context_start_lineno int64 1 913 | line_no int64 16 984 | repo stringclasses 5
values | id int64 0 416 | target_function_prompt stringlengths 201 13.6k | function_signature stringlengths 201 13.6k | solution_position listlengths 2 2 | raw_solution stringlengths 201 13.6k | focal_code stringlengths 201 13.6k | function_name stringlengths 2 38 | start_line int64 1 913 | end_line int64 16 984 | file_path stringlengths 10 52 | context stringlengths 4.52k 9.85k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
538 | 561 | QuantEcon.jl | 200 | function qnwnorm(n::Vector{Int}, mu::Vector, sig2::Matrix = Matrix(I, length(n), length(n)))
n_n, n_mu = length(n), length(mu)
if !(n_n == n_mu)
error("n and mu must have same number of elements")
end
_nodes = Array{Vector{Float64}}(undef, n_n)
_weights = Array{Vector{Float64}}(undef, n_n)... | function qnwnorm(n::Vector{Int}, mu::Vector, sig2::Matrix = Matrix(I, length(n), length(n)))
n_n, n_mu = length(n), length(mu)
if !(n_n == n_mu)
error("n and mu must have same number of elements")
end
_nodes = Array{Vector{Float64}}(undef, n_n)
_weights = Array{Vector{Float64}}(undef, n_n)... | [
538,
561
] | function qnwnorm(n::Vector{Int}, mu::Vector, sig2::Matrix = Matrix(I, length(n), length(n)))
n_n, n_mu = length(n), length(mu)
if !(n_n == n_mu)
error("n and mu must have same number of elements")
end
_nodes = Array{Vector{Float64}}(undef, n_n)
_weights = Array{Vector{Float64}}(undef, n_n)... | function qnwnorm(n::Vector{Int}, mu::Vector, sig2::Matrix = Matrix(I, length(n), length(n)))
n_n, n_mu = length(n), length(mu)
if !(n_n == n_mu)
error("n and mu must have same number of elements")
end
_nodes = Array{Vector{Float64}}(undef, n_n)
_weights = Array{Vector{Float64}}(undef, n_n)... | qnwnorm | 538 | 561 | src/quad.jl | #FILE: QuantEcon.jl/test/test_sampler.jl
##CHUNK 1
@test isapprox(mvns.Q * mvns.Q', mvns.Sigma)
end
@testset "check positive semi-definite zeros" begin
mvns = MVNSampler(mu, zeros(n, n))
@test rand(mvns) == mu
end
@testset "check positive semi-definite ones" begin
mvns ... |
673 | 709 | QuantEcon.jl | 201 | function qnwequi(n::Int, a::Vector, b::Vector, kind::AbstractString = "N")
# error checking
n_a, n_b = length(a), length(b)
if !(n_a == n_b)
error("a and b must have same number of elements")
end
d = n_a
i = reshape(1:n, n, 1)
if kind == "N"
j = 2.0.^((1:d) / (d + 1))
... | function qnwequi(n::Int, a::Vector, b::Vector, kind::AbstractString = "N")
# error checking
n_a, n_b = length(a), length(b)
if !(n_a == n_b)
error("a and b must have same number of elements")
end
d = n_a
i = reshape(1:n, n, 1)
if kind == "N"
j = 2.0.^((1:d) / (d + 1))
... | [
673,
709
] | function qnwequi(n::Int, a::Vector, b::Vector, kind::AbstractString = "N")
# error checking
n_a, n_b = length(a), length(b)
if !(n_a == n_b)
error("a and b must have same number of elements")
end
d = n_a
i = reshape(1:n, n, 1)
if kind == "N"
j = 2.0.^((1:d) / (d + 1))
... | function qnwequi(n::Int, a::Vector, b::Vector, kind::AbstractString = "N")
# error checking
n_a, n_b = length(a), length(b)
if !(n_a == n_b)
error("a and b must have same number of elements")
end
d = n_a
i = reshape(1:n, n, 1)
if kind == "N"
j = 2.0.^((1:d) / (d + 1))
... | qnwequi | 673 | 709 | src/quad.jl | #FILE: QuantEcon.jl/src/markov/markov_approx.jl
##CHUNK 1
Nm::Integer,
n_moments::Integer=2,
method::VAREstimationMethod=Even(),
n_sigmas::Real=sqrt(Nm-1))
# b = zeros(2)
# A = [0.9809 0.0028; 0.041 0.9648]
# Sigma = [7.... |
795 | 809 | QuantEcon.jl | 202 | function quadrect(f::Function, n, a, b, kind = "lege", args...; kwargs...)
if lowercase(kind)[1] == 'l'
nodes, weights = qnwlege(n, a, b)
elseif lowercase(kind)[1] == 'c'
nodes, weights = qnwcheb(n, a, b)
elseif lowercase(kind)[1] == 't'
nodes, weights = qnwtrap(n, a, b)
elseif l... | function quadrect(f::Function, n, a, b, kind = "lege", args...; kwargs...)
if lowercase(kind)[1] == 'l'
nodes, weights = qnwlege(n, a, b)
elseif lowercase(kind)[1] == 'c'
nodes, weights = qnwcheb(n, a, b)
elseif lowercase(kind)[1] == 't'
nodes, weights = qnwtrap(n, a, b)
elseif l... | [
795,
809
] | function quadrect(f::Function, n, a, b, kind = "lege", args...; kwargs...)
if lowercase(kind)[1] == 'l'
nodes, weights = qnwlege(n, a, b)
elseif lowercase(kind)[1] == 'c'
nodes, weights = qnwcheb(n, a, b)
elseif lowercase(kind)[1] == 't'
nodes, weights = qnwtrap(n, a, b)
elseif l... | function quadrect(f::Function, n, a, b, kind = "lege", args...; kwargs...)
if lowercase(kind)[1] == 'l'
nodes, weights = qnwlege(n, a, b)
elseif lowercase(kind)[1] == 'c'
nodes, weights = qnwcheb(n, a, b)
elseif lowercase(kind)[1] == 't'
nodes, weights = qnwtrap(n, a, b)
elseif l... | quadrect | 795 | 809 | src/quad.jl | #FILE: QuantEcon.jl/test/test_quad.jl
##CHUNK 1
# dim 1: num nodes, dim2: method, dim3:func
data1d = Array{Float64}(undef, 6, 6, 3)
kinds = ["trap", "simp", "lege", "N", "W", "H"]
n_nodes = [5, 11, 21, 51, 101, 401] # number of nodes
a, b = -1, 1
for (k_i, k) in enumer... |
834 | 870 | QuantEcon.jl | 203 | function qnwmonomial2(vcv::AbstractMatrix)
n = size(vcv, 1)
@assert n == size(vcv, 2) "Variance covariance matrix must be square"
n_nodes = 2n^2 + 1
z0 = zeros(1, n)
z1 = zeros(2n, n)
# In each node, random variable i takes value either 1 or -1, and
# all other variables take value 0. For e... | function qnwmonomial2(vcv::AbstractMatrix)
n = size(vcv, 1)
@assert n == size(vcv, 2) "Variance covariance matrix must be square"
n_nodes = 2n^2 + 1
z0 = zeros(1, n)
z1 = zeros(2n, n)
# In each node, random variable i takes value either 1 or -1, and
# all other variables take value 0. For e... | [
834,
870
] | function qnwmonomial2(vcv::AbstractMatrix)
n = size(vcv, 1)
@assert n == size(vcv, 2) "Variance covariance matrix must be square"
n_nodes = 2n^2 + 1
z0 = zeros(1, n)
z1 = zeros(2n, n)
# In each node, random variable i takes value either 1 or -1, and
# all other variables take value 0. For e... | function qnwmonomial2(vcv::AbstractMatrix)
n = size(vcv, 1)
@assert n == size(vcv, 2) "Variance covariance matrix must be square"
n_nodes = 2n^2 + 1
z0 = zeros(1, n)
z1 = zeros(2n, n)
# In each node, random variable i takes value either 1 or -1, and
# all other variables take value 0. For e... | qnwmonomial2 | 834 | 870 | src/quad.jl | #FILE: QuantEcon.jl/test/test_mc_tools.jl
##CHUNK 1
((i/(n-1) > p) + (i/(n-1) == p)/2))
P[i+1, i+1] = 1 - P[i+1, i] - P[i+1, i+2]
end
P[end, end-1], P[end, end] = ε/2, 1 - ε/2
return P
end
function Base.isapprox(x::Vector{Vector{<:Real}},
y::... |
911 | 923 | QuantEcon.jl | 204 | function qnwdist(d::Distributions.ContinuousUnivariateDistribution, N::Int,
q0::Real = 0.001, qN::Real = 0.999, method::Union{T,Type{T}} = Quantile) where T
z = _quadnodes(d, N, q0, qN, method)
zprob = zeros(N)
for i in 2:N - 1
zprob[i] = cdf(d, (z[i] + z[i + 1]) / 2) - cdf(d, (z[i] + z[i ... | function qnwdist(d::Distributions.ContinuousUnivariateDistribution, N::Int,
q0::Real = 0.001, qN::Real = 0.999, method::Union{T,Type{T}} = Quantile) where T
z = _quadnodes(d, N, q0, qN, method)
zprob = zeros(N)
for i in 2:N - 1
zprob[i] = cdf(d, (z[i] + z[i + 1]) / 2) - cdf(d, (z[i] + z[i ... | [
911,
923
] | function qnwdist(d::Distributions.ContinuousUnivariateDistribution, N::Int,
q0::Real = 0.001, qN::Real = 0.999, method::Union{T,Type{T}} = Quantile) where T
z = _quadnodes(d, N, q0, qN, method)
zprob = zeros(N)
for i in 2:N - 1
zprob[i] = cdf(d, (z[i] + z[i + 1]) / 2) - cdf(d, (z[i] + z[i ... | function qnwdist(d::Distributions.ContinuousUnivariateDistribution, N::Int,
q0::Real = 0.001, qN::Real = 0.999, method::Union{T,Type{T}} = Quantile) where T
z = _quadnodes(d, N, q0, qN, method)
zprob = zeros(N)
for i in 2:N - 1
zprob[i] = cdf(d, (z[i] + z[i + 1]) / 2) - cdf(d, (z[i] + z[i ... | qnwdist | 911 | 923 | src/quad.jl | #CURRENT FILE: QuantEcon.jl/src/quad.jl
##CHUNK 1
sqrt_vcv = cholesky(vcv).U
R = sqrt(n + 2) .* sqrt_vcv
S = sqrt((n + 2) / 2) * sqrt_vcv
ϵj = [z0; z1 * R; z2 * S]
ωj = vcat(2 / (n + 2) * ones(size(z0, 1)),
(4 - n) / (2 * (n + 2)^2) * ones(size(z1, 1)),
1 / (n + 2)^2 * ... |
43 | 59 | QuantEcon.jl | 205 | function var_quadratic_sum(A::ScalarOrArray, C::ScalarOrArray, H::ScalarOrArray,
bet::Real, x0::ScalarOrArray)
n = size(A, 1)
# coerce shapes
A = reshape([A;], n, n)
C = reshape([C;], n, n)
H = reshape([H;], n, n)
x0 = reshape([x0;], n)
# solve system
Q = sol... | function var_quadratic_sum(A::ScalarOrArray, C::ScalarOrArray, H::ScalarOrArray,
bet::Real, x0::ScalarOrArray)
n = size(A, 1)
# coerce shapes
A = reshape([A;], n, n)
C = reshape([C;], n, n)
H = reshape([H;], n, n)
x0 = reshape([x0;], n)
# solve system
Q = sol... | [
43,
59
] | function var_quadratic_sum(A::ScalarOrArray, C::ScalarOrArray, H::ScalarOrArray,
bet::Real, x0::ScalarOrArray)
n = size(A, 1)
# coerce shapes
A = reshape([A;], n, n)
C = reshape([C;], n, n)
H = reshape([H;], n, n)
x0 = reshape([x0;], n)
# solve system
Q = sol... | function var_quadratic_sum(A::ScalarOrArray, C::ScalarOrArray, H::ScalarOrArray,
bet::Real, x0::ScalarOrArray)
n = size(A, 1)
# coerce shapes
A = reshape([A;], n, n)
C = reshape([C;], n, n)
H = reshape([H;], n, n)
x0 = reshape([x0;], n)
# solve system
Q = sol... | var_quadratic_sum | 43 | 59 | src/quadsums.jl | #FILE: QuantEcon.jl/src/matrix_eqn.jl
##CHUNK 1
- `gamma1::Matrix{Float64}` Represents the value ``X``
"""
function solve_discrete_lyapunov(A::ScalarOrArray,
B::ScalarOrArray,
max_it::Int=50)
# TODO: Implement Bartels-Stewardt
n = size(A, 2)
... |
119 | 130 | QuantEcon.jl | 206 | function b_operator(rlq::RBLQ, P::Matrix)
A, B, Q, R, bet = rlq.A, rlq.B, rlq.Q, rlq.R, rlq.bet
S1 = Q + bet.*B'*P*B
S2 = bet.*B'*P*A
S3 = bet.*A'*P*A
F = S1 \ S2
new_P = R - S2'*F + S3
return F, new_P
end | function b_operator(rlq::RBLQ, P::Matrix)
A, B, Q, R, bet = rlq.A, rlq.B, rlq.Q, rlq.R, rlq.bet
S1 = Q + bet.*B'*P*B
S2 = bet.*B'*P*A
S3 = bet.*A'*P*A
F = S1 \ S2
new_P = R - S2'*F + S3
return F, new_P
end | [
119,
130
] | function b_operator(rlq::RBLQ, P::Matrix)
A, B, Q, R, bet = rlq.A, rlq.B, rlq.Q, rlq.R, rlq.bet
S1 = Q + bet.*B'*P*B
S2 = bet.*B'*P*A
S3 = bet.*A'*P*A
F = S1 \ S2
new_P = R - S2'*F + S3
return F, new_P
end | function b_operator(rlq::RBLQ, P::Matrix)
A, B, Q, R, bet = rlq.A, rlq.B, rlq.Q, rlq.R, rlq.bet
S1 = Q + bet.*B'*P*B
S2 = bet.*B'*P*A
S3 = bet.*A'*P*A
F = S1 \ S2
new_P = R - S2'*F + S3
return F, new_P
end | b_operator | 119 | 130 | src/robustlq.jl | #FILE: QuantEcon.jl/src/lqcontrol.jl
##CHUNK 1
This function updates the `P` and `d` fields on the `lq` instance in addition to
returning them
"""
function update_values!(lq::LQ)
# Simplify notation
Q, R, A, B, N, C, P, d = lq.Q, lq.R, lq.A, lq.B, lq.N, lq.C, lq.P, lq.d
# Some useful matrices
s1 = Q +... |
157 | 175 | QuantEcon.jl | 207 | function robust_rule(rlq::RBLQ)
A, B, C, Q, R = rlq.A, rlq.B, rlq.C, rlq.Q, rlq.R
bet, theta, k, j = rlq.bet, rlq.theta, rlq.k, rlq.j
# Set up LQ version
# I = eye(j)
Z = zeros(k, j)
Ba = [B C]
Qa = [Q Z
Z' -bet.*I.*theta]
lq = QuantEcon.LQ(Qa, R, A, Ba, bet=bet)
# Solve ... | function robust_rule(rlq::RBLQ)
A, B, C, Q, R = rlq.A, rlq.B, rlq.C, rlq.Q, rlq.R
bet, theta, k, j = rlq.bet, rlq.theta, rlq.k, rlq.j
# Set up LQ version
# I = eye(j)
Z = zeros(k, j)
Ba = [B C]
Qa = [Q Z
Z' -bet.*I.*theta]
lq = QuantEcon.LQ(Qa, R, A, Ba, bet=bet)
# Solve ... | [
157,
175
] | function robust_rule(rlq::RBLQ)
A, B, C, Q, R = rlq.A, rlq.B, rlq.C, rlq.Q, rlq.R
bet, theta, k, j = rlq.bet, rlq.theta, rlq.k, rlq.j
# Set up LQ version
# I = eye(j)
Z = zeros(k, j)
Ba = [B C]
Qa = [Q Z
Z' -bet.*I.*theta]
lq = QuantEcon.LQ(Qa, R, A, Ba, bet=bet)
# Solve ... | function robust_rule(rlq::RBLQ)
A, B, C, Q, R = rlq.A, rlq.B, rlq.C, rlq.Q, rlq.R
bet, theta, k, j = rlq.bet, rlq.theta, rlq.k, rlq.j
# Set up LQ version
# I = eye(j)
Z = zeros(k, j)
Ba = [B C]
Qa = [Q Z
Z' -bet.*I.*theta]
lq = QuantEcon.LQ(Qa, R, A, Ba, bet=bet)
# Solve ... | robust_rule | 157 | 175 | src/robustlq.jl | #FILE: QuantEcon.jl/src/lqcontrol.jl
##CHUNK 1
This function updates the `P`, `d`, and `F` fields on the `lq` instance in
addition to returning them
"""
function stationary_values!(lq::LQ)
# simplify notation
Q, R, A, B, N, C = lq.Q, lq.R, lq.A, lq.B, lq.N, lq.C
# solve Riccati equation, obtain P
A0,... |
203 | 229 | QuantEcon.jl | 208 | function robust_rule_simple(rlq::RBLQ,
P::Matrix=zeros(Float64, rlq.n, rlq.n);
max_iter=80,
tol=1e-8)
# Simplify notation
A, B, C, Q, R = rlq.A, rlq.B, rlq.C, rlq.Q, rlq.R
bet, theta, k, j = rlq.bet, rlq.theta, rlq.k, rlq.j
... | function robust_rule_simple(rlq::RBLQ,
P::Matrix=zeros(Float64, rlq.n, rlq.n);
max_iter=80,
tol=1e-8)
# Simplify notation
A, B, C, Q, R = rlq.A, rlq.B, rlq.C, rlq.Q, rlq.R
bet, theta, k, j = rlq.bet, rlq.theta, rlq.k, rlq.j
... | [
203,
229
] | function robust_rule_simple(rlq::RBLQ,
P::Matrix=zeros(Float64, rlq.n, rlq.n);
max_iter=80,
tol=1e-8)
# Simplify notation
A, B, C, Q, R = rlq.A, rlq.B, rlq.C, rlq.Q, rlq.R
bet, theta, k, j = rlq.bet, rlq.theta, rlq.k, rlq.j
... | function robust_rule_simple(rlq::RBLQ,
P::Matrix=zeros(Float64, rlq.n, rlq.n);
max_iter=80,
tol=1e-8)
# Simplify notation
A, B, C, Q, R = rlq.A, rlq.B, rlq.C, rlq.Q, rlq.R
bet, theta, k, j = rlq.bet, rlq.theta, rlq.k, rlq.j
... | robust_rule_simple | 203 | 229 | src/robustlq.jl | #FILE: QuantEcon.jl/src/lqcontrol.jl
##CHUNK 1
This function updates the `P` and `d` fields on the `lq` instance in addition to
returning them
"""
function update_values!(lq::LQ)
# Simplify notation
Q, R, A, B, N, C, P, d = lq.Q, lq.R, lq.A, lq.B, lq.N, lq.C, lq.P, lq.d
# Some useful matrices
s1 = Q +... |
245 | 260 | QuantEcon.jl | 209 | function F_to_K(rlq::RBLQ, F::Matrix)
# simplify notation
R, Q, A, B, C = rlq.R, rlq.Q, rlq.A, rlq.B, rlq.C
bet, theta = rlq.bet, rlq.theta
# set up lq
Q2 = bet * theta
R2 = - R - F'*Q*F
A2 = A - B*F
B2 = C
lq = QuantEcon.LQ(Q2, R2, A2, B2, bet=bet)
neg_P, neg_K, d = stationary... | function F_to_K(rlq::RBLQ, F::Matrix)
# simplify notation
R, Q, A, B, C = rlq.R, rlq.Q, rlq.A, rlq.B, rlq.C
bet, theta = rlq.bet, rlq.theta
# set up lq
Q2 = bet * theta
R2 = - R - F'*Q*F
A2 = A - B*F
B2 = C
lq = QuantEcon.LQ(Q2, R2, A2, B2, bet=bet)
neg_P, neg_K, d = stationary... | [
245,
260
] | function F_to_K(rlq::RBLQ, F::Matrix)
# simplify notation
R, Q, A, B, C = rlq.R, rlq.Q, rlq.A, rlq.B, rlq.C
bet, theta = rlq.bet, rlq.theta
# set up lq
Q2 = bet * theta
R2 = - R - F'*Q*F
A2 = A - B*F
B2 = C
lq = QuantEcon.LQ(Q2, R2, A2, B2, bet=bet)
neg_P, neg_K, d = stationary... | function F_to_K(rlq::RBLQ, F::Matrix)
# simplify notation
R, Q, A, B, C = rlq.R, rlq.Q, rlq.A, rlq.B, rlq.C
bet, theta = rlq.bet, rlq.theta
# set up lq
Q2 = bet * theta
R2 = - R - F'*Q*F
A2 = A - B*F
B2 = C
lq = QuantEcon.LQ(Q2, R2, A2, B2, bet=bet)
neg_P, neg_K, d = stationary... | F_to_K | 245 | 260 | src/robustlq.jl | #FILE: QuantEcon.jl/src/lqcontrol.jl
##CHUNK 1
This function updates the `P`, `d`, and `F` fields on the `lq` instance in
addition to returning them
"""
function stationary_values!(lq::LQ)
# simplify notation
Q, R, A, B, N, C = lq.Q, lq.R, lq.A, lq.B, lq.N, lq.C
# solve Riccati equation, obtain P
A0,... |
276 | 286 | QuantEcon.jl | 210 | function K_to_F(rlq::RBLQ, K::Matrix)
R, Q, A, B, C = rlq.R, rlq.Q, rlq.A, rlq.B, rlq.C
bet, theta = rlq.bet, rlq.theta
A1, B1, Q1, R1 = A+C*K, B, Q, R-bet*theta.*K'*K
lq = QuantEcon.LQ(Q1, R1, A1, B1, bet=bet)
P, F, d = stationary_values(lq)
return F, P
end | function K_to_F(rlq::RBLQ, K::Matrix)
R, Q, A, B, C = rlq.R, rlq.Q, rlq.A, rlq.B, rlq.C
bet, theta = rlq.bet, rlq.theta
A1, B1, Q1, R1 = A+C*K, B, Q, R-bet*theta.*K'*K
lq = QuantEcon.LQ(Q1, R1, A1, B1, bet=bet)
P, F, d = stationary_values(lq)
return F, P
end | [
276,
286
] | function K_to_F(rlq::RBLQ, K::Matrix)
R, Q, A, B, C = rlq.R, rlq.Q, rlq.A, rlq.B, rlq.C
bet, theta = rlq.bet, rlq.theta
A1, B1, Q1, R1 = A+C*K, B, Q, R-bet*theta.*K'*K
lq = QuantEcon.LQ(Q1, R1, A1, B1, bet=bet)
P, F, d = stationary_values(lq)
return F, P
end | function K_to_F(rlq::RBLQ, K::Matrix)
R, Q, A, B, C = rlq.R, rlq.Q, rlq.A, rlq.B, rlq.C
bet, theta = rlq.bet, rlq.theta
A1, B1, Q1, R1 = A+C*K, B, Q, R-bet*theta.*K'*K
lq = QuantEcon.LQ(Q1, R1, A1, B1, bet=bet)
P, F, d = stationary_values(lq)
return F, P
end | K_to_F | 276 | 286 | src/robustlq.jl | #FILE: QuantEcon.jl/src/lqcontrol.jl
##CHUNK 1
This function updates the `P`, `d`, and `F` fields on the `lq` instance in
addition to returning them
"""
function stationary_values!(lq::LQ)
# simplify notation
Q, R, A, B, N, C = lq.Q, lq.R, lq.A, lq.B, lq.N, lq.C
# solve Riccati equation, obtain P
A0,... |
331 | 350 | QuantEcon.jl | 211 | function evaluate_F(rlq::RBLQ, F::Matrix)
R, Q, A, B, C = rlq.R, rlq.Q, rlq.A, rlq.B, rlq.C
bet, theta, j = rlq.bet, rlq.theta, rlq.j
# Solve for policies and costs using agent 2's problem
K_F, P_F = F_to_K(rlq, F)
# I = eye(j)
H = inv(I - C'*P_F*C./theta)
d_F = log(det(H))
# compute O... | function evaluate_F(rlq::RBLQ, F::Matrix)
R, Q, A, B, C = rlq.R, rlq.Q, rlq.A, rlq.B, rlq.C
bet, theta, j = rlq.bet, rlq.theta, rlq.j
# Solve for policies and costs using agent 2's problem
K_F, P_F = F_to_K(rlq, F)
# I = eye(j)
H = inv(I - C'*P_F*C./theta)
d_F = log(det(H))
# compute O... | [
331,
350
] | function evaluate_F(rlq::RBLQ, F::Matrix)
R, Q, A, B, C = rlq.R, rlq.Q, rlq.A, rlq.B, rlq.C
bet, theta, j = rlq.bet, rlq.theta, rlq.j
# Solve for policies and costs using agent 2's problem
K_F, P_F = F_to_K(rlq, F)
# I = eye(j)
H = inv(I - C'*P_F*C./theta)
d_F = log(det(H))
# compute O... | function evaluate_F(rlq::RBLQ, F::Matrix)
R, Q, A, B, C = rlq.R, rlq.Q, rlq.A, rlq.B, rlq.C
bet, theta, j = rlq.bet, rlq.theta, rlq.j
# Solve for policies and costs using agent 2's problem
K_F, P_F = F_to_K(rlq, F)
# I = eye(j)
H = inv(I - C'*P_F*C./theta)
d_F = log(det(H))
# compute O... | evaluate_F | 331 | 350 | src/robustlq.jl | #FILE: QuantEcon.jl/src/lqcontrol.jl
##CHUNK 1
A0, B0 = sqrt(lq.bet) * A, sqrt(lq.bet) * B
P = solve_discrete_riccati(A0, B0, R, Q, N)
# Compute F
s1 = Q .+ lq.bet * (B' * P * B)
s2 = lq.bet * (B' * P * A) .+ N
F = s1 \ s2
# Compute d
d = lq.bet * tr(P * C * C') / (1 - lq.bet)
# B... |
17 | 55 | QuantEcon.jl | 212 | function MVNSampler(mu::Vector{TM}, Sigma::Matrix{TS}) where {TM<:Real,TS<:Real}
ATOL1, RTOL1 = 1e-8, 1e-8
ATOL2, RTOL2 = 1e-8, 1e-14
n = length(mu)
if size(Sigma) != (n, n) # Check Sigma is n x n
throw(ArgumentError(
"Sigma must be 2 dimensional and square matrix of same length to... | function MVNSampler(mu::Vector{TM}, Sigma::Matrix{TS}) where {TM<:Real,TS<:Real}
ATOL1, RTOL1 = 1e-8, 1e-8
ATOL2, RTOL2 = 1e-8, 1e-14
n = length(mu)
if size(Sigma) != (n, n) # Check Sigma is n x n
throw(ArgumentError(
"Sigma must be 2 dimensional and square matrix of same length to... | [
17,
55
] | function MVNSampler(mu::Vector{TM}, Sigma::Matrix{TS}) where {TM<:Real,TS<:Real}
ATOL1, RTOL1 = 1e-8, 1e-8
ATOL2, RTOL2 = 1e-8, 1e-14
n = length(mu)
if size(Sigma) != (n, n) # Check Sigma is n x n
throw(ArgumentError(
"Sigma must be 2 dimensional and square matrix of same length to... | function MVNSampler(mu::Vector{TM}, Sigma::Matrix{TS}) where {TM<:Real,TS<:Real}
ATOL1, RTOL1 = 1e-8, 1e-8
ATOL2, RTOL2 = 1e-8, 1e-14
n = length(mu)
if size(Sigma) != (n, n) # Check Sigma is n x n
throw(ArgumentError(
"Sigma must be 2 dimensional and square matrix of same length to... | MVNSampler | 17 | 55 | src/sampler.jl | #FILE: QuantEcon.jl/src/markov/markov_approx.jl
##CHUNK 1
Nm::Integer,
n_moments::Integer=2,
method::VAREstimationMethod=Even(),
n_sigmas::Real=sqrt(Nm-1))
# b = zeros(2)
# A = [0.9809 0.0028; 0.041 0.9648]
# Sigma = [7.... |
40 | 68 | QuantEcon.jl | 213 | function gridmake!(out, arrays::Union{AbstractVector,AbstractMatrix}...)
