本文整理汇总了Python中bintrees.FastRBTree.prev_item方法的典型用法代码示例。如果您正苦于以下问题：Python FastRBTree.prev_item方法的具体用法？Python FastRBTree.prev_item怎么用？Python FastRBTree.prev_item使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类bintrees.FastRBTree的用法示例。
在下文中一共展示了FastRBTree.prev_item方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: TDigest
# 需要导入模块: from bintrees import FastRBTree [as 别名]
# 或者: from bintrees.FastRBTree import prev_item [as 别名]
#.........这里部分代码省略.........
def batch_update(self, values, w=1):
"""
Update the t-digest with an iterable of values. This assumes all points have the
same weight.
"""
for x in values:
self.update(x, w)
self.compress()
return
def compress(self):
T = TDigest(self.delta, self.K)
C = list(self.C.values())
shuffle(C)
for c_i in C:
T.update(c_i.mean, c_i.count)
self.C = T.C
def percentile(self, q):
"""
Computes the percentile of a specific value in [0,1], ie. computes F^{-1}(q) where F^{-1} denotes
the inverse CDF of the distribution.
"""
if not (0 <= q <= 1):
raise ValueError("q must be between 0 and 1, inclusive.")
t = 0
q *= self.n
for i, key in enumerate(self.C.keys()):
c_i = self.C[key]
k = c_i.count
if q < t + k:
if i == 0:
return c_i.mean
elif i == len(self) - 1:
return c_i.mean
else:
delta = (self.C.succ_item(key)[1].mean - self.C.prev_item(key)[1].mean) / 2.
return c_i.mean + ((q - t) / k - 0.5) * delta
t += k
return self.C.max_item()[1].mean
def quantile(self, q):
"""
Computes the quantile of a specific value, ie. computes F(q) where F denotes
the CDF of the distribution.
"""
t = 0
N = float(self.n)
for i, key in enumerate(self.C.keys()):
c_i = self.C[key]
if i == len(self) - 1:
delta = (c_i.mean - self.C.prev_item(key)[1].mean) / 2.
else:
delta = (self.C.succ_item(key)[1].mean - c_i.mean) / 2.
z = max(-1, (q - c_i.mean) / delta)
if z < 1:
return t / N + c_i.count / N * (z + 1) / 2
t += c_i.count
return 1
def trimmed_mean(self, q1, q2):
"""
Computes the mean of the distribution between the two percentiles q1 and q2.
This is a modified algorithm than the one presented in the original t-Digest paper.
"""
if not (q1 < q2):
raise ValueError("q must be between 0 and 1, inclusive.")
s = k = t = 0
q1 *= self.n
q2 *= self.n
for i, key in enumerate(self.C.keys()):
c_i = self.C[key]
k_i = c_i.count
if q1 < t + k_i:
if i == 0:
delta = self.C.succ_item(key)[1].mean - c_i.mean
elif i == len(self) - 1:
delta = c_i.mean - self.C.prev_item(key)[1].mean
else:
delta = (self.C.succ_item(key)[1].mean - self.C.prev_item(key)[1].mean) / 2.
nu = ((q1 - t) / k_i - 0.5) * delta
s += nu * k_i * c_i.mean
k += nu * k_i
if q2 < t + k_i:
return s/k
t += k_i
return s/k