선형 회귀분석 Linear regression (ver.R)

출처

http://jorditorres.org/first-contact-with-tensorflow/#cap2 (First contact with tensorflow)

https://tensorflow.rstudio.com/ (Tensorflow™ for R)

https://github.com/rstudio/tensorflow (GitHub - rstudio/tensorflow)

http://motioninsocial.com/tufte/ (Tufte in R)

http://www.cookbook-r.com/Graphs/Multiple_graphs_on_one_page_(ggplot2) (Cookbook for R)

좌표 값 생성

num_points <- 1000

x_data <- rnorm(num_points, 0, 0.55)
y_data <- 0.3 + 0.1 * x_data + rnorm(num_points,0,0.03)

plot(y_data~x_data)

require(ggplot2);require(ggthemes)

## Loading required package: ggplot2

## Loading required package: ggthemes

ggplot(data.frame(x_data,y_data),aes(x_data,y_data)) + theme_tufte() + geom_point(size=2.5,alpha=.3)

변수, loss, 최적화 알고리즘 설정.

require(tensorflow)

## Loading required package: tensorflow

# 변수 설정
W <- tf$Variable(tf$random_uniform(shape(1L), -1.0, 1.0))
b <- tf$Variable(tf$zeros(shape(1L)))
y <- W * x_data + b

# loss function 설정 (MSE : Mean Squared Error)
loss <- tf$reduce_mean(tf$square(y - y_data))

# 최적화 알고리즘 설정 (경사하강법)
optimizer <- tf$train$GradientDescentOptimizer(0.5)
train <- optimizer$minimize(loss)

알고리즘 실행

# 변수 초기화
init <- tf$initialize_all_variables()
sess <- tf$Session()
sess$run(init)

# 8번 반복
iter<-8
result<-numeric(8*2);dim(result)<-c(8,2)
for(step in 1:iter){
  sess$run(train)
  cat(step,"-",sess$run(W),sess$run(b),"Loss =",sess$run(loss),"\n")
  result[step,]<-c(sess$run(W),sess$run(b))
}

## 1 - -0.3698774 0.3166558 Loss = 0.06719695 
## 2 - -0.2287394 0.311432 Loss = 0.03330565 
## 3 - -0.1300698 0.3079748 Loss = 0.0167439 
## 4 - -0.06109463 0.3055579 Loss = 0.008650606 
## 5 - -0.0128774 0.3038683 Loss = 0.004695628 
## 6 - 0.02082895 0.3026872 Loss = 0.002762934 
## 7 - 0.04439145 0.3018616 Loss = 0.001818477 
## 8 - 0.06086286 0.3012844 Loss = 0.001356946

최적화 과정 plot으로 확인하기

require(ggplot2);require(ggthemes)

temp_plots<-list()
for(i in 1:iter){
  title_name <- paste("Iteration =",i)
  temp_plots[[i]]<-ggplot(data.frame(x_data,y_data),aes(x_data,y_data)) +
    theme_tufte() + geom_point(size=1,alpha=.3) + geom_abline(slope=result[i,1],intercept=result[i,2],col=2) + ggtitle(label = title_name)
}

multiplot(temp_plots,cols=2)

참고코드

Multiple graphs on one page (ggplot2)

출처 : Cookbook for R (Multiple graphs on one page (ggplot2))

http://www.cookbook-r.com/Graphs/Multiple_graphs_on_one_page_(ggplot2)

# Multiple plot function
#
# ggplot objects can be passed in ..., or to plotlist (as a list of ggplot objects)
# - cols:   Number of columns in layout
# - layout: A matrix specifying the layout. If present, 'cols' is ignored.
#
# If the layout is something like matrix(c(1,2,3,3), nrow=2, byrow=TRUE),
# then plot 1 will go in the upper left, 2 will go in the upper right, and
# 3 will go all the way across the bottom.
#
multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {
  library(grid)

  # Make a list from the ... arguments and plotlist
  plots <- c(list(...), plotlist)

  numPlots = length(plots)

  # If layout is NULL, then use 'cols' to determine layout
  if (is.null(layout)) {
    # Make the panel
    # ncol: Number of columns of plots
    # nrow: Number of rows needed, calculated from # of cols
    layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),
                    ncol = cols, nrow = ceiling(numPlots/cols))
  }

 if (numPlots==1) {
    print(plots[[1]])

  } else {
    # Set up the page
    grid.newpage()
    pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))

    # Make each plot, in the correct location
    for (i in 1:numPlots) {
      # Get the i,j matrix positions of the regions that contain this subplot
      matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))

      print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,
                                      layout.pos.col = matchidx$col))
    }
  }
}

참고

선형 회귀분석 Linear regression (ver.Python)