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Product
Product features · Unweighted isotonic regression
01
Inspect each pooled block beside the observation sequence so a flattened segment can be explained by its least-squares constraint.
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A model available through the configured endpoint.
Capabilities
Context limits and tool support are determined by the model and server.
Unweighted isotonic regression
● ● ●OrderSaffron●
A concrete starting point · Unweighted isotonic regression ×
from math import isfinite
def isotonic_fit(values):
if type(values) is not list or len(values) > 128:
raise ValueError("bounded list")
if any(type(x) not in (int, float) or abs(x) > 10**6 or
not isfinite(x) for x in values):
raise ValueError("bounded finite values")
blocks = []
for value in values:
blocks.append([float(value), 1])
while len(blocks) > 1 and (
blocks[-2][0] / blocks[-2][1] > blocks[-1][0] / blocks[-1][1]):
total, count = blocks.pop()
blocks[-1][0] += total; blocks[-1][1] += count
return [total / count for total, count in blocks for _ in range(count)]✳Fit a nondecreasing sequence to at most 128 finite observations bounded by ±10^6 with equal least-squares weights.
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