GPy/GPy/kern/rbf.py

# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)


from kernpart import kernpart
import numpy as np
import hashlib
from scipy import weave
from ..util.linalg import tdot

class rbf(kernpart):
    """
    Radial Basis Function kernel, aka squared-exponential, exponentiated quadratic or Gaussian kernel:

    .. math::

       k(r) = \sigma^2 \exp \\bigg(- \\frac{1}{2} r^2 \\bigg) \ \ \ \ \  \\text{ where  } r^2 = \sum_{i=1}^d \\frac{ (x_i-x^\prime_i)^2}{\ell_i^2}

    where \ell_i is the lengthscale, \sigma^2 the variance and d the dimensionality of the input.

    :param D: the number of input dimensions
    :type D: int
    :param variance: the variance of the kernel
    :type variance: float
    :param lengthscale: the vector of lengthscale of the kernel
    :type lengthscale: array or list of the appropriate size (or float if there is only one lengthscale parameter)
    :param ARD: Auto Relevance Determination. If equal to "False", the kernel is isotropic (ie. one single lengthscale parameter \ell), otherwise there is one lengthscale parameter per dimension.
    :type ARD: Boolean
    :rtype: kernel object

    .. Note: this object implements both the ARD and 'spherical' version of the function
    """

    def __init__(self,D,variance=1.,lengthscale=None,ARD=False):
        self.D = D
        self.name = 'rbf'
        self.ARD = ARD
        if not ARD:
            self.Nparam = 2
            if lengthscale is not None:
                lengthscale = np.asarray(lengthscale)
                assert lengthscale.size == 1, "Only one lengthscale needed for non-ARD kernel"
            else:
                lengthscale = np.ones(1)
        else:
            self.Nparam = self.D + 1
            if lengthscale is not None:
                lengthscale = np.asarray(lengthscale)
                assert lengthscale.size == self.D, "bad number of lengthscales"
            else:
                lengthscale = np.ones(self.D)

        self._set_params(np.hstack((variance,lengthscale.flatten())))

        #initialize cache
        self._Z, self._mu, self._S = np.empty(shape=(3,1))
        self._X, self._X2, self._params = np.empty(shape=(3,1))

        #a set of optional args to pass to weave
        self.weave_options = {'headers'           : ['<omp.h>'],
                         'extra_compile_args': ['-fopenmp -O3'],  #-march=native'],
                         'extra_link_args'   : ['-lgomp']}



    def _get_params(self):
        return np.hstack((self.variance,self.lengthscale))

    def _set_params(self,x):
        assert x.size==(self.Nparam)
        self.variance = x[0]
        self.lengthscale = x[1:]
        self.lengthscale2 = np.square(self.lengthscale)
        #reset cached results
        self._X, self._X2, self._params = np.empty(shape=(3,1))
        self._Z, self._mu, self._S = np.empty(shape=(3,1)) # cached versions of Z,mu,S

    def _get_param_names(self):
        if self.Nparam == 2:
            return ['variance','lengthscale']
        else:
            return ['variance']+['lengthscale_%i'%i for i in range(self.lengthscale.size)]

    def K(self,X,X2,target):
        self._K_computations(X,X2)
        target += self.variance*self._K_dvar

    def Kdiag(self,X,target):
        np.add(target,self.variance,target)

    def dK_dtheta(self,dL_dK,X,X2,target):
        self._K_computations(X,X2)
        target[0] += np.sum(self._K_dvar*dL_dK)
        if self.ARD:
            dvardLdK = self._K_dvar*dL_dK
            var_len3 = self.variance/np.power(self.lengthscale,3)
            if X2 is None:
                #save computation for the symmetrical case
                dvardLdK += dvardLdK.T
                code = """
                int q,i,j;
                double tmp;
                for(q=0; q<D; q++){
                  tmp = 0;
                  for(i=0; i<N; i++){
                    for(j=0; j<i; j++){
                      tmp += (X(i,q)-X(j,q))*(X(i,q)-X(j,q))*dvardLdK(i,j);
                    }
                  }
                  target(q+1) += var_len3(q)*tmp;
                }
                """
                N,M,D = X.shape[0], X.shape[0], self.D
            else:
                code = """
                int q,i,j;
                double tmp;
                for(q=0; q<D; q++){
                  tmp = 0;
                  for(i=0; i<N; i++){
                    for(j=0; j<M; j++){
                      tmp += (X(i,q)-X2(j,q))*(X(i,q)-X2(j,q))*dvardLdK(i,j);
                    }
                  }
                  target(q+1) += var_len3(q)*tmp;
                }
                """
                N,M,D = X.shape[0], X2.shape[0], self.D
            #[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.D)]
            weave.inline(code, arg_names=['N','M','D','X','X2','target','dvardLdK','var_len3'],
                 type_converters=weave.converters.blitz,**self.weave_options)
        else:
            target[1] += (self.variance/self.lengthscale)*np.sum(self._K_dvar*self._K_dist2*dL_dK)

