下面是我尝试为“Smoothed z-score算法”创建一个Ruby解决方案:
module ThresholdingAlgoMixin
def mean(array)
array.reduce(&:+) / array.size.to_f
end
def stddev(array)
array_mean = mean(array)
Math.sqrt(array.reduce(0.0) { |a, b| a.to_f + ((b.to_f - array_mean) ** 2) } / array.size.to_f)
end
def thresholding_algo(lag: 5, threshold: 3.5, influence: 0.5)
return nil if size < lag * 2
Array.new(size, 0).tap do |signals|
filtered = Array.new(self)
initial_slice = take(lag)
avg_filter = Array.new(lag - 1, 0.0) + [mean(initial_slice)]
std_filter = Array.new(lag - 1, 0.0) + [stddev(initial_slice)]
(lag..size-1).each do |idx|
prev = idx - 1
if (fetch(idx) - avg_filter[prev]).abs > threshold * std_filter[prev]
signals[idx] = fetch(idx) > avg_filter[prev] ? 1 : -1
filtered[idx] = (influence * fetch(idx)) + ((1-influence) * filtered[prev])
end
filtered_slice = filtered[idx-lag..prev]
avg_filter[idx] = mean(filtered_slice)
std_filter[idx] = stddev(filtered_slice)
end
end
end
end
以及示例用法:
test_data = [
1, 1, 1.1, 1, 0.9, 1, 1, 1.1, 1, 0.9, 1, 1.1, 1, 1, 0.9, 1,
1, 1.1, 1, 1, 1, 1, 1.1, 0.9, 1, 1.1, 1, 1, 0.9, 1, 1.1, 1,
1, 1.1, 1, 0.8, 0.9, 1, 1.2, 0.9, 1, 1, 1.1, 1.2, 1, 1.5,
1, 3, 2, 5, 3, 2, 1, 1, 1, 0.9, 1, 1, 3, 2.6, 4, 3, 3.2, 2,
1, 1, 0.8, 4, 4, 2, 2.5, 1, 1, 1
].extend(ThresholdingAlgoMixin)
puts test_data.thresholding_algo.inspect
# Output: [
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1,
# 1, 1, 0, 0, 0, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0
# ]
