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gm.py
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gm.py
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import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
# 正向化
def forward(supplier):
supplier.neg_total_rate = 1 - supplier.neg_total_rate
supplier.neg_abs_rate = 1 - supplier.neg_abs_rate
supplier.p_value = 1 - supplier.p_value
# 预处理
def preProcessing(supplier):
mean = supplier.mean()
supplier /= mean
# 生成虚拟母序列
def genVirtualSeries(supplier):
return supplier.max(axis=1)
# 计算灰色关联系数
def getGcc(supplier, series, rou=0.5):
diff = supplier.sub(series, axis=0).abs()
a = diff.min(axis=0).min()
b = diff.max(axis=0).max()
gcc = ((a + rou * b) / (diff + rou * b)).mean(axis = 0)
return gcc
# 计算指标权重
def getWeight(gcc):
weight = gcc / gcc.sum()
return weight
# 计算得分
def getScores(supplier, weight):
scores = supplier.mul(weight, axis=1).sum(axis=1)
return scores
if __name__ == '__main__':
supplier = pd.read_csv('supplier.csv', header=0, index_col=0)
forward(supplier)
# preProcessing(supplier)
series = genVirtualSeries(supplier)
gcc = getGcc(supplier, series)
weight = getWeight(gcc)
print(weight)
scores = getScores(supplier, weight)
# 保存分数
scores.to_csv('scores.txt', index=False, sep='\t')