plotLossDistribution

Plot CDF and histogram of aggregated window loss distribution

Since R2023a

Syntax

plotLossDistribution(d,data)

plotLossDistribution(d,data,data2)

plotLossDistribution(___,Name=Value)

f = plotLossDistribution(___)

Description

plotLossDistribution(d,data) plots the distribution of window losses computed over the input signal data set data by the detector d.

plotLossDistribution(d,data,data2) specifies a second signal data set data2 over which the detector computes the distribution of window losses. This syntax is useful when you want to compare the loss distribution between two data sets.

plotLossDistribution(___,Name=Value) specifies additional options using name-value arguments.

f = plotLossDistribution(___) returns the figure handle of the plot.

Examples

Plot Window Loss Distribution

Load a convolutional anomaly detector trained with three-channel sinusoidal signals. Display the model, threshold, and window properties of the detector.

load sineWaveAnomalyDetector
D

D = 
  deepSignalAnomalyDetectorCNN with properties:

                IsTrained: 1
              NumChannels: 3

   Model Information
                ModelType: 'conv'
               FilterSize: 8
               NumFilters: 32
      NumDownsampleLayers: 2
         DownsampleFactor: 2
       DropoutProbability: 0.2000

   Threshold Information
                Threshold: 0.0510
          ThresholdMethod: 'contaminationFraction'
       ThresholdParameter: 0.0100

   Window Information
             WindowLength: 1
            OverlapLength: 'auto'
    WindowLossAggregation: 'mean'

Load the file sineWaveAnomalyData.mat, which contains two sets of synthetic three-channel sinusoidal signals.

• sineWaveNormal contains the 10 sinusoids used to train the convolutional anomaly detector. Each signal has a series of small-amplitude impact-like imperfections but otherwise has stable amplitude and frequency.

• sineWaveAbnormal contains three signals of similar length and amplitude to the training data. One of the signals has an abrupt, finite-time change in frequency. Another signal has a finite-duration amplitude change in one of its channels. A third has random spikes in each channel.

Plot three normal signals and the three signals with anomalies.

load sineWaveAnomalyData
 
tiledlayout(3,2,TileSpacing="compact",Padding="compact")
rnd = randperm(length(sineWaveNormal));
for kj = 1:length(sineWaveAbnormal)
    nexttile
    plot(sineWaveNormal{rnd(kj)})
    title("Normal Signal")
    nexttile
    plot(sineWaveAbnormal{kj})
    title("Signal with Anomalies")
end

Use the plotLossDistribution function to display the cumulative distribution function and probability histogram of the window loss distribution computed by the detector over the set of signals with anomalies. The plot also includes the manually specified threshold.

clf
plotLossDistribution(D,sineWaveAbnormal)

Input Arguments

d — Anomaly detector

deepSignalAnomalyDetectorCNN object | deepSignalAnomalyDetectorLSTM object

Anomaly detector, specified as a deepSignalAnomalyDetectorCNN or deepSignalAnomalyDetectorLSTM object. Use the deepSignalAnomalyDetector function to create d.

data, data2 — Signal data set

N_c-column matrix | M-element cell array | timetable | datastore

Signal data set, specified as one of these:

• N_c-column matrix — A single multichannel signal observation (M = 1), where N_c is equal to the value of the NumChannels property of the detector.

• M-element cell array — M multichannel signal observations, where each cell contains an N_c-column matrix.

• Timetable — A single multichannel signal observation, contained in a MATLAB^® timetable. The timetable must contain increasing, uniformly-sampled, and finite values. The timetable can have:

  • A single variable containing an N_c-column matrix, where each column corresponds to a signal channel.

  • N_c variables, where each variable contains a vector that corresponds to a signal channel.

• Datastore — A signalDatastore, audioDatastore (Audio Toolbox), or arrayDatastore object. The detector uses the readall function to read all the signal observations contained in the datastore at once. You can also use a CombinedDatastore or TransformedDatastore object containing any of the supported datastores.

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Example: MiniBatchSize=64,ExecutionEnvironment="cpu" instructs the function to use a mini-batch size of 64 and use the computer CPU to detect anomalies.

MiniBatchSize — Mini-batch size

128 (default) | positive integer scalar

Mini-batch size used by the network to compute reconstructed signals, specified as a positive integer scalar.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

ExecutionEnvironment — Execution environment

"auto" (default) | "gpu" | "cpu"

Execution environment used by the network, specified as one of these:

• "auto" — If available, use the GPU. If the GPU is not available, use the CPU.

• "gpu" — Use the GPU.

• "cpu" — Use the CPU.

Data Types: char | string

Output Arguments

f — Figure handle

figure handle

Figure handle of the plot, returned as a figure handle.

Extended Capabilities

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Usage notes and limitations:

The ExecutionEnvironment option must be "gpu" or "auto" when the input data is:

• A gpuArray

• A cell array containing gpuArray objects

• A datastore that outputs cell arrays containing gpuArray objects

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Version History

Introduced in R2023a