Package 'sGMRFmix'

Title: Sparse Gaussian Markov Random Field Mixtures for Anomaly Detection
Description: An implementation of sparse Gaussian Markov random field mixtures presented by Ide et al. (2016) <doi:10.1109/ICDM.2016.0119>. It provides a novel anomaly detection method for multivariate noisy sensor data. It can automatically handle multiple operational modes. And it can also compute variable-wise anomaly scores.
Authors: Koji Makiyama [cre, aut]
Maintainer: Koji Makiyama <[email protected]>
License: MIT + file LICENSE
Version: 0.3.0
Built: 2024-11-20 03:54:13 UTC
Source: https://github.com/cran/sGMRFmix

Help Index

Compute anomaly scores

Description

Compute anomaly scores

Usage

compute_anomaly_score(obj, x, window_size = 1L, ...)

Arguments

obj

object
x

data.frame to compute anomaly scores
window_size

integer.
...

additional arguments

Value

matrix of anomaly scores

Examples

library(sGMRFmix)

set.seed(314)
train_data <- generate_train_data()
fit <- sGMRFmix(train_data, K = 7, rho = 10)

test_data <- generate_test_data()
compute_anomaly_score(fit, test_data)

Generate test data

Description

Generate test data

Usage

generate_test_data()

Generate test labels

Description

Generate test labels

Usage

generate_test_labels()

Generate train data

Description

Generate train data

Usage

generate_train_data()

Plot multivariate data

Description

Plot multivariate data

Usage

plot_multivariate_data(df, label = NULL, order_by = index(df),
  guide_title = NULL, fix_scale = FALSE, point_size = 1L)

Arguments

df

data.frame of multivariate data
label

data.frame of label for each variables. Or vector of label for each observation.
order_by

vector. An x-axis of plots.
guide_title

character.
fix_scale

logical.
point_size

integer. Point size.

Value

ggplot2 object

Examples

library(sGMRFmix)

test_data <- generate_test_data()
test_label <- generate_test_labels()

plot_multivariate_data(test_data)
plot_multivariate_data(test_data, test_label)

Sparse Gaussian Markov Random Field Mixtures

Description

Sparse Gaussian Markov Random Field Mixtures

Usage

sGMRFmix(x, K, rho, kmeans = FALSE, m0 = rep(0, M), lambda0 = 1,
  alpha = NULL, pi_threshold = 1/K/100, max_iter = 500, tol = 0.1,
  verbose = TRUE)

Arguments

x

data.frame. A training data.
K

integer. Number of mixture components. Set a large enough number because the algorithm identifies major dependency patterns from the data via the sparse mixture model.
rho

double. Constant that multiplies the penalty term. An optimal value should be determined together with the threshold on the anomaly score, so the performance of anomaly detection is maximized.
kmeans

logical. If TRUE, initialize parameters with k-means method. You should set TRUE for non-time series data. Default FALSE.
m0

a numeric vector. Location parameter of Gauss-Laplace prior. Keep default if no prior information is available. Default 0.
lambda0

double. Coefficient for scale parameter of Gauss-Laplace prior. Keep default if no prior information is available. Default 1.
alpha

double. Concentration parameter of Dirichlet prior. Keep default if no prior information is available. Default 1.
pi_threshold

double. Threshold to decide a number of states. If pi < pi_threshold, the states are rejected in the sense of sparse estimation.
max_iter

integer. Maximum number of iterations.
tol

double. The tolerance to declare convergence.
verbose

logical.

Value

sGMRFmix object

Examples

library(sGMRFmix)

set.seed(314)
train_data <- generate_train_data()
fit <- sGMRFmix(train_data, K = 7, rho = 10)
fit