add scatter 3d

git-svn-id: https://scm.gforge.inria.fr/authscm/cfourrie/svn/lingwb/metagger/trunk@2731 dc05b511-7f1d-0410-9f1c-d6f32a2df9e4

bin/plot_learning_curves.py.in

 #!/usr/bin/env python
 
-
-from mpl_toolkits.mplot3d import axes3d
+import matplotlib
 from matplotlib import cm
 import pylab
 import random
 import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import axes3d
+
+
 
 
 
@@ -18,24 +21,28 @@ def plot_3D(triple_list,type="wireframe",x_label="R",y_label="C",z_label="Value"
     x_list,y_list,z_list = zip(*triple_list)
     z_dict = dict([((x,y),z) for (x,y,z) in triple_list])
     X, Y = np.meshgrid( x_list,y_list )
-    Z = np.zeros((len(Y), len(X)))
+    #X = np.array([x_list]*len(x_list))
+    #Y = np.array([y_list]*len(y_list))
+    Z = np.zeros((len(X), len(Y)))
     for i in range(len(X)):
         for j in range(len(Y)):
             Z[j,i] = z_dict[x_list[i],y_list[j]]
-  
+            
     if type=="wireframe": 
-        ax.plot_wireframe(X,Y,Z,rstride=10, cstride=10) 
+        ax.plot_wireframe(X,Y,Z,rstride=10, cstride=10,linestyle="dotted") 
     elif type=="surface": 
         ax.plot_surface(X,Y,Z, rstride=10, cstride=10, cmap=cm.jet) 
     elif type=="contour": 
-        ax.contour3D(X,Y,Z) 
+        ax.contour3D(X,Y,Z)
+    elif type=="scatter":
+        ax.scatter3D(x_list,y_list,z_list)
     else: 
         raise ValueError("Unknown plot type "+str(type)) 
     
     ax.set_xlabel( x_label ) 
     ax.set_ylabel( y_label ) 
-    ax.set_zlabel( z_label ) 
-    
+    ax.set_zlabel( z_label )
+
     #if title: windowtitle(title) 
     #pylab.show._needmain=False 
     
@@ -59,15 +66,45 @@ def plot_2D(x_values,y_values,x_label="X",y_label="Y",title=None):
 
 
 
+def plot_2D_contour(triple_list,x_label="X",y_label="Y",title=None):
+
+    x_list,y_list,z_list = zip(*triple_list)
+    z_dict = dict([((x,y),z) for (x,y,z) in triple_list])
+    X, Y = np.meshgrid( x_list,y_list )
+    Z = np.zeros((len(X), len(Y)))
+    for i in range(len(X)):
+        for j in range(len(Y)):
+            Z[j,i] = z_dict[x_list[i],y_list[j]]
+
+    cmap = cm.get_cmap('jet', 100)    # 10 discrete colors
+
+    im = pylab.imshow(Z, cmap=cmap, interpolation='bilinear')
+    #pylab.axis('off')
+    pylab.colorbar()
+
+    pylab.xlabel( x_label )
+    pylab.ylabel( y_label )
+
+    pylab.show()
+
+    # plt.figure()
+#     CS = plt.contour(X, Y, Z)
+#     plt.clabel(CS, inline=1, fontsize=10)
+#     plt.title(title)
+    return
+
+
+
 
 if __name__ == '__main__':
 
     import sys
     import optparse
-    
+
     usage = "usage: %prog [options] <input_file>"
     parser = optparse.OptionParser(usage=usage)
     parser.add_option("-d", "--dimension", type=int, default=3)
+    parser.add_option("-t", "--type", choices=["wireframe","scatter","surface","contour"], default="wireframe")
     (options, args) = parser.parse_args()
 
     result_file = args[0]
@@ -100,10 +137,11 @@ if __name__ == '__main__':
         # x_label = 'Dictionary size (in # of lemmas)'
         y_label = 'Accuracy on test data'
         # y_label = 'Dictionary size (in # of lemmas)'
-        plot_2D( X,Z, x_label, y_label, title="POS Tagger learning curve" )
+        plot_2D( X, Y, x_label, y_label, title="POS Tagger learning curve" )
+        # plot_2D_contour( scores, x_label, y_label, title="POS Tagger learning curve" )
         
     else:
-        plot_3D(scores,type="wireframe",
+        plot_3D(scores,type=options.type,
                 x_label="Training size (in # of sentences)",
                 y_label="Dictionary size (in # of lemmas)",
                 z_label="Accuracy on test data",