记录一次数据建模的过程:背景是根据5名评委的匿名打分的加权结果反推出各自打分情况。
数据集:
数据集详细描述:
建模:
1、令同一评委的打分方差最小:
2、令所有评委对同一对象的打分方差最小:
3、加权求最优解:
# coding:utf-8
import numpy as np
from sklearn import tree
p = [100,97,94,91]
score = [97.6, 97.6, 97.3, 99.4, 96.1, 97.6, 99.4, 96.7, 97.6, 96.1, 98.2, 96.55]
# [hong,xiong,zhuang,wang,zhang]
alpha = [[0.4, 0.1, 0.1, 0.3, 0.1],# MM
[0.4, 0.1, 0.3, 0.1, 0.1],# RCS
[0.4, 0.1, 0.1, 0.3, 0.1],# Cloud
[0.4, 0.1, 0.3, 0.1, 0.1],# Platform
[0.4, 0.1, 0.1, 0.3, 0.1],# Market
[0.4, 0.3, 0.1, 0.1, 0.1],# RD
[0.4, 0.1, 0.3, 0.1, 0.1],# System
[0.4, 0.3, 0.1, 0.1, 0.1],# Quality
[0.4, 0.1, 0.1, 0.3, 0.1],# General
[0.4, 0.3, 0.1, 0.1, 0.1],# Finance
[0.4, 0.15, 0.15, 0.15, 0.15],# HR
[0.4, 0.15, 0.15, 0.15, 0.15]# Party
]
get_raw_scores = lambda p,score,alpha: [[i+1,score[i],[x for x in [[x1,x2,x3,x4,x5]
for x1 in p for x2 in p for x3 in p for x4 in p for x5 in p]
if score[i] == np.dot(alpha[i],x)]]
for i in range(len(score))]
def make_X(X, x, raw_scores, i):
x_i = raw_scores[i][2]
if i == len(raw_scores) - 1:
for x_i_j in x_i:
new_x = x + [x_i_j]
X.append(new_x)
else:
for x_i_j in x_i:
new_x = x + [x_i_j]
make_X(X, new_x, raw_scores, i+1)
def get_y1(raw_scores,X_set):
y1_set = []
for X in X_set:
X1 = np.array(X)
XT = X1.T
x_j = []
y1 = 0
for XT_j in XT:
x_j.append(np.average(XT_j))
for j in range(5):
y1_j = 0
for i in range(12):
y1_j += np.power((X[i][j] - x_j[j]),2) / 12
y1 += y1_j
y1_set.append(y1)
return y1_set
def get_y2(raw_scores,X_set):
y2_set = []
for X in X_set:
y2 = 0
for i in range(12):
y2_i = 0
x_i = np.average(X[i])
for j in range(5):
y2_i += np.power((X[i][j] - x_i,2) / 5
y2 += y2_i
y2_set.append(y2)
return y2_set
raw_scores = get_raw_scores(p, score, alpha)
# X_set = []
# make_X(X_set, [], raw_scores, 0)