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95 lines
2.8 KiB
95 lines
2.8 KiB
from __future__ import absolute_import
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import math
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class Histogram(object):
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def __init__(self, bin_scheme):
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self._hist = [0.0] * bin_scheme.bins
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self._count = 0.0
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self._bin_scheme = bin_scheme
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def record(self, value):
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self._hist[self._bin_scheme.to_bin(value)] += 1.0
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self._count += 1.0
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def value(self, quantile):
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if self._count == 0.0:
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return float('NaN')
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_sum = 0.0
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quant = float(quantile)
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for i, value in enumerate(self._hist[:-1]):
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_sum += value
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if _sum / self._count > quant:
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return self._bin_scheme.from_bin(i)
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return float('inf')
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@property
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def counts(self):
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return self._hist
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def clear(self):
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for i in range(self._hist):
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self._hist[i] = 0.0
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self._count = 0
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def __str__(self):
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values = ['%.10f:%.0f' % (self._bin_scheme.from_bin(i), value) for
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i, value in enumerate(self._hist[:-1])]
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values.append('%s:%s' % (float('inf'), self._hist[-1]))
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return '{%s}' % ','.join(values)
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class ConstantBinScheme(object):
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def __init__(self, bins, min_val, max_val):
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if bins < 2:
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raise ValueError('Must have at least 2 bins.')
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self._min = float(min_val)
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self._max = float(max_val)
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self._bins = int(bins)
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self._bucket_width = (max_val - min_val) / (bins - 2)
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@property
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def bins(self):
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return self._bins
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def from_bin(self, b):
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if b == 0:
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return float('-inf')
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elif b == self._bins - 1:
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return float('inf')
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else:
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return self._min + (b - 1) * self._bucket_width
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def to_bin(self, x):
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if x < self._min:
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return 0
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elif x > self._max:
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return self._bins - 1
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else:
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return int(((x - self._min) / self._bucket_width) + 1)
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class LinearBinScheme(object):
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def __init__(self, num_bins, max_val):
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self._bins = num_bins
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self._max = max_val
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self._scale = max_val / (num_bins * (num_bins - 1) / 2)
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@property
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def bins(self):
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return self._bins
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def from_bin(self, b):
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if b == self._bins - 1:
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return float('inf')
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else:
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unscaled = (b * (b + 1.0)) / 2.0
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return unscaled * self._scale
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def to_bin(self, x):
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if x < 0.0:
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raise ValueError('Values less than 0.0 not accepted.')
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elif x > self._max:
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return self._bins - 1
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else:
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scaled = x / self._scale
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return int(-0.5 + math.sqrt(2.0 * scaled + 0.25))
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