Type:	Package
Title:	Collection of Data Structures
Version:	0.1.1
Description:	A collection of simple simulation datasets designed for generating Nonlinear Dimension Reduction representations techniques such as t-distributed Stochastic Neighbor Embedding, and Uniform Manifold Approximation and Projection. These datasets serve as a valuable resource for understanding the reliability of Nonlinear Dimension Reduction representations in various contexts.
License:	MIT + file LICENSE
URL:	https://github.com/JayaniLakshika/cardinalR
BugReports:	https://github.com/JayaniLakshika/cardinalR/issues
Depends:	R (≥ 3.5.0)
Imports:	purrr, stats
Suggests:	knitr, langevitour, rmarkdown, testthat (≥ 3.0.0)
VignetteBuilder:	knitr
Config/testthat/edition:	3
Encoding:	UTF-8
Language:	en-GB
LazyData:	true
RoxygenNote:	7.3.1
NeedsCompilation:	no
Packaged:	2024-04-15 06:22:26 UTC; jpiy0001
Author:	Jayani P.G. Lakshika [aut, cre], Dianne Cook [aut], Paul Harrison [aut], Michael Lydeamore [aut], Thiyanga S. Talagala [aut]
Maintainer:	Jayani P.G. Lakshika <jayanilakshika76@gmail.com>
Repository:	CRAN
Date/Publication:	2024-04-16 09:00:06 UTC

Generate Cell Cycle Data with Noise

Description

This function generates a cell cycle dataset with added noise dimensions.

Usage

cell_cycle(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the cell cycle data with added noise.

Examples

set.seed(20240412)
cell_cycle_data <- cell_cycle(
  n = 300, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Generate Clusters with Different Shapes

Description

This function generates clusters with different shapes, including both Gaussian and non-Gaussian clusters.

Usage

clust_diff_shapes(
  n,
  num_gau_clust,
  num_non_gau_clust,
  clust_sd_gau,
  clust_sd_non_gau,
  num_dims,
  a,
  b
)

Arguments

Value

A matrix containing the generated clusters with different shapes.

Examples

# Generate clusters with default parameters
set.seed(20240412)
data <- clust_diff_shapes(
  n = 300, num_gau_clust = 4,
  num_non_gau_clust = 2, clust_sd_gau = 0.05, clust_sd_non_gau = 0.1,
  num_dims = 7, a = 2, b = 4
)

Generate Clusters with Different Shapes and Different Number of Points

Description

This function generates clusters with different shapes, including both Gaussian and non-Gaussian clusters, with different numbers of points in each cluster.

Usage

clust_diff_shapes_pts(
  clust_size_vec,
  num_gau_clust,
  num_non_gau_clust,
  clust_sd_gau,
  clust_sd_non_gau,
  num_dims,
  a,
  b
)

Arguments

Value

A matrix containing the generated clusters with different shapes and different numbers of points.

Examples

# Generate clusters with default parameters
set.seed(20240412)
data <- clust_diff_shapes_pts(
  clust_size_vec = c(50, 50, 50, 50, 100, 100),
  num_gau_clust = 4,
  num_non_gau_clust = 2, clust_sd_gau = 0.05, clust_sd_non_gau = 0.1,
  num_dims = 7, a = 2, b = 4
)

Generate data points along a conic spiral curve with optional noise.

Description

This function generates data points along a conic spiral curve with optional noise.

Usage

conic_spiral_3d(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data points with or without added noise.

Examples

set.seed(20240412)
conic_spiral_3d(n = 100, num_noise = 2, min_n = -0.05, max_n = 0.05)

Generate points on a conic spiral in 3D space.

Description

This function generates points on a conic spiral in 3D space.

Usage

conic_spiral_3d_row(a, b, c, w)

Arguments

Value

A matrix containing the generated points on the conic spiral.

Examples

set.seed(20240412)
conic_spiral_3d_row(1, 2, 0.5, 3)

Generate a 3D cube with optional noise.

Description

This function generates a 3D cube along with optional noise.

Usage

cube_3d(num_dims, num_noise, min_n, max_n)

Arguments

Value

A list containing the generated data matrix and the sample size.

Examples

set.seed(20240412)
cube_3d(num_dims = 3, num_noise = 2, min_n = -0.01, max_n = 0.01)

Generate points on a curvilinear 2D manifold

Description

This function generates points on a curvilinear 2D manifold based on a nonlinear equation.

