求解代碼

python import pandas as pdimport numpy as npimport prop lot as ppltimport seaborn as snsfrom scipy.stats import pearsonrimport matplotlib.pyplot as pltfrom matplotlib.ticker import FormatStrFormatter

import numpy as np# 導(dǎo)入銷(xiāo)量數(shù)據(jù)

X = pd.read_excel('附件 2.xlsx')# 計(jì)算協(xié)方差矩陣

S = np.cov(X)

# 計(jì)算相關(guān)系數(shù)矩陣

R = np.corrcoef(X)

# 分析相關(guān)系數(shù)大小判斷相關(guān)性

from scipy.stats import pearsonr

pvals = heatmap_data.corr (method=lambda x, y: pearsonr (x, y)[1]) -
np.eye(len (heatmap_data.columns))

#轉(zhuǎn)換 P 值為星號(hào) def convert_pvalue_to_asterisks(pvalue) :

if pvalue <= 0.001:

return "***"

elif pvalue <= 0.01:

return "**"

elif pvalue <= 0.05:

return "*"

return ""

pval_star = pvals.applymap (lambda x :convert_pvalue_to_asterisks(x))

# 轉(zhuǎn)換成 numpy 類(lèi)型

corr_star_annot = pval_star.to_numpy ()

# a）seaborn 相關(guān)性矩陣熱力圖 p 值繪制示例一

import matplotlib.pyplot as pltfrom colormaps import parulafrom matplotlib.ticker import FormatStrFormatter
fig,ax = plt.subplots(fig size= (4,3.5),dpi=100,facecolor="w")

sns.heatmap (heatmap_data.corr (),annot=corr_star_annot,fmt='',cmap=parula,vmin=-1, vmax=1,

annot_kws= {"size":13,"fontweight":"bold"},linecolor="k",linewidths=.2
,

cbar_kws= {"aspect":13},ax=ax)

ax.tick_params(bottom=False,

labelbottom=True,labeltop=False,left=False,pad=1,labelsize=12)

ax.yaxis.set_tick_params(labelrotation=0)# 使用 matplotlib.colorbar.Colorbar object

cbar = ax.collections[0].colorbar #分享群：810589811

cbar.ax.tick_params(direction="in",width=.5,labelsize=10)

cbar.ax.yaxis.set_major_formatter (FormatStrFormatter ('%.2f'))

cbar.outline.set_visible(True)

cbar.outline.set_linewidth(.5)

問(wèn)題二求解代碼

python
from pulp import *
#  構(gòu)建模型
model = LpProblem()


#  定義決策變量

x = {}
p = {}
for i in brands:
for j in days:
x[i,j] = LpVariable()
p[i,j] = LpVariable()


#  添加目標(biāo)函數(shù)和約束條件
model += lpSum((p[i,j] - c [i])*x[i,j]) #  最大化收益


for i in brands:
for j in days:
model += a [i] - b[i]*p[i,j] <= x[i,j] <= a [i] + b[i]*p[i,j] #  銷(xiāo)量與價(jià)格約束
python
#  在問(wèn)題 2 代碼基礎(chǔ)上添加:


#  定義單品售賣(mài)變量 y
y = {}
for i in products:
for j in days:
y[i,j] = LpVariable(cat='Binary')


#  添加單品數(shù)量限制
model += lpSum(y[i,j] for i in products for j in days) >= 27
model += lpSum(y[i,j] for i in products for j in days) <= 33


#  添加最小展示量限制
for i in products:
for j in days:
model  +=  x[i,j]>=2.5  *y[i,j

問(wèn)題三?求解代碼

python
#  在問(wèn)題 2 代碼基礎(chǔ)上添加:
#  定義單品售賣(mài)變量 y
y = {}
for i in products:

for j in days:
y[i,j] = LpVariable(cat='Binary')
#  添加單品數(shù)量限制
model += lpSum(y[i,j] for i in products for j in days) >= 27
model += lpSum(y[i,j] for i in products for j in days) <= 33
#  添加最小展示量限制
for i in products:
for j in days:
model += x[i,j] >= 2.5 * y[i,j]

問(wèn)題四 語(yǔ)文建模

到了這里，關(guān)于2023高教社數(shù)學(xué)建模國(guó)賽C題 - 蔬菜類(lèi)商品的自動(dòng)定價(jià)與補(bǔ)貨決策【思路分析（1）+代碼】的文章就介紹完了。如果您還想了解更多內(nèi)容，請(qǐng)?jiān)谟疑辖撬阉鱐OY模板網(wǎng)以前的文章或繼續(xù)瀏覽下面的相關(guān)文章，希望大家以后多多支持TOY模板網(wǎng)！