lens = Int[size(e, 1) for e in arrays]
n = sum(_i -> size(_i, 2), arrays)
l = prod(lens)
@assert size(out) == (l, n)
reverse!(lens)
repititions = cumprod(vcat(1, lens[1:end-1]))
reverse!(repititions)
reverse!(lens... | function gridmake!(out, arrays::Union{AbstractVector,AbstractMatrix}...)
lens = Int[size(e, 1) for e in arrays]
n = sum(_i -> size(_i, 2), arrays)
l = prod(lens)
@assert size(out) == (l, n)
reverse!(lens)
repititions = cumprod(vcat(1, lens[1:end-1]))
reverse!(repititions)
reverse!(lens... | [
40,
68
] | function gridmake!(out, arrays::Union{AbstractVector,AbstractMatrix}...)
lens = Int[size(e, 1) for e in arrays]
n = sum(_i -> size(_i, 2), arrays)
l = prod(lens)
@assert size(out) == (l, n)
reverse!(lens)
repititions = cumprod(vcat(1, lens[1:end-1]))
reverse!(repititions)
reverse!(lens... | function gridmake!(out, arrays::Union{AbstractVector,AbstractMatrix}...)
lens = Int[size(e, 1) for e in arrays]
n = sum(_i -> size(_i, 2), arrays)
l = prod(lens)
@assert size(out) == (l, n)
reverse!(lens)
repititions = cumprod(vcat(1, lens[1:end-1]))
reverse!(repititions)
reverse!(lens... | gridmake! | 40 | 68 | src/util.jl | #FILE: QuantEcon.jl/src/markov/ddp.jl
##CHUNK 1
end
end
@doc doc"""
Define Matrix Multiplication between 3-dimensional matrix and a vector
Matrix multiplication over the last dimension of ``A``
"""
function *(A::AbstractArray{T,3}, v::AbstractVector) where T
shape = size(A)
size(v, 1) == shape[end] || er... |
138 | 148 | QuantEcon.jl | 214 | function is_stable(A::AbstractMatrix)
# Check for stability by testing that eigenvalues are less than 1
stable = true
d = eigvals(A)
if maximum(abs, d) > 1.0
stable = false
end
return stable
end | function is_stable(A::AbstractMatrix)
# Check for stability by testing that eigenvalues are less than 1
stable = true
d = eigvals(A)
if maximum(abs, d) > 1.0
stable = false
end
return stable
end | [
138,
148
] | function is_stable(A::AbstractMatrix)
# Check for stability by testing that eigenvalues are less than 1
stable = true
d = eigvals(A)
if maximum(abs, d) > 1.0
stable = false
end
return stable
end | function is_stable(A::AbstractMatrix)
# Check for stability by testing that eigenvalues are less than 1
stable = true
d = eigvals(A)
if maximum(abs, d) > 1.0
stable = false
end
return stable
end | is_stable | 138 | 148 | src/util.jl | #FILE: QuantEcon.jl/src/lss.jl
##CHUNK 1
#### Arguments
- `lss::LSS` The linear state space system
#### Returns
- `stable::Bool` Whether or not the system is stable
"""
function is_stable(lss::LSS)
# Get version of A without constant row/column
A = remove_constants(lss)
# Check for stability
stab... |
224 | 242 | QuantEcon.jl | 215 | function Base.iterate(sg::SimplexGrid, state)
m = sg.m
x, h = state
x[1] >= sg.n && return nothing
h -= 1
val = x[h+1]
x[h+1] = 0
x[m] = val - 1
x[h] += 1
if val != 1
h = m
end
return x, (x, h)
end | function Base.iterate(sg::SimplexGrid, state)
m = sg.m
x, h = state
x[1] >= sg.n && return nothing
h -= 1
val = x[h+1]
x[h+1] = 0
x[m] = val - 1
x[h] += 1
if val != 1
h = m
end
return x, (x, h)
end | [
224,
242
] | function Base.iterate(sg::SimplexGrid, state)
m = sg.m
x, h = state
x[1] >= sg.n && return nothing
h -= 1
val = x[h+1]
x[h+1] = 0
x[m] = val - 1
x[h] += 1
if val != 1
h = m
end
return x, (x, h)
end | function Base.iterate(sg::SimplexGrid, state)
m = sg.m
x, h = state
x[1] >= sg.n && return nothing
h -= 1
val = x[h+1]
x[h+1] = 0
x[m] = val - 1
x[h] += 1
if val != 1
h = m
end
return x, (x, h)
end | Base.iterate | 224 | 242 | src/util.jl | #FILE: QuantEcon.jl/src/markov/markov_approx.jl
##CHUNK 1
if any(!in(x, X) for x in states)
error("One of the states does not appear in history X")
end
# Count states and store in dictionary
nstates = length(states)
d = Dict{T, Int}(zip(states, 1:nstates))
# Counter matrix and dictiona... |
287 | 298 | QuantEcon.jl | 216 | function simplex_grid(m, n)
# Get number of elements in array and allocate
L = num_compositions(m, n)
out = Matrix{Int}(undef, m, L)
sg = SimplexGrid(m, n)
for (i, x) in enumerate(sg)
copyto!(out, m*(i-1) + 1, x, 1, m)
end
return out
end | function simplex_grid(m, n)
# Get number of elements in array and allocate
L = num_compositions(m, n)
out = Matrix{Int}(undef, m, L)
sg = SimplexGrid(m, n)
for (i, x) in enumerate(sg)
copyto!(out, m*(i-1) + 1, x, 1, m)
end
return out
end | [
287,
298
] | function simplex_grid(m, n)
# Get number of elements in array and allocate
L = num_compositions(m, n)
out = Matrix{Int}(undef, m, L)
sg = SimplexGrid(m, n)
for (i, x) in enumerate(sg)
copyto!(out, m*(i-1) + 1, x, 1, m)
end
return out
end | function simplex_grid(m, n)
# Get number of elements in array and allocate
L = num_compositions(m, n)
out = Matrix{Int}(undef, m, L)
sg = SimplexGrid(m, n)
for (i, x) in enumerate(sg)
copyto!(out, m*(i-1) + 1, x, 1, m)
end
return out
end | simplex_grid | 287 | 298 | src/util.jl | #FILE: QuantEcon.jl/other/ddpsolve.jl
##CHUNK 1
ind = n * x + (1-n:0)
fstar = f[ind]
pstar = P[ind, :]
return pstar, fstar, ind
end
function expandg(g)
# Only need if I supply "transfunc". Not doing so
n, m = size(g)
P = sparse(1:n*m, g(:), 1, n*m, n)
return P
end
function diagmult(a... |
320 | 337 | QuantEcon.jl | 217 | function simplex_index(x, m, n)
# If only one element then only one point in simplex
if m==1
return 1
end
decumsum = reverse(cumsum(reverse(x[2:end])))
idx = binomial(n+m-1, m-1)
for i in 1:m-1
if decumsum[i] == 0
break
end
idx -= num_compositions(m ... | function simplex_index(x, m, n)
# If only one element then only one point in simplex
if m==1
return 1
end
decumsum = reverse(cumsum(reverse(x[2:end])))
idx = binomial(n+m-1, m-1)
for i in 1:m-1
if decumsum[i] == 0
break
end
idx -= num_compositions(m ... | [
320,
337
] | function simplex_index(x, m, n)
# If only one element then only one point in simplex
if m==1
return 1
end
decumsum = reverse(cumsum(reverse(x[2:end])))
idx = binomial(n+m-1, m-1)
for i in 1:m-1
if decumsum[i] == 0
break
end
idx -= num_compositions(m ... | function simplex_index(x, m, n)
# If only one element then only one point in simplex
if m==1
return 1
end
decumsum = reverse(cumsum(reverse(x[2:end])))
idx = binomial(n+m-1, m-1)
for i in 1:m-1
if decumsum[i] == 0
break
end
idx -= num_compositions(m ... | simplex_index | 320 | 337 | src/util.jl | #FILE: QuantEcon.jl/src/markov/mc_tools.jl
##CHUNK 1
@inbounds for k in 1:n-1
scale = sum(A[k, k+1:n])
if scale <= zero(T)
# There is one (and only one) recurrent class contained in
# {1, ..., k};
# compute the solution associated with that recurrent class.
... |
376 | 396 | QuantEcon.jl | 218 | function next_k_array!(a::Vector{<:Integer})
k = length(a)
if k == 1 || a[1] + 1 < a[2]
a[1] += 1
return a
end
a[1] = 1
i = 2
x = a[i] + 1
while i < k && x == a[i+1]
i += 1
a[i-1] = i - 1
x = a[i] + 1
end
a[i] = x
return a
end | function next_k_array!(a::Vector{<:Integer})
k = length(a)
if k == 1 || a[1] + 1 < a[2]
a[1] += 1
return a
end
a[1] = 1
i = 2
x = a[i] + 1
while i < k && x == a[i+1]
i += 1
a[i-1] = i - 1
x = a[i] + 1
end
a[i] = x
return a
end | [
376,
396
] | function next_k_array!(a::Vector{<:Integer})
k = length(a)
if k == 1 || a[1] + 1 < a[2]
a[1] += 1
return a
end
a[1] = 1
i = 2
x = a[i] + 1
while i < k && x == a[i+1]
i += 1
a[i-1] = i - 1
x = a[i] + 1
end
a[i] = x
return a
end | function next_k_array!(a::Vector{<:Integer})
k = length(a)
if k == 1 || a[1] + 1 < a[2]
a[1] += 1
return a
end
a[1] = 1
i = 2
x = a[i] + 1
while i < k && x == a[i+1]
i += 1
a[i-1] = i - 1
x = a[i] + 1
end
a[i] = x
return a
end | next_k_array! | 376 | 396 | src/util.jl | #FILE: QuantEcon.jl/src/markov/random_mc.jl
##CHUNK 1
k == 1 && return ones((k, m))
# if k >= 2
x = Matrix{Float64}(undef, k, m)
r = rand(rng, k-1, m)
x[1:end .- 1, :] = sort(r, dims = 1)
for j in 1:m
x[end, j] = 1 - x[end-1, j]
for i in k-1:-1:2
x[i, j] -= x[i-1, ... |
424 | 436 | QuantEcon.jl | 219 | function k_array_rank(T::Type{<:Integer}, a::Vector{<:Integer})
if T != BigInt
binomial(BigInt(a[end]), BigInt(length(a))) ≤ typemax(T) ||
throw(InexactError(:Binomial, T, a[end]))
end
k = length(a)
idx = one(T)
for i = 1:k
idx += binomial(T(a[i])-one(T), T(i))
end
r... | function k_array_rank(T::Type{<:Integer}, a::Vector{<:Integer})
if T != BigInt
binomial(BigInt(a[end]), BigInt(length(a))) ≤ typemax(T) ||
throw(InexactError(:Binomial, T, a[end]))
end
k = length(a)
idx = one(T)
for i = 1:k
idx += binomial(T(a[i])-one(T), T(i))
end
r... | [
424,
436
] | function k_array_rank(T::Type{<:Integer}, a::Vector{<:Integer})
if T != BigInt
binomial(BigInt(a[end]), BigInt(length(a))) ≤ typemax(T) ||
throw(InexactError(:Binomial, T, a[end]))
end
k = length(a)
idx = one(T)
for i = 1:k
idx += binomial(T(a[i])-one(T), T(i))
end
r... | function k_array_rank(T::Type{<:Integer}, a::Vector{<:Integer})
if T != BigInt
binomial(BigInt(a[end]), BigInt(length(a))) ≤ typemax(T) ||
throw(InexactError(:Binomial, T, a[end]))
end
k = length(a)
idx = one(T)
for i = 1:k
idx += binomial(T(a[i])-one(T), T(i))
end
r... | k_array_rank | 424 | 436 | src/util.jl | #FILE: QuantEcon.jl/other/ddpsolve.jl
##CHUNK 1
# TODO: the stdlib function findmax(arr, dim) should do this now
function indvalmax(a::Matrix{T}, dim::Integer=2) where T
out_size = dim == 2 ? size(a, 1) : size(a, 2)
out_v = Array(T, out_size)
out_i = Array(Int64, out_size)
if dim == 2
for i=1:o... |
121 | 143 | QuantEcon.jl | 220 | function divide_bracket(f::Function, x1::T, x2::T, n::Int=50) where T<:Number
x1 <= x2 || throw(ArgumentError("x1 must be less than x2"))
xs = range(x1, stop=x2, length=n)
dx = xs[2] - xs[1]
x1b = T[]
x2b = T[]
f1 = f(x1)
for x in xs[2:end]
f2 = f(x)
if f1*f2 <= 0.0
... | function divide_bracket(f::Function, x1::T, x2::T, n::Int=50) where T<:Number
x1 <= x2 || throw(ArgumentError("x1 must be less than x2"))
xs = range(x1, stop=x2, length=n)
dx = xs[2] - xs[1]
x1b = T[]
x2b = T[]
f1 = f(x1)
for x in xs[2:end]
f2 = f(x)
if f1*f2 <= 0.0
... | [
121,
143
] | function divide_bracket(f::Function, x1::T, x2::T, n::Int=50) where T<:Number
x1 <= x2 || throw(ArgumentError("x1 must be less than x2"))
xs = range(x1, stop=x2, length=n)
dx = xs[2] - xs[1]
x1b = T[]
x2b = T[]
f1 = f(x1)
for x in xs[2:end]
f2 = f(x)
if f1*f2 <= 0.0
... | function divide_bracket(f::Function, x1::T, x2::T, n::Int=50) where T<:Number
x1 <= x2 || throw(ArgumentError("x1 must be less than x2"))
xs = range(x1, stop=x2, length=n)
dx = xs[2] - xs[1]
x1b = T[]
x2b = T[]
f1 = f(x1)
for x in xs[2:end]
f2 = f(x)
if f1*f2 <= 0.0
... | divide_bracket | 121 | 143 | src/zeros.jl | #CURRENT FILE: QuantEcon.jl/src/zeros.jl
##CHUNK 1
end
if abs(f1) < abs(f2)
x1 += fac*(x1 - x2)
f1 = f(x1)
else
x2 += fac*(x2 - x1)
f2 = f(x2)
end
end
throw(ConvergenceError("failed to find bracket in $ntry iterations"))
end
expa... |
550 | 562 | QuantEcon.jl | 221 | function backward_induction(ddp::DiscreteDP{T}, J::Integer,
v_term::AbstractVector{<:Real}=
zeros(num_states(ddp))) where {T}
n = num_states(ddp)
S = typeof(zero(T)/one(T))
vs = Matrix{S}(undef, n, J+1)
vs[:,end] = v_term
sigmas = Matrix{Int}(u... | function backward_induction(ddp::DiscreteDP{T}, J::Integer,
v_term::AbstractVector{<:Real}=
zeros(num_states(ddp))) where {T}
n = num_states(ddp)
S = typeof(zero(T)/one(T))
vs = Matrix{S}(undef, n, J+1)
vs[:,end] = v_term
sigmas = Matrix{Int}(u... | [
550,
562
] | function backward_induction(ddp::DiscreteDP{T}, J::Integer,
v_term::AbstractVector{<:Real}=
zeros(num_states(ddp))) where {T}
n = num_states(ddp)
S = typeof(zero(T)/one(T))
vs = Matrix{S}(undef, n, J+1)
vs[:,end] = v_term
sigmas = Matrix{Int}(u... | function backward_induction(ddp::DiscreteDP{T}, J::Integer,
v_term::AbstractVector{<:Real}=
zeros(num_states(ddp))) where {T}
n = num_states(ddp)
S = typeof(zero(T)/one(T))
vs = Matrix{S}(undef, n, J+1)
vs[:,end] = v_term
sigmas = Matrix{Int}(u... | backward_induction | 550 | 562 | src/markov/ddp.jl | #CURRENT FILE: QuantEcon.jl/src/markov/ddp.jl
##CHUNK 1
```
for ``j= J, \\ldots, 1``, where the terminal value function ``v_{J+1}`` is
exogenously given by `v_term`.
