def test_step_funciton():
input_vec = np.array([-1, 0, 1])
output_vec = OutputFunciton.step_function(input_vec)
assert [0, 1, 1] == output_vec.tolist()
input_vec = np.array([-1, 0, 1])
output_vec = OutputFunciton.step_function(input_vec, bottom=-1)
assert [-1, 1, 1] == output_vec.tolist()
def test_probability_sigmoid_function():
input_vec = np.array([-100000])
output_vec = OutputFunciton.probability_sigmoid_function(input_vec)
assert [0] == output_vec.tolist()
input_vec = np.array([1])
output_vec = OutputFunciton.probability_sigmoid_function(input_vec)
assert [1] == output_vec.tolist()
def test_function_apply():
input_vec = np.array([0])
# step
output_vec = OutputFunciton.function_apply(input_vec, 0)
assert [1] == output_vec.tolist()
# sigmoid
output_vec = OutputFunciton.function_apply(input_vec, 1)
assert [0.5] == output_vec.tolist()
def get_updated_state(self, weight_mat, init_state, temperature=0.25):
updated_state = np.dot(weight_mat, init_state.T)
result = []
for d in updated_state:
# hopfieldからの違い => 確率素子に変更
#result.append(OutputFunciton.step_function(d, bottom=-1)[0])
result.append(OutputFunciton.probability_sigmoid_function(d, alpha=1/temperature)[0])
return np.array([result])
def get_updated_state(self, weight_mat, init_state):
# ここのみだとアソシアトロン
updated_state = np.dot(weight_mat, init_state.T)
# TODO : 1 or -1 を返すようにする.
result = []
for d in updated_state:
result.append(OutputFunciton.step_function(d, bottom=-1)[0])
return np.array([result])
def test_sigmoid_funciton():
input_vec = np.array([0])
output_vec = OutputFunciton.sigmoid_function(input_vec)
assert [0.5] == output_vec.tolist()
