1 files changed, 0 insertions, 70 deletions
diff --git a/data/class.py b/data/class.py
deleted file mode 100755
index e7975f9..0000000
--- a/data/class.py
+++ /dev/null
@@ -1,70 +0,0 @@
-#! /usr/bin/python
-import copy
-import sys
-from svmutil import *
-
-lower = 0.1
-upper = 10
-
-def normalize_instances(instances, ranges = None) :
-    normalized_instances = copy.deepcopy(instances)
-    if ranges == None :
-        ranges_dict = dict()
-    for attribute in normalized_instances[0].keys() :  # we iterate on the attributes
-        column = [instance[attribute] for instance in normalized_instances]
-        if ranges != None :
-            minimum = ranges[attribute][0]
-            maximum = ranges[attribute][1]
-        else :
-            minimum = min(column)
-            maximum = max(column)
-            ranges_dict[attribute] = [minimum, maximum]
-        for i in range(len(column)) :
-            if column[i] == minimum :
-                column[i] = lower
-            elif column[i] == maximum :
-                column[i] = upper
-            else :
-                column[i] = lower + (upper-lower) * (column[i] - minimum) / (maximum - minimum)
-        # Copying normalized values in memory
-      
-        for elem, instance in zip(column, normalized_instances):
-            instance[attribute] = elem
-  
-    if ranges == None :
-        return normalized_instances, ranges_dict
-    else :
-        return normalized_instances
-
-
-def read_file(filename) :
-    y = []
-    x = []
-    for line in filename:
-        values = line.rstrip().split(',')
-        if values[0] != "# dir":
-            dict = {}
-            for i in range(9):
-                if float(values[i+5])!=-1.:
-                    dict[i+1] = float(values[i+5])
-            if len(dict)==9:
-                y += [int(values[1])]
-                x += [dict]
-                print line.rstrip()
-    #for a,b in zip(y,x):
-    #    result = str(a)
-    #    for i in range(9):
-    #        result += " "+str(i+1)+":"+str(b[i+1])
-    #    print result
-    #return (y,x)
-
-train_filename = sys.argv[1]
-#test_filename = sys.argv[2]
-y1,x1 = read_file(open(train_filename))
-#x1,ranges = normalize_instances(x1)
-#print ranges
-#exit(0)
-#model = svm_train(y1,x1)
-#y2,x2 = read_file(open(test_filename))
-#x2 = normalize_instances(x2,ranges)
-#p_labels,p_acc,p_vals = svm_predict(y2,x2,model)