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caching bugfix for psi2 computations
This commit is contained in:
Max Zwiessele
parent
dcec9d2a25
commit
9b161b7440
2 changed files with 76 additions and 79 deletions
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@ -73,7 +73,7 @@ class RBFInv(RBF):
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self.inv_lengthscale = x[1:]
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self.inv_lengthscale2 = np.square(self.inv_lengthscale)
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# TODO: We can rewrite everything with inv_lengthscale and never need to do the below
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self.lengthscale = 1./self.inv_lengthscale
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self.lengthscale = 1. / self.inv_lengthscale
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self.lengthscale2 = np.square(self.lengthscale)
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# reset cached results
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self._X, self._X2, self._params = np.empty(shape=(3, 1))
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@ -110,7 +110,7 @@ class RBFInv(RBF):
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}
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"""
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num_data, num_inducing, input_dim = X.shape[0], X.shape[0], self.input_dim
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weave.inline(code, arg_names=['num_data','num_inducing','input_dim','X','X2','target','dvardLdK','var_len3', 'len2'], type_converters=weave.converters.blitz, **self.weave_options)
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weave.inline(code, arg_names=['num_data', 'num_inducing', 'input_dim', 'X', 'X2', 'target', 'dvardLdK', 'var_len3', 'len2'], type_converters=weave.converters.blitz, **self.weave_options)
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else:
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code = """
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int q,i,j;
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@ -126,10 +126,10 @@ class RBFInv(RBF):
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}
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"""
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num_data, num_inducing, input_dim = X.shape[0], X2.shape[0], self.input_dim
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#[np.add(target[1+q:2+q],var_len3[q]*np.sum(dvardLdK*np.square(X[:,q][:,None]-X2[:,q][None,:])),target[1+q:2+q]) for q in range(self.input_dim)]
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weave.inline(code, arg_names=['num_data','num_inducing','input_dim','X','X2','target','dvardLdK','var_len3', 'len2'], type_converters=weave.converters.blitz, **self.weave_options)
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# [np.add(target[1+q:2+q],var_len3[q]*np.sum(dvardLdK*np.square(X[:,q][:,None]-X2[:,q][None,:])),target[1+q:2+q]) for q in range(self.input_dim)]
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weave.inline(code, arg_names=['num_data', 'num_inducing', 'input_dim', 'X', 'X2', 'target', 'dvardLdK', 'var_len3', 'len2'], type_converters=weave.converters.blitz, **self.weave_options)
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else:
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target[1] += (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dK)*(-self.lengthscale2)
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target[1] += (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dK) * (-self.lengthscale2)
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def dK_dX(self, dL_dK, X, X2, target):
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self._K_computations(X, X2)
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@ -159,21 +159,21 @@ class RBFInv(RBF):
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def dpsi1_dtheta(self, dL_dpsi1, Z, mu, S, target):
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self._psi_computations(Z, mu, S)
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##d_length = self._psi1[:, :, None] * (-0.5 * ((np.square((self._psi1_dist)/(self.inv_lengthscale * S[:,None,:] + 1))) + ((S[:, None, :])/(self.inv_lengthscale * S[:, None, :] + 1))))
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tmp = 1 + S[:,None,:]*self.inv_lengthscale2
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#inv_len3 = np.power(self.inv_lengthscale,3)
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d_length = -(self._psi1[:, :, None] * ((np.square(self._psi1_dist) * self.inv_lengthscale)/(tmp**2) + (S[:,None,:]*self.inv_lengthscale)/(tmp)))
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# #d_length = self._psi1[:, :, None] * (-0.5 * ((np.square((self._psi1_dist)/(self.inv_lengthscale * S[:,None,:] + 1))) + ((S[:, None, :])/(self.inv_lengthscale * S[:, None, :] + 1))))
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tmp = 1 + S[:, None, :] * self.inv_lengthscale2
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# inv_len3 = np.power(self.inv_lengthscale,3)
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d_length = -(self._psi1[:, :, None] * ((np.square(self._psi1_dist) * self.inv_lengthscale) / (tmp ** 2) + (S[:, None, :] * self.inv_lengthscale) / (tmp)))
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target[0] += np.sum(dL_dpsi1 * self._psi1 / self.variance)
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dpsi1_dlength = d_length * dL_dpsi1[:, :, None]
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if not self.ARD:
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target[1] += dpsi1_dlength.sum()#*(-self.lengthscale2)
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target[1] += dpsi1_dlength.sum() # *(-self.lengthscale2)
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else:
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target[1:] += dpsi1_dlength.sum(0).sum(0)#*(-self.lengthscale2)
