Monthly Archives: June 2017

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データ解析基礎論A. Week10

freq<-c(33,37,7,23)
pref<-factor(c('soba','udon','soba','udon'))
region<-factor(c('east','east','west','west'))
dat<-data.frame(pref,region,freq)
dat.table=table(pref,region)
dat.table[cbind(pref,region)]<-freq

ME.lrt=2*sum((freq)*log(freq/25))
dat.loglinCE_A<-loglin(dat.table,list(1), fit=T,param=T)
dat.loglinCE_B<-loglin(dat.table,list(2), fit=T,param=T)
dat.loglinIND<-loglin(dat.table,list(1,2), fit=T,param=T)

ME.lrt-dat.loglinCE_A$lrt				
1-pchisq(ME.lrt-dat.loglinCE_A$lrt,1)		

dat.loglinCE_A$lrt-dat.loglinIND$lrt	
1-pchisq(dat.loglinCE_A$lrt-dat.loglinIND$lrt,1)	

dat.loglinIND$lrt-dat.loglinSAT$lrt            
1-pchisq(dat.loglinIND$lrt-dat.loglinSAT$lrt,1) 	

freq<-c(9,5,2,4,16,10,26,28)
gender<-factor(c(rep('female',4),c(rep('male',4))))
affil<-factor(rep(c('L','L','E','E'),2))
circle<-factor(rep(c('tennis','astro'),4))
dat<-data.frame(gener,affil,circle,freq)

dat.table<-table(gender,affil,circle)
dat.table[cbind(gender,affil,circle)]<-freq

dat.loglin2<-loglin(dat.table,list(1), fit=T,param=T)

dat.loglin2<-loglin(dat.table,list(1), fit=T,param=T)
dat.loglin3<-loglin(dat.table,list(1,3), fit=T,param=T)
dat.loglin4<-loglin(dat.table,list(1,2,3), fit=T,param=T)
dat.loglin5<-loglin(dat.table,list(c(1,3),2), fit=T,param=T)
dat.loglin6<-loglin(dat.table,list(c(1,3),c(1,2)), fit=T,param=T)
dat.loglin7<-loglin(dat.table,list(c(1,3),c(1,2),c(2,3)), fit=T,param=T)
dat.loglin8<-loglin(dat.table,list(c(1,2,3)), fit=T,param=T)
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認知情報解析　DL　ch5までのまとめ

# ReLU
relu.forwd <- function(x){
  return(pmax(x,0))
}
relu.bckwd <- function(z, dout){
  dout[which(z <= 0)] = 0
  return(dout)
}
# sigmoid
sigmoid.forwd <- function(x){
  return(1/(1+exp(-x)))
}
sigmoid.bckwd <- function(z, dout){
  return(dout*(1-z)*z)
}
# Affine
affine.forwd <- function(x, W, b){
  return(x%*%W + matrix(1, nrow = nrow(x), ncol = 1)%*%b)
}
affine.bckwd <- function(x, W, dout){
  dx = dout%*%t(W)
  dW = t(x)%*%dout
  db = colSums(dout)
  return(list(dx = dx, dW = dW, db = db))
}
# softmax with CE
softmax.forwd <- function(x, target){
  max.x = apply(x,1,max)
  C = ncol(x)
  x = x - max.x%*%matrix(1,nrow=1,ncol=C)
  y = exp(x)/rowSums(exp(x))
  delta = 1e-7;
  R = nrow(as.matrix(y))
  return(list(smx = y, ce = -sum(target*log(y + delta))/R))
}
softmax.bckwd <- function(smx, target){
  R = nrow(smx)
  return((smx-target)/R)
}

activation <- function(A, target, actFun){
  if (actFun == "sigmoid"){
    return(sigmoid.forwd(A))
  }
  if (actFun == "relu"){
    return(relu.forwd(A))
  }
  if (actFun == "softmax"){
    return(softmax.forwd(A, target))
  }
}

