path: root/ot/da.py

# -*- coding: utf-8 -*-
"""
Domain adaptation with optimal transport
"""

import numpy as np
from .bregman import sinkhorn
from .lp import emd
from .utils import unif,dist,kernel
from .optim import cg
from .optim import gcg


def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerItermax = 200,stopInnerThr=1e-9,verbose=False,log=False):
    """
    Solve the entropic regularization optimal transport problem with nonconvex group lasso regularization

    The function solves the following optimization problem:

    .. math::
        \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega_e(\gamma)+ \eta \Omega_g(\gamma)

        s.t. \gamma 1 = a

             \gamma^T 1= b

             \gamma\geq 0
    where :

    - M is the (ns,nt) metric cost matrix
    - :math:`\Omega_e` is the entropic regularization term :math:`\Omega_e(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})`
    - :math:`\Omega_g` is the group lasso  regulaization term :math:`\Omega_g(\gamma)=\sum_{i,c} \|\gamma_{i,\mathcal{I}_c}\|^{1/2}_1`   where  :math:`\mathcal{I}_c` are the index of samples from class c in the source domain.
    - a and b are source and target weights (sum to 1)

    The algorithm used for solving the problem is the generalised conditional gradient as proposed in  [5]_ [7]_


    Parameters
    ----------
    a : np.ndarray (ns,)
        samples weights in the source domain
    labels_a : np.ndarray (ns,)
        labels of samples in the source domain
    b : np.ndarray (nt,)
        samples weights in the target domain
    M : np.ndarray (ns,nt)
        loss matrix
    reg : float
        Regularization term for entropic regularization >0
    eta : float, optional
        Regularization term  for group lasso regularization >0
    numItermax : int, optional
        Max number of iterations
    numInnerItermax : int, optional
        Max number of iterations (inner sinkhorn solver)
    stopInnerThr : float, optional
        Stop threshold on error (inner sinkhorn solver) (>0)
    verbose : bool, optional
        Print information along iterations
    log : bool, optional
        record log if True


    Returns
    -------
    gamma : (ns x nt) ndarray
        Optimal transportation matrix for the given parameters
    log : dict
        log dictionary return only if log==True in parameters


    References
    ----------

    .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1
    .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567.

    See Also
    --------
    ot.lp.emd : Unregularized OT
    ot.bregman.sinkhorn : Entropic regularized OT
    ot.optim.cg : General regularized OT

    """
    p = 0.5
    epsilon = 1e-3

    indices_labels = []
    classes = np.unique(labels_a)
    for c in classes:
        idxc, = np.where(labels_a == c)
        indices_labels.append(idxc)

    W = np.zeros(M.shape)

    for cpt in range(numItermax):
        Mreg = M + eta*W
        transp = sinkhorn(a, b, Mreg, reg, numItermax=numInnerItermax,
                          stopThr=stopInnerThr)
        # the transport has been computed. Check if classes are really
        # separated
        W = np.ones(M.shape)
        for (i, c) in enumerate(classes):
            majs = np.sum(transp[indices_labels[i]], axis=0)
            majs = p*((majs+epsilon)**(p-1))
            W[indices_labels[i]] = majs

    return transp

def sinkhorn_l1l2_gl(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerItermax = 200,stopInnerThr=1e-9,verbose=False,log=False):
    """
    Solve the entropic regularization optimal transport problem with group lasso regularization

    The function solves the following optimization problem:

    .. math::
        \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega_e(\gamma)+ \eta \Omega_g(\gamma)

        s.t. \gamma 1 = a

             \gamma^T 1= b

             \gamma\geq 0
    where :

    - M is the (ns,nt) metric cost matrix
    - :math:`\Omega_e` is the entropic regularization term :math:`\Omega_e(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})`
    - :math:`\Omega_g` is the group lasso  regulaization term :math:`\Omega_g(\gamma)=\sum_{i,c} \|\gamma_{i,\mathcal{I}_c}\|^2`   where  :math:`\mathcal{I}_c` are the index of samples from class c in the source domain.
    - a and b are source and target weights (sum to 1)

    The algorithm used for solving the problem is the generalised conditional gradient as proposed in  [5]_ [7]_