# Parameters
- `ddp::DiscreteDP{T}` : Object that contains the Model Parameters
- `J::Integer`: Number of decision periods
- `v_term::AbstractVector{<... |
699 | 716 | QuantEcon.jl | 222 | function s_wise_max!(
a_indices::AbstractVector, a_indptr::AbstractVector,
vals::AbstractVector, out::AbstractVector
)
n = length(out)
for i in 1:n
if a_indptr[i] != a_indptr[i+1]
m = a_indptr[i]
for j in a_indptr[i]+1:(a_indptr[i+1]-1)
if vals... | function s_wise_max!(
a_indices::AbstractVector, a_indptr::AbstractVector,
vals::AbstractVector, out::AbstractVector
)
n = length(out)
for i in 1:n
if a_indptr[i] != a_indptr[i+1]
m = a_indptr[i]
for j in a_indptr[i]+1:(a_indptr[i+1]-1)
if vals... | [
699,
716
] | function s_wise_max!(
a_indices::AbstractVector, a_indptr::AbstractVector,
vals::AbstractVector, out::AbstractVector
)
n = length(out)
for i in 1:n
if a_indptr[i] != a_indptr[i+1]
m = a_indptr[i]
for j in a_indptr[i]+1:(a_indptr[i+1]-1)
if vals... | function s_wise_max!(
a_indices::AbstractVector, a_indptr::AbstractVector,
vals::AbstractVector, out::AbstractVector
)
n = length(out)
for i in 1:n
if a_indptr[i] != a_indptr[i+1]
m = a_indptr[i]
for j in a_indptr[i]+1:(a_indptr[i+1]-1)
if vals... | s_wise_max! | 699 | 716 | src/markov/ddp.jl | #FILE: QuantEcon.jl/other/ddpsolve.jl
##CHUNK 1
# TODO: the stdlib function findmax(arr, dim) should do this now
function indvalmax(a::Matrix{T}, dim::Integer=2) where T
out_size = dim == 2 ? size(a, 1) : size(a, 2)
out_v = Array(T, out_size)
out_i = Array(Int64, out_size)
if dim == 2
for i=1:o... |
757 | 772 | QuantEcon.jl | 223 | function _has_sorted_sa_indices(
s_indices::AbstractVector, a_indices::AbstractVector
)
L = length(s_indices)
for i in 1:L-1
if s_indices[i] > s_indices[i+1]
return false
end
if s_indices[i] == s_indices[i+1]
if a_indices[i] >= a_indices[i+1]
... | function _has_sorted_sa_indices(
s_indices::AbstractVector, a_indices::AbstractVector
)
L = length(s_indices)
for i in 1:L-1
if s_indices[i] > s_indices[i+1]
return false
end
if s_indices[i] == s_indices[i+1]
if a_indices[i] >= a_indices[i+1]
... | [
757,
772
] | function _has_sorted_sa_indices(
s_indices::AbstractVector, a_indices::AbstractVector
)
L = length(s_indices)
for i in 1:L-1
if s_indices[i] > s_indices[i+1]
return false
end
if s_indices[i] == s_indices[i+1]
if a_indices[i] >= a_indices[i+1]
... | function _has_sorted_sa_indices(
s_indices::AbstractVector, a_indices::AbstractVector
)
L = length(s_indices)
for i in 1:L-1
if s_indices[i] > s_indices[i+1]
return false
end
if s_indices[i] == s_indices[i+1]
if a_indices[i] >= a_indices[i+1]
... | _has_sorted_sa_indices | 757 | 772 | src/markov/ddp.jl | #CURRENT FILE: QuantEcon.jl/src/markov/ddp.jl
##CHUNK 1
if a_indptr[i] != a_indptr[i+1]
m = a_indptr[i]
for j in a_indptr[i]+1:(a_indptr[i+1]-1)
if vals[j] > vals[m]
m = j
end
end
out[i] = vals[m]
out... |
185 | 217 | QuantEcon.jl | 224 | function estimate_mc_discrete(X::Vector{T}, states::Vector{T}) where T
# Get length of simulation
capT = length(X)
# Make sure all of the passed in states appear in X... If not
# throw an error
if any(!in(x, X) for x in states)
error("One of the states does not appear in history X")
end... | function estimate_mc_discrete(X::Vector{T}, states::Vector{T}) where T
# Get length of simulation
capT = length(X)
# Make sure all of the passed in states appear in X... If not
# throw an error
if any(!in(x, X) for x in states)
error("One of the states does not appear in history X")
end... | [
185,
217
] | function estimate_mc_discrete(X::Vector{T}, states::Vector{T}) where T
# Get length of simulation
capT = length(X)
# Make sure all of the passed in states appear in X... If not
# throw an error
if any(!in(x, X) for x in states)
error("One of the states does not appear in history X")
end... | function estimate_mc_discrete(X::Vector{T}, states::Vector{T}) where T
# Get length of simulation
capT = length(X)
# Make sure all of the passed in states appear in X... If not
# throw an error
if any(!in(x, X) for x in states)
error("One of the states does not appear in history X")
end... | estimate_mc_discrete | 185 | 217 | src/markov/markov_approx.jl | #FILE: QuantEcon.jl/src/markov/ddp.jl
##CHUNK 1
num_states, num_actions = size(R)
if size(Q) != (num_states, num_actions, num_states)
throw(ArgumentError("shapes of R and Q must be (n,m) and (n,m,n)"))
end
# check feasibility
R_max = s_wise_max(R)
if any(R_max .== ... |
298 | 425 | QuantEcon.jl | 225 | function discrete_var(b::Union{Real, AbstractVector},
B::Union{Real, AbstractMatrix},
Psi::Union{Real, AbstractMatrix},
Nm::Integer,
n_moments::Integer=2,
method::VAREstimationMethod=Even(),
... | function discrete_var(b::Union{Real, AbstractVector},
B::Union{Real, AbstractMatrix},
Psi::Union{Real, AbstractMatrix},
Nm::Integer,
n_moments::Integer=2,
method::VAREstimationMethod=Even(),
... | [
298,
425
] | function discrete_var(b::Union{Real, AbstractVector},
B::Union{Real, AbstractMatrix},
Psi::Union{Real, AbstractMatrix},
Nm::Integer,
n_moments::Integer=2,
method::VAREstimationMethod=Even(),
... | function discrete_var(b::Union{Real, AbstractVector},
B::Union{Real, AbstractMatrix},
Psi::Union{Real, AbstractMatrix},
Nm::Integer,
n_moments::Integer=2,
method::VAREstimationMethod=Even(),
... | discrete_var | 298 | 425 | src/markov/markov_approx.jl | #FILE: QuantEcon.jl/src/sampler.jl
##CHUNK 1
struct MVNSampler{TM<:Real,TS<:Real,TQ<:BlasReal}
mu::Vector{TM}
Sigma::Matrix{TS}
Q::Matrix{TQ}
end
function MVNSampler(mu::Vector{TM}, Sigma::Matrix{TS}) where {TM<:Real,TS<:Real}
ATOL1, RTOL1 = 1e-8, 1e-8
ATOL2, RTOL2 = 1e-8, 1e-14
n = length(mu)... |
494 | 506 | QuantEcon.jl | 226 | function standardize_var(b::AbstractVector, B::AbstractMatrix,
Psi::AbstractMatrix, M::Integer)
C1 = cholesky(Psi).L
mu = ((I - B)\ I)*b
A1 = C1\(B*C1)
# unconditional variance
Sigma1 = reshape(((I-kron(A1,A1))\I)*vec(Matrix(I, M, M)),M,M)
U, _ = min_var_trace(Sigma1)
... | function standardize_var(b::AbstractVector, B::AbstractMatrix,
Psi::AbstractMatrix, M::Integer)
C1 = cholesky(Psi).L
mu = ((I - B)\ I)*b
A1 = C1\(B*C1)
# unconditional variance
Sigma1 = reshape(((I-kron(A1,A1))\I)*vec(Matrix(I, M, M)),M,M)
U, _ = min_var_trace(Sigma1)
... | [
494,
506
] | function standardize_var(b::AbstractVector, B::AbstractMatrix,
Psi::AbstractMatrix, M::Integer)
C1 = cholesky(Psi).L
mu = ((I - B)\ I)*b
A1 = C1\(B*C1)
# unconditional variance
Sigma1 = reshape(((I-kron(A1,A1))\I)*vec(Matrix(I, M, M)),M,M)
U, _ = min_var_trace(Sigma1)
... | function standardize_var(b::AbstractVector, B::AbstractMatrix,
Psi::AbstractMatrix, M::Integer)
C1 = cholesky(Psi).L
mu = ((I - B)\ I)*b
A1 = C1\(B*C1)
# unconditional variance
Sigma1 = reshape(((I-kron(A1,A1))\I)*vec(Matrix(I, M, M)),M,M)
U, _ = min_var_trace(Sigma1)
... | standardize_var | 494 | 506 | src/markov/markov_approx.jl | #FILE: QuantEcon.jl/other/regression.jl
##CHUNK 1
U, S, V = svd(X1, thin=true)
S_inv = diagm(0 => 1.0 ./ S)
B1 = V*S_inv * U' * Y1
B = de_normalize(X, Y, B1)
else
U, S, V = svd(X, thin=true)
S_inv = diagm(1.0 ./ S)
B = V * S_inv * U' * Y
end
return B
e... |
720 | 759 | QuantEcon.jl | 227 | function discrete_approximation(D::AbstractVector, T::Function, Tbar::AbstractVector,
q::AbstractVector=ones(length(D))/length(D), # Default prior weights
lambda0::AbstractVector=zeros(Tbar))
# Input error checking
N = length(D)
Tx = T(D)... | function discrete_approximation(D::AbstractVector, T::Function, Tbar::AbstractVector,
q::AbstractVector=ones(length(D))/length(D), # Default prior weights
lambda0::AbstractVector=zeros(Tbar))
# Input error checking
N = length(D)
Tx = T(D)... | [
720,
759
] | function discrete_approximation(D::AbstractVector, T::Function, Tbar::AbstractVector,
q::AbstractVector=ones(length(D))/length(D), # Default prior weights
lambda0::AbstractVector=zeros(Tbar))
# Input error checking
N = length(D)
Tx = T(D)... | function discrete_approximation(D::AbstractVector, T::Function, Tbar::AbstractVector,
q::AbstractVector=ones(length(D))/length(D), # Default prior weights
lambda0::AbstractVector=zeros(Tbar))
# Input error checking
N = length(D)
Tx = T(D)... | discrete_approximation | 720 | 759 | src/markov/markov_approx.jl | #FILE: QuantEcon.jl/src/robustlq.jl
##CHUNK 1
"""
function robust_rule_simple(rlq::RBLQ,
P::Matrix=zeros(Float64, rlq.n, rlq.n);
max_iter=80,
tol=1e-8)
# Simplify notation
A, B, C, Q, R = rlq.A, rlq.B, rlq.C, rlq.Q, rlq.R
b... |
871 | 892 | QuantEcon.jl | 228 | function min_var_trace(A::AbstractMatrix)
==(size(A)...) || throw(ArgumentError("input matrix must be square"))
K = size(A, 1) # size of A
d = tr(A)/K # diagonal of U'*A*U should be closest to d
function obj(X, grad)
X = reshape(X, K, K)
return (norm(diag(X'*A*X) .- d))
end
fun... | function min_var_trace(A::AbstractMatrix)
==(size(A)...) || throw(ArgumentError("input matrix must be square"))
K = size(A, 1) # size of A
d = tr(A)/K # diagonal of U'*A*U should be closest to d
function obj(X, grad)
X = reshape(X, K, K)
return (norm(diag(X'*A*X) .- d))
end
fun... | [
871,
892
] | function min_var_trace(A::AbstractMatrix)
==(size(A)...) || throw(ArgumentError("input matrix must be square"))
K = size(A, 1) # size of A
d = tr(A)/K # diagonal of U'*A*U should be closest to d
function obj(X, grad)
X = reshape(X, K, K)
return (norm(diag(X'*A*X) .- d))
end
fun... | function min_var_trace(A::AbstractMatrix)
==(size(A)...) || throw(ArgumentError("input matrix must be square"))
K = size(A, 1) # size of A
d = tr(A)/K # diagonal of U'*A*U should be closest to d
function obj(X, grad)
X = reshape(X, K, K)
return (norm(diag(X'*A*X) .- d))
end
fun... | min_var_trace | 871 | 892 | src/markov/markov_approx.jl | #FILE: QuantEcon.jl/src/quadsums.jl
##CHUNK 1
"""
function var_quadratic_sum(A::ScalarOrArray, C::ScalarOrArray, H::ScalarOrArray,
bet::Real, x0::ScalarOrArray)
n = size(A, 1)
# coerce shapes
A = reshape([A;], n, n)
C = reshape([C;], n, n)
H = reshape([H;], n, n)
x0 ... |
35 | 51 | QuantEcon.jl | 229 | function MarkovChain{T,TM,TV}(p::AbstractMatrix, state_values) where {T,TM,TV}
n, m = size(p)
n != m &&
throw(DimensionMismatch("stochastic matrix must be square"))
minimum(p) <0 &&
throw(ArgumentError("stochastic matrix must have nonnegative elements"))
!check... | function MarkovChain{T,TM,TV}(p::AbstractMatrix, state_values) where {T,TM,TV}
n, m = size(p)
n != m &&
throw(DimensionMismatch("stochastic matrix must be square"))
minimum(p) <0 &&
throw(ArgumentError("stochastic matrix must have nonnegative elements"))
!check... | [
35,
51
] | function MarkovChain{T,TM,TV}(p::AbstractMatrix, state_values) where {T,TM,TV}
n, m = size(p)
n != m &&
throw(DimensionMismatch("stochastic matrix must be square"))
minimum(p) <0 &&
throw(ArgumentError("stochastic matrix must have nonnegative elements"))
!check... | function MarkovChain{T,TM,TV}(p::AbstractMatrix, state_values) where {T,TM,TV}
n, m = size(p)
n != m &&
throw(DimensionMismatch("stochastic matrix must be square"))
minimum(p) <0 &&
throw(ArgumentError("stochastic matrix must have nonnegative elements"))
!check... | MarkovChain{T,TM,TV} | 35 | 51 | src/markov/mc_tools.jl | #FILE: QuantEcon.jl/src/markov/random_mc.jl
##CHUNK 1
return transpose(p)
end
random_stochastic_matrix(n::Integer, k::Integer=n) =
random_stochastic_matrix(Random.GLOBAL_RNG, n, k)
"""
_random_stochastic_matrix([rng], n, m; k=n)
Generate a "non-square column stochstic matrix" of shape `(n, m)`, which co... |
117 | 147 | QuantEcon.jl | 230 | function gth_solve!(A::Matrix{T}) where T<:Real
n = size(A, 1)
x = zeros(T, n)
@inbounds for k in 1:n-1
scale = sum(A[k, k+1:n])
if scale <= zero(T)
# There is one (and only one) recurrent class contained in
# {1, ..., k};
# compute the solution associate... | function gth_solve!(A::Matrix{T}) where T<:Real
n = size(A, 1)
x = zeros(T, n)
@inbounds for k in 1:n-1
scale = sum(A[k, k+1:n])
if scale <= zero(T)
# There is one (and only one) recurrent class contained in
# {1, ..., k};
# compute the solution associate... | [
117,
147
] | function gth_solve!(A::Matrix{T}) where T<:Real
n = size(A, 1)
x = zeros(T, n)
@inbounds for k in 1:n-1
scale = sum(A[k, k+1:n])
if scale <= zero(T)
# There is one (and only one) recurrent class contained in
# {1, ..., k};
# compute the solution associate... | function gth_solve!(A::Matrix{T}) where T<:Real
n = size(A, 1)
x = zeros(T, n)
@inbounds for k in 1:n-1
scale = sum(A[k, k+1:n])
if scale <= zero(T)
# There is one (and only one) recurrent class contained in
# {1, ..., k};
# compute the solution associate... | gth_solve! | 117 | 147 | src/markov/mc_tools.jl | #FILE: QuantEcon.jl/src/markov/random_mc.jl
##CHUNK 1
k == 1 && return ones((k, m))
# if k >= 2
x = Matrix{Float64}(undef, k, m)
r = rand(rng, k-1, m)
x[1:end .- 1, :] = sort(r, dims = 1)
for j in 1:m
x[end, j] = 1 - x[end-1, j]
for i in k-1:-1:2
x[i, j] -= x[i-1, ... |
219 | 230 | QuantEcon.jl | 231 | function period(mc::MarkovChain)
g = DiGraph(mc.p)
recurrent = attracting_components(g)
d = 1
for r in recurrent
pd = period(g[r])
d *= div(pd, gcd(pd, d))
end
return d
end | function period(mc::MarkovChain)
g = DiGraph(mc.p)
recurrent = attracting_components(g)
d = 1
for r in recurrent
pd = period(g[r])
d *= div(pd, gcd(pd, d))
end
return d
end | [
219,
230
] | function period(mc::MarkovChain)
g = DiGraph(mc.p)
recurrent = attracting_components(g)
d = 1
for r in recurrent
pd = period(g[r])
d *= div(pd, gcd(pd, d))
end
return d
end | function period(mc::MarkovChain)
g = DiGraph(mc.p)
recurrent = attracting_components(g)
d = 1
for r in recurrent
pd = period(g[r])
d *= div(pd, gcd(pd, d))
end
return d
end | period | 219 | 230 | src/markov/mc_tools.jl | #FILE: QuantEcon.jl/src/quad.jl
##CHUNK 1
# Recurrance relation for Laguerre polynomials
p3 = p2
p2 = p1
p1 = ((2j - 1 + a - z) * p2 - (j - 1 + a) * p3) ./ j
end
pp = (n * p1 - (n + a) * p2) ./ z
z1 = z
z = z... |
275 | 285 | QuantEcon.jl | 232 | function todense(T::Type, S::SparseMatrixCSC)
A = zeros(T, S.m, S.n)
for Sj in 1:S.n
for Sk in nzrange(S, Sj)
Si = S.rowval[Sk]
Sv = S.nzval[Sk]
A[Si, Sj] = Sv
end
end
return A
end | function todense(T::Type, S::SparseMatrixCSC)
A = zeros(T, S.m, S.n)
for Sj in 1:S.n
for Sk in nzrange(S, Sj)
Si = S.rowval[Sk]
Sv = S.nzval[Sk]
A[Si, Sj] = Sv
end
end
return A
end | [
275,
285
] | function todense(T::Type, S::SparseMatrixCSC)
A = zeros(T, S.m, S.n)
for Sj in 1:S.n
for Sk in nzrange(S, Sj)
Si = S.rowval[Sk]
Sv = S.nzval[Sk]
A[Si, Sj] = Sv
end
end
return A
end | function todense(T::Type, S::SparseMatrixCSC)
A = zeros(T, S.m, S.n)
for Sj in 1:S.n
for Sk in nzrange(S, Sj)
Si = S.rowval[Sk]
Sv = S.nzval[Sk]
A[Si, Sj] = Sv
end
end
return A
end | todense | 275 | 285 | src/markov/mc_tools.jl | #FILE: QuantEcon.jl/src/markov/ddp.jl
##CHUNK 1
while(s_indices[idx] == s)
idx += 1
end
out[s+1] = idx
end
# need this +1 to be consistent with Julia's sparse pointers:
# colptr[i]:(colptr[i+1]-1)
out[num_states+1] = length(s_indices)+1
out
end
function _find_ind... |
81 | 92 | QuantEcon.jl | 233 | function random_stochastic_matrix(rng::AbstractRNG, n::Integer, k::Integer=n)
if !(n > 0)
throw(ArgumentError("n must be a positive integer"))
end
if !(k > 0 && k <= n)
throw(ArgumentError("k must be an integer with 0 < k <= n"))
end
p = _random_stochastic_matrix(rng, n, n, k=k)
... | function random_stochastic_matrix(rng::AbstractRNG, n::Integer, k::Integer=n)
if !(n > 0)
throw(ArgumentError("n must be a positive integer"))
end
if !(k > 0 && k <= n)
throw(ArgumentError("k must be an integer with 0 < k <= n"))
end
p = _random_stochastic_matrix(rng, n, n, k=k)
... | [
81,
92
] | function random_stochastic_matrix(rng::AbstractRNG, n::Integer, k::Integer=n)
if !(n > 0)
throw(ArgumentError("n must be a positive integer"))
end
if !(k > 0 && k <= n)
throw(ArgumentError("k must be an integer with 0 < k <= n"))
end
p = _random_stochastic_matrix(rng, n, n, k=k)
... | function random_stochastic_matrix(rng::AbstractRNG, n::Integer, k::Integer=n)
if !(n > 0)
throw(ArgumentError("n must be a positive integer"))
end
if !(k > 0 && k <= n)
throw(ArgumentError("k must be an integer with 0 < k <= n"))
end
p = _random_stochastic_matrix(rng, n, n, k=k)
... | random_stochastic_matrix | 81 | 92 | src/markov/random_mc.jl | #FILE: QuantEcon.jl/test/test_random_mc.jl
##CHUNK 1
@testset "Test random_stochastic_matrix" begin
n, k = 5, 3
Ps = (random_stochastic_matrix(n), random_stochastic_matrix(n, k))
for P in Ps
@test all(P .>= 0) == true
@test all(x->isapprox(sum(x), 1),
... |
117 | 138 | QuantEcon.jl | 234 | function _random_stochastic_matrix(rng::AbstractRNG, n::Integer, m::Integer;
k::Integer=n)
probvecs = random_probvec(rng, k, m)
k == n && return probvecs
# if k < n
# Randomly sample row indices for each column for nonzero values
row_indices = Vector{Int}(undef, ... | function _random_stochastic_matrix(rng::AbstractRNG, n::Integer, m::Integer;
k::Integer=n)
probvecs = random_probvec(rng, k, m)
k == n && return probvecs
# if k < n
# Randomly sample row indices for each column for nonzero values
row_indices = Vector{Int}(undef, ... | [
117,
138
] | function _random_stochastic_matrix(rng::AbstractRNG, n::Integer, m::Integer;
k::Integer=n)
probvecs = random_probvec(rng, k, m)
k == n && return probvecs
# if k < n
# Randomly sample row indices for each column for nonzero values
row_indices = Vector{Int}(undef, ... | function _random_stochastic_matrix(rng::AbstractRNG, n::Integer, m::Integer;
k::Integer=n)
probvecs = random_probvec(rng, k, m)
k == n && return probvecs
# if k < n
# Randomly sample row indices for each column for nonzero values
row_indices = Vector{Int}(undef, ... | _random_stochastic_matrix | 117 | 138 | src/markov/random_mc.jl | #CURRENT FILE: QuantEcon.jl/src/markov/random_mc.jl
##CHUNK 1
- `m::Integer` : Number of probability vectors.
