인류의 복지와 편익을 위한 인프라 건설을 주도하는토목공학과
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(3학년)인공지능개발실습 기말평가 주요 코딩 (박영훈 교수)
작성일
2023.06.08
작성자
부천대학교 토목공학과
#1번
install.packages("MVA")
library(MVA)
company=read.csv("company.csv")
head(company)
company$불량률=with(company, max(불량률)-불량률)
company$불량률
head(company)
install.packages("psych")
library(psych)
pairs.panels(company)
h_pca=prcomp(company[ ,2:5], scale=TRUE)
h_pca
summary(h_pca)
plot(h_pca, type='l')
summary(h_pca)
plot(h_pca$x[,1],company$최종평가)
cor(h_pca$x[,1],company$최종평가)
biplot(h_pca, choices=c(1,2))
#2번
company=read.csv("company.csv")
head(company)
company=company[ , 2:5]
head(company)
hc1=hclust(dist(company,method="euclidean"),method="complete")
plot(hc1)
hc2=hclust(dist(company,method="euclidean"),method="single")
plot(hc2)
install.packages("mclust")
library(mclust)
company=scale(company)
mc=Mclust(company)
summary(mc)
# 3번 코딩
install.packages("neuralnet")
library(neuralnet)
set.seed(2021)
#수정
attribute=as.data.frame(sample(seq(-5,5,length=50),50, replace=FALSE), ncol=1)
response=(attribute^2)+65
data=cbind(attribute, response)
colnames(data)=c("attribute", "response")
plot(data$attribute,data$response )
fit=neuralnet(response~attribute, data=data, hidden = c(5,5,5,5,5,5,5), threshold=0.01)
plot(fit)
testdata=as.matrix(sample(seq(-5,5,length=25), 25, replace=FALSE), ncol=1)
pred=compute(fit, testdata)
result=cbind(testdata, pred$net.result, (testdata^2)+65)
colnames(result)=c("Attribute", "Prediction", "Actual")
result
round(result,4)
install.packages("Metrics")
library(Metrics)
str(result)
result_frame=as.data.frame(result)
str(result_frame)
mae(result_frame$Prediction, result_frame$Actual)
mape(result_frame$Prediction, result_frame$Actual)
mse(result_frame$Prediction, result_frame$Actual)
rmse(result_frame$Prediction, result_frame$Actual)