Dact <- function(z, smx, target, dout, actFun){
  if (actFun == "sigmoid"){
    return(sigmoid.bckwd(z, dout))
  }
  if (actFun == "relu"){
    return(relu.bckwd(z, dout))
  }
  if (actFun == "softmax"){
    return(softmax.bckwd(smx, target))
  }
}

# function for initialization 
init.network <- function(n.neurons, actFun){
  n.layer = length(n.neurons)
  W = list(); b = list()
  for (i.layer in 1:(n.layer-1)){
    W[[i.layer]] = matrix(rnorm(n.neurons[i.layer]*n.neurons[(i.layer+1)],sd = 0.1),
                          nrow=n.neurons[i.layer])
    b[[i.layer]] =  matrix(rnorm(n.neurons[(i.layer+1)],sd = 0.1), nrow = 1)
  }
  return(list(W = W, b = b, actFun = actFun))
}

cu.nnet = function(train.x, train.y, net, HP = c(10,1000,0.05)){
# HP: Hyperparameters
# HP[1] = batch size
# HP[2] = n iteration
# HP[3] = learning rate
  n.layer <- length(net$W)
  loss = rep(0,HP[2])
  A = list(); z = list(); Dact = list(); Daff = list()
  for (i.iter in 1:HP[2]){
    batch_mask = sample(1:nrow(train.x), HP[1])
    x.batch = train.x[batch_mask,]
    t.batch = train.y[batch_mask,]  
    for (i.layer in 1:n.layer){
      if (i.layer == 1){
        x = x.batch
      } else {
        x = z[[(i.layer - 1)]]
      }
      A[[i.layer]] = affine.forwd(x, net$W[[i.layer]], net$b[[i.layer]])
      z[[i.layer]] = activation(A[[i.layer]], t.batch, net$actFun[i.layer])
    }
    loss[i.iter] = z[[i.layer]]$ce
    smx = z[[i.layer]]$smx
    for (i.layerR in n.layer:1){
      if (i.layerR == n.layer){
        dout = 1
      } else {
        dout = Daff[[(i.layerR+1)]]$dx
      }
      Dact[[i.layerR]] = Dact(z[[i.layerR]], smx, t.batch, dout, net$actFun[i.layerR])
      if (i.layerR==1){
        x = x.batch
      } else {
        x = A[[(i.layerR-1)]]
      }
      Daff[[i.layerR]] = affine.bckwd(x, network$W[[i.layerR]], Dact[[i.layerR]])
    }
    for (i.layer in 1:n.layer){
      net$W[[i.layer]] = net$W[[i.layer]] - HP[3]*Daff[[i.layer]]$dW
      net$b[[i.layer]] = net$b[[i.layer]] - HP[3]*Daff[[i.layer]]$db
    }
  }
  return(list(loss = loss, net = net))
}

# iris
train.x = as.matrix(iris[,1:4])
train.y.temp = as.numeric(iris[,5])
train.y = matrix(0,nrow = nrow(train.x), ncol =3)
train.y[which(train.y.temp==1), 1]=1
train.y[which(train.y.temp==2), 2]=1
train.y[which(train.y.temp==3), 3]=1
actF = c("relu","softmax")
network = init.network(c(4,15,3), actF)
res = cu.nnet(train.x, train.y, network, HP=c(15,2000,0.1))
plot(res$loss,type='l')

# MNIST
train <- read.csv('~/courses/CogMod/CMA2017/MNSTtrain.csv', header=TRUE)
test <- read.csv('~/courses/CogMod/CMA2017/MNSTtest.csv', header=TRUE)
train <- data.matrix(train)
test <- data.matrix(test)
train.x <- as.matrix(train[,-1]/255)
train.y.temp <- train[,1]
train.y = matrix(0,nrow = nrow(train.x), ncol = 10)
for (i in 1:nrow(train.x)){
  train.y[i,(train.y.temp[i]+1)]=1
}
actF = c("relu","relu","softmax")
network = init.network(c(784,100,50,10), actF)
res = cu.nnet(train.x, train.y, network,HP=c(100,2000,0.1))
plot(res$loss,type='l')