# Returns
- `a::Array` : Matrix of shape `(k, m)`, or Vector of shape `(k,)` if `m` is not
specified, containing probability vector(s) as column(s).
"""
function random_probvec(rng::AbstractRNG, k::Integer,... |
169 | 184 | QuantEcon.jl | 235 | function random_discrete_dp(rng::AbstractRNG,
num_states::Integer,
num_actions::Integer,
beta::Real=rand(rng);
k::Integer=num_states,
scale::Real=1)
L = num_states * num_action... | function random_discrete_dp(rng::AbstractRNG,
num_states::Integer,
num_actions::Integer,
beta::Real=rand(rng);
k::Integer=num_states,
scale::Real=1)
L = num_states * num_action... | [
169,
184
] | function random_discrete_dp(rng::AbstractRNG,
num_states::Integer,
num_actions::Integer,
beta::Real=rand(rng);
k::Integer=num_states,
scale::Real=1)
L = num_states * num_action... | function random_discrete_dp(rng::AbstractRNG,
num_states::Integer,
num_actions::Integer,
beta::Real=rand(rng);
k::Integer=num_states,
scale::Real=1)
L = num_states * num_action... | random_discrete_dp | 169 | 184 | src/markov/random_mc.jl | #FILE: QuantEcon.jl/test/test_random_mc.jl
##CHUNK 1
# k > n
@test_throws ArgumentError random_markov_chain(2, 3)
end
@testset "Test random_discrete_dp" begin
num_states, num_actions = 5, 4
num_sa = num_states * num_actions
k = 3
ddp = random_discrete_dp(num_stat... |
210 | 227 | QuantEcon.jl | 236 | function random_probvec(rng::AbstractRNG, k::Integer, m::Integer)
k == 1 && return ones((k, m))
# if k >= 2
x = Matrix{Float64}(undef, k, m)
r = rand(rng, k-1, m)
x[1:end .- 1, :] = sort(r, dims = 1)
for j in 1:m
x[end, j] = 1 - x[end-1, j]
for i in k-1:-1:2
x[i, j... | function random_probvec(rng::AbstractRNG, k::Integer, m::Integer)
k == 1 && return ones((k, m))
# if k >= 2
x = Matrix{Float64}(undef, k, m)
r = rand(rng, k-1, m)
x[1:end .- 1, :] = sort(r, dims = 1)
for j in 1:m
x[end, j] = 1 - x[end-1, j]
for i in k-1:-1:2
x[i, j... | [
210,
227
] | function random_probvec(rng::AbstractRNG, k::Integer, m::Integer)
k == 1 && return ones((k, m))
# if k >= 2
x = Matrix{Float64}(undef, k, m)
r = rand(rng, k-1, m)
x[1:end .- 1, :] = sort(r, dims = 1)
for j in 1:m
x[end, j] = 1 - x[end-1, j]
for i in k-1:-1:2
x[i, j... | function random_probvec(rng::AbstractRNG, k::Integer, m::Integer)
k == 1 && return ones((k, m))
# if k >= 2
x = Matrix{Float64}(undef, k, m)
r = rand(rng, k-1, m)
x[1:end .- 1, :] = sort(r, dims = 1)
for j in 1:m
x[end, j] = 1 - x[end-1, j]
for i in k-1:-1:2
x[i, j... | random_probvec | 210 | 227 | src/markov/random_mc.jl | #FILE: QuantEcon.jl/src/lqnash.jl
##CHUNK 1
function nnash(a, b1, b2, r1, r2, q1, q2, s1, s2, w1, w2, m1, m2;
beta::Float64=1.0, tol::Float64=1e-8, max_iter::Int=1000)
# Apply discounting
a, b1, b2 = map(x->sqrt(beta) * x, Any[a, b1, b2])
dd = 10
its = 0
n = size(a, 1)
# NOTE: i... |
25 | 40 | StatsBase.jl | 237 | function addcounts!(r::AbstractArray, x::AbstractArray{<:Integer}, levels::UnitRange{<:Integer})
# add counts of integers from x that fall within levels to r
checkbounds(r, axes(levels)...)
m0 = first(levels)
m1 = last(levels)
b = m0 - firstindex(levels) # firstindex(levels) == 1 because levels::U... | function addcounts!(r::AbstractArray, x::AbstractArray{<:Integer}, levels::UnitRange{<:Integer})
# add counts of integers from x that fall within levels to r
checkbounds(r, axes(levels)...)
m0 = first(levels)
m1 = last(levels)
b = m0 - firstindex(levels) # firstindex(levels) == 1 because levels::U... | [
25,
40
] | function addcounts!(r::AbstractArray, x::AbstractArray{<:Integer}, levels::UnitRange{<:Integer})
# add counts of integers from x that fall within levels to r
checkbounds(r, axes(levels)...)
m0 = first(levels)
m1 = last(levels)
b = m0 - firstindex(levels) # firstindex(levels) == 1 because levels::U... | function addcounts!(r::AbstractArray, x::AbstractArray{<:Integer}, levels::UnitRange{<:Integer})
# add counts of integers from x that fall within levels to r
checkbounds(r, axes(levels)...)
m0 = first(levels)
m1 = last(levels)
b = m0 - firstindex(levels) # firstindex(levels) == 1 because levels::U... | addcounts! | 25 | 40 | src/counts.jl | #FILE: StatsBase.jl/src/scalarstats.jl
##CHUNK 1
Return all modes (most common numbers) of an array, optionally over a
specified range `r` or weighted via vector `wv`.
"""
function modes(a::AbstractArray{T}, r::UnitRange{T}) where T<:Integer
r0 = r[1]
r1 = r[end]
n = length(r)
cnts = zeros(Int, n)
#... |
42 | 63 | StatsBase.jl | 238 | function addcounts!(r::AbstractArray, x::AbstractArray{<:Integer}, levels::UnitRange{<:Integer}, wv::AbstractWeights)
# add wv weighted counts of integers from x that fall within levels to r
length(x) == length(wv) ||
throw(DimensionMismatch("x and wv must have the same length, got $(length(x)) and $(l... | function addcounts!(r::AbstractArray, x::AbstractArray{<:Integer}, levels::UnitRange{<:Integer}, wv::AbstractWeights)
# add wv weighted counts of integers from x that fall within levels to r
length(x) == length(wv) ||
throw(DimensionMismatch("x and wv must have the same length, got $(length(x)) and $(l... | [
42,
63
] | function addcounts!(r::AbstractArray, x::AbstractArray{<:Integer}, levels::UnitRange{<:Integer}, wv::AbstractWeights)
# add wv weighted counts of integers from x that fall within levels to r
length(x) == length(wv) ||
throw(DimensionMismatch("x and wv must have the same length, got $(length(x)) and $(l... | function addcounts!(r::AbstractArray, x::AbstractArray{<:Integer}, levels::UnitRange{<:Integer}, wv::AbstractWeights)
# add wv weighted counts of integers from x that fall within levels to r
length(x) == length(wv) ||
throw(DimensionMismatch("x and wv must have the same length, got $(length(x)) and $(l... | addcounts! | 42 | 63 | src/counts.jl | #CURRENT FILE: StatsBase.jl/src/counts.jl
##CHUNK 1
If a weighting vector `wv` is specified, the sum of weights is used rather than the
raw counts.
"""
function addcounts!(r::AbstractArray, x::AbstractArray{<:Integer}, levels::UnitRange{<:Integer})
# add counts of integers from x that fall within levels to r
... |
121 | 145 | StatsBase.jl | 239 | function addcounts!(r::AbstractArray, x::AbstractArray{<:Integer}, y::AbstractArray{<:Integer}, levels::NTuple{2,UnitRange{<:Integer}})
# add counts of pairs from zip(x,y) to r
xlevels, ylevels = levels
checkbounds(r, axes(xlevels, 1), axes(ylevels, 1))
mx0 = first(xlevels)
mx1 = last(xlevels)
... | function addcounts!(r::AbstractArray, x::AbstractArray{<:Integer}, y::AbstractArray{<:Integer}, levels::NTuple{2,UnitRange{<:Integer}})
# add counts of pairs from zip(x,y) to r
xlevels, ylevels = levels
checkbounds(r, axes(xlevels, 1), axes(ylevels, 1))
mx0 = first(xlevels)
mx1 = last(xlevels)
... | [
121,
145
] | function addcounts!(r::AbstractArray, x::AbstractArray{<:Integer}, y::AbstractArray{<:Integer}, levels::NTuple{2,UnitRange{<:Integer}})
# add counts of pairs from zip(x,y) to r
xlevels, ylevels = levels
checkbounds(r, axes(xlevels, 1), axes(ylevels, 1))
mx0 = first(xlevels)
mx1 = last(xlevels)
... | function addcounts!(r::AbstractArray, x::AbstractArray{<:Integer}, y::AbstractArray{<:Integer}, levels::NTuple{2,UnitRange{<:Integer}})
# add counts of pairs from zip(x,y) to r
xlevels, ylevels = levels
checkbounds(r, axes(xlevels, 1), axes(ylevels, 1))
mx0 = first(xlevels)
mx1 = last(xlevels)
... | addcounts! | 121 | 145 | src/counts.jl | #FILE: StatsBase.jl/src/signalcorr.jl
##CHUNK 1
for j = 1 : ns
demean_col!(zy, y, j, demean)
sc = sqrt(xx * dot(zy, zy))
for k = 1 : m
r[k,j] = _crossdot(zx, zy, lx, lags[k]) / sc
end
end
return r
end
function crosscor!(r::AbstractArray{<:Real,3}, x::AbstractMatr... |
147 | 179 | StatsBase.jl | 240 | function addcounts!(r::AbstractArray, x::AbstractArray{<:Integer}, y::AbstractArray{<:Integer},
levels::NTuple{2,UnitRange{<:Integer}}, wv::AbstractWeights)
# add counts of pairs from zip(x,y) to r
length(x) == length(y) == length(wv) ||
throw(DimensionMismatch("x, y, and wv must ha... | function addcounts!(r::AbstractArray, x::AbstractArray{<:Integer}, y::AbstractArray{<:Integer},
levels::NTuple{2,UnitRange{<:Integer}}, wv::AbstractWeights)
# add counts of pairs from zip(x,y) to r
length(x) == length(y) == length(wv) ||
throw(DimensionMismatch("x, y, and wv must ha... | [
147,
179
] | function addcounts!(r::AbstractArray, x::AbstractArray{<:Integer}, y::AbstractArray{<:Integer},
levels::NTuple{2,UnitRange{<:Integer}}, wv::AbstractWeights)
# add counts of pairs from zip(x,y) to r
length(x) == length(y) == length(wv) ||
throw(DimensionMismatch("x, y, and wv must ha... | function addcounts!(r::AbstractArray, x::AbstractArray{<:Integer}, y::AbstractArray{<:Integer},
levels::NTuple{2,UnitRange{<:Integer}}, wv::AbstractWeights)
# add counts of pairs from zip(x,y) to r
length(x) == length(y) == length(wv) ||
throw(DimensionMismatch("x, y, and wv must ha... | addcounts! | 147 | 179 | src/counts.jl | #FILE: StatsBase.jl/src/signalcorr.jl
##CHUNK 1
for j = 1 : ns
demean_col!(zy, y, j, demean)
sc = sqrt(xx * dot(zy, zy))
for k = 1 : m
r[k,j] = _crossdot(zx, zy, lx, lags[k]) / sc
end
end
return r
end
function crosscor!(r::AbstractArray{<:Real,3}, x::AbstractMatr... |
272 | 283 | StatsBase.jl | 241 | function _addcounts!(::Type{T}, cm::Dict, x; alg = :auto) where T
# if it's safe to be sorted using radixsort then it should be faster
# albeit using more RAM
if radixsort_safe(T) && (alg == :auto || alg == :radixsort)
addcounts_radixsort!(cm, x)
elseif alg == :radixsort
throw(ArgumentEr... | function _addcounts!(::Type{T}, cm::Dict, x; alg = :auto) where T
# if it's safe to be sorted using radixsort then it should be faster
# albeit using more RAM
if radixsort_safe(T) && (alg == :auto || alg == :radixsort)
addcounts_radixsort!(cm, x)
elseif alg == :radixsort
throw(ArgumentEr... | [
272,
283
] | function _addcounts!(::Type{T}, cm::Dict, x; alg = :auto) where T
# if it's safe to be sorted using radixsort then it should be faster
# albeit using more RAM
if radixsort_safe(T) && (alg == :auto || alg == :radixsort)
addcounts_radixsort!(cm, x)
elseif alg == :radixsort
throw(ArgumentEr... | function _addcounts!(::Type{T}, cm::Dict, x; alg = :auto) where T
# if it's safe to be sorted using radixsort then it should be faster
# albeit using more RAM
if radixsort_safe(T) && (alg == :auto || alg == :radixsort)
addcounts_radixsort!(cm, x)
elseif alg == :radixsort
throw(ArgumentEr... | _addcounts! | 272 | 283 | src/counts.jl | #CURRENT FILE: StatsBase.jl/src/counts.jl
##CHUNK 1
If a weighting vector `wv` is specified, the sum of the weights is used rather than the
raw counts.
`alg` is only allowed for unweighted counting and can be one of:
- `:auto` (default): if `StatsBase.radixsort_safe(eltype(x)) == true` then use
`:... |
286 | 296 | StatsBase.jl | 242 | function addcounts_dict!(cm::Dict{T}, x) where T
for v in x
index = ht_keyindex2!(cm, v)
if index > 0
@inbounds cm.vals[index] += 1
else
@inbounds Base._setindex!(cm, 1, v, -index)
end
end
return cm
end | function addcounts_dict!(cm::Dict{T}, x) where T
for v in x
index = ht_keyindex2!(cm, v)
if index > 0
@inbounds cm.vals[index] += 1
else
@inbounds Base._setindex!(cm, 1, v, -index)
end
end
return cm
end | [
286,
296
] | function addcounts_dict!(cm::Dict{T}, x) where T
for v in x
index = ht_keyindex2!(cm, v)
if index > 0
@inbounds cm.vals[index] += 1
else
@inbounds Base._setindex!(cm, 1, v, -index)
end
end
return cm
end | function addcounts_dict!(cm::Dict{T}, x) where T
for v in x
index = ht_keyindex2!(cm, v)
if index > 0
@inbounds cm.vals[index] += 1
else
@inbounds Base._setindex!(cm, 1, v, -index)
end
end
return cm
end | addcounts_dict! | 286 | 296 | src/counts.jl | #FILE: StatsBase.jl/src/scalarstats.jl
##CHUNK 1
for i = 1:length(a)
@inbounds x = a[i]
if r0 <= x <= r1
@inbounds c = (cnts[x - r0 + 1] += 1)
if c > mc
mc = c
end
end
end
# find all values corresponding to maximum count
ms = T[... |
350 | 370 | StatsBase.jl | 243 | function _addcounts_radix_sort_loop!(cm::Dict{T}, sx::AbstractVector{T}) where T
isempty(sx) && return cm
last_sx = first(sx)
start_i = firstindex(sx)
# now the data is sorted: can just run through and accumulate values before
# adding into the Dict
@inbounds for i in start_i+1:lastindex(sx)
... | function _addcounts_radix_sort_loop!(cm::Dict{T}, sx::AbstractVector{T}) where T
isempty(sx) && return cm
last_sx = first(sx)
start_i = firstindex(sx)
# now the data is sorted: can just run through and accumulate values before
# adding into the Dict
@inbounds for i in start_i+1:lastindex(sx)
... | [
350,
370
] | function _addcounts_radix_sort_loop!(cm::Dict{T}, sx::AbstractVector{T}) where T
isempty(sx) && return cm
last_sx = first(sx)
start_i = firstindex(sx)
# now the data is sorted: can just run through and accumulate values before
# adding into the Dict
@inbounds for i in start_i+1:lastindex(sx)
... | function _addcounts_radix_sort_loop!(cm::Dict{T}, sx::AbstractVector{T}) where T
isempty(sx) && return cm
last_sx = first(sx)
start_i = firstindex(sx)
# now the data is sorted: can just run through and accumulate values before
# adding into the Dict
@inbounds for i in start_i+1:lastindex(sx)
... | _addcounts_radix_sort_loop! | 350 | 370 | src/counts.jl | #FILE: StatsBase.jl/src/ranking.jl
##CHUNK 1
for e in 2:n # e is pass-by-end index of current range
cx = x[p[e]]
if cx != v
# fill average rank to s : e-1
ar = (s + e - 1) / 2
for i = s : e-1
rks[p[i]] = ar
... |
389 | 405 | StatsBase.jl | 244 | function addcounts!(cm::Dict{T}, x::AbstractArray{T}, wv::AbstractVector{W}) where {T,W<:Real}
# add wv weighted counts of integers from x to cm
length(x) == length(wv) ||
throw(DimensionMismatch("x and wv must have the same length, got $(length(x)) and $(length(wv))"))
xv = vec(x) # discard shape... | function addcounts!(cm::Dict{T}, x::AbstractArray{T}, wv::AbstractVector{W}) where {T,W<:Real}
# add wv weighted counts of integers from x to cm
length(x) == length(wv) ||
throw(DimensionMismatch("x and wv must have the same length, got $(length(x)) and $(length(wv))"))
xv = vec(x) # discard shape... | [
389,
405
] | function addcounts!(cm::Dict{T}, x::AbstractArray{T}, wv::AbstractVector{W}) where {T,W<:Real}
# add wv weighted counts of integers from x to cm
length(x) == length(wv) ||
throw(DimensionMismatch("x and wv must have the same length, got $(length(x)) and $(length(wv))"))
xv = vec(x) # discard shape... | function addcounts!(cm::Dict{T}, x::AbstractArray{T}, wv::AbstractVector{W}) where {T,W<:Real}
# add wv weighted counts of integers from x to cm
length(x) == length(wv) ||
throw(DimensionMismatch("x and wv must have the same length, got $(length(x)) and $(length(wv))"))
xv = vec(x) # discard shape... | addcounts! | 389 | 405 | src/counts.jl | #FILE: StatsBase.jl/src/weights.jl
##CHUNK 1
end) @inbounds (@nref $N R j) += f((@nref $N A i) - (@nref $N means j)) * wi
end
return R
end
end
_wsum!(R::AbstractArray, A::AbstractArray, w::AbstractVector, dim::Int, init::Bool) =
_wsum_general!(R, identity, A, w, d... |
5 | 16 | StatsBase.jl | 245 | function _symmetrize!(a::DenseMatrix)
m, n = size(a)
m == n || error("a must be a square matrix.")
for j = 1:n
@inbounds for i = j+1:n
vl = a[i,j]
vr = a[j,i]
a[i,j] = a[j,i] = middle(vl, vr)
end
end
return a
end | function _symmetrize!(a::DenseMatrix)
m, n = size(a)
m == n || error("a must be a square matrix.")
for j = 1:n
@inbounds for i = j+1:n
vl = a[i,j]
vr = a[j,i]
a[i,j] = a[j,i] = middle(vl, vr)
end
end
return a
end | [
5,
16
] | function _symmetrize!(a::DenseMatrix)
m, n = size(a)
m == n || error("a must be a square matrix.")
for j = 1:n
@inbounds for i = j+1:n
vl = a[i,j]
vr = a[j,i]
a[i,j] = a[j,i] = middle(vl, vr)
end
end
return a
end | function _symmetrize!(a::DenseMatrix)
m, n = size(a)
m == n || error("a must be a square matrix.")
