--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/baseline/mlp/mlp_get_error_from_model.py	Wed Mar 10 16:17:59 2010 -0500
@@ -0,0 +1,124 @@
+__docformat__ = 'restructedtext en'
+
+import pdb
+import numpy as np
+import pylab
+import time 
+import pylearn
+from pylearn.io import filetensor as ft
+
+data_path = '/data/lisa/data/nist/by_class/'
+test_data = 'all/all_test_data.ft'
+test_labels = 'all/all_test_labels.ft'
+
+def read_test_data(mlp_model):
+    
+    
+    #read the data
+    h = open(data_path+test_data)
+    i= open(data_path+test_labels)
+    raw_test_data = ft.read(h)
+    raw_test_labels = ft.read(i)
+    i.close()
+    h.close()
+    
+    #read the model chosen
+    a=np.load(mlp_model)
+    W1=a['W1']
+    W2=a['W2']
+    b1=a['b1']
+    b2=a['b2']
+    
+    return (W1,b1,W2,b2,raw_test_data,raw_test_labels)
+    
+    
+    
+
+def get_total_test_error(everything):
+    
+    W1=everything[0]
+    b1=everything[1]
+    W2=everything[2]
+    b2=everything[3]
+    test_data=everything[4]/255.0
+    test_labels=everything[5]
+    total_error_count=0
+    total_exemple_count=0
+    
+    nb_error_count=0
+    nb_exemple_count=0
+    
+    char_error_count=0
+    char_exemple_count=0
+    
+    min_error_count=0
+    min_exemple_count=0
+    
+    maj_error_count=0
+    maj_exemple_count=0
+    
+    for i in range(test_labels.size):
+        total_exemple_count = total_exemple_count +1
+        #get activation for layer 1
+        a0=np.dot(np.transpose(W1),np.transpose(test_data[i])) + b1
+        #add non linear function to layer 1 activation
+        a0_out=np.tanh(a0)
+        
+        #get activation for output layer
+        a1= np.dot(np.transpose(W2),a0_out) + b2
+        #add non linear function for output activation (softmax)
+        a1_exp = np.exp(a1)
+        sum_a1=np.sum(a1_exp)
+        a1_out=a1_exp/sum_a1
+        
+        predicted_class=np.argmax(a1_out)
+        wanted_class=test_labels[i]
+        
+        if(predicted_class!=wanted_class):
+            total_error_count = total_error_count +1
+            
+        #get grouped based error
+        if(wanted_class>9 and wanted_class<35):
+            min_exemple_count=min_exemple_count+1
+            predicted_class=np.argmax(a1_out)
+            if(predicted_class!=wanted_class):
+		min_error_count=min_error_count+1
+        elif(wanted_class<10):
+            nb_exemple_count=nb_exemple_count+1
+            predicted_class=np.argmax(a1_out)
+            if(predicted_class!=wanted_class):
+                nb_error_count=nb_error_count+1
+        elif(wanted_class>34):
+            maj_exemple_count=maj_exemple_count+1
+            predicted_class=np.argmax(a1_out)
+            if(predicted_class!=wanted_class):
+                maj_error_count=maj_error_count+1
+                
+        if(wanted_class>9):
+            char_exemple_count=char_exemple_count+1
+            predicted_class=np.argmax(a1_out)
+            if(predicted_class!=wanted_class):
+                char_error_count=char_error_count+1
+    
+    
+    #convert to float 
+    return ( total_exemple_count,nb_exemple_count,char_exemple_count,min_exemple_count,maj_exemple_count,\
+            total_error_count,nb_error_count,char_error_count,min_error_count,maj_error_count,\
+            total_error_count*100.0/total_exemple_count*1.0,\
+            nb_error_count*100.0/nb_exemple_count*1.0,\
+            char_error_count*100.0/char_exemple_count*1.0,\
+            min_error_count*100.0/min_exemple_count*1.0,\
+            maj_error_count*100.0/maj_exemple_count*1.0)
+            
+            
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+ 
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