Exemplo n.º 1
0
    hyp.mean = np.array([])

    sn = 0.1
    hyp.lik = np.array([np.log(sn)])

  
    ##----------------------------------------------------------##
    ## STANDARD GP (prediction)                                 ##
    ##----------------------------------------------------------## 
    xs = np.arange(2004+1./24.,2024-1./24.,1./12.)     # TEST POINTS
    xs = xs.reshape(len(xs),1)

    vargout = gp(hyp,inffunc,meanfunc,covfunc,likfunc,x,y,xs)
    ym = vargout[0]; ys2 = vargout[1]
    m  = vargout[2]; s2  = vargout[3]
    plotter(xs,ym,ys2,x,y)
    
    ##----------------------------------------------------------##
    ## STANDARD GP (training)                                   ##
    ## OPTIMIZE HYPERPARAMETERS                                 ##
    ##----------------------------------------------------------##
    ## -> parameter training using (off the shelf) conjugent gradient (CG) optimization (NOTE: SCG is faster)
    from time import clock
    t0 = clock()
    vargout = min_wrapper(hyp,gp,'SCG',inffunc,meanfunc,covfunc,likfunc,x,y,None,None,True)
    t1 = clock()

    hyp = vargout[0]
    vargout = gp(hyp,inffunc,meanfunc,covfunc,likfunc,x,y,xs)
    ym = vargout[0]; ys2 = vargout[1]
    m  = vargout[2]; s2  = vargout[3]
Exemplo n.º 2
0
    hyp2.cov = np.array([-1.0,0.0])
    hyp2.mean = np.array([0.5,1.0])
    hyp2.lik = np.array([np.log(0.1)])

    ### PREDICTION
    import time
    t0 = time.time()
    print 'prediction'
    vargout = gp(hyp2,inffunc,meanfunc,covfunc,likfunc,x,y,xstar)
    ym = vargout[0]; ys2 = vargout[1]; m  = vargout[2]; s2 = vargout[3]
    
    print time.time() - t0
    
    
    ### PLOT results
    plotter(xstar,ym,ys2,x,y,[-2, 2, -0.9, 3.9])
    
    ### GET negative log marginal likelihood
    [nlml, post] = gp(hyp2,inffunc,meanfunc,covfunc,likfunc,x,y,None,None,False)
    print "nlml2 = ", nlml


    ###----------------------------------------------------------###
    ### STANDARD GP (example 3)                                  ###
    ###----------------------------------------------------------###
    ### TRAINING: OPTIMIZE hyperparameters
    [hyp2_opt, fopt, gopt, funcCalls] = min_wrapper(hyp2,gp,'CG',inffunc,meanfunc,covfunc,likfunc,x,y,None,None,True)
    print "nlml_opt = ", fopt
    
    #[hyp2_opt, fopt, gopt, funcCalls] = min_wrapper(hyp2,gp,'BFGS',inffunc,meanfunc,covfunc,likfunc,x,y,None,None,True)
    #print fopt
Exemplo n.º 3
0
    hyp = hyperParameters()

    hyp.cov = np.array([np.log(0.25),np.log(1.0),np.log(3.0)])
    hyp.mean = np.array([0.5,1.0])
    sn = 0.1; hyp.lik = np.array([np.log(sn)])

    #_________________________________
    # STANDARD GP:
    ## PREDICTION 
    vargout = gp(hyp,inffunc,meanfunc,covfunc,likfunc,x,y,None,None,False)
    print "nlml = ",vargout[0]
    vargout = gp(hyp,inffunc,meanfunc,covfunc,likfunc,x,y,z)
    ym = vargout[0]; ys2 = vargout[1]
    m  = vargout[2]; s2 = vargout[3]
    ## Plot results
    plotter(z,ym,ys2,x,y,[-1.9, 1.9, -0.9, 3.9])
    
    print "pre" + str(x)
    x = np.delete(x, 0, 0)
    x = np.delete(x, 5, 0)
    print "post" + str(x)
    y = np.delete(y, 0, 0)
    y = np.delete(y, 5, 0)
    vargout = gp(hyp,inffunc,meanfunc,covfunc,likfunc,x,y,None,None,False)
    print "nlml = ",vargout[0]
    vargout = gp(hyp,inffunc,meanfunc,covfunc,likfunc,x,y,z)
    ym = vargout[0]; ys2 = vargout[1]
    m  = vargout[2]; s2 = vargout[3]
    ## Plot results
    plotter(z,ym,ys2,x,y,[-1.9, 1.9, -0.9, 3.9])
    
Exemplo n.º 4
0
    hyp = hyperParameters()

    hyp.cov = np.array([np.log(0.25),np.log(1.0),np.log(3.0)])
    hyp.mean = np.array([0.5,1.0])
    sn = 0.1; hyp.lik = np.array([np.log(sn)])

    #_________________________________
    # STANDARD GP:
    ## PREDICTION 
    vargout = gp(hyp,inffunc,meanfunc,covfunc,likfunc,x,y,None,None,False)
    print "nlml = ",vargout[0]
    vargout = gp(hyp,inffunc,meanfunc,covfunc,likfunc,x,y,z)
    ym = vargout[0]; ys2 = vargout[1]
    m  = vargout[2]; s2 = vargout[3]
    ## Plot results
    plotter(z,ym,ys2,x,y,[-1.9, 1.9, -0.9, 3.9])
    ###########################################################
    covfunc = [ ['kernels.covSEiso'] ]
    ## SET (hyper)parameters
    hyp2 = hyperParameters()

    hyp2.cov = np.array([-1.0,0.0])
    hyp2.lik = np.array([np.log(0.1)])
    vargout = min_wrapper(hyp2,gp,'SCG',inffunc,[],covfunc,likfunc,x,y,None,None,True)
    hyp2 = vargout[0]
    #hyp2.cov = np.array([-0.993396880620537,0.685943441677086])
    #hyp2.lik = np.array([-1.902546786026883])

    vargout = gp(hyp2,inffunc,[],covfunc,likfunc,x,y,None,None,False)
    print "nlml2 = ",vargout[0]