for j = 1:n
@inbounds for i = j+1:n
vl = a[i,j]
vr = a[j,i]
a[i,j] = a[j,i] = middle(vl, vr)
end
end
return a
end | _symmetrize! | 5 | 16 | src/cov.jl | #FILE: StatsBase.jl/src/sampling.jl
##CHUNK 1
n = length(a)
k = length(x)
k <= n || error("length(x) should not exceed length(a)")
i = 0
j = 0
while k > 1
u = rand(rng)
q = (n - k) / n
while q > u # skip
i += 1
n -= 1
q *= (n - k) / n... |
142 | 157 | StatsBase.jl | 246 | function cov2cor!(C::AbstractMatrix, s::AbstractArray = map(sqrt, view(C, diagind(C))))
Base.require_one_based_indexing(C, s)
n = length(s)
size(C) == (n, n) || throw(DimensionMismatch("inconsistent dimensions"))
for j = 1:n
sj = s[j]
for i = 1:(j-1)
C[i,j] = adjoint(C[j,i])
... | function cov2cor!(C::AbstractMatrix, s::AbstractArray = map(sqrt, view(C, diagind(C))))
Base.require_one_based_indexing(C, s)
n = length(s)
size(C) == (n, n) || throw(DimensionMismatch("inconsistent dimensions"))
for j = 1:n
sj = s[j]
for i = 1:(j-1)
C[i,j] = adjoint(C[j,i])
... | [
142,
157
] | function cov2cor!(C::AbstractMatrix, s::AbstractArray = map(sqrt, view(C, diagind(C))))
Base.require_one_based_indexing(C, s)
n = length(s)
size(C) == (n, n) || throw(DimensionMismatch("inconsistent dimensions"))
for j = 1:n
sj = s[j]
for i = 1:(j-1)
C[i,j] = adjoint(C[j,i])
... | function cov2cor!(C::AbstractMatrix, s::AbstractArray = map(sqrt, view(C, diagind(C))))
Base.require_one_based_indexing(C, s)
n = length(s)
size(C) == (n, n) || throw(DimensionMismatch("inconsistent dimensions"))
for j = 1:n
sj = s[j]
for i = 1:(j-1)
C[i,j] = adjoint(C[j,i])
... | cov2cor! | 142 | 157 | src/cov.jl | #FILE: StatsBase.jl/src/rankcorr.jl
##CHUNK 1
C = Matrix{Float64}(I, n, 1)
any(isnan, y) && return fill!(C, NaN)
yrank = tiedrank(y)
for j = 1:n
Xj = view(X, :, j)
if any(isnan, Xj)
C[j,1] = NaN
else
Xjrank = tiedrank(Xj)
C[j,1] = cor(Xjrank, y... |
162 | 184 | StatsBase.jl | 247 | function cov2cor!(C::Union{Symmetric{<:Real},Hermitian}, s::AbstractArray)
n = length(s)
size(C) == (n, n) || throw(DimensionMismatch("inconsistent dimensions"))
A = parent(C)
if C.uplo === 'U'
for j = 1:n
sj = s[j]
for i = 1:(j-1)
A[i,j] = _clampcor(A[i,j... | function cov2cor!(C::Union{Symmetric{<:Real},Hermitian}, s::AbstractArray)
n = length(s)
size(C) == (n, n) || throw(DimensionMismatch("inconsistent dimensions"))
A = parent(C)
if C.uplo === 'U'
for j = 1:n
sj = s[j]
for i = 1:(j-1)
A[i,j] = _clampcor(A[i,j... | [
162,
184
] | function cov2cor!(C::Union{Symmetric{<:Real},Hermitian}, s::AbstractArray)
n = length(s)
size(C) == (n, n) || throw(DimensionMismatch("inconsistent dimensions"))
A = parent(C)
if C.uplo === 'U'
for j = 1:n
sj = s[j]
for i = 1:(j-1)
A[i,j] = _clampcor(A[i,j... | function cov2cor!(C::Union{Symmetric{<:Real},Hermitian}, s::AbstractArray)
n = length(s)
size(C) == (n, n) || throw(DimensionMismatch("inconsistent dimensions"))
A = parent(C)
if C.uplo === 'U'
for j = 1:n
sj = s[j]
for i = 1:(j-1)
A[i,j] = _clampcor(A[i,j... | cov2cor! | 162 | 184 | src/cov.jl | #FILE: StatsBase.jl/src/pairwise.jl
##CHUNK 1
function _pairwise!(::Val{:none}, f, dest::AbstractMatrix, x, y, symmetric::Bool)
@inbounds for (i, xi) in enumerate(x), (j, yj) in enumerate(y)
symmetric && i > j && continue
# For performance, diagonal is special-cased
if f === cor && eltype(d... |
204 | 219 | StatsBase.jl | 248 | function cor2cov!(C::AbstractMatrix, s::AbstractArray)
Base.require_one_based_indexing(C, s)
n = length(s)
size(C) == (n, n) || throw(DimensionMismatch("inconsistent dimensions"))
for j in 1:n
sj = s[j]
for i in 1:(j-1)
C[i,j] = adjoint(C[j,i])
end
C[j,j] = sj... | function cor2cov!(C::AbstractMatrix, s::AbstractArray)
Base.require_one_based_indexing(C, s)
n = length(s)
size(C) == (n, n) || throw(DimensionMismatch("inconsistent dimensions"))
for j in 1:n
sj = s[j]
for i in 1:(j-1)
C[i,j] = adjoint(C[j,i])
end
C[j,j] = sj... | [
204,
219
] | function cor2cov!(C::AbstractMatrix, s::AbstractArray)
Base.require_one_based_indexing(C, s)
n = length(s)
size(C) == (n, n) || throw(DimensionMismatch("inconsistent dimensions"))
for j in 1:n
sj = s[j]
for i in 1:(j-1)
C[i,j] = adjoint(C[j,i])
end
C[j,j] = sj... | function cor2cov!(C::AbstractMatrix, s::AbstractArray)
Base.require_one_based_indexing(C, s)
n = length(s)
size(C) == (n, n) || throw(DimensionMismatch("inconsistent dimensions"))
for j in 1:n
sj = s[j]
for i in 1:(j-1)
C[i,j] = adjoint(C[j,i])
end
C[j,j] = sj... | cor2cov! | 204 | 219 | src/cov.jl | #FILE: StatsBase.jl/src/rankcorr.jl
##CHUNK 1
n = length(x)
n == length(y) || throw(DimensionMismatch("vectors must have same length"))
(any(isnan, x) || any(isnan, y)) && return NaN
return cor(tiedrank(x), tiedrank(y))
end
function corspearman(X::AbstractMatrix{<:Real}, y::AbstractVector{<:Real})
... |
222 | 244 | StatsBase.jl | 249 | function cor2cov!(C::Union{Symmetric{<:Real},Hermitian}, s::AbstractArray)
n = length(s)
size(C) == (n, n) || throw(DimensionMismatch("inconsistent dimensions"))
A = parent(C)
if C.uplo === 'U'
for j in 1:n
sj = s[j]
for i in 1:(j-1)
A[i,j] *= s[i] * sj
... | function cor2cov!(C::Union{Symmetric{<:Real},Hermitian}, s::AbstractArray)
n = length(s)
size(C) == (n, n) || throw(DimensionMismatch("inconsistent dimensions"))
A = parent(C)
if C.uplo === 'U'
for j in 1:n
sj = s[j]
for i in 1:(j-1)
A[i,j] *= s[i] * sj
... | [
222,
244
] | function cor2cov!(C::Union{Symmetric{<:Real},Hermitian}, s::AbstractArray)
n = length(s)
size(C) == (n, n) || throw(DimensionMismatch("inconsistent dimensions"))
A = parent(C)
if C.uplo === 'U'
for j in 1:n
sj = s[j]
for i in 1:(j-1)
A[i,j] *= s[i] * sj
... | function cor2cov!(C::Union{Symmetric{<:Real},Hermitian}, s::AbstractArray)
n = length(s)
size(C) == (n, n) || throw(DimensionMismatch("inconsistent dimensions"))
A = parent(C)
if C.uplo === 'U'
for j in 1:n
sj = s[j]
for i in 1:(j-1)
A[i,j] *= s[i] * sj
... | cor2cov! | 222 | 244 | src/cov.jl | #FILE: StatsBase.jl/src/toeplitzsolvers.jl
##CHUNK 1
# Symmetric Toeplitz solver
function durbin!(r::AbstractVector{T}, y::AbstractVector{T}) where T<:BlasReal
n = length(r)
n <= length(y) || throw(DimensionMismatch("Auxiliary vector cannot be shorter than data vector"))
y[1] = -r[1]
β = one(T)
α = ... |
11 | 21 | StatsBase.jl | 250 | function counteq(a::AbstractArray, b::AbstractArray)
n = length(a)
length(b) == n || throw(DimensionMismatch("Inconsistent lengths."))
c = 0
for i in eachindex(a, b)
@inbounds if a[i] == b[i]
c += 1
end
end
return c
end | function counteq(a::AbstractArray, b::AbstractArray)
n = length(a)
length(b) == n || throw(DimensionMismatch("Inconsistent lengths."))
c = 0
for i in eachindex(a, b)
@inbounds if a[i] == b[i]
c += 1
end
end
return c
end | [
11,
21
] | function counteq(a::AbstractArray, b::AbstractArray)
n = length(a)
length(b) == n || throw(DimensionMismatch("Inconsistent lengths."))
c = 0
for i in eachindex(a, b)
@inbounds if a[i] == b[i]
c += 1
end
end
return c
end | function counteq(a::AbstractArray, b::AbstractArray)
n = length(a)
length(b) == n || throw(DimensionMismatch("Inconsistent lengths."))
c = 0
for i in eachindex(a, b)
@inbounds if a[i] == b[i]
c += 1
end
end
return c
end | counteq | 11 | 21 | src/deviation.jl | #FILE: StatsBase.jl/src/sampling.jl
##CHUNK 1
n = length(a)
length(wv) == n || throw(DimensionMismatch("Inconsistent lengths."))
k = length(x)
w = Vector{Float64}(undef, n)
copyto!(w, wv)
for i = 1:k
u = rand(rng) * wsum
j = 1
c = w[1]
while c < u && j < n
... |
30 | 40 | StatsBase.jl | 251 | function countne(a::AbstractArray, b::AbstractArray)
n = length(a)
length(b) == n || throw(DimensionMismatch("Inconsistent lengths."))
c = 0
for i in eachindex(a, b)
@inbounds if a[i] != b[i]
c += 1
end
end
return c
end | function countne(a::AbstractArray, b::AbstractArray)
n = length(a)
length(b) == n || throw(DimensionMismatch("Inconsistent lengths."))
c = 0
for i in eachindex(a, b)
@inbounds if a[i] != b[i]
c += 1
end
end
return c
end | [
30,
40
] | function countne(a::AbstractArray, b::AbstractArray)
n = length(a)
length(b) == n || throw(DimensionMismatch("Inconsistent lengths."))
c = 0
for i in eachindex(a, b)
@inbounds if a[i] != b[i]
c += 1
end
end
return c
end | function countne(a::AbstractArray, b::AbstractArray)
n = length(a)
length(b) == n || throw(DimensionMismatch("Inconsistent lengths."))
c = 0
for i in eachindex(a, b)
@inbounds if a[i] != b[i]
c += 1
end
end
return c
end | countne | 30 | 40 | src/deviation.jl | #FILE: StatsBase.jl/src/sampling.jl
##CHUNK 1
n = length(a)
length(wv) == n || throw(DimensionMismatch("Inconsistent lengths."))
k = length(x)
w = Vector{Float64}(undef, n)
copyto!(w, wv)
for i = 1:k
u = rand(rng) * wsum
j = 1
c = w[1]
while c < u && j < n
... |
96 | 107 | StatsBase.jl | 252 | function Linfdist(a::AbstractArray{T}, b::AbstractArray{T}) where T<:Number
n = length(a)
length(b) == n || throw(DimensionMismatch("Input dimension mismatch"))
r = 0.0
for i in eachindex(a, b)
@inbounds v = abs(a[i] - b[i])
if r < v
r = v
end
end
return r
end | function Linfdist(a::AbstractArray{T}, b::AbstractArray{T}) where T<:Number
n = length(a)
length(b) == n || throw(DimensionMismatch("Input dimension mismatch"))
r = 0.0
for i in eachindex(a, b)
@inbounds v = abs(a[i] - b[i])
if r < v
r = v
end
end
return r
end | [
96,
107
] | function Linfdist(a::AbstractArray{T}, b::AbstractArray{T}) where T<:Number
n = length(a)
length(b) == n || throw(DimensionMismatch("Input dimension mismatch"))
r = 0.0
for i in eachindex(a, b)
@inbounds v = abs(a[i] - b[i])
if r < v
r = v
end
end
return r
end | function Linfdist(a::AbstractArray{T}, b::AbstractArray{T}) where T<:Number
n = length(a)
length(b) == n || throw(DimensionMismatch("Input dimension mismatch"))
r = 0.0
for i in eachindex(a, b)
@inbounds v = abs(a[i] - b[i])
if r < v
r = v
end
end
return r
end | Linfdist | 96 | 107 | src/deviation.jl | #FILE: StatsBase.jl/src/sampling.jl
##CHUNK 1
n = length(a)
k = length(x)
k <= n || error("length(x) should not exceed length(a)")
i = 0
j = 0
while k > 1
u = rand(rng)
q = (n - k) / n
while q > u # skip
i += 1
n -= 1
q *= (n - k) / n... |
118 | 131 | StatsBase.jl | 253 | function gkldiv(a::AbstractArray{T}, b::AbstractArray{T}) where T<:AbstractFloat
n = length(a)
r = 0.0
for i in eachindex(a, b)
@inbounds ai = a[i]
@inbounds bi = b[i]
if ai > 0
r += (ai * log(ai / bi) - ai + bi)
else
r += bi
end
end
re... | function gkldiv(a::AbstractArray{T}, b::AbstractArray{T}) where T<:AbstractFloat
n = length(a)
r = 0.0
for i in eachindex(a, b)
@inbounds ai = a[i]
@inbounds bi = b[i]
if ai > 0
r += (ai * log(ai / bi) - ai + bi)
else
r += bi
end
end
re... | [
118,
131
] | function gkldiv(a::AbstractArray{T}, b::AbstractArray{T}) where T<:AbstractFloat
n = length(a)
r = 0.0
for i in eachindex(a, b)
@inbounds ai = a[i]
@inbounds bi = b[i]
if ai > 0
r += (ai * log(ai / bi) - ai + bi)
else
r += bi
end
end
re... | function gkldiv(a::AbstractArray{T}, b::AbstractArray{T}) where T<:AbstractFloat
n = length(a)
r = 0.0
for i in eachindex(a, b)
@inbounds ai = a[i]
@inbounds bi = b[i]
if ai > 0
r += (ai * log(ai / bi) - ai + bi)
else
r += bi
end
end
re... | gkldiv | 118 | 131 | src/deviation.jl | #FILE: StatsBase.jl/src/scalarstats.jl
##CHUNK 1
)
# return zero for empty arrays
pzero = zero(eltype(p))
qzero = zero(eltype(q))
return xlogy(pzero, zero(pzero / qzero))
end
# use pairwise summation (https://github.com/JuliaLang/julia/pull/31020)
broadcasted = Broad... |
19 | 42 | StatsBase.jl | 254 | function (ecdf::ECDF)(v::AbstractVector{<:Real})
evenweights = isempty(ecdf.weights)
weightsum = evenweights ? length(ecdf.sorted_values) : sum(ecdf.weights)
ord = sortperm(v)
m = length(v)
r = similar(ecdf.sorted_values, m)
r0 = zero(weightsum)
i = 1
for (j, x) in enumerate(ecdf.sorted_... | function (ecdf::ECDF)(v::AbstractVector{<:Real})
evenweights = isempty(ecdf.weights)
weightsum = evenweights ? length(ecdf.sorted_values) : sum(ecdf.weights)
ord = sortperm(v)
m = length(v)
r = similar(ecdf.sorted_values, m)
r0 = zero(weightsum)
i = 1
for (j, x) in enumerate(ecdf.sorted_... | [
19,
42
] | function (ecdf::ECDF)(v::AbstractVector{<:Real})
evenweights = isempty(ecdf.weights)
weightsum = evenweights ? length(ecdf.sorted_values) : sum(ecdf.weights)
ord = sortperm(v)
m = length(v)
r = similar(ecdf.sorted_values, m)
r0 = zero(weightsum)
i = 1
for (j, x) in enumerate(ecdf.sorted_... | function (ecdf::ECDF)(v::AbstractVector{<:Real})
evenweights = isempty(ecdf.weights)
weightsum = evenweights ? length(ecdf.sorted_values) : sum(ecdf.weights)
ord = sortperm(v)
m = length(v)
r = similar(ecdf.sorted_values, m)
r0 = zero(weightsum)
i = 1
for (j, x) in enumerate(ecdf.sorted_... | unknown_function | 19 | 42 | src/empirical.jl | #FILE: StatsBase.jl/test/empirical.jl
##CHUNK 1
@test extrema(fnecdf) == (minimum(fnecdf), maximum(fnecdf)) == extrema(x)
fnecdf = ecdf([0.5])
@test fnecdf([zeros(5000); ones(5000)]) == [zeros(5000); ones(5000)]
@test extrema(fnecdf) == (minimum(fnecdf), maximum(fnecdf)) == (0.5, 0.5)
@test isnan(ec... |
21 | 50 | StatsBase.jl | 255 | function rle(v::AbstractVector{T}) where T
n = length(v)
vals = T[]
lens = Int[]
n>0 || return (vals,lens)
cv = v[1]
cl = 1
i = 2
@inbounds while i <= n
vi = v[i]
if isequal(vi, cv)
cl += 1
else
push!(vals, cv)
push!(lens, cl... | function rle(v::AbstractVector{T}) where T
n = length(v)
vals = T[]
lens = Int[]
n>0 || return (vals,lens)
cv = v[1]
cl = 1
i = 2
@inbounds while i <= n
vi = v[i]
if isequal(vi, cv)
cl += 1
else
push!(vals, cv)
push!(lens, cl... | [
21,
50
] | function rle(v::AbstractVector{T}) where T
n = length(v)
vals = T[]
lens = Int[]
n>0 || return (vals,lens)
cv = v[1]
cl = 1
i = 2
@inbounds while i <= n
vi = v[i]
if isequal(vi, cv)
cl += 1
else
push!(vals, cv)
push!(lens, cl... | function rle(v::AbstractVector{T}) where T
n = length(v)
vals = T[]
lens = Int[]
n>0 || return (vals,lens)
cv = v[1]
cl = 1
i = 2
@inbounds while i <= n
vi = v[i]
if isequal(vi, cv)
cl += 1
else
push!(vals, cv)
push!(lens, cl... | rle | 21 | 50 | src/misc.jl | #FILE: StatsBase.jl/src/counts.jl
##CHUNK 1
checkbounds(r, axes(xlevels, 1), axes(ylevels, 1))
mx0 = first(xlevels)
mx1 = last(xlevels)
my0 = first(ylevels)
my1 = last(ylevels)
bx = mx0 - 1
by = my0 - 1
for i in eachindex(xv, yv, wv)
xi = xv[i]
yi = yv[i]
if (m... |
60 | 80 | StatsBase.jl | 256 | function inverse_rle(vals::AbstractVector{T}, lens::AbstractVector{<:Integer}) where T
m = length(vals)
mlens = length(lens)
mlens == m || throw(DimensionMismatch(
"number of vals ($m) does not match the number of lens ($mlens)"))
n = sum(lens)
n >= 0 || throw(ArgumentError("... | function inverse_rle(vals::AbstractVector{T}, lens::AbstractVector{<:Integer}) where T
m = length(vals)
mlens = length(lens)
mlens == m || throw(DimensionMismatch(
"number of vals ($m) does not match the number of lens ($mlens)"))
n = sum(lens)
n >= 0 || throw(ArgumentError("... | [
60,
80
] | function inverse_rle(vals::AbstractVector{T}, lens::AbstractVector{<:Integer}) where T
m = length(vals)
mlens = length(lens)
mlens == m || throw(DimensionMismatch(
"number of vals ($m) does not match the number of lens ($mlens)"))
n = sum(lens)
n >= 0 || throw(ArgumentError("... | function inverse_rle(vals::AbstractVector{T}, lens::AbstractVector{<:Integer}) where T
m = length(vals)
mlens = length(lens)
mlens == m || throw(DimensionMismatch(
"number of vals ($m) does not match the number of lens ($mlens)"))
n = sum(lens)
n >= 0 || throw(ArgumentError("... | inverse_rle | 60 | 80 | src/misc.jl | #FILE: StatsBase.jl/src/signalcorr.jl
##CHUNK 1
end
return r
end
function crosscov!(r::AbstractMatrix{<:Real}, x::AbstractVector{<:Real}, y::AbstractMatrix{<:Real}, lags::AbstractVector{<:Integer}; demean::Bool=true)
lx = length(x)
ns = size(y, 2)
m = length(lags)
(size(y, 1) == lx && size(r) =... |
107 | 118 | StatsBase.jl | 257 | function levelsmap(a::AbstractArray{T}) where T
d = Dict{T,Int}()
index = 1
for i = 1 : length(a)
@inbounds k = a[i]
if !haskey(d, k)
d[k] = index
index += 1
end
end
return d
end | function levelsmap(a::AbstractArray{T}) where T
d = Dict{T,Int}()
index = 1
for i = 1 : length(a)
@inbounds k = a[i]
if !haskey(d, k)
d[k] = index
index += 1
end
end
return d
end | [
107,
118
] | function levelsmap(a::AbstractArray{T}) where T
d = Dict{T,Int}()
index = 1
for i = 1 : length(a)
@inbounds k = a[i]
if !haskey(d, k)
d[k] = index
index += 1
end
end
return d
end | function levelsmap(a::AbstractArray{T}) where T
d = Dict{T,Int}()
index = 1
for i = 1 : length(a)
@inbounds k = a[i]
if !haskey(d, k)
d[k] = index
index += 1
end
end
return d
end | levelsmap | 107 | 118 | src/misc.jl | #FILE: StatsBase.jl/src/counts.jl
##CHUNK 1
end
function _addcounts!(::Type{T}, cm::Dict{T}, x; alg = :ignored) where T <: Union{UInt8, UInt16, Int8, Int16}
counts = zeros(Int, 2^(8sizeof(T)))
@inbounds for xi in x
counts[Int(xi) - typemin(T) + 1] += 1
end
for (i, c) in zip(typemin(T):typemax... |
168 | 180 | StatsBase.jl | 258 | function _indicatormat_dense(x::AbstractArray{T}, c::AbstractArray{T}) where T
d = indexmap(c)
m = length(c)
n = length(x)
r = zeros(Bool, m, n)
o = 0
@inbounds for i = 1 : n
xi = x[i]
r[o + d[xi]] = true
o += m
end
return r
end | function _indicatormat_dense(x::AbstractArray{T}, c::AbstractArray{T}) where T
d = indexmap(c)
m = length(c)
n = length(x)
r = zeros(Bool, m, n)
o = 0
@inbounds for i = 1 : n
xi = x[i]
r[o + d[xi]] = true
o += m
end
return r
end | [
168,
180
] | function _indicatormat_dense(x::AbstractArray{T}, c::AbstractArray{T}) where T
d = indexmap(c)
m = length(c)
n = length(x)
r = zeros(Bool, m, n)
o = 0
@inbounds for i = 1 : n
xi = x[i]
r[o + d[xi]] = true
o += m
end
return r
end | function _indicatormat_dense(x::AbstractArray{T}, c::AbstractArray{T}) where T
d = indexmap(c)
m = length(c)
n = length(x)
r = zeros(Bool, m, n)
o = 0
@inbounds for i = 1 : n
xi = x[i]
r[o + d[xi]] = true
o += m
end
return r
end | _indicatormat_dense | 168 | 180 | src/misc.jl | #FILE: StatsBase.jl/src/weights.jl
##CHUNK 1
j_d = i_d
end) @inbounds (@nref $N R j) += f(@nref $N A i) * wi
end
return R
end
end
@generated function _wsum_centralize!(R::AbstractArray{RT}, f::supertype(typeof(abs)),
... |
184 | 194 | StatsBase.jl | 259 | function _indicatormat_sparse(x::AbstractArray{T}, c::AbstractArray{T}) where T
d = indexmap(c)
m = length(c)
n = length(x)
rinds = Vector{Int}(undef, n)
@inbounds for i = 1 : n
rinds[i] = d[x[i]]
end
return sparse(rinds, 1:n, true, m, n)
end | function _indicatormat_sparse(x::AbstractArray{T}, c::AbstractArray{T}) where T
d = indexmap(c)
m = length(c)
n = length(x)
rinds = Vector{Int}(undef, n)
@inbounds for i = 1 : n
rinds[i] = d[x[i]]
end
return sparse(rinds, 1:n, true, m, n)
end | [
184,
194
] | function _indicatormat_sparse(x::AbstractArray{T}, c::AbstractArray{T}) where T
d = indexmap(c)
m = length(c)
n = length(x)
rinds = Vector{Int}(undef, n)
@inbounds for i = 1 : n
rinds[i] = d[x[i]]
end
return sparse(rinds, 1:n, true, m, n)
end | function _indicatormat_sparse(x::AbstractArray{T}, c::AbstractArray{T}) where T
d = indexmap(c)
m = length(c)
n = length(x)
rinds = Vector{Int}(undef, n)
@inbounds for i = 1 : n
rinds[i] = d[x[i]]
end
return sparse(rinds, 1:n, true, m, n)
end | _indicatormat_sparse | 184 | 194 | src/misc.jl | #FILE: StatsBase.jl/src/weights.jl
##CHUNK 1
j_d = i_d
end) @inbounds (@nref $N R j) += f(@nref $N A i) * wi
end
return R
end
end
@generated function _wsum_centralize!(R::AbstractArray{RT}, f::supertype(typeof(abs)),
... |
34 | 56 | StatsBase.jl | 260 | function var!(R::AbstractArray, A::AbstractArray{<:Real}, w::AbstractWeights, dims::Int;
mean=nothing, corrected::Union{Bool, Nothing}=nothing)
corrected = depcheck(:var!, :corrected, corrected)
if mean == 0
mean = Base.reducedim_initarray(A, dims, 0, eltype(R))
elseif mean === nothin... | function var!(R::AbstractArray, A::AbstractArray{<:Real}, w::AbstractWeights, dims::Int;
mean=nothing, corrected::Union{Bool, Nothing}=nothing)
corrected = depcheck(:var!, :corrected, corrected)
if mean == 0
mean = Base.reducedim_initarray(A, dims, 0, eltype(R))
elseif mean === nothin... | [
34,
56
] | function var!(R::AbstractArray, A::AbstractArray{<:Real}, w::AbstractWeights, dims::Int;
mean=nothing, corrected::Union{Bool, Nothing}=nothing)
corrected = depcheck(:var!, :corrected, corrected)
if mean == 0
mean = Base.reducedim_initarray(A, dims, 0, eltype(R))
elseif mean === nothin... | function var!(R::AbstractArray, A::AbstractArray{<:Real}, w::AbstractWeights, dims::Int;
mean=nothing, corrected::Union{Bool, Nothing}=nothing)
corrected = depcheck(:var!, :corrected, corrected)
if mean == 0
mean = Base.reducedim_initarray(A, dims, 0, eltype(R))
elseif mean === nothin... | var! | 34 | 56 | src/moments.jl | #FILE: StatsBase.jl/src/cov.jl
##CHUNK 1
function cov(sc::SimpleCovariance, X::AbstractMatrix; dims::Int=1, mean=nothing)
dims ∈ (1, 2) || throw(ArgumentError("Argument dims can only be 1 or 2 (given: $dims)"))
if mean === nothing
return cov(X; dims=dims, corrected=sc.corrected)
else
return ... |
281 | 295 | StatsBase.jl | 261 | function skewness(v::AbstractArray{<:Real}, m::Real)
n = length(v)
cm2 = 0.0 # empirical 2nd centered moment (variance)
cm3 = 0.0 # empirical 3rd centered moment
for i = 1:n
@inbounds z = v[i] - m
z2 = z * z
cm2 += z2
cm3 += z2 * z
end
cm3 /= n
cm2 /= n
... | function skewness(v::AbstractArray{<:Real}, m::Real)
n = length(v)
cm2 = 0.0 # empirical 2nd centered moment (variance)
cm3 = 0.0 # empirical 3rd centered moment
for i = 1:n
@inbounds z = v[i] - m
z2 = z * z
cm2 += z2
cm3 += z2 * z
end
cm3 /= n
cm2 /= n
... | [
281,
295
] | function skewness(v::AbstractArray{<:Real}, m::Real)
n = length(v)
cm2 = 0.0 # empirical 2nd centered moment (variance)
cm3 = 0.0 # empirical 3rd centered moment
for i = 1:n
@inbounds z = v[i] - m
z2 = z * z
cm2 += z2
cm3 += z2 * z
end
cm3 /= n
cm2 /= n
... | function skewness(v::AbstractArray{<:Real}, m::Real)
n = length(v)
cm2 = 0.0 # empirical 2nd centered moment (variance)
cm3 = 0.0 # empirical 3rd centered moment
for i = 1:n
@inbounds z = v[i] - m
z2 = z * z
cm2 += z2
cm3 += z2 * z
end
cm3 /= n
cm2 /= n
... | skewness | 281 | 295 | src/moments.jl | #CURRENT FILE: StatsBase.jl/src/moments.jl
##CHUNK 1
function skewness(v::AbstractArray{<:Real}, wv::AbstractWeights, m::Real)
n = length(v)
length(wv) == n || throw(DimensionMismatch("Inconsistent array lengths."))
