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library(quadprog)
# 関数の定義
svm.1 <- function(X, y, C) {
eps <- 0.0001
n <- nrow(X)
Dmat <- matrix(nrow = n, ncol = n)
for (i in 1:n)
for (j in 1:n)
Dmat[i, j] <- sum(X[i, ] * X[j, ]) * y[i] * y[j]
Dmat <- Dmat + eps * diag(n)
dvec <- rep(1, n)
Amat <- matrix(nrow = (2 * n + 1), ncol = n)
Amat[1, ] <- y
Amat[2:(n + 1), 1:n] <- -diag(n)
Amat[(n + 2):(2 * n + 1), 1:n] <- diag(n)
Amat <- t(Amat)
bvec <- c(0, rep(-C,n), rep(0,n))
meq <- 1
alpha <- solve.QP(Dmat, dvec, Amat, bvec = bvec, meq = 1)$solution
beta <- drop((alpha * y) %*% X)
index <- (1:n)[eps < alpha & alpha < C - eps]
beta.0 <- mean(y[index] - X[index, ] %*% beta)
return(list(beta = beta, beta.0 = beta.0[1]))
}
# svm.1を用いた実行
a <- rnorm(1)
b <- rnorm(1)
n <- 100
X <- matrix(rnorm(n * 2), ncol = 2, nrow =n)
y <- sign(a * X[, 1] + b * X[, 2] + 0.3 * rnorm(n))
plot(-3:3, -3:3, xlab = "X[, 1]", ylab = "X[, 2]", type = "n")
for (i in 1:n) {
if (y[i] == 1) {
points(X[i, 1], X[i, 2], col = "red")
} else {
points(X[i, 1], X[i, 2], col = "blue")
}
}
qq <- svm.1(X, y, 10)
abline(-qq$beta.0 / qq$beta[2], -qq$beta[1] / qq$beta[2])
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K.poly <- function(x, y) {
return((1 + t(x) %*% y) ^ 2)
}
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K.linear <- function(x, y) {
return(t(x) %*% y)
}
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svm.2 <- function(X, y, C, K) {
eps <- 0.0001
n <- nrow(X)
Dmat <- matrix(nrow = n, ncol = n)
for (i in 1:n)
for(j in 1:n)
Dmat[i, j] <- K(X[i, ], X[j, ]) * y[i] * y[j]
Dmat <- Dmat + eps * diag(n)
dvec <- rep(1, n)
Amat <- matrix(nrow = (2 * n + 1), ncol = n)
Amat[1, ] <- y
Amat[2:(n + 1), 1:n] <- -diag(n)
Amat[(n + 2):(2 * n + 1), 1:n] <- diag(n)
Amat <- t(Amat)
bvec <- c(0, -C * rep(1, n), rep(0, n))
meq <- 1
alpha <- solve.QP(Dmat, dvec, Amat, bvec = bvec, meq = 1)$solution
index <- (1:n)[eps < alpha & alpha < C - eps]
beta <- drop((alpha * y) %*% X)
beta.0 <- mean(y[index] - X[index, ] %*% beta)
return(list(alpha = alpha, beta.0 = beta.0))
}
# 関数の定義
plot.kernel <- function(K, lty) {
qq <- svm.2(X, y, 1, K)
alpha <- qq$alpha
beta.0 <- qq$beta.0
f <- function(u, v) {
x <- c(u, v)
S <- beta.0
for (i in 1:n)
S <- S + alpha[i]*y[i]*K(X[i,],x)
return(S)
}
u <- seq(-2, 2, .1)
v <- seq(-2, 2, .1)
w <- array(dim = c(41, 41))
for (i in 1:41)
for (j in 1:41)
w[i, j] <- f(u[i], v[j])
contour(u, v, w, level = 0, add = TRUE, lty = lty)
}
# 実行
a <- 3
b <- -1
n <- 200
X <- matrix(rnorm(n * 2), ncol = 2, nrow = n)
y <- sign(a * X[, 1] + b * X[, 2] ^ 2 + 0.3 * rnorm(n))
