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Updates for eq_ode1 and eq_ode2 kernels

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This commit is contained in:
cdguarnizo 2016-05-23 01:28:01 -05:00
parent f844143353
commit 52fb928dff
3 changed files with 105 additions and 29 deletions
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39
GPy/kern/src/eq_ode1.py
View file

@ -110,12 +110,32 @@ class EQ_ODE1(Kern):
if not X_flag and X2_flag:
index2 -= self.output_dim
return self._Kfu(X, index, X2, index2) #Kfu
else:
elif X_flag and not X2_flag:
index -= self.output_dim
return self._Kfu(X2, index2, X, index).T #Kuf
elif X_flag and X2_flag:
index -= self.output_dim
index2 -= self.output_dim
return self._Kusu(X, index, X2, index2) #Ku_s u
else:
raise NotImplementedError #Kf_s f
#Calculate the covariance function for diag(Kff(X,X))
def Kdiag(self, X):
if hasattr(X, 'values'):
index = np.int_(np.round(X[:, 1].values))
else:
index = np.int_(np.round(X[:, 1]))
index = index.reshape(index.size,)
X_flag = index[0] >= self.output_dim
if X_flag: #Kuudiag
return np.ones(X[:,0].shape)
else: #Kffdiag
kdiag = self._Kdiag(X)
return np.sum(kdiag, axis=1)
def _Kdiag(self, X):
#This way is not working, indexes are lost after using k._slice_X
#index = np.asarray(X, dtype=np.int)
#index = index.reshape(index.size,)
@ -306,6 +326,23 @@ class EQ_ODE1(Kern):
kuu[indc, indr] = kuu[indr, indc]
return kuu
def _Kusu(self, X, index, X2, index2):
index = index.reshape(index.size,)
index2 = index2.reshape(index2.size,)
t = X[:, 0].reshape(X.shape[0],1)
t2 = X2[:, 0].reshape(1,X2.shape[0])
lq = self.lengthscale.values.reshape(self.rank,)
#Covariance matrix initialization
kuu = np.zeros((t.size, t2.size))
for q in range(self.rank):
ind1 = index == q
ind2 = index2 == q
r = t[ind1]/lq[q] - t2[0,ind2]/lq[q]
r2 = r*r
#Calculation of covariance function
kuu[np.ix_(ind1, ind2)] = np.exp(-r2)
return kuu
#Evaluation of cross-covariance function
def _Kfu(self, X, index, X2, index2):
#terms that move along t

