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Nparams > num_params and Nparam_tranformed > num_params_transformed

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Max Zwiessele 2013-06-05 15:29:18 +01:00
parent 35c2a8b521
commit db78b233b8
25 changed files with 119 additions and 121 deletions
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44
GPy/kern/kern.py
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@ -27,7 +27,7 @@ class kern(parameterised):
"""
self.parts = parts
self.Nparts = len(parts)
self.Nparam = sum([p.Nparam for p in self.parts])
self.num_params = sum([p.num_params for p in self.parts])
self.input_dim = input_dim
@ -80,8 +80,8 @@ class kern(parameterised):
self.param_slices = []
count = 0
for p in self.parts:
self.param_slices.append(slice(count, count + p.Nparam))
count += p.Nparam
self.param_slices.append(slice(count, count + p.num_params))
count += p.num_params
def __add__(self, other):
"""
@ -104,21 +104,21 @@ class kern(parameterised):
newkern = kern(D, self.parts + other.parts, self_input_slices + other_input_slices)
# transfer constraints:
newkern.constrained_indices = self.constrained_indices + [x + self.Nparam for x in other.constrained_indices]
newkern.constrained_indices = self.constrained_indices + [x + self.num_params 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_indices = self.fixed_indices + [self.num_params + x for x in other.fixed_indices]
newkern.fixed_values = self.fixed_values + other.fixed_values
newkern.constraints = self.constraints + other.constraints
newkern.tied_indices = self.tied_indices + [self.Nparam + x for x in other.tied_indices]
newkern.tied_indices = self.tied_indices + [self.num_params + x for x in other.tied_indices]
else:
assert self.input_dim == other.input_dim
newkern = kern(self.input_dim, self.parts + other.parts, self.input_slices + other.input_slices)
# transfer constraints:
newkern.constrained_indices = self.constrained_indices + [i + self.Nparam for i in other.constrained_indices]
newkern.constrained_indices = self.constrained_indices + [i + self.num_params 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_indices = self.fixed_indices + [self.num_params + 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]
newkern.tied_indices = self.tied_indices + [self.num_params + x for x in other.tied_indices]
return newkern
def __mul__(self, other):
@ -158,13 +158,13 @@ class kern(parameterised):
K1_param = []
n = 0
for k1 in K1.parts:
K1_param += [range(n, n + k1.Nparam)]
n += k1.Nparam
K1_param += [range(n, n + k1.num_params)]
n += k1.num_params
n = 0
K2_param = []
for k2 in K2.parts:
K2_param += [range(K1.Nparam + n, K1.Nparam + n + k2.Nparam)]
n += k2.Nparam
K2_param += [range(K1.num_params + n, K1.num_params + n + k2.num_params)]
n += k2.num_params
index_param = []
for p1 in K1_param:
for p2 in K2_param:
@ -172,12 +172,12 @@ class kern(parameterised):
index_param = np.array(index_param)
# 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_ties = K1.tied_indices + [arr + K1.num_params for arr in K2.tied_indices]
prev_constr_ind = [K1.constrained_indices] + [K1.Nparam + i for i in K2.constrained_indices]
prev_constr_ind = [K1.constrained_indices] + [K1.num_params + i for i in K2.constrained_indices]
prev_constr = K1.constraints + K2.constraints
# prev_constr_fix = K1.fixed_indices + [arr + K1.Nparam for arr in K2.fixed_indices]
# prev_constr_fix = K1.fixed_indices + [arr + K1.num_params for arr in K2.fixed_indices]
# prev_constr_fix_values = K1.fixed_values + K2.fixed_values
# follow the previous ties
@ -186,7 +186,7 @@ class kern(parameterised):
index_param[np.where(index_param == j)[0]] = arr[0]
# ties and constrains
for i in range(K1.Nparam + K2.Nparam):
for i in range(K1.num_params + K2.num_params):
index = np.where(index_param == i)[0]
if index.size > 1:
self.tie_params(index)
@ -230,7 +230,7 @@ class kern(parameterised):
:type X2: np.ndarray (M x input_dim)
"""
assert X.shape[1] == self.input_dim
target = np.zeros(self.Nparam)
target = np.zeros(self.num_params)
if X2 is None:
[p.dK_dtheta(dL_dK, X[:, i_s], None, target[ps]) for p, i_s, ps, in zip(self.parts, self.input_slices, self.param_slices)]
else:
@ -259,7 +259,7 @@ class kern(parameterised):
def dKdiag_dtheta(self, dL_dKdiag, X):
assert X.shape[1] == self.input_dim
assert dL_dKdiag.size == X.shape[0]
target = np.zeros(self.Nparam)
target = np.zeros(self.num_params)
[p.dKdiag_dtheta(dL_dKdiag, X[:, i_s], target[ps]) for p, i_s, ps in zip(self.parts, self.input_slices, self.param_slices)]
return self._transform_gradients(target)
@ -275,7 +275,7 @@ class kern(parameterised):
return target
def dpsi0_dtheta(self, dL_dpsi0, Z, mu, S):
target = np.zeros(self.Nparam)
target = np.zeros(self.num_params)
[p.dpsi0_dtheta(dL_dpsi0, Z[:, i_s], mu[:, i_s], S[:, i_s], target[ps]) for p, ps, i_s in zip(self.parts, self.param_slices, self.input_slices)]
return self._transform_gradients(target)
@ -290,7 +290,7 @@ class kern(parameterised):
return target
def dpsi1_dtheta(self, dL_dpsi1, Z, mu, S):
target = np.zeros((self.Nparam))
target = np.zeros((self.num_params))
[p.dpsi1_dtheta(dL_dpsi1, Z[:, i_s], mu[:, i_s], S[:, i_s], target[ps]) for p, ps, i_s in zip(self.parts, self.param_slices, self.input_slices)]
return self._transform_gradients(target)
@ -333,7 +333,7 @@ class kern(parameterised):
return target
def dpsi2_dtheta(self, dL_dpsi2, Z, mu, S):
target = np.zeros(self.Nparam)
target = np.zeros(self.num_params)
[p.dpsi2_dtheta(dL_dpsi2, Z[:, i_s], mu[:, i_s], S[:, i_s], target[ps]) for p, i_s, ps in zip(self.parts, self.input_slices, self.param_slices)]
# compute the "cross" terms