StatRed ass3: Bug fix.

statred/ass3/classifiers.py

@@ -59,3 +59,13 @@ class MEC:
         p = [coeff * e**(-.5 * dot(x - mu, dot(S.I, array([x -
             mu]).T)).tolist()[0][0]) for mu, S, coeff in self.class_data]
         return self.classes[argmax([i.sum() for i in p])]
+
+class SVM:
+    def __init__(self, X, c):
+        self.n, self.N = X.shape
+        self.X, self.c = X, c
+
+    def classify(self, x):
+        d = self.X - tile(x.reshape(self.n, 1), self.N);
+        dsq = sum(d*d, 0)
+        return self.c[argmin(dsq)]

statred/ass3/classify.py

-from pylab import loadtxt, arange, loadtxt, permutation, transpose,\
-    zeros, sum, plot, subplot, array, scatter, logical_and, figure,\
-    savefig, seed
+from pylab import loadtxt, arange, loadtxt, permutation, zeros, sum, \
+    plot, subplot, array, scatter, logical_and, figure, savefig, seed
 from sys import argv, exit
 import classifiers
 
@@ -17,7 +16,7 @@ if method == 'knnb':
     k = int(argv[2])
     if argc == 4:
         seed(int(argv[3]))
-elif method not in ['nnb', 'mec']:
+elif method not in ['nnb', 'mec', 'svm']:
     print 'Unknown classification method "%s"' % argv[1]
     exit()
 elif argc == 3:
@@ -38,15 +37,11 @@ L = ind[0:90] # learning set indices
 T = ind[90:] # test set indices
 
 # Learning set
-X = XC[L,0:4]
-args = [X, XC[L,-1]]
-if method == 'nnb':
-    method_class = classifiers.NNb
-elif method == 'knnb':
-    method_class = classifiers.kNNb
+args = [XC[L,0:4].T, XC[L,-1]]
+if method == 'knnb':
     args.append(k)
-elif method == 'mec':
-    method_class = classifiers.MEC
+method_class = {'nnb': classifiers.NNb, 'knnb': classifiers.kNNb,
+        'mec': classifiers.MEC, 'svm': classifiers.SVM}[method]
 classifier = method_class(*args)
 
 # Classification of test set