    def dKdiag_dtheta(self,dL_dKdiag,X,target):
        #NB: derivative of diagonal elements wrt lengthscale is 0
        target[0] += np.sum(dL_dKdiag)

    def dK_dX(self,dL_dK,X,X2,target):
        self._K_computations(X,X2)
        _K_dist = X[:,None,:]-X2[None,:,:] #don't cache this in _K_computations because it is high memory. If this function is being called, chances are we're not in the high memory arena.
        dK_dX = (-self.variance/self.lengthscale2)*np.transpose(self._K_dvar[:,:,np.newaxis]*_K_dist,(1,0,2))
        target += np.sum(dK_dX*dL_dK.T[:,:,None],0)

    def dKdiag_dX(self,dL_dKdiag,X,target):
        pass


    #---------------------------------------#
    #             PSI statistics            #
    #---------------------------------------#

    def psi0(self,Z,mu,S,target):
        target += self.variance

    def dpsi0_dtheta(self,dL_dpsi0,Z,mu,S,target):
        target[0] += np.sum(dL_dpsi0)

    def dpsi0_dmuS(self,dL_dpsi0,Z,mu,S,target_mu,target_S):
        pass

    def psi1(self,Z,mu,S,target):
        self._psi_computations(Z,mu,S)
        target += self._psi1

    def dpsi1_dtheta(self,dL_dpsi1,Z,mu,S,target):
        self._psi_computations(Z,mu,S)
        denom_deriv = S[:,None,:]/(self.lengthscale**3+self.lengthscale*S[:,None,:])
        d_length = self._psi1[:,:,None]*(self.lengthscale*np.square(self._psi1_dist/(self.lengthscale2+S[:,None,:])) + denom_deriv)
        target[0] += np.sum(dL_dpsi1*self._psi1/self.variance)
        dpsi1_dlength = d_length*dL_dpsi1[:,:,None]
        if not self.ARD:
            target[1] += dpsi1_dlength.sum()
        else:
            target[1:] += dpsi1_dlength.sum(0).sum(0)

    def dpsi1_dZ(self,dL_dpsi1,Z,mu,S,target):
        self._psi_computations(Z,mu,S)
        denominator = (self.lengthscale2*(self._psi1_denom))
        dpsi1_dZ = - self._psi1[:,:,None] * ((self._psi1_dist/denominator))
        target += np.sum(dL_dpsi1.T[:,:,None] * dpsi1_dZ, 0)

    def dpsi1_dmuS(self,dL_dpsi1,Z,mu,S,target_mu,target_S):
        self._psi_computations(Z,mu,S)
        tmp = self._psi1[:,:,None]/self.lengthscale2/self._psi1_denom
        target_mu += np.sum(dL_dpsi1.T[:, :, None]*tmp*self._psi1_dist,1)
        target_S += np.sum(dL_dpsi1.T[:, :, None]*0.5*tmp*(self._psi1_dist_sq-1),1)

    def psi2(self,Z,mu,S,target):
        self._psi_computations(Z,mu,S)
        target += self._psi2

    def dpsi2_dtheta(self,dL_dpsi2,Z,mu,S,target):
        """Shape N,M,M,Ntheta"""
        self._psi_computations(Z,mu,S)
        d_var = 2.*self._psi2/self.variance
        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)

        target[0] += np.sum(dL_dpsi2*d_var)
        dpsi2_dlength = d_length*dL_dpsi2[:,:,:,None]
        if not self.ARD:
            target[1] += dpsi2_dlength.sum()
        else:
            target[1:] += dpsi2_dlength.sum(0).sum(0).sum(0)

    def dpsi2_dZ(self,dL_dpsi2,Z,mu,S,target):
        self._psi_computations(Z,mu,S)
        term1 = self._psi2_Zdist/self.lengthscale2 # M, M, Q
        term2 = self._psi2_mudist/self._psi2_denom/self.lengthscale2 # N, M, M, Q
        dZ = self._psi2[:,:,:,None] * (term1[None] + term2)
        target += (dL_dpsi2[:,:,:,None]*dZ).sum(0).sum(0)

    def dpsi2_dmuS(self,dL_dpsi2,Z,mu,S,target_mu,target_S):
        """Think N,M,M,Q """
        self._psi_computations(Z,mu,S)
        tmp = self._psi2[:,:,:,None]/self.lengthscale2/self._psi2_denom
        target_mu += -2.*(dL_dpsi2[:,:,:,None]*tmp*self._psi2_mudist).sum(1).sum(1)
        target_S += (dL_dpsi2[:,:,:,None]*tmp*(2.*self._psi2_mudist_sq-1)).sum(1).sum(1)