    Parameters
    ----------
    a : np.ndarray (ns,)
        samples weights in the source domain
    labels_a : np.ndarray (ns,)
        labels of samples in the source domain
    b : np.ndarray (nt,)
        samples in the target domain
    M : np.ndarray (ns,nt)
        loss matrix
    reg : float
        Regularization term for entropic regularization >0
    eta : float, optional
        Regularization term  for group lasso regularization >0
    numItermax : int, optional
        Max number of iterations
    numInnerItermax : int, optional
        Max number of iterations (inner sinkhorn solver)
    stopInnerThr : float, optional
        Stop threshold on error (inner sinkhorn solver) (>0)
    verbose : bool, optional
        Print information along iterations
    log : bool, optional
        record log if True


    Returns
    -------
    gamma : (ns x nt) ndarray
        Optimal transportation matrix for the given parameters
    log : dict
        log dictionary return only if log==True in parameters


    References
    ----------

    .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1
    .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567.

    See Also
    --------
    ot.optim.gcg : Generalized conditional gradient for OT problems

    """
    lstlab=np.unique(labels_a)

    def f(G):
        res=0
        for i in range(G.shape[1]):
            for lab in lstlab:
                temp=G[labels_a==lab,i]
                res+=np.linalg.norm(temp)
        return res

    def df(G):
        W=np.zeros(G.shape)
        for i in range(G.shape[1]):
            for lab in lstlab:
                temp=G[labels_a==lab,i]
                n=np.linalg.norm(temp)
                if n:
                    W[labels_a==lab,i]=temp/n
        return W


    return gcg(a,b,M,reg,eta,f,df,G0=None,numItermax = numItermax,numInnerItermax=numInnerItermax, stopThr=stopInnerThr,verbose=verbose,log=log)



def joint_OT_mapping_linear(xs,xt,mu=1,eta=0.001,bias=False,verbose=False,verbose2=False,numItermax = 100,numInnerItermax = 10,stopInnerThr=1e-6,stopThr=1e-5,log=False,**kwargs):
    """Joint OT and linear mapping estimation as proposed in [8]

    The function solves the following optimization problem:

    .. math::
        \min_{\gamma,L}\quad \|L(X_s) -n_s\gamma X_t\|^2_F + \mu<\gamma,M>_F + \eta  \|L -I\|^2_F

        s.t. \gamma 1 = a

             \gamma^T 1= b

             \gamma\geq 0
    where :

    - M is the (ns,nt) squared euclidean cost matrix between samples in Xs and Xt (scaled by ns)
    - :math:`L` is a dxd linear operator that approximates the barycentric mapping
    - :math:`I` is the identity matrix (neutral linear mapping)
    - a and b are uniform source and target weights

    The problem consist in solving jointly an optimal transport matrix
    :math:`\gamma` and a linear mapping that fits the barycentric mapping
    :math:`n_s\gamma X_t`.

    One can also estimate a mapping with constant bias (see supplementary
    material of [8]) using the bias optional argument.

    The algorithm used for solving the problem is the block coordinate
    descent that alternates between updates of G (using conditionnal gradient)
    and the update of L using a classical least square solver.


    Parameters
    ----------
    xs : np.ndarray (ns,d)
        samples in the source domain
    xt : np.ndarray (nt,d)
        samples in the target domain
    mu : float,optional
        Weight for the linear OT loss (>0)
    eta : float, optional
        Regularization term  for the linear mapping L (>0)
    bias : bool,optional
        Estimate linear mapping with constant bias
    numItermax : int, optional
        Max number of BCD iterations
    stopThr : float, optional
        Stop threshold on relative loss decrease (>0)
    numInnerItermax : int, optional
        Max number of iterations (inner CG solver)
    stopInnerThr : float, optional
        Stop threshold on error (inner CG solver) (>0)
    verbose : bool, optional
        Print information along iterations
    log : bool, optional
        record log if True


    Returns
    -------
    gamma : (ns x nt) ndarray
        Optimal transportation matrix for the given parameters
    L : (d x d) ndarray
        Linear mapping matrix (d+1 x d if bias)
    log : dict
        log dictionary return only if log==True in parameters


    References
    ----------

    .. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for discrete optimal transport", Neural Information Processing Systems (NIPS), 2016.

