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Fredrik Bagge Carlson
reinforcementlearning
Commits
e0ffc590
Commit
e0ffc590
authored
7 years ago
by
Fredrik Bagge Carlson
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work on pf
parent
5220be18
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Changes
2
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2 changed files
pf.jl
+31
-29
31 additions, 29 deletions
pf.jl
pf_vec.jl
+149
-0
149 additions, 0 deletions
pf_vec.jl
with
180 additions
and
29 deletions
pf.jl
+
31
−
29
View file @
e0ffc590
using
StatsBase
,
Plots
,
Distributions
using
StatsBase
,
Plots
function
pf!
(
xp
,
w
,
y
,
N
,
g
,
f
,
σw
)
function
pf!
(
xp
,
w
,
y
,
N
,
g
,
f
,
σw
)
T
=
length
(
y
)
T
=
length
(
y
)
...
@@ -7,7 +6,7 @@ function pf!(xp,w, y, N, g, f, σw )
...
@@ -7,7 +6,7 @@ function pf!(xp,w, y, N, g, f, σw )
wT
=
@view
w
[
:
,
1
]
wT
=
@view
w
[
:
,
1
]
xT
=
@view
xp
[
:
,
1
]
xT
=
@view
xp
[
:
,
1
]
fill!
(
wT
,
log
(
1
/
N
))
fill!
(
wT
,
log
(
1
/
N
))
g
(
y
[
1
],
xT
,
wT
)
g
(
wT
,
y
[
1
],
xT
)
wT
.-=
log
(
sum
(
exp
,
wT
))
wT
.-=
log
(
sum
(
exp
,
wT
))
for
t
=
2
:
T
for
t
=
2
:
T
...
@@ -25,16 +24,10 @@ function pf!(xp,w, y, N, g, f, σw )
...
@@ -25,16 +24,10 @@ function pf!(xp,w, y, N, g, f, σw )
copy!
(
wT
,
wT1
)
copy!
(
wT
,
wT1
)
end
end
g
(
y
[
t
],
xT
,
wT
)
g
(
wT
,
y
[
t
],
xT
)
# Normalize weights
logsumexp!
(
wT
)
offset
=
maximum
(
wT
)
normConstant
=
0.0
for
i
=
1
:
N
normConstant
+=
exp
(
w
[
i
,
t
]
-
offset
)
end
wT
.-=
log
(
sum
(
exp
,
wT
))
end
end
xh
=
zeros
(
Float64
,
T
)
xh
=
zeros
(
Float64
,
T
)
...
@@ -46,16 +39,26 @@ function pf!(xp,w, y, N, g, f, σw )
...
@@ -46,16 +39,26 @@ function pf!(xp,w, y, N, g, f, σw )
return
xh
return
xh
end
end
@inline
logsumexp!
(
w
)
=
w
.-=
log
(
sum
(
exp
,
w
))
# function logsumexp!(w)
# offset = maximum(wT)
# normConstant = 0.0
# for i = 1:N
# normConstant += exp(w[i,t]-offset)
# end
# wT .-= log(normConstant) + offset
# end
function
resample
(
w
)
function
resample
(
w
)
N
=
length
(
w
)
N
=
length
(
w
)
j
=
Array
(
Int64
,
N
)
j
=
Array
{
Int64
}(
N
)
# bins = cumsum(exp(w)) # devec
# bins = cumsum(exp(w)) # devec
bins
=
Array
(
Float64
,
N
)
bins
=
Array
{
Float64
}(
N
)
bins
[
1
]
=
exp
(
w
[
1
])
# devec
bins
[
1
]
=
exp
(
w
[
1
])
for
i
=
2
:
N
for
i
=
2
:
N
bins
[
i
]
=
bins
[
i
-
1
]
+
exp
(
w
[
i
])
bins
[
i
]
=
bins
[
i
-
1
]
+
exp
(
w
[
i
])
end
end
s
=
collect
(
(
rand
()
/
N
+
0
)
:
1
/
N
:
bins
[
end
]
)
s
=
(
rand
()
/
N
+
0
)
:
1
/
N
:
bins
[
end
]
bo
=
1
bo
=
1
for
i
=
1
:
N
for
i
=
1
:
N
...
@@ -90,16 +93,15 @@ f(xn,x,t::Int64) = begin
...
@@ -90,16 +93,15 @@ f(xn,x,t::Int64) = begin
end
end
f
(
x
::
Float64
,
t
::
Int64
)
=
0.5
*
x
+
25
*
x
./
(
1
+
x
^
2
)
+
8
*
cos
(
1.2
*
(
t
-
1
))
f
(
x
::
Float64
,
t
::
Int64
)
=
0.5
*
x
+
25
*
x
./
(
1
+
x
^
2
)
+
8
*
cos
(
1.2
*
(
t
-
1
))
# g(y[t]-0.05xp[:,t].^2, wT)
const
pg
=
Distributions
.
