Merge branch 'devel' of github.com:SheffieldML/GPy into devel

GPy/examples/tutorials.py

@@ -17,28 +17,27 @@ def tuto_GP_regression():
     X = np.random.uniform(-3.,3.,(20,1))
     Y = np.sin(X) + np.random.randn(20,1)*0.05
 
-    kernel = GPy.kern.rbf(D=1, variance=1., lengthscale=1.)
+    kernel = GPy.kern.rbf(input_dim=1, variance=1., lengthscale=1.)
 
     m = GPy.models.GPRegression(X, Y, kernel)
 
     print m
     m.plot()
 
+    m.ensure_default_constraints() 
     m.constrain_positive('')
 
-    m.unconstrain('')                            # Required to remove the previous constrains
+    m.unconstrain('')               # may be used to remove the previous constrains
-    m.constrain_positive('rbf_variance')
+    m.constrain_positive('.*rbf_variance')
-    m.constrain_bounded('lengthscale',1.,10. )
+    m.constrain_bounded('.*lengthscale',1.,10. )
-    m.constrain_fixed('noise',0.0025)
+    m.constrain_fixed('.*noise',0.0025)
 
     m.optimize()
 
     m.optimize_restarts(num_restarts = 10)
 
-    ###########################
+    #######################################################
-    #  2-dimensional example  #
+    #######################################################
-    ###########################
-
     # sample inputs and outputs
     X = np.random.uniform(-3.,3.,(50,2))
     Y = np.sin(X[:,0:1]) * np.sin(X[:,1:2])+np.random.randn(50,1)*0.05
@@ -53,22 +52,19 @@ def tuto_GP_regression():
     m.constrain_positive('')
 
     # optimize and plot
-    pb.figure()
     m.optimize('tnc', max_f_eval = 1000)
-
     m.plot()
     print(m)
-
+    return(m)
 
 def tuto_kernel_overview():
     """The detailed explanations of the commands used in this file can be found in the tutorial section"""
-    pb.ion()
+    ker1 = GPy.kern.rbf(1)  # Equivalent to ker1 = GPy.kern.rbf(input_dim=1, variance=1., lengthscale=1.)
-
+    ker2 = GPy.kern.rbf(input_dim=1, variance = .75, lengthscale=2.)
-    ker1 = GPy.kern.rbf(1)  # Equivalent to ker1 = GPy.kern.rbf(D=1, variance=1., lengthscale=1.)
-    ker2 = GPy.kern.rbf(D=1, variance = .75, lengthscale=2.)
     ker3 = GPy.kern.rbf(1, .5, .5)
-
+    
     print ker2
+
     ker1.plot()
     ker2.plot()
     ker3.plot()
@@ -77,28 +73,13 @@ def tuto_kernel_overview():
     k2 = GPy.kern.Matern32(1, 0.5, 0.2)
 
     # Product of kernels
-    k_prod = k1.prod(k2)
+    k_prod = k1.prod(k2)                        # By default, tensor=False
-    k_prodorth = k1.prod_orthogonal(k2)
+    k_prodtens = k1.prod(k2,tensor=True)
 
     # Sum of kernels
-    k_add = k1.add(k2)
+    k_add = k1.add(k2)                          # By default, tensor=False
-    k_addorth = k1.add_orthogonal(k2)
+    k_addtens = k1.add(k2,tensor=True)    
-
+    
-    pb.figure(figsize=(8,8))
-    pb.subplot(2,2,1)
-    k_prod.plot()
-    pb.title('prod')
-    pb.subplot(2,2,2)
-    k_prodorth.plot()
-    pb.title('prod_orthogonal')
-    pb.subplot(2,2,3)
-    k_add.plot()
-    pb.title('add')
-    pb.subplot(2,2,4)
-    k_addorth.plot()
-    pb.title('add_orthogonal')
-    pb.subplots_adjust(wspace=0.3, hspace=0.3)
-
     k1 = GPy.kern.rbf(1,1.,2)
     k2 = GPy.kern.periodic_Matern52(1,variance=1e3, lengthscale=1, period = 1.5, lower=-5., upper = 5)
 
@@ -109,18 +90,6 @@ def tuto_kernel_overview():
     X = np.linspace(-5,5,501)[:,None]
     Y = np.random.multivariate_normal(np.zeros(501),k.K(X),1)
 
-    # plot
-    pb.figure(figsize=(10,4))
-    pb.subplot(1,2,1)
-    k.plot()
-    pb.subplot(1,2,2)
-    pb.plot(X,Y.T)
-    pb.ylabel("Sample path")
-    pb.subplots_adjust(wspace=0.3)
-
-    k = (k1+k2)*(k1+k2)
-    print k.parts[0].name, '\n', k.parts[1].name, '\n', k.parts[2].name, '\n', k.parts[3].name
-
     k1 = GPy.kern.rbf(1)
     k2 = GPy.kern.Matern32(1)
     k3 = GPy.kern.white(1)
@@ -128,16 +97,16 @@ def tuto_kernel_overview():
     k = k1 + k2 + k3
     print k
 
