def example2(m=100, scale=0.01, stride=2):
    #m,k = 100,1
    upper = 6
    xs1 = np.linspace(1,upper,m)[:,np.newaxis]
    y1true = np.sum(np.sin(xs1**2),1)[:,np.newaxis]+xs1
    y1 = y1true + 0.05*np.random.randn(m,1)

    ridgecoeff = 1e-10
    #stride = 2 #use only some points as trainig points e.g 2 means every 2nd
    gp1 = GaussProcess(xs1[::stride,:],y1[::stride,:], kernel=kernel_euclid,
                       ridgecoeff=1e-10)
    yhatr1 = gp1.predict(xs1)
    plt.figure()
    plt.plot(y1true, y1,'bo',y1true, yhatr1,'r.')
    plt.title('euclid kernel: true versus noisy (blue) and estimated (red) observations')
    plt.figure()
    plt.plot(y1,'bo-',y1true,'go-',yhatr1,'r.-')
    plt.title('euclid kernel: true (green), noisy (blue) and estimated (red) '+
              'observations')

    gp2 = GaussProcess(xs1[::stride,:],y1[::stride,:], kernel=kernel_rbf,
                       scale=scale, ridgecoeff=1e-2)
    yhatr2 = gp2.predict(xs1)
    plt.figure()
    plt.plot(y1true, y1,'bo',y1true, yhatr2,'r.')
    plt.title('rbf kernel: true versus noisy (blue) and estimated (red) observations')
    plt.figure()
    plt.plot(y1,'bo-',y1true,'go-',yhatr2,'r.-')
    plt.title('rbf kernel: true (green), noisy (blue) and estimated (red) '+
              'observations')
    #gp2.plot(y1)
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/sandbox/regression/kernridgeregress_class.py/example2/operator `/` -> `+`