seed

Builtin index · Statistics & ML

seed(n)

Input: n · Output: no output

What it is

Reseeds the shared random stream with integer n, so subsequent calls to rand/randn/etc. without their own seed= become reproducible from this point on.

Overloads

One call shape — this function is not overloaded.

Code	Input	Output	Description
`seed(n)`	`n` —not documented yet	no output	without their own `seed=` become reproducible from this point on.

Examples

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

seed(42)
a = rand(1, 3)
seed(42)
b = rand(1, 3)
print("a = {a}")
print("b = {b}")

from repl-diagnostics

a = rand(3)
print("shape a: {size(a)}")

seed(5)
p = rand(2, 2)
junk = rand(2, 2, seed=42)
q = rand(2, 2)

seed(5)
p2 = rand(2, 2)
q2 = rand(2, 2)

print("q == q2: {q == q2}")

output

shape a: [3, 1]
q == q2: [1, 1; 1, 1]

from repl-diagnostics

seed(42)                             # reseed the shared stream

u = rand(3, seed = 1)                # an explicit seed still wins

print("rand draws: {round(u, 3)}")
print("chi2pdf(2, 3):  {round(chi2pdf(2, 3), 4)}")
print("normpdf(0):     {round(normpdf(0), 4)}")

walk = random_walk(20, drift = 0.1, volatility = 1.0, seed = 3)
print("random walk, last value: {round(walk[19], 3)}")

output

rand draws: [0.746, 0.971, 0.444]
chi2pdf(2, 3):  0.2076
normpdf(0):     0.3989
random walk, last value: -3.552

from statistics-ml

w = param([0.5, -0.3; 0.2, 0.8])
b = param([0.1, -0.1])
x = [1.0, 2.0; -0.5, 1.5]      # two examples, two features each

z = dense(x, w, b)
a = relu(z)
p = sigmoid(a)
loss = sum(p .^ 2)
g = grad(loss, wrt = w)
print("relu:    {round(a, 4)}")
print("sigmoid: {round(p, 4)}")
print("dL/dw:   {round(g, 4)}")
tape_reset()                    # clear the tape before the next computation


scores = [2.0, 1.0, 0.1; 0.5, 0.5, 0.5]
probs = softmax_rows(scores)
print("softmax row 0: {round(probs[0, :], 4)}")

seed(3)
kept = dropout(ones(1, 8), 0.5)
print("dropout output: {kept}")
print("survivors:      {sum(kept > 0)} of 8, each scaled by 1/(1-rate)")

output

relu:    [1, 1.4; 0, 1.25]
sigmoid: [0.7311, 0.8022; 0.5, 0.7773]
dL/dw:   [0.2875, 0.12; 0.5749, 0.9128]
softmax row 0: [0.659, 0.2424, 0.0986]
dropout output: [2, 2, 0, 0, 2, 0, 2, 0]
survivors:      4 of 8, each scaled by 1/(1-rate)

from statistics-ml

How it works

seed(n) reseeds the persistent random stream.

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