inst/site/readme_examples.R

In jMotif/jmotif-R: Time Series Analysis Toolkit Based on Symbolic Aggregate Discretization, i.e. SAX

#1 setup
install.packages("devtools")
library(devtools)
install_github('jMotif/jmotif-R')

# 2
x <- seq(0, pi * 4, 0.02)
y <- sin(x) * 5 + rnorm(length(x))

plot(x, y, type = "l", col = "blue",
     main = "A scaled sine wave with a random noise and its z-normalization")

lines(x, znorm(y, 0.01), type = "l", col = "red")
abline(h = c(1,-1), lty = 2, col = "gray50")
legend(0, -4, c("scaled sine wave","z-normalized wave"),
       lty = c(1,1), lwd = c(1,1), col = c("blue","red"), cex = 0.8)

# 3
y <- c(-1, -2, -1, 0, 2, 1, 1, 0)
plot(y, type = "l", col = "blue",
    main = "8-points time series and it PAA transform into 3 points")
points(y, pch = 16, lwd = 5, col = "blue")

abline(v = c(1, 1 + 7 / 3 , 1 + 7 / 3 * 2, 8), lty = 3, lwd = 2, col = "gray50")

y_paa3 <- paa(y, 3)

segments(1, y_paa3[1], 1 + 7 / 3, y_paa3[1], lwd = 1, col = "red")
points(x = 1 + 7 / 3 / 2, y = y_paa3[1], col = "red", pch = 23, lwd = 5)

segments(1 + 7 / 3, y_paa3[2], 1 + 7 / 3 * 2, y_paa3[2], lwd = 1, col = "red")
points(x = 1 + 7 / 3 + 7 / 3 / 2, y = y_paa3[2], col = "red", pch = 23, lwd = 5)

segments(1 + 7 / 3 * 2, y_paa3[3], 8, y_paa3[3], lwd = 1, col = "red")
points(x = 1 + 7 / 3 * 2 + 7 / 3 / 2, y = y_paa3[3], col = "red", pch = 23, lwd = 5)

# 4
y <- seq(-2,2, length = 100)
x <- dnorm(y, mean = 0, sd = 1)
lines(x,y, type = "l", lwd = 5, col = "magenta")
abline(h = alphabet_to_cuts(3)[2:3], lty = 2, lwd = 2, col = "magenta")
text(0.7, -1, "a", cex = 2, col = "magenta")
text(0.7, 0, "b", cex = 2, col = "magenta")
text(0.7, 1, "c", cex = 2, col = "magenta")

series_to_chars(y_paa3, 3)
series_to_string(y_paa3, 3)

# 5
# load Cylinder-Bell-Funnel data
data("CBF")
str(CBF)

# set the discretization parameters
#
w <- 60 # the sliding window size
p <- 6  # the PAA size
a <- 6  # the SAX alphabet size

# convert the train classes to wordbags (the dataset has three labels: 1, 2, 3)
#
cylinder <- manyseries_to_wordbag(CBF[["data_train"]][CBF[["labels_train"]] == 1,],
                                  w, p, a, "exact", 0.01)
bell <- manyseries_to_wordbag(CBF[["data_train"]][CBF[["labels_train"]] == 2,],
                              w, p, a, "exact", 0.01)
funnel <- manyseries_to_wordbag(CBF[["data_train"]][CBF[["labels_train"]] == 3,],
                                w, p, a, "exact", 0.01)

# compute tf*idf weights for three bags
#
tfidf = bags_to_tfidf(
  list("cylinder" = cylinder, "bell" = bell, "funnel" = funnel) )
tail(tfidf)

# make up a sample time-series
#
sample <- (CBF[["data_train"]][CBF[["labels_train"]] == 3,])[1,]
sample_bag <- sax_via_window(sample, w, p, a, "exact", 0.01)
df <- data.frame(index = as.numeric(names(sample_bag)),
                words = unlist(sample_bag))

# weight the found patterns
#
weighted_patterns <- merge(df, tfidf)
specificity <- rep(0, length(sample))
for (i in 1:length(weighted_patterns$words)) {
  pattern <- weighted_patterns[i,]
  for (j in 1:w) {
    specificity[pattern$index + j] <- specificity[pattern$index + j] +
      pattern$funnel - pattern$bell - pattern$cylinder
  }
}

# plot the weighted patterns
#
library(ggplot2)
library(scales)
ggplot(data = data.frame(x = c(1:length(sample)), y = sample, col = rescale(specificity)),
       aes(x = x, y = y, color = col)) + geom_line(size = 1.2) + theme_bw() +
  ggtitle("The funnel class-characteristic pattern example") +
  scale_colour_gradientn(name = "Class specificity:  ",limits = c(0,1),
            colours = c("red", "yellow", "green", "lightblue", "darkblue"),
            breaks = c(0, 0.5, 1), labels = c("negative", "neutral", "high"),
            guide = guide_colorbar(title.theme = element_text(size = 14, angle = 0),
                      title.vjust = 1, barheight = 0.6, barwidth = 6,
                      label.theme = element_text(size = 10, angle = 0))) +
  theme(legend.position = "bottom", plot.title = element_text(size = 18),
        axis.title.x = element_blank(), axis.title.y = element_blank(),
        axis.text.x = element_text(size = 12),axis.text.y = element_blank(),
        panel.grid.major.y = element_blank(), panel.grid.minor.y = element_blank(),
        axis.ticks.y = element_blank())

# classify the test data
#
labels_predicted <- rep(-1, length(CBF[["labels_test"]]))
labels_test <- CBF[["labels_test"]]
data_test <- CBF[["data_test"]]
for (i in c(1:length(data_test[,1]))) {
  series <- data_test[i,]
  bag <- series_to_wordbag(series, w, p, a, "exact", 0.01)
  cosines <- cosine_sim(list("bag" = bag, "tfidf" = tfidf))
  labels_predicted[i] <- which(cosines$cosines == max(cosines$cosines))
}

# compute the classification error
#
error <- length(which((labels_test != labels_predicted))) / length(labels_test)
error

# findout which time series were misclassified
#
which((labels_test != labels_predicted))