########################################################
#   with additional methods

init.network <- function(n.neurons, actFun, Opt, sdv){
  n.layer = length(n.neurons)
  W = list(); b = list(); 
  mW = list(); mb = list();    # momentum
  hW = list(); hb = list();    # adaGrad
  aMW = list(); aMb = list();  # adam M
  aVW = list(); aVb = list();  # adam V
  for (i.layer in 1:(n.layer-1)){
    if (nargs() == 3) {
      if (actFun[i.layer]=="sigmoid"){
        # Xavier
        sdv = 1/sqrt(n.neurons[i.layer])
      } else {
        # He - assumes ReLU
        sdv = sqrt(2/n.neurons[i.layer])
      }
    }
    W[[i.layer]] = matrix(rnorm(n.neurons[i.layer]*n.neurons[(i.layer+1)], sd = sdv),
                          nrow=n.neurons[i.layer])
    b[[i.layer]] =  matrix(rnorm(n.neurons[(i.layer+1)], sd = sdv), nrow = 1)
    if (Opt == "momentum"){
      mW[[i.layer]] = matrix(0, nrow=n.neurons[i.layer], ncol=n.neurons[(i.layer+1)])
      mb[[i.layer]] = matrix(0, nrow = 1, ncol=n.neurons[(i.layer+1)])
    } 
    if (Opt == "adagrad"){
      hW[[i.layer]] = matrix(0, nrow=n.neurons[i.layer], ncol=n.neurons[(i.layer+1)])
      hb[[i.layer]] = matrix(0, nrow = 1, ncol=n.neurons[(i.layer+1)])
    }
    if (Opt == "adam"){
      aMW[[i.layer]] = matrix(0, nrow=n.neurons[i.layer], ncol=n.neurons[(i.layer+1)])
      aMb[[i.layer]] = matrix(0, nrow = 1, ncol=n.neurons[(i.layer+1)])
      aVW[[i.layer]] = matrix(0, nrow=n.neurons[i.layer], ncol=n.neurons[(i.layer+1)])
      aVb[[i.layer]] = matrix(0, nrow = 1, ncol=n.neurons[(i.layer+1)])
    }
  }
  return(list(W = W, b = b, actFun = actFun, optimizer = Opt,
              mW=mW, mb=mb,
              hW=hW, hb=hb,
              aMW=aMW,aMb=aMb,aVW=aVW))
}

OPT<-function(net, Daff, HP){
  if (net$optimizer == "momentum"){
    return(Opt.mom(net, Daff, HP))
  }
  if (net$optimizer == "adagrad"){
    return(Opt.adagrad(net, Daff, HP))
  }
  if (net$optimizer == "adam"){
    return(Opt.adam(net, Daff, HP))
  }
}

Opt.mom <- function(net, Daff, HP){
# HP[3] = learning rate
# HP[4] = weight decay
# HP[5] = momentum
  n.layer <- length(net$W)
  for (i.layer in 1:n.layer){
    net$mW[[i.layer]] = HP[5]*net$mW[[i.layer]] 
                        - HP[3]*Daff[[i.layer]]$dW - HP[4]*net$W[[i.layer]]
    net$mb[[i.layer]] = HP[5]*net$mb[[i.layer]] 
                        - HP[3]*Daff[[i.layer]]$db - HP[4]*net$b[[i.layer]]
    net$W[[i.layer]] = net$W[[i.layer]] + net$mW[[i.layer]]
    net$b[[i.layer]] = net$b[[i.layer]] + net$mb[[i.layer]] 
  }
  return(net=net)
}

