继续统计算法,这次也没什么特别的,还没到那么深入,也是比较基础的
1、方差-样本
2、协方差(标准差)-样本
3、变异系数
4、相关系数
依然是先造个list,这次把这个功能写个函数,方便以后调用,另外上一篇写过的函数这次也会继承
def create_rand_list(min_num,max_num,count_list):
case_list = []
while len(case_list) < count_list:
rand_float = random.uniform(min_num,max_num)
if rand_float in case_list:
continue
case_list.append(rand_float)
case_list = [round(case,2) for case in case_list]
return case_list
下面是历史函数
sum_fun() #累加
len_fun() #统计个数
multiply_fun() #累乘
sum_mean_fun() #算数平均数
sum_mean_rate() #算数平均数计算回报
median_fun() #中位数
modes_fun() #众数
ext_minus_fun() #极差
geom_mean_fun() #几何平均数
geom_mean_rate() #几何平均回报
新函数代码
import random
先生成一个随机list,已有函数,不赘述
rand_list = [15.79, 6.83, 12.83, 22.32, 17.92, 6.29, 10.19, 10.13, 24.23, 25.56]
1、方差-样本S^2,list中的每个元素减整个list的平均数的平方累加,结果比个数-1,方差总量不-1
def var_fun(rand_list):
mean_num = sum_mean_fun(rand_list) #计算平均数 len_num = len_fun(rand_list) #计算总量 var_list = [(x-mean_num)**2 for x in rand_list] var_sum = sum_fun(var_list) var_num = var_sum/(len_num - 1) return var_num
2、协方差(标准差)-样本S,这个简单,用方差开平方就可以了
def covar_fun(rand_list):
var_num = var_fun(rand_list) covar_num = var_num ** 0.5 return covar_num
3、变异系数CV,变异程度度量,协方差/算数平均数*100%
说明(百度百科):在进行数据统计分析时,如果变异系数大于15%,则要考虑该数据可能不正常,应该剔除
def trans_coef_fun(rand_list):
covar_num = covar_fun(rand_list) mean_num = sum_mean_fun(rand_list) trans_coef_num = covar_num / mean_num return trans_coef_num
4、相关系数-样本r,表示两个维之间的线性关系,-1 < r < 1,越接近1关系维间的关系越强
因为是两个维,因此需要输入两维的list,算法比较麻烦
'''
((x1-mean(x))(y1-mean(y))+(x2-mean(x))(y2-mean(y))+...(xn-mean(x))(yn-mean(y)))
/((x1-mean(x))^2+(x2-mean(x))^2+...(xn-mean(x))^2)^0.5*((y1-mean(y))^2+(y2-mean(y))^2+...(yn-mean(y))^2)^0.5
'''
x_list = rand_list
y_list = [4.39, 13.84, 9.21, 9.91, 15.69, 14.92, 25.77, 23.99, 8.15, 25.07]
def pearson_fun(x_list,y_list):
x_mean = sum_mean_fun(x_list) y_mean = sum_mean_fun(y_list) len_num = len_fun(x_list) if len_num == len_fun(y_list): xy_multiply_list = [(x_list[i]-x_mean)*(y_list[i]-y_mean) for i in range(len_num)] xy_multiply_num = sum_fun(xy_multiply_list) else: print 'input list wrong,another input try' return None x_covar_son_list = [(x-x_mean)**2 for x in x_list] y_covar_son_list = [(y-y_mean)**2 for y in y_list] x_covar_son_num = sum_fun(x_covar_son_list) y_covar_son_num = sum_fun(y_covar_son_list) xy_covar_son_multiply_num = (x_covar_son_num ** 0.5) * (y_covar_son_num ** 0.5) pearson_num = xy_multiply_num / xy_covar_son_multiply_num return pearson_num
Original: https://www.cnblogs.com/xiu123/p/9420799.html
Author: 咻_python
Title: 统计算法_数值/线性关系度量
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