    See Also
    --------
    ot.lp.emd : Unregularized OT
    ot.optim.cg : General regularized OT

    """

    ns,nt,d=xs.shape[0],xt.shape[0],xt.shape[1]

    if bias:
        xs1=np.hstack((xs,np.ones((ns,1))))
        xstxs=xs1.T.dot(xs1)
        I=np.eye(d+1)
        I[-1]=0
        I0=I[:,:-1]
        sel=lambda x : x[:-1,:]
    else:
        xs1=xs
        xstxs=xs1.T.dot(xs1)
        I=np.eye(d)
        I0=I
        sel=lambda x : x

    if log:
        log={'err':[]}

    a,b=unif(ns),unif(nt)
    M=dist(xs,xt)*ns
    G=emd(a,b,M)

    vloss=[]

    def loss(L,G):
        """Compute full loss"""
        return np.sum((xs1.dot(L)-ns*G.dot(xt))**2)+mu*np.sum(G*M)+eta*np.sum(sel(L-I0)**2)

    def solve_L(G):
        """ solve L problem with fixed G (least square)"""
        xst=ns*G.dot(xt)
        return np.linalg.solve(xstxs+eta*I,xs1.T.dot(xst)+eta*I0)

    def solve_G(L,G0):
        """Update G with CG algorithm"""
        xsi=xs1.dot(L)
        def f(G):
            return np.sum((xsi-ns*G.dot(xt))**2)
        def df(G):
            return -2*ns*(xsi-ns*G.dot(xt)).dot(xt.T)
        G=cg(a,b,M,1.0/mu,f,df,G0=G0,numItermax=numInnerItermax,stopThr=stopInnerThr)
        return G


    L=solve_L(G)

    vloss.append(loss(L,G))

    if verbose:
        print('{:5s}|{:12s}|{:8s}'.format('It.','Loss','Delta loss')+'\n'+'-'*32)
        print('{:5d}|{:8e}|{:8e}'.format(0,vloss[-1],0))


    # init loop
    if numItermax>0:
        loop=1
    else:
        loop=0
    it=0

    while loop:

        it+=1

        # update G
        G=solve_G(L,G)

        #update L
        L=solve_L(G)

        vloss.append(loss(L,G))

        if it>=numItermax:
            loop=0

        if abs(vloss[-1]-vloss[-2])/abs(vloss[-2])<stopThr:
            loop=0

        if verbose:
            if it%20==0:
                print('{:5s}|{:12s}|{:8s}'.format('It.','Loss','Delta loss')+'\n'+'-'*32)
            print('{:5d}|{:8e}|{:8e}'.format(it,vloss[-1],(vloss[-1]-vloss[-2])/abs(vloss[-2])))
    if log:
        log['loss']=vloss
        return G,L,log
    else:
        return G,L


def joint_OT_mapping_kernel(xs,xt,mu=1,eta=0.001,kerneltype='gaussian',sigma=1,bias=False,verbose=False,verbose2=False,numItermax = 100,numInnerItermax = 10,stopInnerThr=1e-6,stopThr=1e-5,log=False,**kwargs):
    """Joint OT and nonlinear mapping estimation with kernels as proposed in [8]

    The function solves the following optimization problem:

    .. math::
        \min_{\gamma,L\in\mathcal{H}}\quad \|L(X_s) -n_s\gamma X_t\|^2_F + \mu<\gamma,M>_F + \eta  \|L\|^2_\mathcal{H}

        s.t. \gamma 1 = a

             \gamma^T 1= b

             \gamma\geq 0
    where :

    - M is the (ns,nt) squared euclidean cost matrix between samples in Xs and Xt (scaled by ns)
    - :math:`L` is a ns x d linear operator on a kernel matrix that approximates the barycentric mapping
    - a and b are uniform source and target weights

    The problem consist in solving jointly an optimal transport matrix
    :math:`\gamma` and the nonlinear mapping that fits the barycentric mapping
    :math:`n_s\gamma X_t`.

    One can also estimate a mapping with constant bias (see supplementary
    material of [8]) using the bias optional argument.

