Method fit

supervised_class/nb.py:18–28

(self, X, Y, smoothing=1e-2)

class NaiveBayes(object):
    def fit(self, X, Y, smoothing=1e-2):
        self.gaussians = dict()
        self.priors = dict()
        labels = set(Y)
        for c in labels:
            current_x = X[Y == c]
            self.gaussians[c] = {
                'mean': current_x.mean(axis=0),
                'var': current_x.var(axis=0) + smoothing,
            }
            self.priors[c] = float(len(Y[Y == c])) / len(Y)

    def score(self, X, Y):
        P = self.predict(X)