cm2 = 0.0 # empirical 2nd centered moment (variance)
cm3 = 0.0 # empirical 3rd cent... |
297 | 315 | StatsBase.jl | 262 | function skewness(v::AbstractArray{<:Real}, wv::AbstractWeights, m::Real)
n = length(v)
length(wv) == n || throw(DimensionMismatch("Inconsistent array lengths."))
cm2 = 0.0 # empirical 2nd centered moment (variance)
cm3 = 0.0 # empirical 3rd centered moment
@inbounds for i = 1:n
x_i = v... | function skewness(v::AbstractArray{<:Real}, wv::AbstractWeights, m::Real)
n = length(v)
length(wv) == n || throw(DimensionMismatch("Inconsistent array lengths."))
cm2 = 0.0 # empirical 2nd centered moment (variance)
cm3 = 0.0 # empirical 3rd centered moment
@inbounds for i = 1:n
x_i = v... | [
297,
315
] | function skewness(v::AbstractArray{<:Real}, wv::AbstractWeights, m::Real)
n = length(v)
length(wv) == n || throw(DimensionMismatch("Inconsistent array lengths."))
cm2 = 0.0 # empirical 2nd centered moment (variance)
cm3 = 0.0 # empirical 3rd centered moment
@inbounds for i = 1:n
x_i = v... | function skewness(v::AbstractArray{<:Real}, wv::AbstractWeights, m::Real)
n = length(v)
length(wv) == n || throw(DimensionMismatch("Inconsistent array lengths."))
cm2 = 0.0 # empirical 2nd centered moment (variance)
cm3 = 0.0 # empirical 3rd centered moment
@inbounds for i = 1:n
x_i = v... | skewness | 297 | 315 | src/moments.jl | #FILE: StatsBase.jl/test/cov.jl
##CHUNK 1
X = randn(3, 8)
Z1 = X .- mean(X, dims = 1)
Z2 = X .- mean(X, dims = 2)
w1 = rand(3)
w2 = rand(8)
# varcorrection is negative if sum of weights is smaller than 1
if f === fweights
w1[1] += 1
w2[1] += 1
end
wv1 = f(w1)
... |
328 | 341 | StatsBase.jl | 263 | function kurtosis(v::AbstractArray{<:Real}, m::Real)
n = length(v)
cm2 = 0.0 # empirical 2nd centered moment (variance)
cm4 = 0.0 # empirical 4th centered moment
for i = 1:n
@inbounds z = v[i] - m
z2 = z * z
cm2 += z2
cm4 += z2 * z2
end
cm4 /= n
cm2 /= n
... | function kurtosis(v::AbstractArray{<:Real}, m::Real)
n = length(v)
cm2 = 0.0 # empirical 2nd centered moment (variance)
cm4 = 0.0 # empirical 4th centered moment
for i = 1:n
@inbounds z = v[i] - m
z2 = z * z
cm2 += z2
cm4 += z2 * z2
end
cm4 /= n
cm2 /= n
... | [
328,
341
] | function kurtosis(v::AbstractArray{<:Real}, m::Real)
n = length(v)
cm2 = 0.0 # empirical 2nd centered moment (variance)
cm4 = 0.0 # empirical 4th centered moment
for i = 1:n
@inbounds z = v[i] - m
z2 = z * z
cm2 += z2
cm4 += z2 * z2
end
cm4 /= n
cm2 /= n
... | function kurtosis(v::AbstractArray{<:Real}, m::Real)
n = length(v)
cm2 = 0.0 # empirical 2nd centered moment (variance)
cm4 = 0.0 # empirical 4th centered moment
for i = 1:n
@inbounds z = v[i] - m
z2 = z * z
cm2 += z2
cm4 += z2 * z2
end
cm4 /= n
cm2 /= n
... | kurtosis | 328 | 341 | src/moments.jl | #FILE: StatsBase.jl/src/scalarstats.jl
##CHUNK 1
elseif normalize
m * mad_constant
else
m
end
end
# Interquartile range
"""
iqr(x)
Compute the interquartile range (IQR) of collection `x`, i.e. the 75th percentile
minus the 25th percentile.
"""
iqr(x) = (q = quantile(x, [.25, .75]); q[2... |
343 | 362 | StatsBase.jl | 264 | function kurtosis(v::AbstractArray{<:Real}, wv::AbstractWeights, m::Real)
n = length(v)
length(wv) == n || throw(DimensionMismatch("Inconsistent array lengths."))
cm2 = 0.0 # empirical 2nd centered moment (variance)
cm4 = 0.0 # empirical 4th centered moment
@inbounds for i = 1 : n
x_i = v... | function kurtosis(v::AbstractArray{<:Real}, wv::AbstractWeights, m::Real)
n = length(v)
length(wv) == n || throw(DimensionMismatch("Inconsistent array lengths."))
cm2 = 0.0 # empirical 2nd centered moment (variance)
cm4 = 0.0 # empirical 4th centered moment
@inbounds for i = 1 : n
x_i = v... | [
343,
362
] | function kurtosis(v::AbstractArray{<:Real}, wv::AbstractWeights, m::Real)
n = length(v)
length(wv) == n || throw(DimensionMismatch("Inconsistent array lengths."))
cm2 = 0.0 # empirical 2nd centered moment (variance)
cm4 = 0.0 # empirical 4th centered moment
@inbounds for i = 1 : n
x_i = v... | function kurtosis(v::AbstractArray{<:Real}, wv::AbstractWeights, m::Real)
n = length(v)
length(wv) == n || throw(DimensionMismatch("Inconsistent array lengths."))
cm2 = 0.0 # empirical 2nd centered moment (variance)
cm4 = 0.0 # empirical 4th centered moment
@inbounds for i = 1 : n
x_i = v... | kurtosis | 343 | 362 | src/moments.jl | #FILE: StatsBase.jl/src/scalarstats.jl
##CHUNK 1
function sem(x::AbstractArray, weights::ProbabilityWeights; mean=nothing)
if isempty(x)
# Return the NaN of the type that we would get for a nonempty x
return var(x, weights; mean=mean, corrected=true) / 0
else
_mean = mean === nothing ? S... |
381 | 400 | StatsBase.jl | 265 | function cumulant(v::AbstractArray{<:Real}, krange::Union{Integer, AbstractRange{<:Integer}}, wv::AbstractWeights,
m::Real=mean(v, wv))
if minimum(krange) <= 0
throw(ArgumentError("Cumulant orders must be strictly positive."))
end
k = maximum(krange)
cmoms = zeros(typeof(m), k)... | function cumulant(v::AbstractArray{<:Real}, krange::Union{Integer, AbstractRange{<:Integer}}, wv::AbstractWeights,
m::Real=mean(v, wv))
if minimum(krange) <= 0
throw(ArgumentError("Cumulant orders must be strictly positive."))
end
k = maximum(krange)
cmoms = zeros(typeof(m), k)... | [
381,
400
] | function cumulant(v::AbstractArray{<:Real}, krange::Union{Integer, AbstractRange{<:Integer}}, wv::AbstractWeights,
m::Real=mean(v, wv))
if minimum(krange) <= 0
throw(ArgumentError("Cumulant orders must be strictly positive."))
end
k = maximum(krange)
cmoms = zeros(typeof(m), k)... | function cumulant(v::AbstractArray{<:Real}, krange::Union{Integer, AbstractRange{<:Integer}}, wv::AbstractWeights,
m::Real=mean(v, wv))
if minimum(krange) <= 0
throw(ArgumentError("Cumulant orders must be strictly positive."))
end
k = maximum(krange)
cmoms = zeros(typeof(m), k)... | cumulant | 381 | 400 | src/moments.jl | #CURRENT FILE: StatsBase.jl/src/moments.jl
##CHUNK 1
w_i = wv[i]
z = x_i - m
z2 = z * z
z2w = z2 * w_i
cm2 += z2w
cm4 += z2w * z2
end
sw = sum(wv)
cm4 /= sw
cm2 /= sw
return (cm4 / (cm2 * cm2)) - 3.0
end
kurtosis(v::AbstractArray{<:Real}) = kurtosis(v... |
1 | 20 | StatsBase.jl | 266 | function _pairwise!(::Val{:none}, f, dest::AbstractMatrix, x, y, symmetric::Bool)
@inbounds for (i, xi) in enumerate(x), (j, yj) in enumerate(y)
symmetric && i > j && continue
# For performance, diagonal is special-cased
if f === cor && eltype(dest) !== Union{} && i == j && xi === yj
... | function _pairwise!(::Val{:none}, f, dest::AbstractMatrix, x, y, symmetric::Bool)
@inbounds for (i, xi) in enumerate(x), (j, yj) in enumerate(y)
symmetric && i > j && continue
# For performance, diagonal is special-cased
if f === cor && eltype(dest) !== Union{} && i == j && xi === yj
... | [
1,
20
] | function _pairwise!(::Val{:none}, f, dest::AbstractMatrix, x, y, symmetric::Bool)
@inbounds for (i, xi) in enumerate(x), (j, yj) in enumerate(y)
symmetric && i > j && continue
# For performance, diagonal is special-cased
if f === cor && eltype(dest) !== Union{} && i == j && xi === yj
... | function _pairwise!(::Val{:none}, f, dest::AbstractMatrix, x, y, symmetric::Bool)
@inbounds for (i, xi) in enumerate(x), (j, yj) in enumerate(y)
symmetric && i > j && continue
# For performance, diagonal is special-cased
if f === cor && eltype(dest) !== Union{} && i == j && xi === yj
... | _pairwise! | 1 | 20 | src/pairwise.jl | #FILE: StatsBase.jl/src/rankcorr.jl
##CHUNK 1
n = length(x)
n == length(y) || throw(DimensionMismatch("vectors must have same length"))
(any(isnan, x) || any(isnan, y)) && return NaN
return cor(tiedrank(x), tiedrank(y))
end
function corspearman(X::AbstractMatrix{<:Real}, y::AbstractVector{<:Real})
... |
49 | 80 | StatsBase.jl | 267 | function _pairwise!(::Val{:pairwise}, f, dest::AbstractMatrix, x, y, symmetric::Bool)
check_vectors(x, y, :pairwise)
@inbounds for (j, yj) in enumerate(y)
ynminds = .!ismissing.(yj)
@inbounds for (i, xi) in enumerate(x)
symmetric && i > j && continue
if xi === yj
... | function _pairwise!(::Val{:pairwise}, f, dest::AbstractMatrix, x, y, symmetric::Bool)
check_vectors(x, y, :pairwise)
@inbounds for (j, yj) in enumerate(y)
ynminds = .!ismissing.(yj)
@inbounds for (i, xi) in enumerate(x)
symmetric && i > j && continue
if xi === yj
... | [
49,
80
] | function _pairwise!(::Val{:pairwise}, f, dest::AbstractMatrix, x, y, symmetric::Bool)
check_vectors(x, y, :pairwise)
@inbounds for (j, yj) in enumerate(y)
ynminds = .!ismissing.(yj)
@inbounds for (i, xi) in enumerate(x)
symmetric && i > j && continue
if xi === yj
... | function _pairwise!(::Val{:pairwise}, f, dest::AbstractMatrix, x, y, symmetric::Bool)
check_vectors(x, y, :pairwise)
@inbounds for (j, yj) in enumerate(y)
ynminds = .!ismissing.(yj)
@inbounds for (i, xi) in enumerate(x)
symmetric && i > j && continue
if xi === yj
... | _pairwise! | 49 | 80 | src/pairwise.jl | #FILE: StatsBase.jl/src/rankcorr.jl
##CHUNK 1
n = length(x)
n == length(y) || throw(DimensionMismatch("vectors must have same length"))
(any(isnan, x) || any(isnan, y)) && return NaN
return cor(tiedrank(x), tiedrank(y))
end
function corspearman(X::AbstractMatrix{<:Real}, y::AbstractVector{<:Real})
... |
82 | 103 | StatsBase.jl | 268 | function _pairwise!(::Val{:listwise}, f, dest::AbstractMatrix, x, y, symmetric::Bool)
check_vectors(x, y, :listwise)
nminds = .!ismissing.(first(x))
@inbounds for xi in Iterators.drop(x, 1)
nminds .&= .!ismissing.(xi)
end
if x !== y
@inbounds for yj in y
nminds .&= .!ismi... | function _pairwise!(::Val{:listwise}, f, dest::AbstractMatrix, x, y, symmetric::Bool)
check_vectors(x, y, :listwise)
nminds = .!ismissing.(first(x))
@inbounds for xi in Iterators.drop(x, 1)
nminds .&= .!ismissing.(xi)
end
if x !== y
@inbounds for yj in y
nminds .&= .!ismi... | [
82,
103
] | function _pairwise!(::Val{:listwise}, f, dest::AbstractMatrix, x, y, symmetric::Bool)
check_vectors(x, y, :listwise)
nminds = .!ismissing.(first(x))
@inbounds for xi in Iterators.drop(x, 1)
nminds .&= .!ismissing.(xi)
end
if x !== y
@inbounds for yj in y
nminds .&= .!ismi... | function _pairwise!(::Val{:listwise}, f, dest::AbstractMatrix, x, y, symmetric::Bool)
check_vectors(x, y, :listwise)
nminds = .!ismissing.(first(x))
@inbounds for xi in Iterators.drop(x, 1)
nminds .&= .!ismissing.(xi)
end
if x !== y
@inbounds for yj in y
nminds .&= .!ismi... | _pairwise! | 82 | 103 | src/pairwise.jl | #CURRENT FILE: StatsBase.jl/src/pairwise.jl
##CHUNK 1
function _pairwise!(f, dest::AbstractMatrix, x, y;
symmetric::Bool=false, skipmissing::Symbol=:none)
if !(skipmissing in (:none, :pairwise, :listwise))
throw(ArgumentError("skipmissing must be one of :none, :pairwise or :listwise"))... |
105 | 122 | StatsBase.jl | 269 | function _pairwise!(f, dest::AbstractMatrix, x, y;
symmetric::Bool=false, skipmissing::Symbol=:none)
if !(skipmissing in (:none, :pairwise, :listwise))
throw(ArgumentError("skipmissing must be one of :none, :pairwise or :listwise"))
end
x′ = x isa Union{AbstractArray, Tuple, Nam... | function _pairwise!(f, dest::AbstractMatrix, x, y;
symmetric::Bool=false, skipmissing::Symbol=:none)
if !(skipmissing in (:none, :pairwise, :listwise))
throw(ArgumentError("skipmissing must be one of :none, :pairwise or :listwise"))
end
x′ = x isa Union{AbstractArray, Tuple, Nam... | [
105,
122
] | function _pairwise!(f, dest::AbstractMatrix, x, y;
symmetric::Bool=false, skipmissing::Symbol=:none)
if !(skipmissing in (:none, :pairwise, :listwise))
throw(ArgumentError("skipmissing must be one of :none, :pairwise or :listwise"))
end
x′ = x isa Union{AbstractArray, Tuple, Nam... | function _pairwise!(f, dest::AbstractMatrix, x, y;
symmetric::Bool=false, skipmissing::Symbol=:none)
if !(skipmissing in (:none, :pairwise, :listwise))
throw(ArgumentError("skipmissing must be one of :none, :pairwise or :listwise"))
end
x′ = x isa Union{AbstractArray, Tuple, Nam... | _pairwise! | 105 | 122 | src/pairwise.jl | #FILE: StatsBase.jl/test/pairwise.jl
##CHUNK 1
length(xm), length(ym)), xm, ym,
skipmissing=:something)
# variable with only missings
xm = [fill(missing, 10), rand(10)]
ym = [rand(10), rand(10)]
... |
16 | 26 | StatsBase.jl | 270 | function _partialcor(x::AbstractVector, μx, y::AbstractVector, μy, Z::AbstractMatrix)
p = size(Z, 2)
p == 1 && return _partialcor(x, μx, y, μy, vec(Z))
z₀ = view(Z, :, 1)
Zmz₀ = view(Z, :, 2:p)
μz₀ = mean(z₀)
rxz = _partialcor(x, μx, z₀, μz₀, Zmz₀)
rzy = _partialcor(z₀, μz₀, y, μy, Zmz... | function _partialcor(x::AbstractVector, μx, y::AbstractVector, μy, Z::AbstractMatrix)
p = size(Z, 2)
p == 1 && return _partialcor(x, μx, y, μy, vec(Z))
z₀ = view(Z, :, 1)
Zmz₀ = view(Z, :, 2:p)
μz₀ = mean(z₀)
rxz = _partialcor(x, μx, z₀, μz₀, Zmz₀)
rzy = _partialcor(z₀, μz₀, y, μy, Zmz... | [
16,
26
] | function _partialcor(x::AbstractVector, μx, y::AbstractVector, μy, Z::AbstractMatrix)
p = size(Z, 2)
p == 1 && return _partialcor(x, μx, y, μy, vec(Z))
z₀ = view(Z, :, 1)
Zmz₀ = view(Z, :, 2:p)
μz₀ = mean(z₀)
rxz = _partialcor(x, μx, z₀, μz₀, Zmz₀)
rzy = _partialcor(z₀, μz₀, y, μy, Zmz... | function _partialcor(x::AbstractVector, μx, y::AbstractVector, μy, Z::AbstractMatrix)
p = size(Z, 2)
p == 1 && return _partialcor(x, μx, y, μy, vec(Z))
z₀ = view(Z, :, 1)
Zmz₀ = view(Z, :, 2:p)
μz₀ = mean(z₀)
rxz = _partialcor(x, μx, z₀, μz₀, Zmz₀)
rzy = _partialcor(z₀, μz₀, y, μy, Zmz... | _partialcor | 16 | 26 | src/partialcor.jl | #FILE: StatsBase.jl/src/signalcorr.jl
##CHUNK 1
ns = size(y, 2)
m = length(lags)
(size(y, 1) == lx && size(r) == (m, ns)) || throw(DimensionMismatch())
check_lags(lx, lags)
T = typeof(zero(eltype(x)) / 1)
zx::Vector{T} = demean ? x .- mean(x) : x
S = typeof(zero(eltype(y)) / 1)
zy = Vec... |
28 | 62 | StatsBase.jl | 271 | function _partialcor(x::AbstractVector, μx, y::AbstractVector, μy, z::AbstractVector)
μz = mean(z)
# Initialize all of the accumulators to 0 of the appropriate types
Σxx = abs2(zero(eltype(x)) - zero(μx))
Σyy = abs2(zero(eltype(y)) - zero(μy))
Σzz = abs2(zero(eltype(z)) - zero(μz))
Σxy = zero(Σ... | function _partialcor(x::AbstractVector, μx, y::AbstractVector, μy, z::AbstractVector)
μz = mean(z)
# Initialize all of the accumulators to 0 of the appropriate types
Σxx = abs2(zero(eltype(x)) - zero(μx))
Σyy = abs2(zero(eltype(y)) - zero(μy))
Σzz = abs2(zero(eltype(z)) - zero(μz))
Σxy = zero(Σ... | [
28,
62
] | function _partialcor(x::AbstractVector, μx, y::AbstractVector, μy, z::AbstractVector)
μz = mean(z)
# Initialize all of the accumulators to 0 of the appropriate types
Σxx = abs2(zero(eltype(x)) - zero(μx))
Σyy = abs2(zero(eltype(y)) - zero(μy))
Σzz = abs2(zero(eltype(z)) - zero(μz))
Σxy = zero(Σ... | function _partialcor(x::AbstractVector, μx, y::AbstractVector, μy, z::AbstractVector)
μz = mean(z)
# Initialize all of the accumulators to 0 of the appropriate types
Σxx = abs2(zero(eltype(x)) - zero(μx))
Σyy = abs2(zero(eltype(y)) - zero(μy))
Σzz = abs2(zero(eltype(z)) - zero(μz))
Σxy = zero(Σ... | _partialcor | 28 | 62 | src/partialcor.jl | #FILE: StatsBase.jl/src/signalcorr.jl
##CHUNK 1
ns = size(y, 2)
m = length(lags)
(size(y, 1) == lx && size(r) == (m, ns)) || throw(DimensionMismatch())
check_lags(lx, lags)
T = typeof(zero(eltype(x)) / 1)
zx::Vector{T} = demean ? x .- mean(x) : x
S = typeof(zero(eltype(y)) / 1)
zy = Vec... |
27 | 44 | StatsBase.jl | 272 | function corspearman(X::AbstractMatrix{<:Real}, y::AbstractVector{<:Real})
size(X, 1) == length(y) ||
throw(DimensionMismatch("X and y have inconsistent dimensions"))
n = size(X, 2)
C = Matrix{Float64}(I, n, 1)
any(isnan, y) && return fill!(C, NaN)
yrank = tiedrank(y)
for j = 1:n
... | function corspearman(X::AbstractMatrix{<:Real}, y::AbstractVector{<:Real})
size(X, 1) == length(y) ||
throw(DimensionMismatch("X and y have inconsistent dimensions"))
n = size(X, 2)
C = Matrix{Float64}(I, n, 1)
any(isnan, y) && return fill!(C, NaN)
yrank = tiedrank(y)
for j = 1:n
... | [
27,
44
] | function corspearman(X::AbstractMatrix{<:Real}, y::AbstractVector{<:Real})
size(X, 1) == length(y) ||
throw(DimensionMismatch("X and y have inconsistent dimensions"))
n = size(X, 2)
C = Matrix{Float64}(I, n, 1)
any(isnan, y) && return fill!(C, NaN)
yrank = tiedrank(y)
for j = 1:n