-    k.constrain_positive('var')
+    k.constrain_positive('.*var')
     k.constrain_fixed(np.array([1]),1.75)
-    k.tie_params('len')
+    k.tie_params('.*len')
     k.unconstrain('white')
     k.constrain_bounded('white',lower=1e-5,upper=.5)
     print k
-
+    
     k_cst = GPy.kern.bias(1,variance=1.)
     k_mat = GPy.kern.Matern52(1,variance=1., lengthscale=3)
-    Kanova = (k_cst + k_mat).prod_orthogonal(k_cst + k_mat)
+    Kanova = (k_cst + k_mat).prod(k_cst + k_mat,tensor=True)
     print Kanova
 
     # sample inputs and outputs
@@ -148,7 +117,7 @@ def tuto_kernel_overview():
     m = GPy.models.GPRegression(X, Y, Kanova)
     pb.figure(figsize=(5,5))
     m.plot()
-
+   
     pb.figure(figsize=(20,3))
     pb.subplots_adjust(wspace=0.5)
     pb.subplot(1,5,1)
@@ -156,41 +125,17 @@ def tuto_kernel_overview():
     pb.subplot(1,5,2)
     pb.ylabel("=   ",rotation='horizontal',fontsize='30')
     pb.subplot(1,5,3)
-    m.plot(which_functions=[False,True,False,False])
+    m.plot(which_parts=[False,True,False,False])
     pb.ylabel("cst          +",rotation='horizontal',fontsize='30')
     pb.subplot(1,5,4)
-    m.plot(which_functions=[False,False,True,False])
+    m.plot(which_parts=[False,False,True,False])
     pb.ylabel("+   ",rotation='horizontal',fontsize='30')
     pb.subplot(1,5,5)
     pb.ylabel("+   ",rotation='horizontal',fontsize='30')
-    m.plot(which_functions=[False,False,False,True])
+    m.plot(which_parts=[False,False,False,True])
 
-    ker1 = GPy.kern.rbf(D=1)  # Equivalent to ker1 = GPy.kern.rbf(D=1, variance=1., lengthscale=1.)
+    return(m)
-    ker2 = GPy.kern.rbf(D=1, variance = .75, lengthscale=3.)
-    ker3 = GPy.kern.rbf(1, .5, .25)
 
-    ker1.plot()
-    ker2.plot()
-    ker3.plot()
-    #pb.savefig("Figures/tuto_kern_overview_basicdef.png")
-
-    kernels = [GPy.kern.rbf(1), GPy.kern.exponential(1), GPy.kern.Matern32(1), GPy.kern.Matern52(1),  GPy.kern.Brownian(1), GPy.kern.bias(1), GPy.kern.linear(1), GPy.kern.spline(1), GPy.kern.periodic_exponential(1), GPy.kern.periodic_Matern32(1), GPy.kern.periodic_Matern52(1), GPy.kern.white(1)]
-    kernel_names = ["GPy.kern.rbf", "GPy.kern.exponential", "GPy.kern.Matern32", "GPy.kern.Matern52", "GPy.kern.Brownian", "GPy.kern.bias", "GPy.kern.linear", "GPy.kern.spline", "GPy.kern.periodic_exponential", "GPy.kern.periodic_Matern32", "GPy.kern.periodic_Matern52", "GPy.kern.white"]
-
-    pb.figure(figsize=(16,12))
-    pb.subplots_adjust(wspace=.5, hspace=.5)
-    for i, kern in enumerate(kernels):
-       pb.subplot(3,4,i+1)
-       kern.plot(x=7.5,plot_limits=[0.00001,15.])
-       pb.title(kernel_names[i]+ '\n')
-
-    # actual plot for the noise
-    i = 11
-    X = np.linspace(0.,15.,201)
-    WN = 0*X
-    WN[100] = 1.
-    pb.subplot(3,4,i+1)
-    pb.plot(X,WN,'b')
 
 def model_interaction():
     X = np.random.randn(20,1)

doc/tuto_GP_regression.rst

@@ -25,7 +25,7 @@ The first step is to define the covariance kernel we want to use for the model.
 
     kernel = GPy.kern.rbf(input_dim=1, variance=1., lengthscale=1.)
 
-The parameter ``D`` stands for the dimension of the input space. The parameters ``variance`` and ``lengthscale`` are optional. Many other kernels are implemented such as:
+The parameter ``input_dim`` stands for the dimension of the input space. The parameters ``variance`` and ``lengthscale`` are optional. Many other kernels are implemented such as:
 
 * linear (``GPy.kern.linear``)
 * exponential kernel (``GPy.kern.exponential``)
@@ -69,7 +69,7 @@ There are various ways to constrain the parameters of the kernel. The most basic
 
 but it is also possible to set a range on to constrain one parameter to be fixed. The parameter of ``m.constrain_positive`` is a regular expression that matches the name of the parameters to be constrained (as seen in ``print m``). For example, if we want the variance to be positive, the lengthscale to be in [1,10] and the noise variance to be fixed we can write::
 
-    m.unconstrain('')                            # Required to remove the previous constrains
+    m.unconstrain('')               # may be used to remove the previous constrains
     m.constrain_positive('.*rbf_variance')
     m.constrain_bounded('.*lengthscale',1.,10. )
     m.constrain_fixed('.*noise',0.0025)