Usage

curv_2d(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated points on the curvilinear 2D manifold.

Examples

set.seed(20240412)
curvilinear_points <- curv_2d(
  n = 100, num_noise = 2, min_n = -0.01,
  max_n = 0.01
)

Generate Curvy Branching Clusters with Noise

Description

This function generates data with curvy branching clusters along with added noise.

Usage

curvy_branch(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate curvy branching clusters with noise with custom parameters
set.seed(20240412)
data <- curvy_branch(n = 200, num_noise = 2, min_n = -0.05, max_n = 0.05)

Generate Curvy Branching Cluster Data

Description

This function generates curvy branching cluster data with three clusters of different shapes.

Usage

curvy_branch_clust(n, clust_vec, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate curvy branching cluster data with custom parameters
set.seed(20240412)
data <- curvy_branch_clust(
  n = 300, clust_vec = c(100, 150, 50),
  num_noise = 2, min_n = -0.05, max_n = 0.05
)

Generate Curvy Branching Cluster Data with Background Noise

Description

This function generates data with four clusters, two of which follow a curvilinear pattern and the other two are distributed randomly.

Usage

curvy_branch_clust_bkg(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate curvy branching cluster data with background noise with custom parameters
set.seed(20240412)
data <- curvy_branch_clust_bkg(
  n = 400, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Generate Curvy Cell Cycle Data with Noise

Description

This function generates a curvy cell cycle dataset with added noise dimensions.

Usage

curvy_cycle(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the curvy cell cycle data with added noise.

Examples

set.seed(20240412)
curvy_cell_cycle_data <- curvy_cycle(
  n = 300, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Generate Curvy Tree Data with Noise

Description

This function generates a dataset representing a curvy tree structure, with added noise.

Usage

curvy_tree(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the curvy tree data with added noise.

Examples

set.seed(20240412)
tree_data <- curvy_tree(n = 300, num_noise = 2, min_n = -0.05, max_n = 0.05)

Generate data representing small spheres within a larger encompassing sphere with added noise.

Description

This function generates data points representing small spheres within a larger encompassing sphere and adds noise to the data if specified.

Usage

diff_sphere(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data points with or without added noise.

Examples

set.seed(20240412)
diff_sphere(
  n = 390, num_noise = 2,
  min_n = -0.05, max_n = 0.05
)

Generate points sampled from the Dini surface with optional noise.

Description

This function generates points sampled from the Dini surface along with optional noise.

Usage

dini_surface_3d(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data points with or without added noise.

Examples

set.seed(20240412)
dini_surface_3d(n = 100, num_noise = 2, min_n = -0.05, max_n = 0.05)

Generate points on a Dini's surface.

Description

This function generates points on a Dini's surface.

Usage

dini_surface_3d_row(a = 1, b = 1)

Arguments

Value

A matrix containing the generated points on the surface.

Examples

set.seed(20240412)
dini_surface_3d_row(a = 1, b = 1)

Generate Eight Branching Data with Noise

Description

This function generates a dataset representing eight branching patterns, with added noise.

Usage

eight_branch(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the eight branching data with added noise.

Examples

set.seed(20240412)
branching_data <- eight_branch(
  n = 400, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Generate Four-Branching Data with Noise

Description

This function generates a dataset representing four branches with added noise.

Usage

four_branch(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the four-branching data with added noise.

Examples

set.seed(20240412)
four_branching_data <- four_branch(
  n = 400, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Generate Four Different Long Clusters with Noise

Description

This function generates a dataset consisting of four different long clusters with added noise.

Usage

four_long_clust(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the four different long clusters with added noise.

Examples

set.seed(20240412)
four_diff_long_clusters <- four_long_clust(
  n = 200, num_noise = 2,
  min_n = -0.05, max_n = 0.05
)

Generate Four Long Clusters with Background Noise

Description

This function generates data with four long clusters along with background noise.

Usage

four_long_clust_bkg(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate four long clusters with background noise with custom parameters
set.seed(20240412)
data <- four_long_clust_bkg(
  n = 400, num_noise = 4, min_n = -0.05,
  max_n = 0.05
)

Generate synthetic data with Gaussian clusters

Description

Generate Gaussian Clusters

Usage

gau_clust(
  n,
  num_clust,
  mean_matrix,
  var_vec,
  num_dims,
  num_noise,
  min_n,
  max_n
)

Arguments

Details

This function generates Gaussian clusters with specified parameters.

