This function generates Gaussian clusters with different numbers of points per cluster.
Usage
gau_clust_diff(
clust_size_vec,
num_clust,
mean_matrix,
var_vec,
num_dims,
num_noise,
min_n,
max_n
)Arguments
- clust_size_vec
A vector specifying the number of points in each cluster.
- num_clust
The number of clusters to generate.
- mean_matrix
A matrix where each row represents the mean vector for a cluster.
- var_vec
A vector specifying the variance for each cluster.
- num_dims
The number of effective dimensions for the data points.
- num_noise
The number of additional noise dimensions to be generated.
- min_n
The minimum value for the noise added to the data points.
- max_n
The maximum value for the noise added to the data points.
Examples
# Generate Gaussian clusters with custom parameters
set.seed(20240412)
data <- gau_clust_diff(
clust_size_vec = c(50, 100, 200, 50),
num_clust = 4, mean_matrix =
rbind(
c(1, 0, 0, 0, 0, 0), c(0, 1, 0, 0, 0, 0),
c(0, 0, 1, 0, 0, 0), c(0, 0, 0, 1, 0, 0)
),
var_vec = c(0.02, 0.05, 0.06, 0.1),
num_dims = 6, num_noise = 4,
min_n = -0.05, max_n = 0.05
)