cu.nnet = function(train.x, train.y, net, HP = c(10,1000,0.05,0.01,0.1,0.999,0.9)){
# HP: Hyperparameters
# HP[1] = batch size
# HP[2] = n iteration
# HP[3] = learning rate
# HP[4] = weight decay
# HP[5] = momentum
# HP[6] = beta1 (adam)
# HP[7] = beta2 (adam)
  n.layer <- length(net$W)
  loss = rep(0,HP[2])
  A = list(); z = list(); Dact = list(); Daff = list()
  for (i.iter in 1:HP[2]){
    batch_mask = sample(1:nrow(train.x), HP[1])
    x.batch = train.x[batch_mask,]
    t.batch = train.y[batch_mask,]
    for (i.layer in 1:n.layer){
      if (i.layer == 1){
        x = x.batch
      } else {
        x = z[[(i.layer - 1)]]
      }
      A[[i.layer]] = affine.forwd(x, net$W[[i.layer]], net$b[[i.layer]])
      z[[i.layer]] = activation(A[[i.layer]], t.batch, net$actFun[i.layer])
    }
    loss[i.iter] = z[[i.layer]]$ce
    smx = z[[i.layer]]$smx
    for (i.layerR in n.layer:1){
      if (i.layerR == n.layer){
        dout = 1
      } else {
        dout = Daff[[(i.layerR+1)]]$dx
      }
      Dact[[i.layerR]] = Dact(z[[i.layerR]], smx, t.batch, dout, net$actFun[i.layerR])
      if (i.layerR==1){
        x = x.batch
      } else {
        x = A[[(i.layerR-1)]]
      }
      Daff[[i.layerR]] = affine.bckwd(x, net$W[[i.layerR]], Dact[[i.layerR]])
    }
    net = OPT(net, Daff, HP)
  }
  return(list(loss = loss, net = net))
}

# iris
train.x = as.matrix(iris[,1:4])
train.y.temp = as.numeric(iris[,5])
train.y = matrix(0,nrow = nrow(train.x), ncol =3)
train.y[which(train.y.temp==1), 1]=1
train.y[which(train.y.temp==2), 2]=1
train.y[which(train.y.temp==3), 3]=1
actF = c("relu","softmax")
network = init.network(c(4,15,3), actF, "momentum")
res = cu.nnet(train.x, train.y, network, HP=c(15,1000,0.01,0.0001,0.9,0.999,0.9))
hist(res$net$W[[1]])
plot(res$loss,type='l')
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データ解析基礎論A week09

dat<-read.csv("http://www.matsuka.info/data_folder/datWA01.txt")
plot(dat$shoesize, dat$gender, pch=20, cex=3, xlab="Shoe size", 
     ylab="gender", col='blue', ylim = c(0.5, 2.5), yaxt="n")
axis(2, at = c(1,2),labels=c("female","male"))
dat.lm<-lm(as.numeric(dat$gender)~dat$shoesize,data=dat)
abline(dat.lm,lwd=4,col='red')

chisq.test(c(72,23,16,49),p=rep(40,4),rescale.p=T)
M=matrix(c(52,48,8,42),nrow=2)
chisq.test(M,correct=F)

plot(dat$shoesize, dat$gender, pch=20, cex=3, xlab="Shoe size", 
     ylab="gender", col='blue', ylim = c(0.5, 2.5), yaxt="n")
axis(2, at = c(1,2),labels=c("female","male"))
plot(dat$shoesize, dat$gender, pch=20, cex=3, xlab="Shoe size", 
     ylab="gender", col='blue', ylim = c(0.5, 2.5), yaxt="n")
axis(2, at = c(1,2),labels=c("female","male"))
abline(dat.lm,lwd=4,col='red')

p = seq(0.1,0.9,length.out = 20)
plot(p/(1-p),col="red",pch=20,cex=3)
plot(log(p/(1-p)),col="red",pch=20,cex=3)


