vstack

Builtin index · Core maths

result = hstack(a, b, ...)

Input: a, b, ... · Output: result

What it is

Named equivalents of bracket-literal block concatenation ([a, b] horizontally / [a; b] vertically). Each of the 2+ required arguments is a scalar, Vec, or Mat (real or complex — any complex argument makes the whole result complex); hstack needs matching row counts, vstack needs matching column counts.

Overloads

One call shape — this function is not overloaded.

Code	Input	Output	Description
`result = hstack(a, b, ...)`	`a` —Named equivalents of bracket-literal block concatenation (`[a, b]` horizontally / `[a.` `b` —b] `vertically).` `...` repeatablenot documented yet	`Mat` CMator `CMat` if any input was complex	Returns a `Mat` (or `CMat` if any input was complex).

Examples

From the documentation, and run by the build -- the output and the figure below are what the engine actually produced. Simplest first.

G = vstack(randn(40, 2, seed = 31) + 4,
           randn(40, 2, seed = 32) - 4,
           randn(40, 2, seed = 33))
labels = kmeans(G, 3, seed = 5)
centers = kmeans_centers(G, 3, seed = 5)
xs = G[:, 0]
ys = G[:, 1]
for k in 0 to 2
    inside = labels == k
    print("cluster {k}: {len(xs[inside])} points")
    scatter(xs[inside], ys[inside], label = "cluster {k}")
end for
scatter(centers[:, 0], centers[:, 1], marker = "x", color = "#0f172a", size = 12, label = "centroids")
legend()
title("kmeans, k = 3")

output

cluster 0: 40 points
· scatter: figure 6 (40 points)
cluster 1: 39 points
· scatter: figure 7 (39 points)
cluster 2: 41 points
· scatter: figure 8 (41 points)
· scatter: figure 9 (3 points)

from statistics-ml

G = vstack(randn(40, 2, seed = 31) + 4,
           randn(40, 2, seed = 32) - 4,
           randn(40, 2, seed = 33))
d = dbscan(G, 1.5, 4)
print("dbscan labels: {distinct(d)}")

proj  = pca(G, 2)                    # the (n, 2) projected coordinates

comps = pca_components(G, 2)         # the (2, 2) loading matrix

ev    = pca_explained_variance(G, 2)
print("pca projected shape: {rows(proj)} x {cols(proj)}")
print("pca components:\n{round(comps, 3)}")
print("explained variance: {round(ev, 3)}")

small = vstack(randn(6, 2, seed = 41) + 3, randn(6, 2, seed = 42) - 3)
low = tsne(small, n_components = 2, perplexity = 3, seed = 1, max_iter = 100)
print("tsne output rows: {rows(low)}")

output

dbscan labels: (0, 1, 2)
pca projected shape: 120 x 2
pca components:
[0.694, 0.72; 0.72, -0.694]
explained variance: [0.969, 0.031]
tsne output rows: 12

from statistics-ml

How it works

hstack(a, b, ...) / vstack(a, b, ...) — explicit, named equivalents of the bracket-literal block-concatenation magic ([a, b] / [a; b], see eval_matrix_literal_blocks), for scripts that would rather spell the operation out than rely on [...]'s implicit dispatch — same "also give it a plain function name" precedent as panel/subplot. Both need 2+ arrays; a single argument would make the call redundant.

From the note above this builtin in the interpreter, where the reasoning was written down as it was built.

vstack

What it is

Overloads

Examples

How it works

See also