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def _isotonic_regression(np.ndarray[DOUBLE, ndim=1] y, | |
np.ndarray[DOUBLE, ndim=1] weight, | |
np.ndarray[DOUBLE, ndim=1] solution): | |
cdef: | |
Py_ssize_t current, i | |
unsigned int len_active_set | |
DOUBLE v, w | |
len_active_set = y.shape[0] | |
active_set = [[weight[i] * y[i], weight[i], [i, ]] | |
for i in range(len_active_set)] | |
current = 0 | |
while current < len_active_set - 1: | |
while current < len_active_set -1 and \ | |
(active_set[current][0] * active_set[current + 1][1] <= | |
active_set[current][1] * active_set[current + 1][0]): | |
current += 1 | |
if current == len_active_set - 1: | |
break | |
# merge two groups | |
active_set[current][0] += active_set[current + 1][0] | |
active_set[current][1] += active_set[current + 1][1] | |
active_set[current][2] += active_set[current + 1][2] | |
active_set.pop(current + 1) | |
len_active_set -= 1 | |
while current > 0 and \ | |
(active_set[current - 1][0] * active_set[current][1] > | |
active_set[current - 1][1] * active_set[current][0]): | |
current -= 1 | |
active_set[current][0] += active_set[current + 1][0] | |
active_set[current][1] += active_set[current + 1][1] | |
active_set[current][2] += active_set[current + 1][2] | |
active_set.pop(current + 1) | |
len_active_set -= 1 | |
for v, w, idx in active_set: | |
solution[idx] = v / w | |
return solution |
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