def test_weighted_avg_price_is_correct_with_same_weights(self):
np.random.seed(0)
num_samples = 16
num_underlyings = 7
prices = np.random.uniform(size=[num_samples, num_underlyings]).astype(
np.float32)
weighted_avg_prices = payoffs.weighted_avg_price(prices)
with self.session() as session:
weighted_avg_prices_eval = session.run(weighted_avg_prices)
self.assertAllClose(weighted_avg_prices_eval, np.mean(prices, axis=-1))
def test_weighted_avg_price_is_correct_with_specified_weights(self):
np.random.seed(0)
num_samples = 16
num_underlyings = 7
prices = np.random.uniform(size=[num_samples, num_underlyings]).astype(
np.float32)
weights = np.random.uniform(size=[num_underlyings]).astype(np.float32)
weighted_avg_prices = payoffs.weighted_avg_price(prices, weights)
with self.session() as session:
weighted_avg_prices_eval = session.run(weighted_avg_prices)
normalized_weights = np.expand_dims(weights / np.sum(weights), 0)
self.assertAllClose(weighted_avg_prices_eval,
np.sum(prices * normalized_weights, axis=-1))