Value

A matrix containing the generated Gaussian clusters.

Examples

set.seed(20240412)
gau_clust(
  n = 300, num_clust = 5,
  mean_matrix = rbind(
    c(1, 0, 0, 0), c(0, 1, 0, 0), c(0, 0, 1, 0),
    c(0, 0, 0, 1), c(0, 0, 0, 0)
  ), var_vec = c(0.05, 0.05, 0.05, 0.05, 0.05),
  num_dims = 4, num_noise = 2, min_n = -0.05, max_n = 0.05
)

Generate Gaussian Clusters with Different Points

Description

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

Value

A matrix containing the generated Gaussian clusters with different 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
)

Generate Cluster and Curvilinear Data with Noise

Description

This function generates data with two clusters, one following a curvilinear pattern and the other distributed randomly.

Usage

gau_curvy_clust(n, clust_size_vec, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate cluster and curvilinear data with custom parameters
set.seed(20240412)
data <- gau_curvy_clust(
  n = 300, clust_size_vec = c(100, 200), num_noise = 3,
  min_n = -0.05, max_n = 0.05
)

Generate Clusters and Curvilinear Data with Noise

Description

This function generates data with clusters and curvilinear patterns along with added background noise.

Usage

gau_curvy_clust_bkg(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate clusters and curvilinear data with noise with custom parameters
set.seed(20240412)
data <- gau_curvy_clust_bkg(
  n = 260, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Generate Background Noise Data

Description

This function generates background noise data with specified parameters such as the number of samples, number of dimensions, mean, and standard deviation.

Usage

gen_bkg_noise(n, num_dims, mean, sd)

Arguments

Value

A matrix containing the generated background noise data, with n rows and num_dims columns.

Examples

# Generate background noise with custom mean and standard deviation
set.seed(20240412)
gen_bkg_noise(n = 50, num_dims = 3, mean = 5, sd = 2)

Generate Random Noise Dimensions

Description

This function generates random noise dimensions to be added to the coordinates of a sphere.

Usage

gen_noise_dims(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated random noise dimensions.

Examples

# Generate random noise dimensions with 3 dimensions, minimum value -1, and maximum value 1
set.seed(20240412)
gen_noise_dims(n = 50, num_noise = 3, min_n = -0.01, max_n = 0.01)

Generate Mirror S-curve Datasets with Noise

Description

This function generates mirror S-curve datasets with added noise dimensions.

Usage

mirror_scurves(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the combined mirror S-curve datasets with added noise.

Examples

set.seed(20240412)
mirror_s_curve_data <- mirror_scurves(
  n = 200, num_noise = 2,
  min_n = -0.05, max_n = 0.05
)

Generate a 5-D Mobius Strip

Description

This function generates a dataset representing a 5-dimensional Mobius strip.

Usage

mobius_5d(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated Mobius strip.

Examples

set.seed(20240412)
mobius_data <- mobius_5d(n = 100, num_noise = 2, min_n = -0.05, max_n = 0.05)

Generate a Single Row for a 5-D Mobius Strip

Description

This function generates a single row of data representing a point on a 5-dimensional Mobius strip.

Usage

mobius_5d_row()

Value

A vector containing the coordinates of the point on the Mobius strip.

Examples

set.seed(20240412)
mobius_row <- mobius_5d_row()

Generate Mobius Cluster with Noise

Description

This function generates a dataset consisting of a mobius cluster with added noise.

Usage

mobius_clust(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the mobius cluster with added noise.

Examples

mobius_cluster <- mobius_clust(
  n = 200, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Mobius clust dataset with noise dimensions

Description

The 'mobius_clust_data' dataset contains a 3-dimensional Mobius and Gaussian cluster with added noise dimensions. Each data point is represented by five dimensions (x1 to x5).

Usage

data(mobius_clust_data)

Format

A data frame with 500 rows and 5 columns:

x1, x2, x3, x4, x5

High-dimensional coordinates

Source

This dataset is generated for illustrative purposes.

Examples

# Load the mobius_clust_data dataset
data(mobius_clust_data)

# Display the first few rows of the dataset
head(mobius_clust_data)

tSNE embedding for mobius_clust_data dataset which with noise dimensions tSNE parameters set to perplexity: 15.

Description

The 'mobius_clust_tsne_param1' dataset contains the tSNE (t-distributed Stochastic Neighbor Embedding) embeddings of a five-dimensional mobius_clust_data. Each data point is represented by two tSNE coordinates (emb1 and emb2).

