This functions create data partitions and compute assessment metrics.

1 2 3 4 5 | ```
## S4 method for signature 'twdtwTimeSeries'
splitDataset(object, times = 1, p = 0.1, ...)
## S4 method for signature 'list'
twdtwAssess(object, matrix = FALSE)
``` |

`object` |
an object of class |

`times` |
Number of partitions to create. |

`p` |
the percentage of data that goes to training.
See |

`...` |
Other arguments to be passed to |

`matrix` |
logical. If TRUE retrieves the confusion matrix. FALSE retrieves User's Accuracy (UA) and Producer's Accuracy (PA). Dafault is FALSE. |

`splitDataset`

:This function splits the a set of time series into training and validation. The function uses stratified sampling and a simple random sampling for each stratum. Each data partition returned by this function has the temporal patterns and a set of time series for validation.

`twdtwAssess`

:The function

`splitDataset`

performs the assessment of the classification based on the labels of the classified time series (Reference) and the labels of the classification (Predicted). This function returns a data.frame with User's and Produce's Accuracy or a list for confusion matrices.

Victor Maus, vwmaus1@gmail.com

`twdtwMatches-class`

,
`twdtwApply`

, and
`twdtwClassify`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
## Not run:
load(system.file("lucc_MT/field_samples_ts.RData", package="dtwSat"))
set.seed(1)
partitions = splitDataset(field_samples_ts, p=0.1, times=5,
freq = 8, formula = y ~ s(x, bs="cc"))
log_fun = logisticWeight(alpha=-0.1, beta=50)
twdtw_res = lapply(partitions, function(x){
res = twdtwApply(x = x$ts, y = x$patterns, weight.fun = log_fun, n=1)
twdtwClassify(x = res)
})
assessment = twdtwAssess(twdtw_res)
head(assessment, 5)
## End(Not run)
``` |

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