    #---------------------------------------#
    #            Precomputations            #
    #---------------------------------------#

    def _K_computations(self,X,X2):
        if not (np.array_equal(X,self._X) and np.array_equal(X2,self._X2) and np.array_equal(self._params , self._get_params())):
            self._X = X.copy()
            self._params == self._get_params().copy()
            if X2 is None:
                self._X2 = None
                X = X/self.lengthscale
                Xsquare = np.sum(np.square(X),1)
                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_dvar = np.exp(-0.5*self._K_dist2)

    def _psi_computations(self,Z,mu,S):
        #here are the "statistics" for psi1 and psi2
        if not np.array_equal(Z, self._Z):
            #Z has changed, compute Z specific stuff
            self._psi2_Zhat = 0.5*(Z[:,None,:] +Z[None,:,:]) # M,M,Q
            self._psi2_Zdist = 0.5*(Z[:,None,:]-Z[None,:,:]) # M,M,Q
            self._psi2_Zdist_sq = np.square(self._psi2_Zdist/self.lengthscale) # M,M,Q
            self._Z = Z

        if not (np.array_equal(Z, self._Z) and np.array_equal(mu, self._mu) and np.array_equal(S, self._S)):
            #something's changed. recompute EVERYTHING

            #psi1
            self._psi1_denom = S[:,None,:]/self.lengthscale2 + 1.
            self._psi1_dist = Z[None,:,:]-mu[:,None,:]
            self._psi1_dist_sq = np.square(self._psi1_dist)/self.lengthscale2/self._psi1_denom
            self._psi1_exponent = -0.5*np.sum(self._psi1_dist_sq+np.log(self._psi1_denom),-1)
            self._psi1 = self.variance*np.exp(self._psi1_exponent)

            #psi2
            self._psi2_denom = 2.*S[:,None,None,:]/self.lengthscale2+1. # N,M,M,Q
            self._psi2_mudist, self._psi2_mudist_sq, self._psi2_exponent, _ = self.weave_psi2(mu,self._psi2_Zhat)
            #self._psi2_mudist = mu[:,None,None,:]-self._psi2_Zhat #N,M,M,Q
            #self._psi2_mudist_sq = np.square(self._psi2_mudist)/(self.lengthscale2*self._psi2_denom)
            #self._psi2_exponent = np.sum(-self._psi2_Zdist_sq -self._psi2_mudist_sq -0.5*np.log(self._psi2_denom),-1) #N,M,M
            self._psi2 = np.square(self.variance)*np.exp(self._psi2_exponent) # N,M,M

            #store matrices for caching
            self._Z, self._mu, self._S = Z, mu,S

    def weave_psi2(self,mu,Zhat):
        N,Q = mu.shape
        M = Zhat.shape[0]

        mudist = np.empty((N,M,M,Q))
        mudist_sq = np.empty((N,M,M,Q))
        psi2_exponent = np.zeros((N,M,M))
        psi2 = np.empty((N,M,M))

        psi2_Zdist_sq = self._psi2_Zdist_sq
        _psi2_denom = self._psi2_denom.squeeze().reshape(N,self.D)
        half_log_psi2_denom = 0.5*np.log(self._psi2_denom).squeeze().reshape(N,self.D)
        variance_sq = float(np.square(self.variance))
        if self.ARD:
            lengthscale2 = self.lengthscale2
        else:
            lengthscale2 = np.ones(Q)*self.lengthscale2
        code = """
        double tmp;

        #pragma omp parallel for private(tmp)
        for (int n=0; n<N; n++){
            for (int m=0; m<M; m++){
               for (int mm=0; mm<(m+1); mm++){
                   for (int q=0; q<Q; q++){
                       //compute mudist
                       tmp = mu(n,q) - Zhat(m,mm,q);
                       mudist(n,m,mm,q) = tmp;
                       mudist(n,mm,m,q) = tmp;

                       //now mudist_sq
                       tmp = tmp*tmp/lengthscale2(q)/_psi2_denom(n,q);
                       mudist_sq(n,m,mm,q) = tmp;
                       mudist_sq(n,mm,m,q) = tmp;

                       //now psi2_exponent
                       tmp = -psi2_Zdist_sq(m,mm,q) - tmp - half_log_psi2_denom(n,q);
                       psi2_exponent(n,mm,m) += tmp;
                       if (m !=mm){
                           psi2_exponent(n,m,mm) += tmp;
                       }
                   //psi2 would be computed like this, but np is faster
                   //tmp = variance_sq*exp(psi2_exponent(n,m,mm));
                   //psi2(n,m,mm) = tmp;
                   //psi2(n,mm,m) = tmp;
                   }
                }
            }
        }

        """

        support_code = """
        #include <omp.h>
        #include <math.h>
        """
        weave.inline(code, support_code=support_code, libraries=['gomp'],
                     arg_names=['N','M','Q','mu','Zhat','mudist_sq','mudist','lengthscale2','_psi2_denom','psi2_Zdist_sq','psi2_exponent','half_log_psi2_denom','psi2','variance_sq'],
                     type_converters=weave.converters.blitz,**self.weave_options)

        return mudist,mudist_sq, psi2_exponent, psi2