    The algorithm used for solving the problem is the block coordinate
    descent that alternates between updates of G (using conditionnal gradient)
    and the update of L using a classical kernel least square solver.


    Parameters
    ----------
    xs : np.ndarray (ns,d)
        samples in the source domain
    xt : np.ndarray (nt,d)
        samples in the target domain
    mu : float,optional
        Weight for the linear OT loss (>0)
    eta : float, optional
        Regularization term  for the linear mapping L (>0)
    bias : bool,optional
        Estimate linear mapping with constant bias
    kerneltype : str,optional
        kernel used by calling function ot.utils.kernel (gaussian by default)
    sigma : float, optional
        Gaussian kernel bandwidth.
    numItermax : int, optional
        Max number of BCD iterations
    stopThr : float, optional
        Stop threshold on relative loss decrease (>0)
    numInnerItermax : int, optional
        Max number of iterations (inner CG solver)
    stopInnerThr : float, optional
        Stop threshold on error (inner CG solver) (>0)
    verbose : bool, optional
        Print information along iterations
    log : bool, optional
        record log if True


    Returns
    -------
    gamma : (ns x nt) ndarray
        Optimal transportation matrix for the given parameters
    L : (ns x d) ndarray
        Nonlinear mapping matrix (ns+1 x d if bias)
    log : dict
        log dictionary return only if log==True in parameters


    References
    ----------

    .. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for discrete optimal transport", Neural Information Processing Systems (NIPS), 2016.

    See Also
    --------
    ot.lp.emd : Unregularized OT
    ot.optim.cg : General regularized OT

    """

    ns,nt=xs.shape[0],xt.shape[0]

    K=kernel(xs,xs,method=kerneltype,sigma=sigma)
    if bias:
        K1=np.hstack((K,np.ones((ns,1))))
        I=np.eye(ns+1)
        I[-1]=0
        Kp=np.eye(ns+1)
        Kp[:ns,:ns]=K

        # ls regu
        #K0 = K1.T.dot(K1)+eta*I
        #Kreg=I

        # RKHS regul
        K0 = K1.T.dot(K1)+eta*Kp
        Kreg=Kp

    else:
        K1=K
        I=np.eye(ns)

        # ls regul
        #K0 = K1.T.dot(K1)+eta*I
        #Kreg=I

        # proper kernel ridge
        K0=K+eta*I
        Kreg=K




    if log:
        log={'err':[]}

    a,b=unif(ns),unif(nt)
    M=dist(xs,xt)*ns
    G=emd(a,b,M)

    vloss=[]

    def loss(L,G):
        """Compute full loss"""
        return np.sum((K1.dot(L)-ns*G.dot(xt))**2)+mu*np.sum(G*M)+eta*np.trace(L.T.dot(Kreg).dot(L))

    def solve_L_nobias(G):
        """ solve L problem with fixed G (least square)"""
        xst=ns*G.dot(xt)
        return np.linalg.solve(K0,xst)

    def solve_L_bias(G):
        """ solve L problem with fixed G (least square)"""
        xst=ns*G.dot(xt)
        return np.linalg.solve(K0,K1.T.dot(xst))

    def solve_G(L,G0):
        """Update G with CG algorithm"""
        xsi=K1.dot(L)
        def f(G):
            return np.sum((xsi-ns*G.dot(xt))**2)
        def df(G):
            return -2*ns*(xsi-ns*G.dot(xt)).dot(xt.T)
        G=cg(a,b,M,1.0/mu,f,df,G0=G0,numItermax=numInnerItermax,stopThr=stopInnerThr)
        return G

    if bias:
        solve_L=solve_L_bias
    else:
        solve_L=solve_L_nobias

    L=solve_L(G)

    vloss.append(loss(L,G))

    if verbose:
        print('{:5s}|{:12s}|{:8s}'.format('It.','Loss','Delta loss')+'\n'+'-'*32)
        print('{:5d}|{:8e}|{:8e}'.format(0,vloss[-1],0))


