`BoltzmannMachinesPlots.BoltzmannMachinesPlots`

— ModuleContains all plotting functions for displaying information collected in module `BoltzmannMachines`

. Most important function is `plotevaluation`

.

`BoltzmannMachinesPlots.crossvalidationcurve`

— Function```
crossvalidationcurve(monitor)
crossvalidationcurve(monitor, evaluationkey)
```

Plots the results of cross-validation experiments conducted over the course of the training. For each training epoch, the results of the evaluations for the different data sets used in the cross-validation are displayed in form of box plots. The mean values of the evaluations are also displayed. Such a plot be used to determine the optimal number of training epochs if the other parameters are given.

The `monitor`

argument contains the monitoring results of an evaluation criterion. Similar to `plotevaluation`

, an `evaluationkey`

can be specified if the `monitor`

object contains multiple evaluations.

See also: `BoltzmannMachines.crossvalidation`

, `BoltzmannMachines.monitored_fitrbm`

, `BoltzmannMachines.monitored_fitdbm`

.

`BoltzmannMachinesPlots.plotestimatedprob`

— MethodPlots the information about the estimated lower bound of the log probability that has been gathered while training a BMs.

`BoltzmannMachinesPlots.plotevaluation`

— Function```
plotevaluation(monitor; ...)
plotevaluation(monitor, evaluationkey; ...)
```

Plots a curve that shows the values of the evaluation contained in the `monitor`

and specified by the `evaluationkey`

over the course of the training epochs. If no `evaluationkey`

is specified, the evaluation type of the first monitor element is used.

Optional keyword argument `sdrange`

: For evaluations with keys `BoltzmannMachines.monitorloglikelihood`

and `BoltzmannMachines.monitorlogproblowerbound`

, there is additional information about the standard deviation of the estimator. With the parameter `sdrange`

, it is possible to display this information as a ribbon around the curve. The ribbon indicates the area around the curve that contains the values that deviate at maximum `sdrange`

times the standard deviation from the estimator. Default value for `sdrange`

is 2.0.

`BoltzmannMachinesPlots.scatter`

— Method`scatter(xy; ...)`

Creates a two-dimensional scatter plot from the first two columns of the matrix `xy`

. Each of the rows in the matrix is displayed as one point. The two columns of the matrix contain the x- and y-values of the points.

**Optional named arguments:**

`labels`

: a vector of labels of the same length as the columns in the matrix`xy`

. Each entry contains a label for a row of`xy`

.`opacity`

: the opacity of the dots`xlabel`

: a label for the x-axis`ylabel`

: a label for the y-axis

```
scatterhidden(bm, x; ...)
scatterhidden(h; ...)
```

Creates a scatter plot of the logarithmized activation potential of hidden nodes. The activation is either induced by the dataset `x`

in the Boltzmann machine `bm`

or it is directly specified as matrix `h`

. This function can be used to inspect pairs of hidden nodes. For getting a reduced view on a larger number of hidden nodes, consider employing `BoltzmannMachines.top2latentdims`

and `scatter`

.

**Optional keyword arguments:**

`hiddennodes`

: Tuple of integers, default`(1,2)`

, selecting the first two nodes of the (last) hidden layer.`labels`

: a vector containing string labels for each of the data points