Method fit

unsupervised_class2/gaussian_nb.py:19–29

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

class GaussianNB(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)