[SSGPLVM] add plotting class

GPy/plotting/matplot_dep/__init__.py

@@ -15,3 +15,5 @@ import latent_space_visualizations
 import netpbmfile
 import inference_plots
 import maps
+import img_plots
+from ssgplvm import SSGPLVM_plot

GPy/plotting/matplot_dep/img_plots.py

@@ -0,0 +1,56 @@
+"""
+The module contains the tools for ploting 2D image visualizations
+"""
+
+import numpy as np
+from matplotlib.cm import jet
+
+width_max = 15
+height_max = 12
+
+def _calculateFigureSize(x_size, y_size, fig_ncols, fig_nrows, pad):
+    width = (x_size*fig_ncols+pad*(fig_ncols-1))
+    height = (y_size*fig_nrows+pad*(fig_nrows-1))
+    if width > float(height)/height_max*width_max:
+        return (width_max, float(width_max)/width*height)
+    else:
+        return (float(height_max)/height*width, height_max)
+
+def plot_2D_images(figure, arr, symmetric=False, pad=None, zoom=None, mode=None, interpolation='nearest'):
+    ax = figure.add_subplot(111)
+    if len(arr.shape)==2:
+        arr = arr.reshape(*((1,)+arr.shape))
+    fig_num = arr.shape[0]
+    y_size = arr.shape[1]
+    x_size = arr.shape[2]
+    fig_ncols = int(np.ceil(np.sqrt(fig_num)))
+    fig_nrows = int(np.ceil((float)(fig_num)/fig_ncols))
+    if pad==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.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95)
+    
+    if symmetric:
+        # symmetric around zero: fix zero as the middle color
+        mval = max(abs(arr.max()),abs(arr.min()))
+        arr = arr/(2.*mval)+0.5
+    else:
+        minval,maxval = arr.max(),arr.min()
+        arr = (arr-minval)/(maxval-minval)
+
+    if mode=='L':
+        arr_color = np.empty(arr.shape+(3,))
+        arr_color[:] = arr.reshape(*(arr.shape+(1,)))
+    elif mode==None or mode=='jet':
+        arr_color = jet(arr)
+    
+    buf = np.ones((y_size*fig_nrows+pad*(fig_nrows-1), x_size*fig_ncols+pad*(fig_ncols-1), 3),dtype=arr.dtype)
+    
+    for y in xrange(fig_nrows):
+        for x in xrange(fig_ncols):
+            if y*fig_ncols+x<fig_num:
+                buf[y*y_size+y*pad:(y+1)*y_size+y*pad, x*x_size+x*pad:(x+1)*x_size+x*pad] = arr_color[y*fig_ncols+x,:,:,:3]
+    img_plot = ax.imshow(buf, interpolation=interpolation)
+    ax.axis('off')

GPy/plotting/matplot_dep/ssgplvm.py

@@ -0,0 +1,29 @@
+"""
+The module plotting results for SSGPLVM
+"""
+
+import pylab
+
+from ...models import SSGPLVM
+from img_plots import plot_2D_images
+from ...util.misc import param_to_array
+
+class SSGPLVM_plot(object):
+    def __init__(self,model, imgsize):
+        assert isinstance(model,SSGPLVM)
+        self.model = model
+        self.imgsize= imgsize
+        assert model.Y.shape[1] == imgsize[0]*imgsize[1]
+        
+    def plot_inducing(self):
+        fig1 = pylab.figure()
+        mean = self.model.posterior.mean
+        arr = mean.reshape(*(mean.shape[0],self.imgsize[1],self.imgsize[0]))
+        plot_2D_images(fig1, arr)
+        fig1.gca().set_title('The mean of inducing points')
+        
+        fig2 = pylab.figure()
+        covar = self.model.posterior.covariance
+        plot_2D_images(fig2, covar)
+        fig2.gca().set_title('The variance of inducing points')
+