Usage

data(mobius_clust_tsne_param1)

Format

## 'mobius_clust_tsne_param1' A data frame with 500 rows and 2 columns:

emb1

Numeric, first tSNE 2D embeddings.

emb2

Numeric, second tSNE 2D embeddings.

Source

This dataset is generated for illustrative purposes.

Examples

# Load the mobius_clust_tsne_param1 dataset
data(mobius_clust_tsne_param1)

# Display the first few rows of the dataset
head(mobius_clust_tsne_param1)

tSNE embedding for mobius_clust_data dataset which with noise dimensions tSNE parameters set to perplexity: 30.

Description

The 'mobius_clust_tsne_param2' dataset contains the tSNE (t-distributed Stochastic Neighbor Embedding) embeddings of a five-dimensional mobius_clust_data. Each data point is represented by two tSNE coordinates (emb1 and emb2).

Usage

data(mobius_clust_tsne_param2)

Format

## 'mobius_clust_tsne_param2' A data frame with 500 rows and 2 columns:

emb1

Numeric, first tSNE 2D embeddings.

emb2

Numeric, second tSNE 2D embeddings.

Source

This dataset is generated for illustrative purposes.

Examples

# Load the mobius_clust_tsne_param2 dataset
data(mobius_clust_tsne_param2)

# Display the first few rows of the dataset
head(mobius_clust_tsne_param2)

tSNE embedding for mobius_clust_data dataset which with noise dimensions tSNE parameters set to perplexity: 5.

Description

The 'mobius_clust_tsne_param3' dataset contains the tSNE (t-distributed Stochastic Neighbor Embedding) embeddings of a five-dimensional mobius_clust_data. Each data point is represented by two tSNE coordinates (emb1 and emb2).

Usage

data(mobius_clust_tsne_param3)

Format

## 'mobius_clust_tsne_param3' A data frame with 500 rows and 2 columns:

emb1

Numeric, first tSNE 2D embeddings.

emb2

Numeric, second tSNE 2D embeddings.

Source

This dataset is generated for illustrative purposes.

Examples

# Load the mobius_clust_tsne_param1 dataset
data(mobius_clust_tsne_param3)

# Display the first few rows of the dataset
head(mobius_clust_tsne_param3)

UMAP embedding for mobius_clust_data dataset which with noise dimensions UMAP parameters set to n-neigbors: 15 and min-dist: 0.1.

Description

The 'mobius_clust_umap_param1' dataset contains the UMAP (Uniform Manifold Approximation and Projection) embeddings of a five-dimensional mobius_clust_data. Each data point is represented by two UMAP coordinates (emb1 and emb2).

Usage

data(mobius_clust_umap_param1)

Format

## 'mobius_clust_umap_param1' A data frame with 500 rows and 2 columns:

emb1

Numeric, first UMAP 2D embeddings.

emb2

Numeric, second UMAP 2D embeddings.

Source

This dataset is generated for illustrative purposes.

Examples

# Load the mobius_clust_umap_param1 dataset
data(mobius_clust_umap_param1)

# Display the first few rows of the dataset
head(mobius_clust_umap_param1)

UMAP embedding for mobius_clust_data dataset which with noise dimensions UMAP parameters set to n-neigbors: 30 and min-dist: 0.08.

Description

The 'mobius_clust_umap_param2' dataset contains the UMAP (Uniform Manifold Approximation and Projection) embeddings of a five-dimensional mobius_clust_data. Each data point is represented by two UMAP coordinates (emb1 and emb2).

Usage

data(mobius_clust_umap_param2)

Format

## 'mobius_clust_umap_param2' A data frame with 500 rows and 2 columns:

emb1

Numeric, first UMAP 2D embeddings.

emb2

Numeric, second UMAP 2D embeddings.

Source

This dataset is generated for illustrative purposes.

Examples

# Load the mobius_clust_umap_param2 dataset
data(mobius_clust_umap_param2)

# Display the first few rows of the dataset
head(mobius_clust_umap_param2)

UMAP embedding for mobius_clust_data dataset which with noise dimensions UMAP parameters set to n-neigbors: 5 and min-dist: 0.9.

Description

The 'mobius_clust_umap_param3' dataset contains the UMAP (Uniform Manifold Approximation and Projection) embeddings of a five-dimensional mobius_clust_data. Each data point is represented by two UMAP coordinates (emb1 and emb2).

