rEMM (version 1.0-5)

synthetic_stream: Create a Synthetic Data Stream

Description

This function creates a synthetic data stream with data points in roughly $[0, 1]^p$ by choosing points form k clusters following a sequence through these clusters. Each cluster has a density function following a d-dimensional normal distributions. In the test set outliers are introduced.

Usage

synthetic_stream(k = 10, d = 2, n_subseq = 100, p_transition = 0.5, p_swap = 0,
n_train = 5000, n_test = 1000, p_outlier = 0.01, rangeVar = c(0, 0.005))

Arguments

k

number of clusters.

d

dimensionality of data set.

n_subseq

length of subsequence which will be repeat to create the data set.

p_transition

probability that the next position in the subsequence will belong to a different cluster.

p_swap

probability that two data points are swapped. This represents measurement errors (e.g., a data points arrive out of order) or that the data stream does not exactly follow the subsequence.

n_train

size of training set (without outliers).

n_test

size of test set (with outliers).

p_outlier

probability that a data point is replaced by an outlier (a randomly chosen point in $[0,1]^p$).

rangeVar

Used to create the random covariance matrices for the clusters. See genPositiveDefMat() in clusterGeneration for details.

Value

• A list with the following elements:
• test
  test data.
• train
  training data.
• sequence_test
  sequence of the test data points through the clusters.
• sequence_train
  sequence of the training data points through the clusters.
• swap_test
  index where points are swapped.
• swap_train
  index where points are swapped.
• outlier_position
  logical vector for outliers in test data.
• model
  centers and covariance matrices for the clusters.

Details

The data generation process creates a data set consisting of k clusters in roughly $[0,1]^d$. The data points for each cluster are be drawn from a multivariate normal distribution given a random mean and a random variance/covariance matrix for each cluster. The temporal aspect is modeled by a fixed subsequence (of length n_subseq) through the k clusters. In each step in the subsequence we have a transition probability p_transition that the next data point is in the same cluster or in a randomly chosen other cluster, thus we can create slowly or fast changing data. For the complete sequence, the subsequence is repeated to create n_test/n_train data points. The data set is generated by drawing a data point from the cluster corresponding to each position in the sequence. Outliers are introduced by replacing data points in the data set with probability $p_outlier by randomly chosen data points in $[0,1]^d$. Finally, to introduce imperfection in the temporal sequence (e.g., because the data does not follow exactly a repeating sequence or because observations do not arrive in the correct order), we swap two consecutive observations with probability p_swap.

Examples

## create only test data (with outliers)
ds <- synthetic_stream(n_train=0)

## plot test data
plot(ds$test, pch = ds$sequence_test, col ="gray")
text(ds$model$mu[,1], ds$model$mu[,2], 1:10)

## mark outliers
points(ds$test[ds$outlier_position,], pch=3, lwd=2, col="red")