Changes in plotting functions.

GPy/examples/classification.py

@@ -84,7 +84,7 @@ def toy_linear_1d_classification(seed=default_seed):
     likelihood = GPy.likelihoods.EP(data['Y'][:, 0:1],distribution)
 
     # Model definition
-    m = GPy.models.GP(data['X'],kernel,likelihood=likelihood)
+    m = GPy.models.GP(data['X'],likelihood=likelihood,kernel=kernel)
 
     # Optimize
     """
@@ -98,9 +98,9 @@ def toy_linear_1d_classification(seed=default_seed):
 
     # Plot
     pb.subplot(211)
-    m.plot_GP()
+    m.plot_internal()
     pb.subplot(212)
-    m.plot_output()
+    m.plot()
     print(m)
 
     return m

GPy/likelihoods/Gaussian.py

@@ -42,7 +42,7 @@ class Gaussian(likelihood):
         """
         mean = mu*self._std + self._mean
         true_var = (var + self._variance)*self._std**2
-        _5pc = mean + mean - 2.*np.sqrt(var)
+        _5pc = mean + - 2.*np.sqrt(var)
         _95pc = mean + 2.*np.sqrt(var)
         return mean, _5pc, _95pc
 

GPy/likelihoods/likelihood_functions.py

@@ -52,8 +52,8 @@ class probit(likelihood_function):
         mu = mu.flatten()
         var = var.flatten()
         mean = stats.norm.cdf(mu/np.sqrt(1+var))
-        p_05 = np.zeros([mu.size])
-        p_95 = np.ones([mu.size])
+        p_05 = np.zeros(mu.shape)#np.zeros([mu.size])
+        p_95 = np.zeros(mu.shape)#np.ones([mu.size])
         return mean, p_05, p_95
 
 class Poisson(likelihood_function):

GPy/models/GP.py

@@ -7,7 +7,7 @@ import pylab as pb
 from .. import kern
 from ..core import model
 from ..util.linalg import pdinv,mdot
-from ..util.plot import gpplot,x_frame, Tango
+from ..util.plot import gpplot,x_frame1D,x_frame2D, Tango
 from ..likelihoods import EP
 
 class GP(model):
@@ -175,7 +175,7 @@ class GP(model):
         return mean, _5pc, _95pc
 
 
-    def plot_GP(self,samples=0,plot_limits=None,which_data='all',which_functions='all',resolution=None,full_cov=False):
+    def plot_internal(self,samples=0,plot_limits=None,which_data='all',which_functions='all',resolution=None,full_cov=False):
         """
         Plot the GP's view of the world, where the data is normalised and the likelihood is Gaussian
 
@@ -200,22 +200,49 @@ class GP(model):
         if which_data=='all':
             which_data = slice(None)
 
-        Xnew, xmin, xmax = x_frame(self.X, plot_limits=plot_limits)
+        if self.X.shape[1] == 1:
+            Xnew, xmin, xmax = x_frame1D(self.X, plot_limits=plot_limits)
+            m,v = self._raw_predict(Xnew, slices=which_functions)
+            gpplot(Xnew,m,m-np.sqrt(v),m+np.sqrt(v))
+            pb.plot(self.X[which_data],self.likelihood.Y[which_data],'kx',mew=1.5)
+            pb.xlim(xmin,xmax)
+        elif X.shape[1]==2:
+            resolution = resolution or 50
+            Xnew, xmin, xmax,xx,yy = x_frame2D(self.X, plot_limits=plot_limits)
+            m,v = self._raw_predict(Xnew, slices=which_functions)
+            m = m.reshape(resolution,resolution)
+            pb.contour(xx,yy,zz,vmin=zz.min(),vmax=zz.max(),cmap=pb.cm.jet)
+            pb.scatter(Xorig[:,0],Xorig[:,1],40,Yorig,linewidth=0,cmap=pb.cm.jet,vmin=zz.min(),vmax=zz.max())
+            pb.xlim(xmin[0],xmax[0])
+            pb.ylim(xmin[1],xmax[1])
+        else:
+            raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
 
