def test_array_gen(self):
for args, expected in zip(self.shapes, self.sequences):
data = array_gen(args)
self.assertEqual(data, expected)
with_kwargs = [({
"shape": (2, 1, 1),
"v": ['hail', 'pythonics!']
}, [[['hail']], [['pythonics!']]])]
for kwargs, expecteds in with_kwargs:
result = array_gen(**kwargs)
self.assertEqual(result, expecteds)
def __init__(self, input_size: int, output_size: int):
self.weights = Matrix(array_gen((output_size, input_size), 0.0))
def update(self, rollouts, sigma_r):
step = Matrix(array_gen(self.weights.shape, 0.0))
for r_pos, r_neg, d in rollouts:
step += (r_pos - r_neg) * d
self.weights += hp.learning_rate / (hp.n_best_directions *
sigma_r) * step
def __init__(self, n_inputs):
self.n = Matrix(array_gen((n_inputs, ), 0.0))
self.mean = Matrix(array_gen((n_inputs, ), 0.0))
self.mean_diff = Matrix(array_gen((n_inputs, ), 0.0))
self.var = Matrix(array_gen((n_inputs, ), 0.0))