Usage

data(mobius_clust_umap_param3)

Format

## 'mobius_clust_umap_param3' A data frame with 500 rows and 2 columns:

emb1

Numeric, first UMAP 2D embeddings.

emb2

Numeric, second UMAP 2D embeddings.

Source

This dataset is generated for illustrative purposes.

Examples

# Load the mobius_clust_umap_param3 dataset
data(mobius_clust_umap_param3)

# Display the first few rows of the dataset
head(mobius_clust_umap_param3)

Generate points on a nonlinear 2D manifold

Description

This function generates points on a nonlinear 2D manifold based on a given equation.

Usage

nonlinear_2d(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated points on the nonlinear 2D manifold.

Examples

set.seed(20240412)
nonlinear_points <- nonlinear_2d(
  n = 100, num_noise = 2, min_n = -0.01,
  max_n = 0.01
)

Generate Nonlinear Connected Data with Noise

Description

This function generates a dataset representing nonlinear connected clusters with added noise.

Usage

nonlinear_connect(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the nonlinear connected data with noise.

Examples

set.seed(20240412)
nonlinear_connect <- nonlinear_connect(
  n = 400, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Generate Nonlinear Mirror Data with Noise

Description

This function generates a dataset representing two mirror-image clusters with added noise.

Usage

nonlinear_mirror(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the nonlinear mirror data with noise.

Examples

set.seed(20240412)
nonlinear_mirror <- nonlinear_mirror(
  n = 400, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Generate Doublets with Noise

Description

This function generates data with one set of doublets (pairs of clusters) along with added background noise.

Usage

one_doublet(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate doublets with noise with custom parameters
set.seed(20240412)
data <- one_doublet(n = 220, num_noise = 2, min_n = -0.05, max_n = 0.05)

Generate Doublets with Background Noise

Description

This function generates data with doublets (pairs of clusters) along with added background noise.

Usage

one_doublet_bkg(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate doublets with background noise with custom parameters
set.seed(20240412)
data <- one_doublet_bkg(n = 250, num_noise = 2, min_n = -0.05, max_n = 0.05)

Generate Doublets with Different Pattern Clusters and Noise

Description

This function generates data with one set of doublets (pairs of clusters) having different patterns, along with added background noise.

Usage

one_doublet_diff_patterns(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate doublets with different pattern clusters and noise with custom parameters
set.seed(20240412)
data <- one_doublet_diff_patterns(
  n = 280, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Generate Doublets with Different Variance Clusters and Noise

Description

This function generates data with one set of doublets (pairs of clusters) having clusters with different variance, along with added background noise.

Usage

one_doublet_diff_var_clust(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate doublets with different variance clusters and noise with custom parameters
set.seed(20240412)
data <- one_doublet_diff_var_clust(
  n = 260, num_noise = 2,
  min_n = -0.05, max_n = 0.05
)

Generate Doublets with Four Clusters and Noise

Description

This function generates data with one set of doublets (pairs of clusters) containing four clusters, along with added background noise.

Usage

one_doublet_four_clusts(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate doublets with four clusters and noise with custom parameters
set.seed(20240412)
data <- one_doublet_four_clusts(
  n = 440, num_noise = 2,
  min_n = -0.05, max_n = 0.05
)

Generate Grid Data with Noise

Description

This function generates a grid dataset with specified grid points along the x and y axes, and optionally adds noise dimensions.

Usage

one_grid(nx, ny, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the grid data with added noise.

Examples

set.seed(20240412)
one_grid <- one_grid(nx = 10, ny = 10, num_noise = 2, min_n = -0.05, max_n = 0.05)

Generate One Grid with Different Values and Background Noise

Description

This function generates a grid dataset with different values and background noise.

Usage

one_grid_bkg(n_value, num_noise, min_n, max_n)

Arguments

Value

A list containing the one grid datasets with background noise and the sample size.

Examples

set.seed(20240412)
one_grid_bkg <- one_grid_bkg(
  n_value = 10, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Generate points on a plane in 2D space

Description

This function generates points on a plane in 3D space based on the provided coefficients, intercepts, and ranges for the parameters.

Usage

plane(
  n,
  coef_x1,
  coef_x2,
  coef_y1,
  coef_y2,
  intercept_x,
  intercept_y,
  u_min,
  u_max,
  v_min,
  v_max,
  num_noise,
  min_n,
  max_n
)

Arguments

Value

A matrix containing the generated points on the plane.

