本文整理汇总了Python中minisom.MiniSom.activation_response方法的典型用法代码示例。如果您正苦于以下问题：Python MiniSom.activation_response方法的具体用法？Python MiniSom.activation_response怎么用？Python MiniSom.activation_response使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类minisom.MiniSom的用法示例。
在下文中一共展示了MiniSom.activation_response方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: setUp
# 需要导入模块: from minisom import MiniSom [as 别名]
# 或者: from minisom.MiniSom import activation_response [as 别名]
class TestMinisom:
def setUp(self):
self.som = MiniSom(5, 5, 1)
for w in self.som.weights: # checking weights normalization
assert_almost_equal(1.0, np.linalg.norm(w))
self.som.weights = np.zeros((5, 5)) # fake weights
self.som.weights[2, 3] = 5.0
self.som.weights[1, 1] = 2.0
def test_fast_norm(self):
assert minisom.fast_norm(np.array([1, 3])) == sqrt(1 + 9)
def test_gaussian(self):
bell = minisom.gaussian((2, 2), 1, self.som.neigx, self.som.neigy)
assert bell.max() == 1.0
assert bell.argmax() == 12 # unravel(12) = (2,2)
def test_win_map(self):
winners = self.som.win_map([5.0, 2.0])
assert winners[(2, 3)][0] == 5.0
assert winners[(1, 1)][0] == 2.0
def test_activation_reponse(self):
response = self.som.activation_response([5.0, 2.0])
assert response[2, 3] == 1
assert response[1, 1] == 1
def test_activate(self):
assert self.som.activate(5.0).argmin() == 13.0 # unravel(13) = (2,3)
def test_quantization_error(self):
self.som.quantization_error([5, 2]) == 0.0
self.som.quantization_error([4, 1]) == 0.5
def test_quantization(self):
q = self.som.quantization(np.array([4, 2]))
assert q[0] == 5.0
assert q[1] == 2.0
# def test_random_seed(self):
# som1 = MiniSom(5, 5, 2, sigma=1.0, learning_rate=0.5, random_seed=1)
# som2 = MiniSom(5, 5, 2, sigma=1.0, learning_rate=0.5, random_seed=1)
# # same initialization
# assert_array_almost_equal(som1.weights, som2.weights)
# data = np.random.rand(100, 2)
# som1 = MiniSom(5, 5, 2, sigma=1.0, learning_rate=0.5, random_seed=1)
# som1.train_random(data, 10)
# som2 = MiniSom(5, 5, 2, sigma=1.0, learning_rate=0.5, random_seed=1)
# som2.train_random(data, 10)
# # same state after training
# assert_array_almost_equal(som1.weights, som2.weights)
def test_train_batch(self):
som = MiniSom(5, 5, 2, sigma=1.0, learning_rate=0.5, random_seed=1)
data = np.array([[4, 2], [3, 1]])
q1 = som.quantization_error(data)
som.train_batch(data, 10)
assert q1 > som.quantization_error(data)
# def test_train_random(self):
# som = MiniSom(5, 5, 2, sigma=1.0, learning_rate=0.5, random_seed=1)
# data = np.array([[4, 2], [3, 1]])
# q1 = som.quantization_error(data)
# som.train_random(data, 10)
# assert q1 > som.quantization_error(data)
def test_random_weights_init(self):
som = MiniSom(2, 2, 2, sigma=0.1, random_seed=1)
som.random_weights_init(np.array([[1.0, .0]]))
for w in som.weights:
assert_array_equal(w[0], np.array([1.0, .0]))