changeset 249:90181ab320f0

- Scripts for plotting summary performance graphs.

--- a/experiments/scripts/cnbh-syllables/results_plotting/plot_munged_results.py	Tue Oct 26 16:48:03 2010 +0000
+++ b/experiments/scripts/cnbh-syllables/results_plotting/plot_munged_results.py	Mon Nov 01 00:31:00 2010 +0000
@@ -4,11 +4,9 @@
 
 """
 
-import matplotlib as mpl
-mpl.use('PDF')
 import numpy as np
 import pylab as p
-#import matplotlib.pyplot as plt
+import matplotlib.pyplot as plt
 
 f=open("results_test_all.csv","r")
 results = dict()
@@ -18,40 +16,29 @@
     results.setdefault(values[3],dict())
     results[values[3]].setdefault(values[0], dict())
     results[values[3]][values[0]].setdefault(values[1], dict())
-    results[values[3]][values[0]][values[1]].setdefault(int(values[4]), dict())
-    results[values[3]][values[0]][values[1]][int(values[4])].setdefault(int(values[5]), dict())
-    results[values[3]][values[0]][values[1]][int(values[4])][int(values[5])].setdefault(int(values[6]), dict())
     if values[2] == 'clean':
-       snr = 50
-       results[values[3]][values[0]][values[1]][int(values[4])][int(values[5])][int(values[6])][snr] = float(values[7])
+       snr = 40
     else:
        snr = int(values[2])
-       results[values[3]][values[0]][values[1]][int(values[4])][int(values[5])][int(values[6])][snr] = float(values[7])
+       results[values[3]][values[0]][values[1]][snr] = float(values[4])
 #    results[values[3]].append((values[1],values[2],values[2],values[4]))
 
-ax = mpl.pyplot.subplot(111)
+ax = plt.subplot(111)
+
 train_set = 'inner'
-for hmm_iterations in [2,3,15]:
-  for hmm_states in [3,4]:
-    for hmm_components in [3,4]:
-      lines = []
-      labels = []
-      ax.cla()
-      for feature_type in ('mfcc', 'mfcc_vtln', 'aim'):
-        for feature_subtype in results[train_set][feature_type].keys():
-          try:
-            this_line = results[train_set][feature_type][feature_subtype][hmm_states][hmm_components][hmm_iterations].items()
-            this_line.sort(cmp=lambda x,y: x[0] - y[0])
-            xs, ys = zip(*this_line)
-            xs = list(xs)
-            ys = list(ys)
-            line, = ax.plot(xs,ys,'-o',linewidth=2)
-            lines.append(line)
-            labels.append(feature_type + "_" + feature_subtype)
-          except KeyError:
-            print "Data not found"
-      p.legend(lines, labels, 'upper left', shadow=True)
-      p.xlabel('SNR/dB')
-      p.ylabel('Recognition performance %')
-      output_file = ("recognition_vs_snr_%diterations_%dstates_%d_components.pdf" % (hmm_iterations, hmm_states, hmm_components))
-      p.savefig(output_file)
\ No newline at end of file
+lines = []
+labels = []
+for feature_type in ('mfcc', 'mfcc_vtln', 'aim'):
+  for feature_subtype in results[train_set][feature_type].keys():
+    this_line = results[train_set][feature_type][feature_subtype].items()
+    this_line.sort(cmp=lambda x,y: x[0] - y[0])
+    xs, ys = zip(*this_line)
+    xs = list(xs)
+    ys = list(ys)
+    line, = ax.plot(xs,ys,'-o',linewidth=2)
+    lines.append(line)
+    labels.append(feature_type + "_" + feature_subtype)
+p.legend(lines, labels, 'upper left', shadow=True)
+p.xlabel('SNR/dB')
+p.ylabel('Recognition performance %')
+plt.show()