... | function corspearman(X::AbstractMatrix{<:Real}, y::AbstractVector{<:Real})
size(X, 1) == length(y) ||
throw(DimensionMismatch("X and y have inconsistent dimensions"))
n = size(X, 2)
C = Matrix{Float64}(I, n, 1)
any(isnan, y) && return fill!(C, NaN)
yrank = tiedrank(y)
for j = 1:n
... | corspearman | 27 | 44 | src/rankcorr.jl | #CURRENT FILE: StatsBase.jl/src/rankcorr.jl
##CHUNK 1
n = size(Y, 2)
C = Matrix{Float64}(I, 1, n)
any(isnan, x) && return fill!(C, NaN)
xrank = tiedrank(x)
for j = 1:n
Yj = view(Y, :, j)
if any(isnan, Yj)
C[1,j] = NaN
else
Yjrank = tiedrank(Yj)
... |
46 | 63 | StatsBase.jl | 273 | function corspearman(x::AbstractVector{<:Real}, Y::AbstractMatrix{<:Real})
size(Y, 1) == length(x) ||
throw(DimensionMismatch("x and Y have inconsistent dimensions"))
n = size(Y, 2)
C = Matrix{Float64}(I, 1, n)
any(isnan, x) && return fill!(C, NaN)
xrank = tiedrank(x)
for j = 1:n
... | function corspearman(x::AbstractVector{<:Real}, Y::AbstractMatrix{<:Real})
size(Y, 1) == length(x) ||
throw(DimensionMismatch("x and Y have inconsistent dimensions"))
n = size(Y, 2)
C = Matrix{Float64}(I, 1, n)
any(isnan, x) && return fill!(C, NaN)
xrank = tiedrank(x)
for j = 1:n
... | [
46,
63
] | function corspearman(x::AbstractVector{<:Real}, Y::AbstractMatrix{<:Real})
size(Y, 1) == length(x) ||
throw(DimensionMismatch("x and Y have inconsistent dimensions"))
n = size(Y, 2)
C = Matrix{Float64}(I, 1, n)
any(isnan, x) && return fill!(C, NaN)
xrank = tiedrank(x)
for j = 1:n
... | function corspearman(x::AbstractVector{<:Real}, Y::AbstractMatrix{<:Real})
size(Y, 1) == length(x) ||
throw(DimensionMismatch("x and Y have inconsistent dimensions"))
n = size(Y, 2)
C = Matrix{Float64}(I, 1, n)
any(isnan, x) && return fill!(C, NaN)
xrank = tiedrank(x)
for j = 1:n
... | corspearman | 46 | 63 | src/rankcorr.jl | #CURRENT FILE: StatsBase.jl/src/rankcorr.jl
##CHUNK 1
C = Matrix{Float64}(I, n, 1)
any(isnan, y) && return fill!(C, NaN)
yrank = tiedrank(y)
for j = 1:n
Xj = view(X, :, j)
if any(isnan, Xj)
C[j,1] = NaN
else
Xjrank = tiedrank(Xj)
C[j,1] = cor(X... |
65 | 90 | StatsBase.jl | 274 | function corspearman(X::AbstractMatrix{<:Real})
n = size(X, 2)
C = Matrix{Float64}(I, n, n)
anynan = Vector{Bool}(undef, n)
for j = 1:n
Xj = view(X, :, j)
anynan[j] = any(isnan, Xj)
if anynan[j]
C[:,j] .= NaN
C[j,:] .= NaN
C[j,j] = 1
... | function corspearman(X::AbstractMatrix{<:Real})
n = size(X, 2)
C = Matrix{Float64}(I, n, n)
anynan = Vector{Bool}(undef, n)
for j = 1:n
Xj = view(X, :, j)
anynan[j] = any(isnan, Xj)
if anynan[j]
C[:,j] .= NaN
C[j,:] .= NaN
C[j,j] = 1
... | [
65,
90
] | function corspearman(X::AbstractMatrix{<:Real})
n = size(X, 2)
C = Matrix{Float64}(I, n, n)
anynan = Vector{Bool}(undef, n)
for j = 1:n
Xj = view(X, :, j)
anynan[j] = any(isnan, Xj)
if anynan[j]
C[:,j] .= NaN
C[j,:] .= NaN
C[j,j] = 1
... | function corspearman(X::AbstractMatrix{<:Real})
n = size(X, 2)
C = Matrix{Float64}(I, n, n)
anynan = Vector{Bool}(undef, n)
for j = 1:n
Xj = view(X, :, j)
anynan[j] = any(isnan, Xj)
if anynan[j]
C[:,j] .= NaN
C[j,:] .= NaN
C[j,j] = 1
... | corspearman | 65 | 90 | src/rankcorr.jl | #FILE: StatsBase.jl/src/cov.jl
##CHUNK 1
"""
function cov2cor!(C::AbstractMatrix, s::AbstractArray = map(sqrt, view(C, diagind(C))))
Base.require_one_based_indexing(C, s)
n = length(s)
size(C) == (n, n) || throw(DimensionMismatch("inconsistent dimensions"))
for j = 1:n
sj = s[j]
for i = ... |
92 | 116 | StatsBase.jl | 275 | function corspearman(X::AbstractMatrix{<:Real}, Y::AbstractMatrix{<:Real})
size(X, 1) == size(Y, 1) ||
throw(ArgumentError("number of rows in each array must match"))
nr = size(X, 2)
nc = size(Y, 2)
C = Matrix{Float64}(undef, nr, nc)
for j = 1:nr
Xj = view(X, :, j)
if any(isn... | function corspearman(X::AbstractMatrix{<:Real}, Y::AbstractMatrix{<:Real})
size(X, 1) == size(Y, 1) ||
throw(ArgumentError("number of rows in each array must match"))
nr = size(X, 2)
nc = size(Y, 2)
C = Matrix{Float64}(undef, nr, nc)
for j = 1:nr
Xj = view(X, :, j)
if any(isn... | [
92,
116
] | function corspearman(X::AbstractMatrix{<:Real}, Y::AbstractMatrix{<:Real})
size(X, 1) == size(Y, 1) ||
throw(ArgumentError("number of rows in each array must match"))
nr = size(X, 2)
nc = size(Y, 2)
C = Matrix{Float64}(undef, nr, nc)
for j = 1:nr
Xj = view(X, :, j)
if any(isn... | function corspearman(X::AbstractMatrix{<:Real}, Y::AbstractMatrix{<:Real})
size(X, 1) == size(Y, 1) ||
throw(ArgumentError("number of rows in each array must match"))
nr = size(X, 2)
nc = size(Y, 2)
C = Matrix{Float64}(undef, nr, nc)
for j = 1:nr
Xj = view(X, :, j)
if any(isn... | corspearman | 92 | 116 | src/rankcorr.jl | #FILE: StatsBase.jl/src/cov.jl
##CHUNK 1
"""
function cov2cor!(C::AbstractMatrix, s::AbstractArray = map(sqrt, view(C, diagind(C))))
Base.require_one_based_indexing(C, s)
n = length(s)
size(C) == (n, n) || throw(DimensionMismatch("inconsistent dimensions"))
for j = 1:n
sj = s[j]
for i = ... |
189 | 200 | StatsBase.jl | 276 | function corkendall(X::AbstractMatrix{<:Real})
n = size(X, 2)
C = Matrix{Float64}(I, n, n)
for j = 2:n
permx = sortperm(X[:,j])
for i = 1:j - 1
C[j,i] = corkendall!(X[:,j], X[:,i], permx)
C[i,j] = C[j,i]
end
end
return C
end | function corkendall(X::AbstractMatrix{<:Real})
n = size(X, 2)
C = Matrix{Float64}(I, n, n)
for j = 2:n
permx = sortperm(X[:,j])
for i = 1:j - 1
C[j,i] = corkendall!(X[:,j], X[:,i], permx)
C[i,j] = C[j,i]
end
end
return C
end | [
189,
200
] | function corkendall(X::AbstractMatrix{<:Real})
n = size(X, 2)
C = Matrix{Float64}(I, n, n)
for j = 2:n
permx = sortperm(X[:,j])
for i = 1:j - 1
C[j,i] = corkendall!(X[:,j], X[:,i], permx)
C[i,j] = C[j,i]
end
end
return C
end | function corkendall(X::AbstractMatrix{<:Real})
n = size(X, 2)
C = Matrix{Float64}(I, n, n)
for j = 2:n
permx = sortperm(X[:,j])
for i = 1:j - 1
C[j,i] = corkendall!(X[:,j], X[:,i], permx)
C[i,j] = C[j,i]
end
end
return C
end | corkendall | 189 | 200 | src/rankcorr.jl | #FILE: StatsBase.jl/test/rankcorr.jl
##CHUNK 1
# AbstractMatrix{<:Real}
@test corkendall(X) ≈ [c11 c12; c12 c22]
@test c11 == 1.0
@test c22 == 1.0
@test c12 == 3/sqrt(20)
# Finished testing for overflow, so redefine n for speedier tests
n = 100
@test corkendall(repeat(X, n), repeat(X, n)) ≈ [c11 c12; c12 c22]
@te... |
202 | 213 | StatsBase.jl | 277 | function corkendall(X::AbstractMatrix{<:Real}, Y::AbstractMatrix{<:Real})
nr = size(X, 2)
nc = size(Y, 2)
C = Matrix{Float64}(undef, nr, nc)
for j = 1:nr
permx = sortperm(X[:,j])
for i = 1:nc
C[j,i] = corkendall!(X[:,j], Y[:,i], permx)
end
end
return C
end | function corkendall(X::AbstractMatrix{<:Real}, Y::AbstractMatrix{<:Real})
nr = size(X, 2)
nc = size(Y, 2)
C = Matrix{Float64}(undef, nr, nc)
for j = 1:nr
permx = sortperm(X[:,j])
for i = 1:nc
C[j,i] = corkendall!(X[:,j], Y[:,i], permx)
end
end
return C
end | [
202,
213
] | function corkendall(X::AbstractMatrix{<:Real}, Y::AbstractMatrix{<:Real})
nr = size(X, 2)
nc = size(Y, 2)
C = Matrix{Float64}(undef, nr, nc)
for j = 1:nr
permx = sortperm(X[:,j])
for i = 1:nc
C[j,i] = corkendall!(X[:,j], Y[:,i], permx)
end
end
return C
end | function corkendall(X::AbstractMatrix{<:Real}, Y::AbstractMatrix{<:Real})
nr = size(X, 2)
nc = size(Y, 2)
C = Matrix{Float64}(undef, nr, nc)
for j = 1:nr
permx = sortperm(X[:,j])
for i = 1:nc
C[j,i] = corkendall!(X[:,j], Y[:,i], permx)
end
end
return C
end | corkendall | 202 | 213 | src/rankcorr.jl | #FILE: StatsBase.jl/test/rankcorr.jl
##CHUNK 1
# AbstractMatrix{<:Real}
@test corkendall(X) ≈ [c11 c12; c12 c22]
@test c11 == 1.0
@test c22 == 1.0
@test c12 == 3/sqrt(20)
# Finished testing for overflow, so redefine n for speedier tests
n = 100
@test corkendall(repeat(X, n), repeat(X, n)) ≈ [c11 c12; c12 c22]
@te... |
254 | 293 | StatsBase.jl | 278 | function merge_sort!(v::AbstractVector, lo::Integer, hi::Integer, t::AbstractVector=similar(v, 0))
# Use of widen below prevents possible overflow errors when
# length(v) exceeds 2^16 (32 bit) or 2^32 (64 bit)
nswaps = widen(0)
@inbounds if lo < hi
hi - lo <= SMALL_THRESHOLD && return insertion_... | function merge_sort!(v::AbstractVector, lo::Integer, hi::Integer, t::AbstractVector=similar(v, 0))
# Use of widen below prevents possible overflow errors when
# length(v) exceeds 2^16 (32 bit) or 2^32 (64 bit)
nswaps = widen(0)
@inbounds if lo < hi
hi - lo <= SMALL_THRESHOLD && return insertion_... | [
254,
293
] | function merge_sort!(v::AbstractVector, lo::Integer, hi::Integer, t::AbstractVector=similar(v, 0))
# Use of widen below prevents possible overflow errors when
# length(v) exceeds 2^16 (32 bit) or 2^32 (64 bit)
nswaps = widen(0)
@inbounds if lo < hi
hi - lo <= SMALL_THRESHOLD && return insertion_... | function merge_sort!(v::AbstractVector, lo::Integer, hi::Integer, t::AbstractVector=similar(v, 0))
# Use of widen below prevents possible overflow errors when
# length(v) exceeds 2^16 (32 bit) or 2^32 (64 bit)
nswaps = widen(0)
@inbounds if lo < hi
hi - lo <= SMALL_THRESHOLD && return insertion_... | merge_sort! | 254 | 293 | src/rankcorr.jl | #FILE: StatsBase.jl/src/sampling.jl
##CHUNK 1
# set threshold
@inbounds threshold = pq[1].first
@inbounds for i in s+1:n
w = wv.values[i]
w < 0 && error("Negative weight found in weight vector at index $i")
w > 0 || continue
key = w/randexp(rng)
# if key is larger t... |
306 | 324 | StatsBase.jl | 279 | function insertion_sort!(v::AbstractVector, lo::Integer, hi::Integer)
if lo == hi return widen(0) end
nswaps = widen(0)
@inbounds for i = lo + 1:hi
j = i
x = v[i]
while j > lo
if x < v[j - 1]
nswaps += 1
v[j] = v[j - 1]
j -=... | function insertion_sort!(v::AbstractVector, lo::Integer, hi::Integer)
if lo == hi return widen(0) end
nswaps = widen(0)
@inbounds for i = lo + 1:hi
j = i
x = v[i]
while j > lo
if x < v[j - 1]
nswaps += 1
v[j] = v[j - 1]
j -=... | [
306,
324
] | function insertion_sort!(v::AbstractVector, lo::Integer, hi::Integer)
if lo == hi return widen(0) end
nswaps = widen(0)
@inbounds for i = lo + 1:hi
j = i
x = v[i]
while j > lo
if x < v[j - 1]
nswaps += 1
v[j] = v[j - 1]
j -=... | function insertion_sort!(v::AbstractVector, lo::Integer, hi::Integer)
if lo == hi return widen(0) end
nswaps = widen(0)
@inbounds for i = lo + 1:hi
j = i
x = v[i]
while j > lo
if x < v[j - 1]
nswaps += 1
v[j] = v[j - 1]
j -=... | insertion_sort! | 306 | 324 | src/rankcorr.jl | #FILE: StatsBase.jl/src/sampling.jl
##CHUNK 1
k = length(x)
k > 0 || return x
# initialize priority queue
pq = Vector{Pair{Float64,Int}}(undef, k)
i = 0
s = 0
@inbounds for _s in 1:n
s = _s
w = wv.values[s]
w < 0 && error("Negative weight found in weight vector at in... |
60 | 80 | StatsBase.jl | 280 | function _competerank!(rks::AbstractArray, x::AbstractArray, p::AbstractArray{<:Integer})
n = _check_randparams(rks, x, p)
@inbounds if n > 0
p1 = p[1]
v = x[p1]
rks[p1] = k = 1
for i in 2:n
pi = p[i]
xi = x[pi]
if xi != v
v =... | function _competerank!(rks::AbstractArray, x::AbstractArray, p::AbstractArray{<:Integer})
n = _check_randparams(rks, x, p)
@inbounds if n > 0
p1 = p[1]
v = x[p1]
rks[p1] = k = 1
for i in 2:n
pi = p[i]
xi = x[pi]
if xi != v
v =... | [
60,
80
] | function _competerank!(rks::AbstractArray, x::AbstractArray, p::AbstractArray{<:Integer})
n = _check_randparams(rks, x, p)
@inbounds if n > 0
p1 = p[1]
v = x[p1]
rks[p1] = k = 1
for i in 2:n
pi = p[i]
xi = x[pi]
if xi != v
v =... | function _competerank!(rks::AbstractArray, x::AbstractArray, p::AbstractArray{<:Integer})
n = _check_randparams(rks, x, p)
@inbounds if n > 0
p1 = p[1]
v = x[p1]
rks[p1] = k = 1
for i in 2:n
pi = p[i]
xi = x[pi]
if xi != v
v =... | _competerank! | 60 | 80 | src/ranking.jl | #FILE: StatsBase.jl/src/sampling.jl
##CHUNK 1
k <= n || error("length(x) should not exceed length(a)")
inds = Vector{Int}(undef, n)
for i = 1:n
@inbounds inds[i] = i
end
@inbounds for i = 1:k
j = rand(rng, i:n)
t = inds[j]
inds[j] = inds[i]
inds[i] = t
... |
97 | 117 | StatsBase.jl | 281 | function _denserank!(rks::AbstractArray, x::AbstractArray, p::AbstractArray{<:Integer})
n = _check_randparams(rks, x, p)
@inbounds if n > 0
p1 = p[1]
v = x[p1]
rks[p1] = k = 1
for i in 2:n
pi = p[i]
xi = x[pi]
if xi != v
v = x... | function _denserank!(rks::AbstractArray, x::AbstractArray, p::AbstractArray{<:Integer})
n = _check_randparams(rks, x, p)
@inbounds if n > 0
p1 = p[1]
v = x[p1]
rks[p1] = k = 1
for i in 2:n
pi = p[i]
xi = x[pi]
if xi != v
v = x... | [
97,
117
] | function _denserank!(rks::AbstractArray, x::AbstractArray, p::AbstractArray{<:Integer})
n = _check_randparams(rks, x, p)
@inbounds if n > 0
p1 = p[1]
v = x[p1]
rks[p1] = k = 1
for i in 2:n
pi = p[i]
xi = x[pi]
if xi != v
v = x... | function _denserank!(rks::AbstractArray, x::AbstractArray, p::AbstractArray{<:Integer})
n = _check_randparams(rks, x, p)
@inbounds if n > 0
p1 = p[1]
v = x[p1]
rks[p1] = k = 1
for i in 2:n
pi = p[i]
xi = x[pi]
if xi != v
v = x... | _denserank! | 97 | 117 | src/ranking.jl | #FILE: StatsBase.jl/src/sampling.jl
##CHUNK 1
k <= n || error("length(x) should not exceed length(a)")
inds = Vector{Int}(undef, n)
for i = 1:n
@inbounds inds[i] = i
end
@inbounds for i = 1:k
j = rand(rng, i:n)
t = inds[j]
inds[j] = inds[i]
inds[i] = t
... |
134 | 163 | StatsBase.jl | 282 | function _tiedrank!(rks::AbstractArray, x::AbstractArray, p::AbstractArray{<:Integer})
n = _check_randparams(rks, x, p)
@inbounds if n > 0
v = x[p[1]]
s = 1 # starting index of current range
for e in 2:n # e is pass-by-end index of current range
cx = x[p[e]]
if... | function _tiedrank!(rks::AbstractArray, x::AbstractArray, p::AbstractArray{<:Integer})
n = _check_randparams(rks, x, p)
@inbounds if n > 0
v = x[p[1]]
s = 1 # starting index of current range
for e in 2:n # e is pass-by-end index of current range
cx = x[p[e]]
if... | [
134,
163
] | function _tiedrank!(rks::AbstractArray, x::AbstractArray, p::AbstractArray{<:Integer})
n = _check_randparams(rks, x, p)
@inbounds if n > 0
v = x[p[1]]
s = 1 # starting index of current range
for e in 2:n # e is pass-by-end index of current range
cx = x[p[e]]
if... | function _tiedrank!(rks::AbstractArray, x::AbstractArray, p::AbstractArray{<:Integer})
n = _check_randparams(rks, x, p)
@inbounds if n > 0
v = x[p[1]]
s = 1 # starting index of current range
for e in 2:n # e is pass-by-end index of current range
cx = x[p[e]]
if... | _tiedrank! | 134 | 163 | src/ranking.jl | #FILE: StatsBase.jl/src/sampling.jl
##CHUNK 1
# set threshold
@inbounds threshold = pq[1].first
@inbounds for i in s+1:n
w = wv.values[i]
w < 0 && error("Negative weight found in weight vector at index $i")
w > 0 || continue
key = w/randexp(rng)
# if key is larger t... |
53 | 78 | StatsBase.jl | 283 | function cronbachalpha(covmatrix::AbstractMatrix{<:Real})
if !isposdef(covmatrix)
throw(ArgumentError("Covariance matrix must be positive definite. " *
"Maybe you passed the data matrix instead of its covariance matrix? " *
"If so, call `cronbachalpha(... | function cronbachalpha(covmatrix::AbstractMatrix{<:Real})
if !isposdef(covmatrix)
throw(ArgumentError("Covariance matrix must be positive definite. " *
"Maybe you passed the data matrix instead of its covariance matrix? " *
"If so, call `cronbachalpha(... | [
53,
78
] | function cronbachalpha(covmatrix::AbstractMatrix{<:Real})
if !isposdef(covmatrix)
throw(ArgumentError("Covariance matrix must be positive definite. " *
"Maybe you passed the data matrix instead of its covariance matrix? " *
"If so, call `cronbachalpha(... | function cronbachalpha(covmatrix::AbstractMatrix{<:Real})
if !isposdef(covmatrix)
throw(ArgumentError("Covariance matrix must be positive definite. " *
"Maybe you passed the data matrix instead of its covariance matrix? " *
"If so, call `cronbachalpha(... | cronbachalpha | 53 | 78 | src/reliability.jl | #FILE: StatsBase.jl/src/signalcorr.jl
##CHUNK 1
z::Vector{T} = demean ? x .- mean(x) : x
for k = 1 : m # foreach lag value
r[k] = _autodot(z, lx, lags[k]) / lx
end
return r
end
function autocov!(r::AbstractMatrix{<:Real}, x::AbstractMatrix{<:Real}, lags::AbstractVector{<:Integer}; demean::Bool... |
123 | 136 | StatsBase.jl | 284 | function trimvar(x::AbstractVector; prop::Real=0.0, count::Integer=0)
n = length(x)
n > 0 || throw(ArgumentError("x can not be empty."))
