np.hstack((self.constrained_positive_indices, self.Nparam + other.constrained_positive_indices))
newkern.constrained_negative_indices = np.hstack((self.constrained_negative_indices, self.Nparam + other.constrained_negative_indices))
newkern.constrained_bounded_indices = self.constrained_bounded_indices + [self.Nparam + x for x in other.constrained_bounded_indices]
newkern.constrained_bounded_lowers = self.constrained_bounded_lowers + other.constrained_bounded_lowers
newkern.constrained_indices = self.constrained_indices + [x+self.Nparam for x in other.constrained_indices]
newkern.constraints = self.constraints + other.constraints
newkern.fixed_indices = self.fixed_indices + [self.Nparam + x for x in other.fixed_indices]
newkern.fixed_values = self.fixed_values + other.fixed_values
newkern.constraints = self.constraints + other.constraints
newkern.constrained_bounded_uppers = self.constrained_bounded_uppers + other.constrained_bounded_uppers
newkern.constrained_fixed_indices = self.constrained_fixed_indices + [self.Nparam + x for x in other.constrained_fixed_indices]
newkern.constrained_fixed_values = self.constrained_fixed_values + other.constrained_fixed_values
newkern.tied_indices = self.tied_indices + [self.Nparam + x for x in other.tied_indices]
return newkern
@ -208,15 +202,11 @@ class kern(parameterised):
# Get the ties and constrains of the kernels before the multiplication
prev_ties = K1.tied_indices + [arr + K1.Nparam for arr in K2.tied_indices]
prev_constr_pos = np.append(K1.constrained_positive_indices, K1.Nparam + K2.constrained_positive_indices)
prev_constr_neg = np.append(K1.constrained_negative_indices, K1.Nparam + K2.constrained_negative_indices)
prev_constr_ind = [K1.constrained_indices] + [K1.Nparam + i for i in K2.constrained_indices]
prev_constr = K1.constraints + K2.constraints
prev_constr_fix = K1.constrained_fixed_indices + [arr + K1.Nparam for arr in K2.constrained_fixed_indices]
prev_constr_fix_values = K1.constrained_fixed_values + K2.constrained_fixed_values
prev_constr_bou = K1.constrained_bounded_indices + [arr + K1.Nparam for arr in K2.constrained_bounded_indices]
prev_constr_bou_low = K1.constrained_bounded_lowers + K2.constrained_bounded_lowers
prev_constr_bou_upp = K1.constrained_bounded_uppers + K2.constrained_bounded_uppers
prev_constr_fix = K1.fixed_indices + [arr + K1.Nparam for arr in K2.fixed_indices]
prev_constr_fix_values = K1.fixed_values + K2.fixed_values
# follow the previous ties
for arr in prev_ties:
@ -228,14 +218,8 @@ class kern(parameterised):
index = np.where(index_param == i)[0]
if index.size > 1:
self.tie_params(index)
for i in prev_constr_pos:
self.constrain_positive(np.where(index_param == i)[0])
for i in prev_constr_neg:
self.constrain_neg(np.where(index_param == i)[0])
for j, i in enumerate(prev_constr_fix):
self.constrain_fixed(np.where(index_param == i)[0], prev_constr_fix_values[j])
for j, i in enumerate(prev_constr_bou):
self.constrain_bounded(np.where(index_param == i)[0], prev_constr_bou_low[j], prev_constr_bou_upp[j])
for i,t in zip(prev_constr_ind,prev_constr):
self.constrain(np.where(index_param == i)[0],t)
def _get_params(self):
return np.hstack([p._get_params() for p in self.parts])

4
GPy/kern/rbf.py
View file

@ -188,12 +188,12 @@ class rbf(kernpart):
self._X2 = None
X = X/self.lengthscale
Xsquare = np.sum(np.square(X),1)
self._K_dist2 = (-2.*tdot(X) + Xsquare[:,None] + Xsquare[None,:])
self._K_dist2 = -2.*tdot(X) + (Xsquare[:,None] + Xsquare[None,:])
else:
self._X2 = X2.copy()
X = X/self.lengthscale
X2 = X2/self.lengthscale
self._K_dist2 = (-2.*np.dot(X, X2.T) + np.sum(np.square(X),1)[:,None] + np.sum(np.square(X2),1)[None,:])
self._K_dist2 = -2.*np.dot(X, X2.T) + (np.sum(np.square(X),1)[:,None] + np.sum(np.square(X2),1)[None,:])
self._K_dvar = np.exp(-0.5*self._K_dist2)
def _psi_computations(self,Z,mu,S):