[84.15, 74.25, 97.2, 83.25, 102.6, 88.8, 77.55, 91.8, 68.45, 102.6, 86.4, 64.35, 81.6, 97.2, 81.0, 76.5, 83.25, 29.7, 93.6, 79.8]按照这组数据的概率分布采样10个数的具体步骤

可以使用随机数生成器来进行采样，具体步骤如下：

1. 计算数据的概率分布，即将每个数据除以所有数据的和，得到每个数据出现的概率。

2. 计算累积概率分布，即将每个数据的概率累加起来，得到每个数据出现的累积概率。

3. 生成10个随机数，每个随机数在到1之间。

4. 对于每个随机数，找到第一个累积概率大于等于该随机数的数据，即为采样结果。

5. 重复步骤4，直到生成10个采样结果。

注意：在步骤4中，可以使用二分查找等算法来加速查找过程。

相关问题

java建一个string类型的数组并填入数据：0.15,0.7,1.52,2.65,4.08,6.06,7.98,9.91,11.86,14.05,15.9,17.84,20.06,21.99,23.94,25.9,27.84,30.06,31.97,33.9,35.61,37.03,38.26,38.99,39.42,39.53,39.53,39.77,40.24,40.8,41.28,41.77,42.3,42.78,43.27,43.75,44.3,44.78,45.27,45.76,46.31,46.79,47.27,47.75,48.31,48.79,49.27,49.76,50.3,50.78,51.27,51.76,52.31,52.8,53.28,53.77,54.25,54.74,55.3,55.78,56.27,56.75,57.3,57.79,58.28,58.76,59.25,59.81,60.29,60.78,61.26,61.75,62.3,62.78,63.27,63.75,64.31,64.79,65.28,65.76,66.24,66.8,67.28,67.76,68.25,68.81,69.28,69.77,70.25,70.81,71.29,71.78,72.26,72.75,73.3,73.79,74.25,74.8,75.29,75.77,76.24,76.8,77.28,77.77,78.25,78.81,79.29,79.78,80.26,80.75,81.31,81.79,82.27,82.76,83.3,83.79,84.27,84.76,85.25,85.8,86.29,86.77,87.26,87.74,88.3,88.78,89.27,89.76,90.31,90.79,91.28,91.76,92.31,92.8,93.28,93.77,94.25,94.81,95.29,95.77,96.25,96.8,97.28,97.77,98.31,98.8,99.28,99.77,100.25,100.8,101.29,101.77,102.26,102.79,103.28,103.76,104.24,104.8,105.28,105.77,106.25,106.81,107.28,107.77,108.26,108.81,109.29,109.77,110.25,110.8,111.28,111.77,112.26,112.81,113.29,113.78,114.26,114.75,115.3,115.78,116.27,116.75,117.3,117.79,118.27,118.76,119.31,119.79,120.28,120.76,121.31,121.8,122.28,122.76,123.31,123.79,124.28,124.76,125.24,125.8,126.28,126.77,127.25,127.8,128.28,128.77,129.25,129.8,130.27,130.75,131.24,131.8,132.28,132.76,133.25,133.8,134.28,134.77,135.26,135.81,136.3,136.78,137.26,137.75,138.3,138.79,139.27,139.76,140.25,140.8,141.29,141.78,142.26,142.75,143.3,143.78,144.26,144.81,145.28,145.76,146.3,146.77,147.25,147.8,148.28,148.76,149.25,149.8,150.29,150.76,151.31,151.79,152.26,152.81,153.28,153.69,

String[] arr = {"0.15","0.7","1.52","2.65","4.08","6.06","7.98","9.91","11.86","14.05","15.9","17.84","20.06","21.99","23.94","25.9","27.84","30.06","31.97","33.9","35.61","37.03","38.26","38.99","39.42","39.53","39.53","39.77","40.24","40.8","41.28","41.77","42.3","42.78","43.27","43.75","44.3","44.78","45.27","45.76","46.31","46.79","47.27","47.75","48.31","48.79","49.27","49.76","50.3","50.78","51.27","51.76","52.31","52.8","53.28","53.77","54.25","54.74","55.3","55.78","56.27","56.75","57.3","57.79","58.28","58.76","59.25","59.81","60.29","60.78","61.26","61.75","62.3","62.78","63.27","63.75","64.31","64.79","65.28","65.76","66.24","66.8","67.28","67.76","68.25","68.81","69.28","69.77","70.25","70.81","71.29","71.78","72.26","72.75","73.3","73.79","74.25","74.8","75.29","75.77","76.24","76.8","77.28","77.77","78.25","78.81","79.29","79.78","80.26","80.75","81.31","81.79","82.27","82.76","83.3","83.79","84.27","84.76","85.25","85.8","86.29","86.77","87.26","87.74","88.3","88.78","89.27","89.76","90.31","90.79","91.28","91.76","92.31","92.8","93.28","93.77","94.25","94.81","95.29","95.77","96.25","96.8","97.28","97.77","98.31","98.8","99.28","99.77","100.25","100.8","101.29","101.77","102.26","102.79","103.28","103.76","104.24","104.8","105.28","105.77","106.25","106.81","107.28","107.77","108.26","108.81","109.29","109.77","110.25","110.8","111.28","111.77","112.26","112.81","113.29","113.78","114.26","114.75","115.3","115.78","116.27","116.75","117.3","117.79","118.27","118.76","119.31","119.79","120.28","120.76","121.31","121.8","122.28","122.76","123.31","123.79","124.28","124.76","125.24","125.8","126.28","126.77","127.25","127.8","128.28","128.77","129.25","129.8","130.27","130.75","131.24","131.8","132.28","132.76","133.25","133.8","134.28","134.77","135.26","135.81","136.3","136.78","137.26","137.75","138.3","138.79","139.27","139.76","140.25","140.8","141.29","141.78","142.26","142.75","143.3","143.78","144.26","144.81","145.28","145.76","146.3","146.77","147.25","147.8","148.28","148.76","149.25","149.8","150.29","150.76","151.31","151.79","152.26","152.81","153.28","153.69"};

pd.quantile

pd.quantile是Pandas库中的一个函数，用于计算给定数据集的分位数。该函数的语法如下：

pd.quantile(q, interpolation='linear')

其中，参数q是一个浮点数或一个list/array-like对象，表示要计算的分位数。interpolation参数是可选的，默认为'linear'，表示使用线性插值来计算分位数。

例如，假设有一个包含100个数值的Series对象s，我们想计算它的50%和75%分位数，可以使用pd.quantile函数：

import pandas as pd

s = pd.Series(range(100))
q1 = pd.quantile(s, q=0.5)
q2 = pd.quantile(s, q=0.75)

print(q1, q2)

输出结果将分别是49.5和74.25，即数据集中的中位数和75%分位数。