Examples

set.seed(20240412)
plane_points <- plane(
  n = 100, coef_x1 = 1, coef_x2 = 1,
  coef_y1 = -1, coef_y2 = 1, intercept_x = -10,
  intercept_y = 8, u_min = 10, u_max = 30, v_min = 10, v_max = 20,
  num_noise = 2, min_n = -0.05, max_n = 0.05
)

Generate 2D Plane with Hole and Noise

Description

This function generates a dataset representing a 2D plane with a hole in the middle, with added noise.

Usage

plane_2d_hole(n, num_noise, min_n, max_n)

Arguments

Value

A list containing the 2D plane data with a hole and the sample size.

Examples

set.seed(20240412)
plane_data <- plane_2d_hole(
  n = 100, num_noise = 2,
  min_n = -0.05, max_n = 0.05
)

Generate data points on a Roman surface with optional noise.

Description

This function generates data points on a Roman surface with optional noise.

Usage

roman_surface_3d(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data points with or without added noise.

Examples

set.seed(20240412)
roman_surface_3d(n = 100, num_noise = 2, min_n = -0.05, max_n = 0.05)

Generate points on a Roman surface in 3D space.

Description

This function generates points on a Roman surface in 3D space.

Usage

roman_surface_3d_row(a = 1)

Arguments

Value

A matrix containing the generated points on the Roman surface in 3D space.

Examples

set.seed(20240412)
roman_surface_3d_row(a = 1)

Generate S-curve Data

Description

This function generates S-curve data, which is a commonly used dataset for testing and visualizing dimensionality reduction algorithms.

Usage

scurve(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated S-curve data.

Examples

set.seed(20240412)
s_curve_data <- scurve(
  n = 100, num_noise = 2,
  min_n = -0.05, max_n = 0.05
)

Generate S-curve Data with a Hole

Description

This function generates S-curve data with a hole by filtering out samples that are not close to a specified anchor point.

Usage

scurve_hole(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated S-curve data with a hole.

Examples

set.seed(20240412)
s_curve_hole_data <- scurve_hole(
  n = 100, num_noise = 2,
  min_n = -0.05, max_n = 0.05
)

Generate Seven-Branching Data with Noise

Description

This function generates a dataset representing seven branches with added noise.

Usage

seven_branch(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the seven-branching data with added noise.

Examples

set.seed(20240412)
seven_branching_data <- seven_branch(
  n = 210, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Generate Sine Curve Data with Noise

Description

This function generates a dataset representing a sine curve with added noise.

Usage

sine_curve(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the sine curve data with noise.

Examples

set.seed(20240412)
sine_curve <- sine_curve(n = 100, num_noise = 2, min_n = -0.05, max_n = 0.05)

Generate Coordinates for a Sphere

Description

This function generates the coordinates for a sphere in three-dimensional space.

Usage

sphere(radius, resolution, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the Cartesian coordinates of the points on the sphere.

Examples

# Generate coordinates for a sphere with radius 1 and resolution 20
set.seed(20240412)
sphere(
  radius = 1, resolution = 20, num_noise = 3, min_n = -0.05,
  max_n = 0.05
)

Generate a spiral dataset with optional noise.

Description

This function generates a dataset arranged in a spiral pattern with optional noise.

Usage

spiral_3d(n, num_dims, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data points with or without added noise.

Examples

set.seed(20240412)
spiral_3d(n = 100, num_dims = 10, num_noise = 2, min_n = -0.05, max_n = 0.05)

Generate Swiss Roll Data

Description

This function generates data points in the shape of a Swiss roll.

Usage

swiss_roll(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated Swiss roll data points.

Examples

# Generate Swiss roll data with noise with custom parameters
set.seed(20240412)
data <- swiss_roll(n = 200, num_noise = 2, min_n = -0.05, max_n = 0.05)

Generate Three Circular Clusters with Noise

Description

This function generates three circular clusters in 4D space with added noise dimensions.

Usage

three_circulars(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the three circular clusters with added noise.

Examples

set.seed(20240412)
circular_clusters_data <- three_circulars(
  n = 300, num_noise = 2,
  min_n = -0.05, max_n = 0.05
)

Generate three clusters of data points with optional noise.

Description

This function generates three clusters of data points along with optional noise.

Usage

three_clust_diff_dist(n, num_dims, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data points with or without added noise.

