Description Usage Arguments Details Value Examples

View source: R/synthetic_stream.R

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.

1 2 | ```
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))
``` |

`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 |

`rangeVar` |
Used to create the random covariance matrices for the
clusters. See |

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`

.

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. |

1 2 3 4 5 6 7 8 9 |

```
Loading required package: proxy
Attaching package: 'proxy'
The following objects are masked from 'package:stats':
as.dist, dist
The following object is masked from 'package:base':
as.matrix
Loading required package: igraph
Attaching package: 'igraph'
The following objects are masked from 'package:stats':
decompose, spectrum
The following object is masked from 'package:base':
union
Attaching package: 'rEMM'
The following objects are masked from 'package:igraph':
as.igraph, clusters
```

rEMM documentation built on May 30, 2017, 12:40 a.m.

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