dat.lr<-glm(gender~shoesize, family=binomial, data=dat)
x=seq((min(dat$shoesize)-2), (max(dat$shoesize)+2), 0.1)
keisu  = coef(dat.lr)
y.prob=1/(1+exp(-1*(keisu[1]+keisu[2]*x)))
plot(dat$shoesize, dat$gender, pch=20, cex=3, xlab="Shoe size", 
     ylab="gender", col='blue', ylim = c(0.5, 2.5), yaxt="n")
axis(2, at = c(1,2),labels=c("female","male"))
lines(x,y.prob+1,lty=2,lwd=4,col='green')
abline(dat.lm,lwd=4,col='red')

anova(dat.lr, test ="Chisq")

dat.lr0<-glm(gender~1,family="binomial",data=dat)
dat.lrS<-glm(gender~shoesize,family=binomial,data=dat)
dat.lrH<-glm(gender~h,family="binomial",data=dat)


dat<-read.csv("http://www.matsuka.info/data_folder/cda7-16.csv")
dat.glmAllAdd=glm(survival~age+Ncigarettes+NdaysGESTATION,family=binomial,data=dat)
dat.glmAllMult=glm(survival~age*Ncigarettes*NdaysGESTATION,family=binomial,data=dat)
stepAIC(dat.glmAllMult)
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dlls

import numpy as np
import matplotlib.pylab as plt

def numGrad(f, x):
    h = 1e-4 # 0.0001
    grad = np.zeros_like(x)
    
    for idx in range(x.size):
        tmp_val = x[idx]
        x[idx] = float(tmp_val) + h
        fxhP = f(x) # f(x+h)
        
        x[idx] = tmp_val - h 
        fxhM = f(x) # f(x-h)
        grad[idx] = (fxhP - fxhM) / (2*h)
        
        x[idx] = tmp_val # 値を元に戻す
        
    return grad


def gradDesc(f, init_x, lr=0.01, step_num=100):
    x = init_x
    x_history = []

    for i in range(step_num):
        x_history.append( x.copy() )

        grad = numGrad(f, x)
        x -= lr * grad

    return x, np.array(x_history)


def function_2(x):
    return (x[0]**2)/20 + x[1]**2

init_x = np.array([-3.0, 4.0])    

lr = 0.9
step_num = 100
x, x_history = gradDesc(function_2, init_x, lr=lr, step_num=step_num)

plt.plot( [-5, 5], [0,0], '--b')
plt.plot( [0,0], [-5, 5], '--b')
plt.plot(x_history[:,0], x_history[:,1], '-o')

plt.xlim(-3.5, 3.5)
plt.ylim(-4.5, 4.5)
plt.xlabel("X0")
plt.ylabel("X1")
plt.show()
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認知情報解析　ch06.1

func02R = function(x){
  return(1/20*x[1]^2 + x[2]^2)
}
numerical.grad <- function(func, x){
  h = 1e-4
  R = nrow(x)
  C = ncol(x)
  grad = matrix(0, R, C)
  for (i.col in 1:C){
    for (i.row in 1:R){
      temp.x = x[i.row,i.col]
      x[i.row, i.col] = temp.x + h
      plusH = do.call(func, list(x))
      x[i.row, i.col] = temp.x - h
      minusH = do.call(func,list(x))
      grad[i.row, i.col] = (plusH - minusH)/(2*h)
      x[i.row, i.col] = temp.x
    }
  }
  return(grad)
}

require(plot3D)
x = seq(-10,10,0.2)
y = seq(-10,10,0.2)
M = mesh(x,y)
Z = as.vector(1/20*M$x^2)+as.vector(M$y^2) 
Z.mesh = matrix(Z,nrow(M$x))
contour(x,y,Z.mesh,drawlabels = F,nlevels=40)

grad.desc <- function(func, init.x, lr, n.iter){
  x = init.x
  x.hist = init.x
  for (i.iter in 1:n.iter) {
    grad = numerical.grad(func, x)
    x = x - lr*grad
    x.hist = rbind(x.hist,x)
  }
  return(x.hist)
}
x.init = matrix(c(-7,2),nrow = 1)
gd = grad.desc("func02R",x.init,0.9,100)
lines(gd,type='o',col = 'green',pch=20)
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データ解析基礎論A week07