Examples

set.seed(20240412)
three_clust_diff_dist(
  n = 150, num_dims = 7, num_noise = 4, min_n = -0.05,
  max_n = 0.05
)

Generate Three Cluster Mirror with Noise

Description

This function generates data with three clusters forming a mirror image, along with added noise.

Usage

three_clust_mirror(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate three cluster mirror with noise with custom parameters
set.seed(20240412)
data <- three_clust_mirror(n = 300, num_noise = 2, min_n = -0.05, max_n = 0.05)

Generate Three Different Linear Data with Noise

Description

This function generates a dataset consisting of three different linear patterns with added noise.

Usage

three_diff_linear(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the three different linear data with added noise.

Examples

set.seed(20240412)
three_diff_linear <- three_diff_linear(
  n = 150, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Generate Doublets with Three Clusters and Noise

Description

This function generates data with three sets of doublets (pairs of clusters) along with added background noise.

Usage

three_doublets(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate doublets with three clusters and noise with custom parameters
set.seed(20240412)
data <- three_doublets(
  n = 420, num_noise = 2,
  min_n = -0.05, max_n = 0.05
)

Generate Three Grids with Noise

Description

This function generates three grid datasets with noise dimensions.

Usage

three_grid(n_value, num_noise, min_n, max_n)

Arguments

Value

A list containing three grid datasets with added noise and the sample size of each dataset.

Examples

set.seed(20240412)
three_grids <- three_grid(
  n_value = 19, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Generate Three Linear Clusters with Noise

Description

This function generates data with three linear clusters, along with added noise.

Usage

three_long_clust(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate three linear clusters with noise with custom parameters
set.seed(20240412)
data <- three_long_clust(n = 300, num_noise = 2, min_n = -0.05, max_n = 0.05)

Generate Three Nonlinear Clusters with Noise

Description

This function generates data with three nonlinear clusters, along with added noise.

Usage

three_nonlinear(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate three nonlinear clusters with noise with custom parameters
set.seed(20240412)
data <- three_nonlinear(n = 300, num_noise = 2, min_n = -0.05, max_n = 0.05)

Generate a torus-shaped dataset with optional noise.

Description

This function generates a torus-shaped dataset along with optional noise.

Usage

torus_3d(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated torus-shaped data points with or without added noise.

Examples

set.seed(20240412)
torus_3d(n = 100, num_noise = 2, min_n = -0.05, max_n = 0.05)

Generate a row of data points for a 3D torus.

Description

This function generates a row of data points for a 3D torus with given radii.

Usage

torus_3d_row(radius)

Arguments

Value

A vector representing a row of data points for the 3D torus.

Examples

set.seed(20240412)
torus_3d_row(c(2, 1))

Generate Tree-like Data with Noise

Description

This function generates a dataset representing a tree-like structure, with added noise.

Usage

tree(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the tree-like data with added noise.

Examples

set.seed(20240412)
tree_data <- tree(n = 300, num_noise = 2, min_n = -0.05, max_n = 0.05)

Generate Triangular 3D Datasets with Noise

Description

This function generates triangular 3D datasets with added noise dimensions.

Usage

tri_3d(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the triangular 3D datasets with added noise.

Examples

set.seed(20240412)
triangular_3d_data <- tri_3d(
  n = 100, num_noise = 2,
  min_n = -0.05, max_n = 0.05
)

Generate Triangular Plane with Background Noise

Description

This function generates a triangular plane dataset with background noise dimensions.

Usage

tri_plane_bkg(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the triangular plane dataset with background noise.

Examples

set.seed(20240412)
triangular_plane_data <- tri_plane_bkg(
  n = 216,
  num_noise = 2, min_n = -0.05, max_n = 0.05
)

Generate Linked Data

Description

This function generates linked data points.

Usage

two_circulars(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated linked data points.

Examples

# Generate linked data with noise with custom parameters
set.seed(20240412)
data <- two_circulars(n = 200, num_noise = 2, min_n = -0.05, max_n = 0.05)

Generate Two Curvilinear Data with Noise

Description

This function generates a dataset representing two curvilinear clusters with added noise.

Usage

two_curvilinear(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the two curvilinear data with noise.

Examples

set.seed(20240412)
two_curvilinear <- two_curvilinear(
  n = 250, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Generate Two Curvilinear Clusters with Noise

Description

This function generates data with two curvilinear clusters along with added noise.