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#target[1:] = target[1:]*(-self.lengthscale2)
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target[1:] += dpsi1_dlength.sum(0).sum(0) # *(-self.lengthscale2)
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# target[1:] = target[1:]*(-self.lengthscale2)
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def dpsi1_dZ(self, dL_dpsi1, Z, mu, S, target):
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self._psi_computations(Z, mu, S)
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dpsi1_dZ = -self._psi1[:, :, None] * ((self.inv_lengthscale2*self._psi1_dist)/self._psi1_denom)
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dpsi1_dZ = -self._psi1[:, :, None] * ((self.inv_lengthscale2 * self._psi1_dist) / self._psi1_denom)
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target += np.sum(dL_dpsi1[:, :, None] * dpsi1_dZ, 0)
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def dpsi1_dmuS(self, dL_dpsi1, Z, mu, S, target_mu, target_S):
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@ -186,15 +186,15 @@ class RBFInv(RBF):
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"""Shape N,num_inducing,num_inducing,Ntheta"""
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self._psi_computations(Z, mu, S)
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d_var = 2.*self._psi2 / self.variance
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#d_length = 2.*self._psi2[:, :, :, None] * (self._psi2_Zdist_sq * self._psi2_denom + self._psi2_mudist_sq + S[:, None, None, :] / self.lengthscale2) / (self.lengthscale * self._psi2_denom)
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# d_length = 2.*self._psi2[:, :, :, None] * (self._psi2_Zdist_sq * self._psi2_denom + self._psi2_mudist_sq + S[:, None, None, :] / self.lengthscale2) / (self.lengthscale * self._psi2_denom)
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d_length = -2.*self._psi2[:, :, :, None] * (self._psi2_Zdist_sq * self._psi2_denom + self._psi2_mudist_sq + S[:, None, None, :] * self.inv_lengthscale2) / (self.inv_lengthscale * self._psi2_denom)
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target[0] += np.sum(dL_dpsi2 * d_var)
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dpsi2_dlength = d_length * dL_dpsi2[:, :, :, None]
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if not self.ARD:
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target[1] += dpsi2_dlength.sum()#*(-self.lengthscale2)
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target[1] += dpsi2_dlength.sum() # *(-self.lengthscale2)
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else:
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target[1:] += dpsi2_dlength.sum(0).sum(0).sum(0)#*(-self.lengthscale2)
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#target[1:] = target[1:]*(-self.lengthscale2)
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target[1:] += dpsi2_dlength.sum(0).sum(0).sum(0) # *(-self.lengthscale2)
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# target[1:] = target[1:]*(-self.lengthscale2)
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def dpsi2_dZ(self, dL_dpsi2, Z, mu, S, target):
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self._psi_computations(Z, mu, S)
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@ -217,7 +217,7 @@ class RBFInv(RBF):
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def _K_computations(self, X, X2):
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if not (np.array_equal(X, self._X) and np.array_equal(X2, self._X2) and np.array_equal(self._params , self._get_params())):
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self._X = X.copy()
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self._params == self._get_params().copy()
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self._params = self._get_params().copy()
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if X2 is None:
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self._X2 = None
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X = X * self.inv_lengthscale
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@ -233,41 +233,40 @@ class RBFInv(RBF):
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def _psi_computations(self, Z, mu, S):
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# here are the "statistics" for psi1 and psi2
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if not np.array_equal(Z, self._Z):
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#Z has changed, compute Z specific stuff
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self._psi2_Zhat = 0.5*(Z[:,None,:] +Z[None,:,:]) # M,M,Q
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self._psi2_Zdist = 0.5*(Z[:,None,:]-Z[None,:,:]) # M,M,Q
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self._psi2_Zdist_sq = np.square(self._psi2_Zdist*self.inv_lengthscale) # M,M,Q
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self._Z = Z
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# Z has changed, compute Z specific stuff
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self._psi2_Zhat = 0.5 * (Z[:, None, :] + Z[None, :, :]) # M,M,Q
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self._psi2_Zdist = 0.5 * (Z[:, None, :] - Z[None, :, :]) # M,M,Q
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self._psi2_Zdist_sq = np.square(self._psi2_Zdist * self.inv_lengthscale) # M,M,Q
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if not (np.array_equal(Z, self._Z) and np.array_equal(mu, self._mu) and np.array_equal(S, self._S)):
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#something's changed. recompute EVERYTHING
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# something's changed. recompute EVERYTHING
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#psi1
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self._psi1_denom = S[:,None,:]*self.inv_lengthscale2 + 1.