91
GPy/kern/src/eq_ode2.py
View file

@ -44,7 +44,7 @@ class EQ_ODE2(Kern):
lengthscale = np.asarray(lengthscale)
assert lengthscale.size in [1, self.rank], "Bad number of lengthscales"
if lengthscale.size != self.rank:
lengthscale = np.ones(self.input_dim)*lengthscale
lengthscale = np.ones(self.rank)*lengthscale
if W is None:
#W = 0.5*np.random.randn(self.output_dim, self.rank)/np.sqrt(self.rank)
@ -71,7 +71,7 @@ class EQ_ODE2(Kern):
#index = index.reshape(index.size,)
if hasattr(X, 'values'):
X = X.values
index = np.int_(X[:, 1])
index = np.int_(np.round(X[:, 1]))
index = index.reshape(index.size,)
X_flag = index[0] >= self.output_dim
if X2 is None:
@ -79,7 +79,7 @@ class EQ_ODE2(Kern):
#Calculate covariance function for the latent functions
index -= self.output_dim
return self._Kuu(X, index)
else:
else: #Kff full
raise NotImplementedError
else:
#This way is not working, indexes are lost after using k._slice_X
@ -87,19 +87,40 @@ class EQ_ODE2(Kern):
#index2 = index2.reshape(index2.size,)
if hasattr(X2, 'values'):
X2 = X2.values
index2 = np.int_(X2[:, 1])
index2 = np.int_(np.round(X2[:, 1]))
index2 = index2.reshape(index2.size,)
X2_flag = index2[0] >= self.output_dim
#Calculate cross-covariance function
if not X_flag and X2_flag:
index2 -= self.output_dim
return self._Kfu(X, index, X2, index2) #Kfu
else:
elif X_flag and not X2_flag:
index -= self.output_dim
return self._Kfu(X2, index2, X, index).T #Kuf
elif X_flag and X2_flag:
index -= self.output_dim
index2 -= self.output_dim
return self._Kusu(X, index, X2, index2) #Ku_s u
else:
raise NotImplementedError #Kf_s f
#Calculate the covariance function for diag(Kff(X,X))
def Kdiag(self, X):
if hasattr(X, 'values'):
index = np.int_(np.round(X[:, 1].values))
else:
index = np.int_(np.round(X[:, 1]))
index = index.reshape(index.size,)
X_flag = index[0] >= self.output_dim
if X_flag: #Kuudiag
return np.ones(X[:,0].shape)
else: #Kffdiag
kdiag = self._Kdiag(X)
return np.sum(kdiag, axis=1)
#Calculate the covariance function for diag(Kff(X,X))
def _Kdiag(self, X):
#This way is not working, indexes are lost after using k._slice_X
#index = np.asarray(X, dtype=np.int)
#index = index.reshape(index.size,)
@ -132,7 +153,7 @@ class EQ_ODE2(Kern):
#Terms that move along q
lq = self.lengthscale.values.reshape(1, self.lengthscale.size)
S2 = S*S
kdiag = np.empty((t.size, ))
kdiag = np.empty((t.size, lq.size))
indD = np.arange(B.size)
#(1) When wd is real
@ -187,8 +208,8 @@ class EQ_ODE2(Kern):
upv[t1[:, 0] == 0, :] = 0.
#Covariance calculation
kdiag[ind3t] = np.sum(np.real(K01[ind]*upm), axis=1)
kdiag[ind3t] += np.sum(np.real((c0[ind]*ec)*upv), axis=1)
kdiag[ind3t] = np.real(K01[ind]*upm)
kdiag[ind3t] += np.real((c0[ind]*ec)*upv)
#(2) When w_d is complex
if np.any(wbool):
@ -265,7 +286,7 @@ class EQ_ODE2(Kern):
upvc[t1[:, 0] == 0, :] = 0.
#Covariance calculation
kdiag[ind2t] = np.sum(K011[ind]*upm + K012[ind]*upmc + (c0[ind]*ec)*upv + (c0[ind]*ec2)*upvc, axis=1)
kdiag[ind2t] = K011[ind]*upm + K012[ind]*upmc + (c0[ind]*ec)*upv + (c0[ind]*ec2)*upvc
return kdiag
def update_gradients_full(self, dL_dK, X, X2 = None):
@ -336,16 +357,17 @@ class EQ_ODE2(Kern):
index = index.reshape(index.size,)
glq, gS, gB, gC = self._gkdiag(X, index)
tmp = dL_dKdiag.reshape(index.size, 1)*glq
if dL_dKdiag.size == X.shape[0]:
dL_dKdiag = np.reshape(dL_dKdiag, (index.size, 1))
tmp = dL_dKdiag*glq
self.lengthscale.gradient = tmp.sum(0)
#TODO: Avoid the reshape by a priori knowing the shape of dL_dKdiag
tmpB = dL_dKdiag*gB.reshape(dL_dKdiag.shape)
tmpC = dL_dKdiag*gC.reshape(dL_dKdiag.shape)
tmp = dL_dKdiag.reshape(index.size, 1)*gS
tmpB = dL_dKdiag*gB
tmpC = dL_dKdiag*gC
tmp = dL_dKdiag*gS
for d in np.unique(index):
ind = np.where(index == d)
self.B.gradient[d] = tmpB[ind].sum()
self.C.gradient[d] = tmpC[ind].sum()
