def _train(self, t):
sample = self._samples[t]
# bmu
bmu_id = self._bmu(sample)
bmu_position = self._positions[bmu_id]
# update weight
D = normVectors(self._positions - bmu_position)
L = self._learningRestraint(t)
T = self._neighborhoodFunction(t, D)
# update nodes
for ci in range(3):
self._nodes[:, ci] += L * T * (sample[ci] - self._nodes[:, ci])
def _train(self, t):
sample = self._samples[t % len(self._samples)]
# print sample
# bmu
bmu_id = self._bmu(sample)
bmu_position = self._positions[bmu_id]
#在一维度情况下 bmu_id 和 bmu_position相等
# update weight
D = normVectors(self._positions - bmu_position)
# L是一个从0.16逐渐减小的数值
L = self._learningRestraint(t)
# T是一个根据D计算出来的影响范围
T = self._neighborhoodFunction(t, D)
# update nodes
# print self._nodes
for ci in range(3):
self._nodes[:, ci] += L * T * (sample[ci] - self._nodes[:, ci])
def _bmu(self, sample):
norms = normVectors(self._nodes - sample)
bmu_id = np.argmin(norms)
return bmu_id
def _bmu(self, sample):
norms = normVectors(self._nodes - sample)
bmu_id = np.argmin(norms)
return bmu_id