if count == 0
0 <= prop < 0.5 || throw(ArgumentError("prop must satisfy 0 ≤ prop < 0.5."))
count = floor(Int, n * prop)
else
0 <= count < n/2 ... | function trimvar(x::AbstractVector; prop::Real=0.0, count::Integer=0)
n = length(x)
n > 0 || throw(ArgumentError("x can not be empty."))
if count == 0
0 <= prop < 0.5 || throw(ArgumentError("prop must satisfy 0 ≤ prop < 0.5."))
count = floor(Int, n * prop)
else
0 <= count < n/2 ... | [
123,
136
] | function trimvar(x::AbstractVector; prop::Real=0.0, count::Integer=0)
n = length(x)
n > 0 || throw(ArgumentError("x can not be empty."))
if count == 0
0 <= prop < 0.5 || throw(ArgumentError("prop must satisfy 0 ≤ prop < 0.5."))
count = floor(Int, n * prop)
else
0 <= count < n/2 ... | function trimvar(x::AbstractVector; prop::Real=0.0, count::Integer=0)
n = length(x)
n > 0 || throw(ArgumentError("x can not be empty."))
if count == 0
0 <= prop < 0.5 || throw(ArgumentError("prop must satisfy 0 ≤ prop < 0.5."))
count = floor(Int, n * prop)
else
0 <= count < n/2 ... | trimvar | 123 | 136 | src/robust.jl | #FILE: StatsBase.jl/test/counts.jl
##CHUNK 1
@test addcounts!(fill(0.0, 1, 5), reshape(x, 10, 50, 10), 1:5, w) ≈ c0 # Perhaps this should not be allowed
@test x == x0
@test w == w0
end
@testset "2D integer counts" begin
x = rand(1:4, n)
y = rand(1:5, n)
w = weights(rand(n))
x0 = deepcopy(... |
14 | 29 | StatsBase.jl | 285 | function direct_sample!(rng::AbstractRNG, a::UnitRange, x::AbstractArray)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
s = Sampler(rng, 1:length(a))
b = a[1] - 1
if b == 0
for i = 1:length(x)
@inbounds x[i] = rand(rng, s)... | function direct_sample!(rng::AbstractRNG, a::UnitRange, x::AbstractArray)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
s = Sampler(rng, 1:length(a))
b = a[1] - 1
if b == 0
for i = 1:length(x)
@inbounds x[i] = rand(rng, s)... | [
14,
29
] | function direct_sample!(rng::AbstractRNG, a::UnitRange, x::AbstractArray)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
s = Sampler(rng, 1:length(a))
b = a[1] - 1
if b == 0
for i = 1:length(x)
@inbounds x[i] = rand(rng, s)... | function direct_sample!(rng::AbstractRNG, a::UnitRange, x::AbstractArray)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
s = Sampler(rng, 1:length(a))
b = a[1] - 1
if b == 0
for i = 1:length(x)
@inbounds x[i] = rand(rng, s)... | direct_sample! | 14 | 29 | src/sampling.jl | #CURRENT FILE: StatsBase.jl/src/sampling.jl
##CHUNK 1
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
n = length(a)
k = length(x)
... |
40 | 50 | StatsBase.jl | 286 | function direct_sample!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
s = Sampl... | function direct_sample!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
s = Sampl... | [
40,
50
] | function direct_sample!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
s = Sampl... | function direct_sample!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
s = Sampl... | direct_sample! | 40 | 50 | src/sampling.jl | #CURRENT FILE: StatsBase.jl/src/sampling.jl
##CHUNK 1
"""
function knuths_sample!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray;
initshuffle::Bool=true)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
Base.mightalias(a, x... |
65 | 87 | StatsBase.jl | 287 | function sample_ordered!(sampler!, rng::AbstractRNG, a::AbstractArray, x::AbstractArray)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
... | function sample_ordered!(sampler!, rng::AbstractRNG, a::AbstractArray, x::AbstractArray)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
... | [
65,
87
] | function sample_ordered!(sampler!, rng::AbstractRNG, a::AbstractArray, x::AbstractArray)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
... | function sample_ordered!(sampler!, rng::AbstractRNG, a::AbstractArray, x::AbstractArray)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
... | sample_ordered! | 65 | 87 | src/sampling.jl | #FILE: StatsBase.jl/src/pairwise.jl
##CHUNK 1
[view(yi, nminds′) for yi in y],
symmetric)
end
function _pairwise!(f, dest::AbstractMatrix, x, y;
symmetric::Bool=false, skipmissing::Symbol=:none)
if !(skipmissing in (:none, :pairwise, :listwise))
... |
143 | 176 | StatsBase.jl | 288 | function knuths_sample!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray;
initshuffle::Bool=true)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not ... | function knuths_sample!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray;
initshuffle::Bool=true)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not ... | [
143,
176
] | function knuths_sample!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray;
initshuffle::Bool=true)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not ... | function knuths_sample!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray;
initshuffle::Bool=true)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not ... | knuths_sample! | 143 | 176 | src/sampling.jl | #CURRENT FILE: StatsBase.jl/src/sampling.jl
##CHUNK 1
"""
direct_sample!([rng], a::AbstractArray, x::AbstractArray)
Direct sampling: for each `j` in `1:k`, randomly pick `i` from `1:n`,
and set `x[j] = a[i]`, with `n=length(a)` and `k=length(x)`.
This algorithm consumes `k` random numbers.
"""
function direct_sa... |
204 | 226 | StatsBase.jl | 289 | function fisher_yates_sample!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
n =... | function fisher_yates_sample!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
n =... | [
204,
226
] | function fisher_yates_sample!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
n =... | function fisher_yates_sample!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
n =... | fisher_yates_sample! | 204 | 226 | src/sampling.jl | #CURRENT FILE: StatsBase.jl/src/sampling.jl
##CHUNK 1
"""
direct_sample!([rng], a::AbstractArray, x::AbstractArray)
Direct sampling: for each `j` in `1:k`, randomly pick `i` from `1:n`,
and set `x[j] = a[i]`, with `n=length(a)` and `k=length(x)`.
This algorithm consumes `k` random numbers.
"""
function direct_sa... |
244 | 272 | StatsBase.jl | 290 | function self_avoid_sample!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
n = l... | function self_avoid_sample!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
n = l... | [
244,
272
] | function self_avoid_sample!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
n = l... | function self_avoid_sample!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
n = l... | self_avoid_sample! | 244 | 272 | src/sampling.jl | #CURRENT FILE: StatsBase.jl/src/sampling.jl
##CHUNK 1
"""
direct_sample!([rng], a::AbstractArray, x::AbstractArray)
Direct sampling: for each `j` in `1:k`, randomly pick `i` from `1:n`,
and set `x[j] = a[i]`, with `n=length(a)` and `k=length(x)`.
This algorithm consumes `k` random numbers.
"""
function direct_sa... |
286 | 315 | StatsBase.jl | 291 | function seqsample_a!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
n = length(... | function seqsample_a!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
n = length(... | [
286,
315
] | function seqsample_a!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
n = length(... | function seqsample_a!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
n = length(... | seqsample_a! | 286 | 315 | src/sampling.jl | #CURRENT FILE: StatsBase.jl/src/sampling.jl
##CHUNK 1
However, if `k` is large and approaches ``n``, the rejection rate would increase
drastically, resulting in poorer performance.
"""
function self_avoid_sample!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray)
1 == firstindex(a) == firstindex(x) ||
th... |
328 | 361 | StatsBase.jl | 292 | function seqsample_c!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
n = length(... | function seqsample_c!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
n = length(... | [
328,
361
] | function seqsample_c!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
n = length(... | function seqsample_c!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
n = length(... | seqsample_c! | 328 | 361 | src/sampling.jl | #CURRENT FILE: StatsBase.jl/src/sampling.jl
##CHUNK 1
However, if `k` is large and approaches ``n``, the rejection rate would increase
drastically, resulting in poorer performance.
"""
function self_avoid_sample!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray)
1 == firstindex(a) == firstindex(x) ||
th... |
488 | 525 | StatsBase.jl | 293 | function sample!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray;
replace::Bool=true, ordered::Bool=false)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
n = length(a)
k = length(x)
k == 0 && return x
if replace # w... | function sample!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray;
replace::Bool=true, ordered::Bool=false)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
n = length(a)
k = length(x)
k == 0 && return x
if replace # w... | [
488,
525
] | function sample!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray;
replace::Bool=true, ordered::Bool=false)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
n = length(a)
k = length(x)
k == 0 && return x
if replace # w... | function sample!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray;
replace::Bool=true, ordered::Bool=false)
1 == firstindex(a) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
n = length(a)
k = length(x)
k == 0 && return x
if replace # w... | sample! | 488 | 525 | src/sampling.jl | #CURRENT FILE: StatsBase.jl/src/sampling.jl
##CHUNK 1
if replace
if ordered
sample_ordered!(rng, a, wv, x) do rng, a, wv, x
sample!(rng, a, wv, x; replace=true, ordered=false)
end
else
if n < 40
direct_sample!(rng, a, wv, x)
... |
586 | 600 | StatsBase.jl | 294 | function sample(rng::AbstractRNG, wv::AbstractWeights)
1 == firstindex(wv) ||
throw(ArgumentError("non 1-based arrays are not supported"))
wsum = sum(wv)
isfinite(wsum) || throw(ArgumentError("only finite weights are supported"))
t = rand(rng) * wsum
n = length(wv)
i = 1
cw = wv[1]
... | function sample(rng::AbstractRNG, wv::AbstractWeights)
1 == firstindex(wv) ||
throw(ArgumentError("non 1-based arrays are not supported"))
wsum = sum(wv)
isfinite(wsum) || throw(ArgumentError("only finite weights are supported"))
t = rand(rng) * wsum
n = length(wv)
i = 1
cw = wv[1]
... | [
586,
600
] | function sample(rng::AbstractRNG, wv::AbstractWeights)
1 == firstindex(wv) ||
throw(ArgumentError("non 1-based arrays are not supported"))
wsum = sum(wv)
isfinite(wsum) || throw(ArgumentError("only finite weights are supported"))
t = rand(rng) * wsum
n = length(wv)
i = 1
cw = wv[1]
... | function sample(rng::AbstractRNG, wv::AbstractWeights)
1 == firstindex(wv) ||
throw(ArgumentError("non 1-based arrays are not supported"))
wsum = sum(wv)
isfinite(wsum) || throw(ArgumentError("only finite weights are supported"))
t = rand(rng) * wsum
n = length(wv)
i = 1
cw = wv[1]
... | sample | 586 | 600 | src/sampling.jl | #FILE: StatsBase.jl/src/weights.jl
##CHUNK 1
@propagate_inbounds function Base.getindex(wv::W, i::AbstractArray) where W <: AbstractWeights
@boundscheck checkbounds(wv, i)
@inbounds v = wv.values[i]
W(v, sum(v))
end
Base.getindex(wv::W, ::Colon) where {W <: AbstractWeights} = W(copy(wv.values), sum(wv))
... |
618 | 632 | StatsBase.jl | 295 | function direct_sample!(rng::AbstractRNG, a::AbstractArray,
wv::AbstractWeights, x::AbstractArray)
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
Base.mightalias(x, wv) &&
throw(ArgumentError("output array x must n... | function direct_sample!(rng::AbstractRNG, a::AbstractArray,
wv::AbstractWeights, x::AbstractArray)
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
Base.mightalias(x, wv) &&
throw(ArgumentError("output array x must n... | [
618,
632
] | function direct_sample!(rng::AbstractRNG, a::AbstractArray,
wv::AbstractWeights, x::AbstractArray)
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
Base.mightalias(x, wv) &&
throw(ArgumentError("output array x must n... | function direct_sample!(rng::AbstractRNG, a::AbstractArray,
wv::AbstractWeights, x::AbstractArray)
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
Base.mightalias(x, wv) &&
throw(ArgumentError("output array x must n... | direct_sample! | 618 | 632 | src/sampling.jl | #CURRENT FILE: StatsBase.jl/src/sampling.jl
##CHUNK 1
when the corresponding sample is picked.
Noting `k=length(x)` and `n=length(a)`, this algorithm consumes ``O(k)`` random numbers,
and has overall time complexity ``O(n k)``.
"""
function naive_wsample_norep!(rng::AbstractRNG, a::AbstractArray,
... |
649 | 666 | StatsBase.jl | 296 | function alias_sample!(rng::AbstractRNG, a::AbstractArray, wv::AbstractWeights, x::AbstractArray)
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
1 == firstindex(a) == firstindex(wv) ||
throw(ArgumentError("non 1-based arrays are not suppo... | function alias_sample!(rng::AbstractRNG, a::AbstractArray, wv::AbstractWeights, x::AbstractArray)
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
1 == firstindex(a) == firstindex(wv) ||
throw(ArgumentError("non 1-based arrays are not suppo... | [
649,
666
] | function alias_sample!(rng::AbstractRNG, a::AbstractArray, wv::AbstractWeights, x::AbstractArray)
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
1 == firstindex(a) == firstindex(wv) ||
throw(ArgumentError("non 1-based arrays are not suppo... | function alias_sample!(rng::AbstractRNG, a::AbstractArray, wv::AbstractWeights, x::AbstractArray)
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
1 == firstindex(a) == firstindex(wv) ||
throw(ArgumentError("non 1-based arrays are not suppo... | alias_sample! | 649 | 666 | src/sampling.jl | #FILE: StatsBase.jl/src/deprecates.jl
##CHUNK 1
a::AbstractVector{Float64},
alias::AbstractVector{Int})
Base.depwarn("make_alias_table! is both internal and deprecated, use AliasTables.jl instead", :make_alias_table!)
# Arguments:
#
# w [in]: ... |
681 | 711 | StatsBase.jl | 297 | function naive_wsample_norep!(rng::AbstractRNG, a::AbstractArray,
wv::AbstractWeights, x::AbstractArray)
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
Base.mightalias(x, wv) &&
throw(ArgumentError("output ar... | function naive_wsample_norep!(rng::AbstractRNG, a::AbstractArray,
wv::AbstractWeights, x::AbstractArray)
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
Base.mightalias(x, wv) &&
throw(ArgumentError("output ar... | [
681,
711
] | function naive_wsample_norep!(rng::AbstractRNG, a::AbstractArray,
wv::AbstractWeights, x::AbstractArray)
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
Base.mightalias(x, wv) &&
throw(ArgumentError("output ar... | function naive_wsample_norep!(rng::AbstractRNG, a::AbstractArray,
wv::AbstractWeights, x::AbstractArray)
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
Base.mightalias(x, wv) &&
throw(ArgumentError("output ar... | naive_wsample_norep! | 681 | 711 | src/sampling.jl | #CURRENT FILE: StatsBase.jl/src/sampling.jl
##CHUNK 1
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
Base.mightalias(x, wv) &&
throw(ArgumentError("output array x must not share memory with weights array wv"))
1 == firstindex(a) == fi... |
728 | 753 | StatsBase.jl | 298 | function efraimidis_a_wsample_norep!(rng::AbstractRNG, a::AbstractArray,
wv::AbstractWeights, x::AbstractArray)
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
Base.mightalias(x, wv) &&
throw(ArgumentEr... | function efraimidis_a_wsample_norep!(rng::AbstractRNG, a::AbstractArray,
wv::AbstractWeights, x::AbstractArray)
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
Base.mightalias(x, wv) &&
throw(ArgumentEr... | [
728,
753
] | function efraimidis_a_wsample_norep!(rng::AbstractRNG, a::AbstractArray,
wv::AbstractWeights, x::AbstractArray)
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
Base.mightalias(x, wv) &&
throw(ArgumentEr... | function efraimidis_a_wsample_norep!(rng::AbstractRNG, a::AbstractArray,
wv::AbstractWeights, x::AbstractArray)
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
Base.mightalias(x, wv) &&
throw(ArgumentEr... | efraimidis_a_wsample_norep! | 728 | 753 | src/sampling.jl | #CURRENT FILE: StatsBase.jl/src/sampling.jl
##CHUNK 1
throw(ArgumentError("output array x must not share memory with input array a"))
Base.mightalias(x, wv) &&
throw(ArgumentError("output array x must not share memory with weights array wv"))
1 == firstindex(a) == firstindex(wv) == firstindex(x)... |
770 | 826 | StatsBase.jl | 299 | function efraimidis_ares_wsample_norep!(rng::AbstractRNG, a::AbstractArray,
wv::AbstractWeights, x::AbstractArray)
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
Base.mightalias(x, wv) &&
throw(Argu... | function efraimidis_ares_wsample_norep!(rng::AbstractRNG, a::AbstractArray,
wv::AbstractWeights, x::AbstractArray)
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
Base.mightalias(x, wv) &&
throw(Argu... | [
770,
826
] | function efraimidis_ares_wsample_norep!(rng::AbstractRNG, a::AbstractArray,
wv::AbstractWeights, x::AbstractArray)
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
Base.mightalias(x, wv) &&
throw(Argu... | function efraimidis_ares_wsample_norep!(rng::AbstractRNG, a::AbstractArray,
wv::AbstractWeights, x::AbstractArray)
Base.mightalias(a, x) &&
throw(ArgumentError("output array x must not share memory with input array a"))
Base.mightalias(x, wv) &&
throw(Argu... | efraimidis_ares_wsample_norep! | 770 | 826 | src/sampling.jl | #CURRENT FILE: StatsBase.jl/src/sampling.jl
##CHUNK 1
Base.mightalias(x, wv) &&
throw(ArgumentError("output array x must not share memory with weights array wv"))
1 == firstindex(a) == firstindex(wv) == firstindex(x) ||
throw(ArgumentError("non 1-based arrays are not supported"))
isfinite(su... |
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