self.B.gradient[d] = tmpB[ind, :].sum()
self.C.gradient[d] = tmpC[ind, :].sum()
self.W.gradient[d, :] = tmp[ind].sum(0)
def gradients_X(self, dL_dK, X, X2=None):
@ -410,6 +432,23 @@ class EQ_ODE2(Kern):
kuu[indc, indr] = kuu[indr, indc]
return kuu
def _Kusu(self, X, index, X2, index2):
index = index.reshape(index.size,)
index2 = index2.reshape(index2.size,)
t = X[:, 0].reshape(X.shape[0],1)
t2 = X2[:, 0].reshape(1,X2.shape[0])
lq = self.lengthscale.values.reshape(self.rank,)
#Covariance matrix initialization
kuu = np.zeros((t.size, t2.size))
for q in range(self.rank):
ind1 = index == q
ind2 = index2 == q
r = t[ind1]/lq[q] - t2[0,ind2]/lq[q]
r2 = r*r
#Calculation of covariance function
kuu[np.ix_(ind1, ind2)] = np.exp(-r2)
return kuu
#Evaluation of cross-covariance function
def _Kfu(self, X, index, X2, index2):
#terms that move along t
@ -632,8 +671,8 @@ class EQ_ODE2(Kern):
lq = self.lengthscale.values.reshape(1, self.rank)
lq2 = lq*lq
gB = np.empty((t.size,))
gC = np.empty((t.size,))
gB = np.empty((t.size, lq.size))
gC = np.empty((t.size, lq.size))
glq = np.empty((t.size, lq.size))
gS = np.empty((t.size, lq.size))
@ -723,8 +762,8 @@ class EQ_ODE2(Kern):
Ba4_1 = (S2lq*lq)*dgam_dB/w2
Ba4 = Ba4_1*c
gB[ind3t] = np.sum(np.real(Ba1[ind]*upm) - np.real(((Ba2_1[ind] + Ba2_2[ind]*t1)*egamt - Ba3[ind]*egamct)*upv)\
+ np.real(Ba4[ind]*upmd) + np.real((Ba4_1[ind]*ec)*upvd), axis=1)
gB[ind3t] = np.real(Ba1[ind]*upm) - np.real(((Ba2_1[ind] + Ba2_2[ind]*t1)*egamt - Ba3[ind]*egamct)*upv)\
+ np.real(Ba4[ind]*upmd) + np.real((Ba4_1[ind]*ec)*upvd)
# gradient wrt C
dw_dC = - alphad*dw_dB
@ -738,8 +777,8 @@ class EQ_ODE2(Kern):
Ca4_1 = (S2lq*lq)*dgam_dC/w2
Ca4 = Ca4_1*c
gC[ind3t] = np.sum(np.real(Ca1[ind]*upm) - np.real(((Ca2_1[ind] + Ca2_2[ind]*t1)*egamt - (Ca3_1[ind] + Ca3_2[ind]*t1)*egamct)*upv)\
+ np.real(Ca4[ind]*upmd) + np.real((Ca4_1[ind]*ec)*upvd), axis=1)
gC[ind3t] = np.real(Ca1[ind]*upm) - np.real(((Ca2_1[ind] + Ca2_2[ind]*t1)*egamt - (Ca3_1[ind] + Ca3_2[ind]*t1)*egamct)*upv)\
+ np.real(Ca4[ind]*upmd) + np.real((Ca4_1[ind]*ec)*upvd)
#Gradient wrt lengthscale
#DxQ terms
@ -868,10 +907,10 @@ class EQ_ODE2(Kern):
Ba2_1c = c0*(dgamc_dB*(0.5/gamc2 - 0.25*lq2) + 0.5/(w2*gamc))
Ba2_2c = c0*dgamc_dB/gamc
gB[ind2t] = np.sum(Ba1[ind]*upm - ((Ba2_1[ind] + Ba2_2[ind]*t1)*egamt - Ba3[ind]*egamct)*upv\
gB[ind2t] = Ba1[ind]*upm - ((Ba2_1[ind] + Ba2_2[ind]*t1)*egamt - Ba3[ind]*egamct)*upv\
+ Ba4[ind]*upmd + (Ba4_1[ind]*ec)*upvd\
+ Ba1c[ind]*upmc - ((Ba2_1c[ind] + Ba2_2c[ind]*t1)*egamct - Ba3c[ind]*egamt)*upvc\
+ Ba4c[ind]*upmdc + (Ba4_1c[ind]*ec2)*upvdc, axis=1)
+ Ba4c[ind]*upmdc + (Ba4_1c[ind]*ec2)*upvdc
##Gradient wrt C
dw_dC = 0.5*alphad/w
@ -895,10 +934,10 @@ class EQ_ODE2(Kern):
Ca4_1c = S2lq2*(dgamc_dC/w2)
Ca4c = Ca4_1c*c2
gC[ind2t] = np.sum(Ca1[ind]*upm - ((Ca2_1[ind] + Ca2_2[ind]*t1)*egamt - (Ca3_1[ind] + Ca3_2[ind]*t1)*egamct)*upv\
gC[ind2t] = Ca1[ind]*upm - ((Ca2_1[ind] + Ca2_2[ind]*t1)*egamt - (Ca3_1[ind] + Ca3_2[ind]*t1)*egamct)*upv\
+ Ca4[ind]*upmd + (Ca4_1[ind]*ec)*upvd\
+ Ca1c[ind]*upmc - ((Ca2_1c[ind] + Ca2_2c[ind]*t1)*egamct - (Ca3_1c[ind] + Ca3_2c[ind]*t1)*egamt)*upvc\
+ Ca4c[ind]*upmdc + (Ca4_1c[ind]*ec2)*upvdc, axis=1)
+ Ca4c[ind]*upmdc + (Ca4_1c[ind]*ec2)*upvdc
#Gradient wrt lengthscale
#DxQ terms

4
GPy/models/ibp_lfm.py
View file

@ -58,7 +58,7 @@ class VarDTC_minibatch_IBPLFM(VarDTC_minibatch):
else:
b = beta
psi0 = kern.Kdiag(X_slice) #Kff^q
psi0 = kern._Kdiag(X_slice) #Kff^q
psi1 = kern.K(X_slice, Z) #Kfu
indX = X_slice.values
@ -223,7 +223,7 @@ class VarDTC_minibatch_IBPLFM(VarDTC_minibatch):
Y_slice = YYT_factor[n_start:n_end]
X_slice = X[n_start:n_end]
psi0 = kern.Kdiag(X_slice) #Kffdiag
psi0 = kern._Kdiag(X_slice) #Kffdiag
psi1 = kern.K(X_slice, Z) #Kfu
betapsi1 = np.einsum('n,nm->nm', beta, psi1)