fix stick man example

GPy/core/gp.py

@@ -121,7 +121,7 @@ class GP(Model):
            If full_cov and self.input_dim > 1, the return shape of var is Nnew x Nnew x self.input_dim. If self.input_dim == 1, the return shape is Nnew x Nnew.
            This is to allow for different normalizations of the output dimensions.
 
-        """
+        """        
         #predict the latent function values
         mu, var = self._raw_predict(Xnew, full_cov=full_cov)
 

GPy/examples/dimensionality_reduction.py

@@ -409,12 +409,12 @@ def stick(kernel=None, optimize=True, verbose=True, plot=True):
     # optimize
     m = GPy.models.GPLVM(data['Y'], 2, kernel=kernel)
     if optimize: m.optimize(messages=verbose, max_f_eval=10000)
-    if plot and GPy.plotting.matplot_dep.visualize.visual_available:
+    if plot:
         plt.clf
         ax = m.plot_latent()
-        y = m.likelihood.Y[0, :]
+        y = m.Y[0, :]
         data_show = GPy.plotting.matplot_dep.visualize.stick_show(y[None, :], connect=data['connect'])
-        GPy.plotting.matplot_dep.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
+        vis = GPy.plotting.matplot_dep.visualize.lvm(m.X[0, :].copy(), m, data_show, latent_axes=ax)
         raw_input('Press enter to finish')
 
     return m

GPy/inference/latent_function_inference/exact_gaussian_inference.py

@@ -32,7 +32,7 @@ class ExactGaussianInference(object):
             return Y
         else:
             #if Y in self.cache, return self.Cache[Y], else store Y in cache and return L.
-            print "WARNING: N>D of Y, we need caching of L, such that L*L^T = Y, returning Y still!"
+            #print "WARNING: N>D of Y, we need caching of L, such that L*L^T = Y, returning Y still!"
             return Y
 
     def inference(self, kern, X, likelihood, Y, Y_metadata=None):

GPy/inference/latent_function_inference/var_dtc_gpu.py

@@ -109,7 +109,9 @@ class VarDTC_GPU(object):
             x0, x1 = 0.,0.
         y0, y1 = self._estimateMemoryOccupation(N, M, D)
         
-        return int((self.gpu_memory-y0-x0)/(x1+y1))
+        opt_batchsize = min(int((self.gpu_memory-y0-x0)/(x1+y1)), N)
+        
+        return opt_batchsize
         
     def _get_YYTfactor(self, Y):
         """

GPy/plotting/matplot_dep/img_plots.py

@@ -29,7 +29,7 @@ def plot_2D_images(figure, arr, symmetric=False, pad=None, zoom=None, mode=None,
         pad = max(int(min(y_size,x_size)/10),1)
     
     figsize = _calculateFigureSize(x_size, y_size, fig_ncols, fig_nrows, pad)
-    figure.set_size_inches(figsize,forward=True)
+    #figure.set_size_inches(figsize,forward=True)
     #figure.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95)
     
     if symmetric:
@@ -37,7 +37,7 @@ def plot_2D_images(figure, arr, symmetric=False, pad=None, zoom=None, mode=None,
         mval = max(abs(arr.max()),abs(arr.min()))
         arr = arr/(2.*mval)+0.5
     else:
-        minval,maxval = arr.max(),arr.min()
+        minval,maxval = arr.min(),arr.max()
         arr = (arr-minval)/(maxval-minval)
 
     if mode=='L':

GPy/plotting/matplot_dep/visualize.py

@@ -85,6 +85,7 @@ class vector_show(matplotlib_show):
 
 
 class lvm(matplotlib_show):
+    
     def __init__(self, vals, model, data_visualize, latent_axes=None, sense_axes=None, latent_index=[0,1]):
         """Visualize a latent variable model
 
@@ -98,7 +99,7 @@ class lvm(matplotlib_show):
                 vals = param_to_array(model.X.mean)
             else:
                 vals = param_to_array(model.X)
-        
+         
         vals = param_to_array(vals)
         matplotlib_show.__init__(self, vals, axes=latent_axes)
 
@@ -121,7 +122,7 @@ class lvm(matplotlib_show):
         self.move_on = False
         self.latent_index = latent_index
         self.latent_dim = model.input_dim
-
+ 
         # The red cross which shows current latent point.
         self.latent_values = vals
         self.latent_handle = self.latent_axes.plot([0],[0],'rx',mew=2)[0]
@@ -130,10 +131,10 @@ class lvm(matplotlib_show):
 
     def modify(self, vals):
         """When latent values are modified update the latent representation and ulso update the output visualization."""
-        self.vals = vals.copy()
+        self.vals = vals[None,:].copy()
         y = self.model.predict(self.vals)[0]
         self.data_visualize.modify(y)
-        self.latent_handle.set_data(self.vals[self.latent_index[0]], self.vals[self.latent_index[1]])
+        self.latent_handle.set_data(self.vals[:,self.latent_index[0]], self.vals[:,self.latent_index[1]])
         self.axes.figure.canvas.draw()