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self._psi1_dist = Z[None,:,:]-mu[:,None,:]
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self._psi1_dist_sq = (np.square(self._psi1_dist)*self.inv_lengthscale2)/self._psi1_denom
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self._psi1_exponent = -0.5*np.sum(self._psi1_dist_sq+np.log(self._psi1_denom),-1)
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self._psi1 = self.variance*np.exp(self._psi1_exponent)
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# psi1
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self._psi1_denom = S[:, None, :] * self.inv_lengthscale2 + 1.
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self._psi1_dist = Z[None, :, :] - mu[:, None, :]
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self._psi1_dist_sq = (np.square(self._psi1_dist) * self.inv_lengthscale2) / self._psi1_denom
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self._psi1_exponent = -0.5 * np.sum(self._psi1_dist_sq + np.log(self._psi1_denom), -1)
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self._psi1 = self.variance * np.exp(self._psi1_exponent)
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#psi2
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self._psi2_denom = 2.*S[:,None,None,:]*self.inv_lengthscale2+1. # N,M,M,Q
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self._psi2_mudist, self._psi2_mudist_sq, self._psi2_exponent, _ = self.weave_psi2(mu,self._psi2_Zhat)
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#self._psi2_mudist = mu[:,None,None,:]-self._psi2_Zhat #N,M,M,Q
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#self._psi2_mudist_sq = np.square(self._psi2_mudist)/(self.lengthscale2*self._psi2_denom)
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#self._psi2_exponent = np.sum(-self._psi2_Zdist_sq -self._psi2_mudist_sq -0.5*np.log(self._psi2_denom),-1) #N,M,M,Q
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self._psi2 = np.square(self.variance)*np.exp(self._psi2_exponent) # N,M,M,Q
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# psi2
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self._psi2_denom = 2.*S[:, None, None, :] * self.inv_lengthscale2 + 1. # N,M,M,Q
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self._psi2_mudist, self._psi2_mudist_sq, self._psi2_exponent, _ = self.weave_psi2(mu, self._psi2_Zhat)
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# self._psi2_mudist = mu[:,None,None,:]-self._psi2_Zhat #N,M,M,Q
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# self._psi2_mudist_sq = np.square(self._psi2_mudist)/(self.lengthscale2*self._psi2_denom)
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# self._psi2_exponent = np.sum(-self._psi2_Zdist_sq -self._psi2_mudist_sq -0.5*np.log(self._psi2_denom),-1) #N,M,M,Q
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self._psi2 = np.square(self.variance) * np.exp(self._psi2_exponent) # N,M,M,Q
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#store matrices for caching
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self._Z, self._mu, self._S = Z, mu,S
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# store matrices for caching
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self._Z, self._mu, self._S = Z, mu, S
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def weave_psi2(self,mu,Zhat):
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N,input_dim = mu.shape
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def weave_psi2(self, mu, Zhat):
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N, input_dim = mu.shape
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num_inducing = Zhat.shape[0]
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mudist = np.empty((N,num_inducing,num_inducing,input_dim))
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mudist_sq = np.empty((N,num_inducing,num_inducing,input_dim))
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psi2_exponent = np.zeros((N,num_inducing,num_inducing))
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psi2 = np.empty((N,num_inducing,num_inducing))
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mudist = np.empty((N, num_inducing, num_inducing, input_dim))
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mudist_sq = np.empty((N, num_inducing, num_inducing, input_dim))
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psi2_exponent = np.zeros((N, num_inducing, num_inducing))
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psi2 = np.empty((N, num_inducing, num_inducing))
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psi2_Zdist_sq = self._psi2_Zdist_sq
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_psi2_denom = self._psi2_denom.squeeze().reshape(N, self.input_dim)
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@ -317,7 +316,7 @@ class RBFInv(RBF):
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#include <math.h>
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"""
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weave.inline(code, support_code=support_code, libraries=['gomp'],
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arg_names=['N','num_inducing','input_dim','mu','Zhat','mudist_sq','mudist','inv_lengthscale2','_psi2_denom','psi2_Zdist_sq','psi2_exponent','half_log_psi2_denom','psi2','variance_sq'],
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arg_names=['N', 'num_inducing', 'input_dim', 'mu', 'Zhat', 'mudist_sq', 'mudist', 'inv_lengthscale2', '_psi2_denom', 'psi2_Zdist_sq', 'psi2_exponent', 'half_log_psi2_denom', 'psi2', 'variance_sq'],
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type_converters=weave.converters.blitz, **self.weave_options)
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return mudist, mudist_sq, psi2_exponent, psi2
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