    # init loop
    if numItermax>0:
        loop=1
    else:
        loop=0
    it=0

    while loop:

        it+=1

        # update G
        G=solve_G(L,G)

        #update L
        L=solve_L(G)

        vloss.append(loss(L,G))

        if it>=numItermax:
            loop=0

        if abs(vloss[-1]-vloss[-2])/abs(vloss[-2])<stopThr:
            loop=0

        if verbose:
            if it%20==0:
                print('{:5s}|{:12s}|{:8s}'.format('It.','Loss','Delta loss')+'\n'+'-'*32)
            print('{:5d}|{:8e}|{:8e}'.format(it,vloss[-1],(vloss[-1]-vloss[-2])/abs(vloss[-2])))
    if log:
        log['loss']=vloss
        return G,L,log
    else:
        return G,L


class OTDA(object):
    """Class for domain adaptation with optimal transport as proposed in [5]


    References
    ----------

    .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1

    """

    def __init__(self,metric='sqeuclidean'):
        """ Class initialization"""
        self.xs=0
        self.xt=0
        self.G=0
        self.metric=metric
        self.computed=False


    def fit(self,xs,xt,ws=None,wt=None,norm=None):
        """ Fit domain adaptation between samples is xs and xt (with optional weights)"""
        self.xs=xs
        self.xt=xt

        if wt is None:
            wt=unif(xt.shape[0])
        if ws is None:
            ws=unif(xs.shape[0])

        self.ws=ws
        self.wt=wt

        self.M=dist(xs,xt,metric=self.metric)
        self.normalizeM(norm)
        self.G=emd(ws,wt,self.M)
        self.computed=True

    def interp(self,direction=1):
        """Barycentric interpolation for the source (1) or target (-1) samples

        This Barycentric interpolation solves for each source (resp target)
        sample xs (resp xt) the following optimization problem:

        .. math::
            arg\min_x \sum_i \gamma_{k,i} c(x,x_i^t)

        where k is the index of the sample in xs

        For the moment only squared euclidean distance is provided but more
        metric  could be used in the future.

        """
        if direction>0: # >0 then source to target
            G=self.G
            w=self.ws.reshape((self.xs.shape[0],1))
            x=self.xt
        else:
            G=self.G.T
            w=self.wt.reshape((self.xt.shape[0],1))
            x=self.xs

        if self.computed:
            if self.metric=='sqeuclidean':
                return np.dot(G/w,x) # weighted mean
            else:
                print("Warning, metric not handled yet, using weighted average")
                return np.dot(G/w,x) # weighted mean
                return None
        else:
            print("Warning, model not fitted yet, returning None")
            return None


    def predict(self,x,direction=1):
        """ Out of sample mapping using the formulation from [6]

        For each sample x to map, it finds the nearest source sample xs and
        map the samle x to the position xst+(x-xs) wher xst is the barycentric
        interpolation of source sample xs.

        References
        ----------

        .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.

        """
        if direction>0: # >0 then source to target
            xf=self.xt
            x0=self.xs
        else:
            xf=self.xs
            x0=self.xt

        D0=dist(x,x0) # dist netween new samples an source
        idx=np.argmin(D0,1) # closest one
        xf=self.interp(direction)# interp the source samples
        return xf[idx,:]+x-x0[idx,:] # aply the delta to the interpolation

    def normalizeM(self, norm):
        """ Apply normalization to the loss matrix
        
        
        Parameters
        ----------
        norm : str
            type of normalization from 'median','max','log','loglog'
            
        """
        
        if norm == "median":
            self.M /= float(np.median(self.M))
        elif norm == "max":
            self.M /= float(np.max(self.M))
        elif norm == "log":
            self.M = np.log(1 + self.M)
        elif norm == "loglog":
            self.M = np.log(1 + np.log(1 + self.M))
            


class OTDA_sinkhorn(OTDA):
    """Class for domain adaptation with optimal transport with entropic regularization"""

    def fit(self,xs,xt,reg=1,ws=None,wt=None,norm=None,**kwargs):
        """ Fit regularized domain adaptation between samples is xs and xt (with optional weights)"""
        self.xs=xs
        self.xt=xt

        if wt is None:
            wt=unif(xt.shape[0])
        if ws is None:
            ws=unif(xs.shape[0])

