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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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