plot(-3:3, -3:3, xlab = "X[, 1]", ylab = "X[, 2]", type = "n")
for (i in 1:n) {
if (y[i] == 1) {
points(X[i, 1], X[i, 2], col = "red")
} else {
points(X[i, 1], X[i, 2], col = "blue")
}
}
plot.kernel(K.linear, 1)
plot.kernel(K.poly, 2)
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library(e1071)
x <- matrix(rnorm(200 * 2), ncol = 2)
x[1:100, ] <- x[1:100, ] + 2
x[101:150, ] <- x[101:150, ] - 2
y <- c(rep(1, 150), rep(2, 50))
dat <- data.frame(x = x, y = as.factor(y))
train <- sample(200, 100)
svmfit <- svm(y ~ ., data = dat[train, ], kernel = "radial",
gamma = 1, cost = 1)
plot(svmfit, dat[train, ])
x <- matrix(rnorm(200 * 2), ncol = 2)
x[1:100, ] <- x[1:100, ] + 2
x[101:150, ] <- x[101:150, ] - 2
y <- c(rep(1, 150), rep(2, 50))
dat <- data.frame(x = x, y = as.factor(y))
train <- sample(200, 100)
svmfit <- svm(y ~ ., data = dat[train, ], kernel = "radial",
gamma = 1, cost = 100)
plot(svmfit, dat[train, ])
tune.out <- tune(svm, y ~ ., data = dat[train, ], kernel = "radial",
ranges = list(cost = c(0.1, 1, 10, 100, 1000),
gamma = c(0.5, 1, 2, 3, 4)))
summary (tune.out)
##
## Parameter tuning of 'svm':
##
## - sampling method: 10-fold cross validation
##
## - best parameters:
## cost gamma
## 1 3
##
## - best performance: 0.09
##
## - Detailed performance results:
## cost gamma error dispersion
## 1 1e-01 0.5 0.25 0.12692955
## 2 1e+00 0.5 0.11 0.07378648
## 3 1e+01 0.5 0.12 0.07888106
## 4 1e+02 0.5 0.11 0.07378648
## 5 1e+03 0.5 0.14 0.06992059
## 6 1e-01 1.0 0.22 0.14757296
## 7 1e+00 1.0 0.11 0.07378648
## 8 1e+01 1.0 0.11 0.07378648
## 9 1e+02 1.0 0.13 0.08232726
## 10 1e+03 1.0 0.18 0.07888106
## 11 1e-01 2.0 0.22 0.14757296
## 12 1e+00 2.0 0.10 0.06666667
## 13 1e+01 2.0 0.12 0.09189366
## 14 1e+02 2.0 0.19 0.07378648
## 15 1e+03 2.0 0.18 0.11352924
## 16 1e-01 3.0 0.25 0.12692955
## 17 1e+00 3.0 0.09 0.07378648
## 18 1e+01 3.0 0.12 0.09189366
## 19 1e+02 3.0 0.18 0.09189366
## 20 1e+03 3.0 0.16 0.08432740
## 21 1e-01 4.0 0.25 0.12692955
## 22 1e+00 4.0 0.09 0.07378648
## 23 1e+01 4.0 0.14 0.09660918
## 24 1e+02 4.0 0.15 0.11785113
## 25 1e+03 4.0 0.17 0.08232726
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df <- iris
x <- df[, 1:4]
y <- as.factor(df[, 5])
m <- 30
train <- sample(1:150, 150 - m)
x.train <- x[train, ]
y.train <- y[train]
dat <- data.frame(x.train, y.train)
svmfit <- svm(y.train ~ ., data = dat, kernel = "radial", cost = 10, gamma = 1)
x.test <- x[-train, ]
y.test <- y[-train]
table(y.test, predict(svmfit, x.test))
##
## y.test setosa versicolor virginica
## setosa 11 0 0
## versicolor 0 11 1
## virginica 0 1 6