StatRed ass3: Started work on part 3.

statred/ass3/classifiers.py

 from pylab import argmin, argmax, tile, unique, argwhere, array, mean, \
         newaxis, dot, pi, e, matrix
+from svm import svm_model, svm_problem, svm_parameter, LINEAR
 
 class NNb:
     def __init__(self, X, c):
@@ -42,8 +43,8 @@ class MEC:
         self.estimate()
 
     def estimate(self):
-        """Estimate the mean and covariance matrix each class in the learning
-        set."""
+        """Estimate the mean and covariance matrix for each class in the
+        learning set."""
         self.class_data = []
         for c in self.classes:
             indices = argwhere(array(map(lambda x: x if x == c else 0,
@@ -56,16 +57,17 @@ class MEC:
             self.class_data.append((mu, S, coeff))
 
     def classify(self, x):
+        """Use the sum of all entries in the pdf matrix to determine the class
+        with the greatest probability."""
         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
+        px = svm_problem(c.tolist(), X.T)
+        pm = svm_parameter(kernel_type=LINEAR)
+        self.model = svm_model(px, pm)
 
     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)]
+        return self.model.predict(x)