R inplementation of wanderlust

Description

R inplementation of wanderlust

Usage

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Rwanderlust(data, s, l = 15, k = 15, num_graphs = 1,
  num_waypoints = 250, waypoints_seed = 123, flock_waypoints = 2,
  metric = "euclidean", voting_scheme = "exponential",
  band_sample = FALSE, partial_order = NULL, verbose = TRUE)

Arguments

data

Input data matrix.

s

Starting point ID.

l

l nearest neighbours.

k

k nearest neighbours, k < l.

num_graphs

Number of repreated graphs.

num_waypoints

Number of waypoints to guide the trajectory detection.

waypoints_seed

The seed for reproducing the results.

flock_waypoints

The number of times for flocking the waypoints, default is 2.

metric

Distance calculation metric for nearest neighbour detection.

voting_scheme

The scheme of voting.

band_sample

Boolean, if band the sample

partial_order

default NULL

verbose

Boolean, if print the details

Value

a list containing Trajectory, Order, Waypoints

Author(s)

Hao Chen

Examples

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set.seed(15)
shuffled_iris <- iris[sample(150, 150, replace = FALSE), ]
data <- shuffled_iris[,1:4]
data_label <- shuffled_iris[,5]
wishbone <- Rwanderlust(data = data, num_waypoints = 100, waypoints_seed = 2)
pd1 <- data.frame(id = wishbone$Trajectory, label=data_label, stringsAsFactors = FALSE)
pd2 <- data.frame(id = seq_along(row.names(data)), label=data_label, stringsAsFactors = FALSE)
#ggplot(pd1, aes(x=id, y=id, colour = label)) + geom_point() + theme_bw()
#ggplot(pd2, aes(x=id, y=id, colour = label)) + geom_point() + theme_bw()

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