        self.ws=ws
        self.wt=wt

        self.M=dist(xs,xt,metric=self.metric)
        self.normalizeM(norm)
        self.G=sinkhorn(ws,wt,self.M,reg,**kwargs)
        self.computed=True


class OTDA_lpl1(OTDA):
    """Class for domain adaptation with optimal transport with entropic and group regularization"""


    def fit(self,xs,ys,xt,reg=1,eta=1,ws=None,wt=None,norm=None,**kwargs):
        """ Fit regularized domain adaptation between samples is xs and xt (with optional weights),  See ot.da.sinkhorn_lpl1_mm for fit parameters"""
        self.xs=xs
        self.xt=xt

        if wt is None:
            wt=unif(xt.shape[0])
        if ws is None:
            ws=unif(xs.shape[0])

        self.ws=ws
        self.wt=wt

        self.M=dist(xs,xt,metric=self.metric)
        self.normalizeM(norm)
        self.G=sinkhorn_lpl1_mm(ws,ys,wt,self.M,reg,eta,**kwargs)
        self.computed=True

class OTDA_l1l2(OTDA):
    """Class for domain adaptation with optimal transport with entropic and group lasso regularization"""


    def fit(self,xs,ys,xt,reg=1,eta=1,ws=None,wt=None,norm=None,**kwargs):
        """ Fit regularized domain adaptation between samples is xs and xt (with optional weights),  See ot.da.sinkhorn_lpl1_gl for fit parameters"""
        self.xs=xs
        self.xt=xt

        if wt is None:
            wt=unif(xt.shape[0])
        if ws is None:
            ws=unif(xs.shape[0])

        self.ws=ws
        self.wt=wt

        self.M=dist(xs,xt,metric=self.metric)
        self.normalizeM(norm)
        self.G=sinkhorn_l1l2_gl(ws,ys,wt,self.M,reg,eta,**kwargs)
        self.computed=True

class OTDA_mapping_linear(OTDA):
    """Class for optimal transport with joint linear mapping estimation as in [8]"""


    def __init__(self):
        """ Class initialization"""


        self.xs=0
        self.xt=0
        self.G=0
        self.L=0
        self.bias=False
        self.computed=False
        self.metric='sqeuclidean'

    def fit(self,xs,xt,mu=1,eta=1,bias=False,**kwargs):
        """ Fit domain adaptation between samples is xs and xt (with optional
            weights)"""
        self.xs=xs
        self.xt=xt
        self.bias=bias


        self.ws=unif(xs.shape[0])
        self.wt=unif(xt.shape[0])

        self.G,self.L=joint_OT_mapping_linear(xs,xt,mu=mu,eta=eta,bias=bias,**kwargs)
        self.computed=True

    def mapping(self):
        return lambda x: self.predict(x)


    def predict(self,x):
        """ Out of sample mapping estimated during the call to fit"""
        if self.computed:
            if self.bias:
                x=np.hstack((x,np.ones((x.shape[0],1))))
            return x.dot(self.L) # aply the delta to the interpolation
        else:
            print("Warning, model not fitted yet, returning None")
            return None

class OTDA_mapping_kernel(OTDA_mapping_linear):
    """Class for optimal transport with joint nonlinear mapping estimation as in [8]"""



    def fit(self,xs,xt,mu=1,eta=1,bias=False,kerneltype='gaussian',sigma=1,**kwargs):
        """ Fit domain adaptation between samples is xs and xt """
        self.xs=xs
        self.xt=xt
        self.bias=bias

        self.ws=unif(xs.shape[0])
        self.wt=unif(xt.shape[0])
        self.kernel=kerneltype
        self.sigma=sigma
        self.kwargs=kwargs


        self.G,self.L=joint_OT_mapping_kernel(xs,xt,mu=mu,eta=eta,bias=bias,**kwargs)
        self.computed=True


    def predict(self,x):
        """ Out of sample mapping estimated during the call to fit"""

        if self.computed:
            K=kernel(x,self.xs,method=self.kernel,sigma=self.sigma,**self.kwargs)
            if self.bias:
                K=np.hstack((K,np.ones((x.shape[0],1))))
            return K.dot(self.L)
        else:
            print("Warning, model not fitted yet, returning None")
            return None