def predict(self, xs):
if self.bias:
xs = add_feature_bias(xs)
return np.rint(self.hfunc(xs, self.ws))
def train(self, xs, ys):
if self.bias:
xs = add_feature_bias(xs)
g = GradientDescentOptimizer(self.hfunc, self.costfunc, convergence=1e-6, \
learning_rate=0.001, regularization=self.l, max_iters=1e4)
self.ws = g.optimize(xs, ys)
