(self, X)
| 32 | return np.mean(P == Y) |
| 33 | |
| 34 | def predict(self, X): |
| 35 | N, D = X.shape |
| 36 | K = len(self.gaussians) |
| 37 | P = np.zeros((N, K)) |
| 38 | for c, g in iteritems(self.gaussians): |
| 39 | mean, var = g['mean'], g['var'] |
| 40 | P[:,c] = mvn.logpdf(X, mean=mean, cov=var) + np.log(self.priors[c]) |
| 41 | return np.argmax(P, axis=1) |
| 42 | |
| 43 | |
| 44 | if __name__ == '__main__': |