def _average(self):
sum_xi = None
for v in self._xi:
if not sum_xi:
sum_xi = [0.0] * len(v)
sum_xi = add(sum_xi, v)
average = div(sum_xi, len(self._xi))
return average
def _weightedAverage(values, weight):
sum_values = None
for v in values:
if not sum_values:
sum_values = [0.0] * len(v)
sum_values = add(sum_values, v)
weighted_average = div(sum_values, weight)
return weighted_average
def compute(self):
init_yi = self._average()
yi = init_yi
converged = False
it = 0
while not converged:
diffi = [sub(xj, yi) for xj in self._xi]
normi = [sqrt(dot(di, di)) for di in diffi]
weight = sum([1 / ni for ni in normi])
val = [div(self._xi[i], normi[i]) for i in range(len(self._xi))]
yip1 = _weightedAverage(val, weight)
diff = sub(yip1, yi)
yi = yip1
it += 1
# print(it)
if sqrt(dot(diff, diff)) < self._eps:
converged = True
return yi