dat<-read.csv("http://www.matsuka.info/data_folder/dktb312.txt")
dat2<-data.frame(result=dat$result,
                 c1=c(rep(0,8),rep(1,8),rep(0,16)),
                 c2=c(rep(0,16),rep(1,8),rep(0,8)),
                 c3=c(rep(0,24),rep(1,8)))
dat2.lm<-lm(result~.,data=dat2)


dat3<-data.frame(result=dat$result, 
                 c1=c(rep(-3,8), rep(1,24)), 
                 c2=c(rep(0,8),rep(-2,8),rep(1,16)),
                 c3=c(rep(0,16),rep(-1,8),rep(1,8)))
dat3.lm<-lm(result~c1+c2+c3,data=dat3)


dat<-read.csv("http://www.matsuka.info/data_folder/dktb321.txt")
plot(dat$result~dat$duration,data=dat[dat$method=="method.x",])
result<-dat$result[dat$method=="method.X"]
CL=c(rep(-3,5),rep(-1,5),rep(1,5),rep(3,5))
CQ=c(rep(-1,5),rep(1,5),rep(1,5),rep(-1,5))
CC=c(rep(-3,5),rep(1,5),rep(-1,5),rep(3,5))

# or
dat2<-dat[dat$method=="method.X",]
dat2.lm<-lm(result~duration, data=dat2,
  contrasts=list(duration="contr.poly"))

result2=result;
result2[16:20]=result2[16:20]-3
plot(tapply(result2,dat$duration[dat$method=="method.X"],mean),
     pch=20,col="red",lwd=3, type="o",cex=3,ylab="mean",xlab="time")
trend2.lm<-lm(result2~CL+CQ+CC)

Cont=contr.poly(4) 

##
subj.gender = c(rep("male",30),rep("female",30))
pic.gender = rep(c(rep("male",15),rep("female",15)),2)
# 15m-m, 15m-f, 15f-m, 15f-f 
eye.fix = c(round(rnorm(15,50,5)),
           round(rnorm(15,70,5)),
           round(rnorm(15,65,5)),
           round(rnorm(15,50,5)))
datE = data.frame(eye.fix = eye.fix, subj.gender= subj.gender,pic.gender=pic.gender)
interaction.plot(datE$subj.gender, datE$pic.gender, datE$eye.fix, 
                 trace.label = "Gender of Stimuli", xlab = "Gender of Participants",
                 ylab = "Number of fixation",lwd = 3,type = "o",pch = c(17,20),cex =3,
                 col = c('skyblue','coral'),legend =T)
datE.lm <- lm(eye.fix~subj.gender*pic.gender,data=datE)

dat<-read.csv("http://www.matsuka.info/data_folder/ancova01.csv")
dat$pretest=dat$pretest*0.1

dat<-read.csv("http://www.matsuka.info/data_folder/tdkReg01.csv") 
dat.lm01<-lm(sales~price, data=as.data.frame(scale(dat)))
plot(dat.lm01,which=1)
plot(dat.lm01,which=2)


par(mfrow=c(2,2))
plot(dat.lm01)

dat<-read.csv("http://peach.l.chiba-u.ac.jp/course_folder/dktb312.csv")
dat$method=factor(dat$method, levels(dat$method)[c(1,3,4,2)])

dat<-read.csv("http://peach.l.chiba-u.ac.jp/course_folder/forbesdata.txt") 
boil.lm<-lm(log(pressure)~temp, data=boil)
par(mfrow=c(2,2))
plot(boil.lm)

boil.lm<-lm(log(pressure)~temp, data=boil[-12,])
plot(boil.lm)

dat<-read.csv("http://www.matsuka.info/data_folder/tdkReg02.csv")
plot(dat)
dat.lm<-lm(sales~., data=dat) 
install.packages("DAAG")
library(DAAG)
vif(dat.lm)