Usage

two_curvy(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate two curvilinear clusters with noise with custom parameters
set.seed(20240412)
data <- two_curvy(n = 200, num_noise = 2, min_n = -0.05, max_n = 0.05)

Generate Two Curvilinear Differentiated Clusters with Noise

Description

This function generates data with two curvilinear clusters that are differentiated from each other, along with added noise.

Usage

two_curvy_diff_pts(cluster_size_vec, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate two curvilinear differentiated clusters with noise with custom parameters
set.seed(20240412)
data <- two_curvy_diff_pts(
  cluster_size_vec = c(50, 100), num_noise = 2,
  min_n = -0.05, max_n = 0.05
)

Generate Two Curvy Pancakes with Noise

Description

This function generates a dataset representing two curvy pancake-shaped clusters with added noise.

Usage

two_curvy_panckakes(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the two curvy pancakes data with noise.

Examples

set.seed(20240412)
two_curvy_panckakes <- two_curvy_panckakes(
  n = 300, num_noise = 2,
  min_n = -0.05, max_n = 0.05
)

Generate Two Doublets with Background Noise

Description

This function generates data with two doublets along with added background noise.

Usage

two_doublets_bkg(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate two doublets with background noise with custom parameters
set.seed(20240412)
data <- two_doublets_bkg(n = 200, num_noise = 2, min_n = -0.05, max_n = 0.05)

Generate Doublets in Parallel with Noise

Description

This function generates data with two sets of doublets (pairs of clusters) running in parallel, along with added background noise.

Usage

two_doublets_parallel(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate doublets in parallel with noise with custom parameters
set.seed(20240412)
data <- two_doublets_parallel(n = 440, num_noise = 2, min_n = -0.05, max_n = 0.05)

Generate Two Grids with Noise

Description

This function generates two grid datasets with noise dimensions.

Usage

two_grid(n_value, num_noise, min_n, max_n)

Arguments

Value

A list containing two grid datasets with added noise and the sample size of each dataset.

Examples

set.seed(20240412)
two_grids <- two_grid(n_value = 19, num_noise = 2, min_n = -0.05, max_n = 0.05)

Generate One Grid with Different Offset

Description

This function generates a single grid dataset with a different offset.

Usage

two_grid_comb(n_value, num_noise, min_n, max_n)

Arguments

Value

A list containing the grid dataset with different offsets and the sample size.

Examples

set.seed(20240412)
two_grid_comb <- two_grid_comb(
  n_value = 10, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Generate Two Grids with Background Noise

Description

This function generates two grid datasets with background noise.

Usage

two_grid_comb_bkg(n_value, num_noise, min_n, max_n)

Arguments

Value

A list containing the two grid datasets with background noise and the sample size.

Examples

set.seed(20240412)
two_grid_comb_bkg <- two_grid_comb_bkg(
  n_value = 10, num_noise = 2,
  min_n = -0.05, max_n = 0.05
)

Generate Long Cluster Data

Description

This function generates a dataset consisting of two long clusters with added noise.

Usage

two_long_clust(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the long cluster data with added noise.

Examples

set.seed(20240412)
long_cluster <- two_long_clust(
  n = 200, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Generate Two Linear Differentiated Clusters with Noise

Description

This function generates data with two linear clusters that are differentiated from each other, along with added noise.

Usage

two_long_clust_diff(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate two linear differentiated clusters with noise with custom parameters
set.seed(20240412)
data <- two_long_clust_diff(
  n = 300, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Generate Two Nonlinear Clusters with Noise

Description

This function generates data with two nonlinear clusters along with added noise.

Usage

two_nonlinear(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate two nonlinear clusters with noise with custom parameters
set.seed(20240412)
data <- two_nonlinear(n = 200, num_noise = 2, min_n = -0.05, max_n = 0.50)

Generate Two S-Curve Data with Noise

Description

This function generates two S-curve data with noise.

Usage

two_scurve_hole(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the two S-curve datasets with added noise.

Examples

# Generate two S-curve data with noise with custom parameters
set.seed(20240412)
data <- two_scurve_hole(
  n = 200, num_noise = 2,
  min_n = -0.05, max_n = 0.05
)

Generate Two S-curve Datasets with Noise

Description

This function generates two S-curve datasets with added noise dimensions.

Usage

two_scurves(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the combined S-curve datasets with added noise.

Examples

set.seed(20240412)
two_s_curve_data <- two_scurves(
  n = 200, num_noise = 2,
  min_n = -0.05, max_n = 0.05
)