def test_univariate_scalar_adagrad_optimization(self):
def objective_func(x):
return x * np.log(x)
var_init = np.array([2])
optimizer = Optimizer(objective_func, var_init)
min_value, var_value = optimizer.adagrad_optimize(
tolerance=None, num_iterations=100000)
self.assertAlmostEqual(min_value, -1 / np.e, places=3)
self.assertAlmostEqual(var_value[0], 1 / np.e, places=3)
def test_multivariate_vector_adagrad_optimization(self):
def objective_func(x):
return x[0]**2 + x[0] * x[1] + x[1]**2
var_init = np.array([0.2, 0.5])
optimizer = Optimizer(objective_func, var_init, scalar=False)
min_value, var_value = optimizer.adagrad_optimize(tolerance=None,
num_iterations=10000)
self.assertAlmostEqual(min_value, 0, places=4)
self.assertAlmostEqual(var_value[0], 0, places=4)
self.assertAlmostEqual(var_value[1], 0, places=4)