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total = 0 | |
N = 300 | |
start_time = time() | |
for a in 0:(N - 1) | |
for b in 0:(N - 1) | |
for c in 0:(N - 1) | |
if a^2 + b^2 == c^2 | |
total = total + 1 |
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using Distributions | |
d = Dirichlet([100.0, 17.0, 31.0, 45.0]) | |
X = rand(d, 1_000_000) | |
fixed_point(X) | |
@elapsed alpha = fixed_point(X) | |
norm(d.alpha - alpha, Inf) |
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# Implementation of fixed point algorithm described in | |
# "Estimating a Dirichlet distribution" by Thomas P. Minka, 2000 | |
function invdigamma(y::Float64) | |
# Closed form initial estimates | |
if y >= -2.22 | |
x_old = exp(y) + 0.5 | |
x_new = x_old | |
else | |
x_old = -1.0 / (y - digamma(1.0)) | |
x_new = x_old |
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fit(TDLearner, choices) | |
fit(HMM, signal) | |
fit(Lasso, Y ~ X, data, lambda = 1.0) | |
loglikelihood(model) | |
AIC(model) | |
BIC(model) |
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# Uniform | |
a <- runif(10000, 0, 1) | |
b <- runif(10000, 0, 1) | |
c <- runif(10000, 0, 1) | |
df <- cbind(a, b, c) | |
df <- transform(df, s = a + b + c) |
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function repeat{T}(v::Array{T}, | |
dims::Integer...; | |
repetitions::Integer = 1) | |
n = length(v) | |
n_res = n * repetitions * prod(dims) | |
res = Array(T, tuple(repetitions * n * dims[1], dims[2:end]...)...) | |
for i in 0:(n_res - 1) | |
index = mod(fld(i, repetitions), n) | |
res[i + 1] = v[index + 1] | |
end |
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function bfs(start_node::Any, | |
next_nodes::Function, | |
at_node::Function) | |
to_process = Array(Any, 0) | |
depths = Array(Int, 0) | |
push!(to_process, start_node) | |
push!(depths, 0) | |
while !isempty(to_process) |
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# K clusters in N-dimensional space | |
# TODO: Fill out Distribution methods | |
# TODO: Restore empty cluster pruning | |
# TODO: Add method for getting point assignments (for all mixtures?) | |
# TODO: Make custom cont. univ. mixture type? | |
immutable MixtureMultivariateNormals <: ContinuousMultivariateDistribution | |
mu::Matrix{Float64} | |
sigma::Array{Float64} | |
p::Vector{Float64} | |
normals::Vector{MultivariateNormal} |
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function betainc(x, pin, qin) | |
# Are these appropriate? | |
eps1 = eps(0.5) | |
alneps = log(eps1) | |
sml = eps(0.0) | |
alnsml = log(sml) | |
if x < 0.0 || x > 1.0 | |
error("x must lie in (0, 1)") | |
end |
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type PrimeArray | |
n::BigInt | |
end | |
PrimeArray() = PrimeArray(BigInt(1)) | |
function Base.getindex(a::PrimeArray, i::Integer) | |
if i < 0 || i > length(Base.PRIMES) | |
throw(BoundsError()) | |
end |