-        m,v = self._raw_predict(Xnew, slices=which_functions)
-        gpplot(Xnew,m,m-np.sqrt(v),m+np.sqrt(v))
-        pb.plot(self.X[which_data],self.likelihood.Y[which_data],'kx',mew=1.5)
-        pb.xlim(xmin,xmax)
-
-    def plot_output(self,samples=0,plot_limits=None,which_data='all',which_functions='all',resolution=None,full_cov=False):
+    def plot(self,samples=0,plot_limits=None,which_data='all',which_functions='all',resolution=None,full_cov=False):
         if which_functions=='all':
             which_functions = [True]*self.kern.Nparts
         if which_data=='all':
             which_data = slice(None)
-        Xnew, xmin, xmax = x_frame(self.X, plot_limits=plot_limits)
-        m, lower, upper = self.predict(Xnew, slices=which_functions)
-        gpplot(Xnew,m, lower, upper)
-        pb.plot(self.X[which_data],self.likelihood.data[which_data],'kx',mew=1.5)
-        ymin,ymax = self.likelihood.data.min()*1.2,self.likelihood.data.max()*1.2
-        pb.xlim(xmin,xmax)
-        pb.ylim(ymin,ymax)
+
+        if self.X.shape[1] == 1:
+            Xnew, xmin, xmax = x_frame1D(self.X, plot_limits=plot_limits)
+            m, lower, upper = self.predict(Xnew, slices=which_functions)
+            gpplot(Xnew,m, lower, upper)
+            pb.plot(self.X[which_data],self.likelihood.data[which_data],'kx',mew=1.5)
+            ymin,ymax = self.likelihood.data.min()*1.2,self.likelihood.data.max()*1.2
+            pb.xlim(xmin,xmax)
+            pb.ylim(ymin,ymax)
+        elif X.shape[1]==2:
+            resolution = resolution or 50
+            Xnew, xmin, xmax,xx,yy = x_frame2D(self.X, plot_limits=plot_limits)
+            m,v = self.predict(Xnew, slices=which_functions)
+            m = m.reshape(resolution,resolution)
+            pb.contour(xx,yy,zz,vmin=zz.min(),vmax=zz.max(),cmap=pb.cm.jet)
+            pb.scatter(Xorig[:,0],Xorig[:,1],40,Yorig,linewidth=0,cmap=pb.cm.jet,vmin=zz.min(),vmax=zz.max())
+            pb.xlim(xmin[0],xmax[0])
+            pb.ylim(xmin[1],xmax[1])
+        else:
+            raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
+
+
+

GPy/util/plot.py

@@ -70,10 +70,11 @@ def align_subplots(N,M,xlim=None, ylim=None):
         else:
             removeUpperTicks()
 
-def x_frame(X,plot_limits=None,resolution=None):
+def x_frame1D(X,plot_limits=None,resolution=None):
     """
     Internal helper function for making plots, returns a set of input values to plot as well as lower and upper limits
     """
+    assert X.shape[1] ==1, "x_frame1D is defined for one-dimensional inputs"
     if plot_limits is None:
         xmin,xmax = X.min(0),X.max(0)
         xmin, xmax = xmin-0.2*(xmax-xmin), xmax+0.2*(xmax-xmin)
@@ -82,12 +83,24 @@ def x_frame(X,plot_limits=None,resolution=None):
     else:
         raise ValueError, "Bad limits for plotting"
 
-    if X.shape[1]==1:
-        Xnew = np.linspace(xmin,xmax,resolution or 200)[:,None]
-    elif X.shape[1]==2:
-        resolution = resolution or 50
-        xx,yy = np.mgrid[xmin[0]:xmax[0]:1j*resolution,xmin[1]:xmax[1]:1j*resolution]
-        Xnew = np.vstack((xx.flatten(),yy.flatten())).T
-    else:
-        raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
+    Xnew = np.linspace(xmin,xmax,resolution or 200)[:,None]
     return Xnew, xmin, xmax
+
+def x_frame2D(X,plot_limits=None,resolution=None):
+    """
+    Internal helper function for making plots, returns a set of input values to plot as well as lower and upper limits
+    """
+    assert X.shape[1] ==2, "x_frame2D is defined for two-dimensional inputs"
+    if plot_limits is None:
+        xmin,xmax = X.min(0),X.max(0)
+        xmin, xmax = xmin-0.2*(xmax-xmin), xmax+0.2*(xmax-xmin)
+    elif len(plot_limits)==2:
+        xmin, xmax = plot_limits
+    else:
+        raise ValueError, "Bad limits for plotting"
+
+    resolution = resolution or 50
+    xx,yy = np.mgrid[xmin[0]:xmax[0]:1j*resolution,xmin[1]:xmax[1]:1j*resolution]
+    Xnew = np.vstack((xx.flatten(),yy.flatten())).T
+    return Xnew, xx,yy,xmin, xmax
+