Normal
(
0
,
σv
)
const
den
=
0.5
/
σv
^
2
function
g
(
w
,
y
::
Float64
,
x
)
function
g
(
y
::
Float64
,
x
,
w
)
@inbounds
for
i
=
1
:
length
(
w
)
@inbounds
for
i
=
1
:
length
(
w
)
w
[
i
]
-=
den
*
(
y
-
0.05
x
[
i
]
^
2
)
^
2
w
[
i
]
-=
logpdf
(
pg
,
y
-
0.05
x
[
i
]
^
2
)
end
end
end
function
g
(
x
)
ret
=
@.
-
0.5
*
(
x
/
σv
)
^
2
end
end
rms
(
x
)
=
sqrt
(
mean
(
abs2
,
x
))
rms
(
x
)
=
sqrt
(
mean
(
abs2
,
x
))
function
run_test
()
function
run_test
()
...
@@ -107,14 +109,14 @@ function run_test()
...
@@ -107,14 +109,14 @@ function run_test()
time_steps
=
[
20
,
50
,
100
,
200
]
time_steps
=
[
20
,
50
,
100
,
200
]
RMSE
=
zeros
(
length
(
particle_count
),
length
(
time_steps
))
# Store the RMS errors
RMSE
=
zeros
(
length
(
particle_count
),
length
(
time_steps
))
# Store the RMS errors
propagated_particles
=
0
propagated_particles
=
0
@progress
for
(
Ti
,
T
)
in
enumerate
(
time_steps
)
for
(
Ti
,
T
)
in
enumerate
(
time_steps
)
@progress
for
(
Ni
,
N
)
in
enumerate
(
particle_count
)
for
(
Ni
,
N
)
in
enumerate
(
particle_count
)
montecarlo_runs
=
maximum
(
particle_count
)
*
maximum
(
time_steps
)
/
T
/
N
montecarlo_runs
=
maximum
(
particle_count
)
*
maximum
(
time_steps
)
/
T
/
N
# montecarlo_runs = 1
# montecarlo_runs = 1
xp
=
Array
(
Float64
,
(
N
,
T
)
)
xp
=
Array
{
Float64
}
(
N
,
T
)
w
=
Array
(
Float64
,
(
N
,
T
)
)
w
=
Array
{
Float64
}
(
N
,
T
)
x
=
Array
(
Float64
,
T
)
x
=
Array
{
Float64
}(
T
)
y
=
Array
(
Float64
,
T
)
y
=
Array
{
Float64
}(
T
)
E
=
@parallel
(
+
)
for
mc_iter
=
1
:
montecarlo_runs
E
=
@parallel
(
+
)
for
mc_iter
=
1
:
montecarlo_runs
x
[
1
]
=
σw
*
randn
()
x
[
1
]
=
σw
*
randn
()
...
...
This diff is collapsed.
Click to expand it.
pf_vec.jl
0 → 100644
+
149
−
0
View file @
e0ffc590
using
StatsBase
,
Plots
,
Distributions
,
StaticArrays
function
init_pf
(
N
,
p0
)
xprev
=
rand
(
p0
,
N
)
x
=
similar
(
x
)
w
=
fill
(
log
(
1
/
N
),
N
)
x
,
xprev
,
w
end
function
pf!
(
x
,
xprev
,
w
,
u
,
y
,
g
,
f
)
N
=
length
(
x
)
if
shouldresample
(
w
)
j
=
resample
(
w
)
f
(
x
,
xprev
,
u
,
j
)
fill!
(
w
,
log
(
1
/
N
))
else
# Resample not needed
f
(
x
,
xprev
,
u
,
1
:
N
)
end
g
(
w
,
y
,
x
)
logsumexp!
(
w
)
x
end
function
weigthed_mean
(
x
,
w
)
xh
=
zeros
(
size
(
x
[
1
]))
@inbounds
@simd
for
i
=
eachindex
(
x
)
xh
.+=
x
[
i
]
.*
exp
(
w
[
i
])
end
return
xh
end
@inline
logsumexp!
(
w
)
=
w
.-=
log
(
sum
(
exp
,
w
))
# function logsumexp!(w)
# offset = maximum(wT)
# normConstant = 0.0
# for i = 1:N
# normConstant += exp(w[i,t]-offset)
# end
# wT .-= log(normConstant) + offset
# end
function
resample
(
w
)
N
=
length
(
w
)
j
=
Array
{
Int64
}(
N
)
bins
=
Array
{
Float64
}(
N
)
bins
[
1
]
=
exp
(
w
[
1
])
for
i
=
2
:
N
bins
[
i
]
=
bins
[
i
-
1
]
+
exp
(
w
[
i
])
end
s
=
(
rand
()
/
N
+
0
)
:
1
/
N
:
bins
[
end
]
bo
=
1
for
i
=
1
:
N
@inbounds
for
b
=
bo
:
N
if
s
[
i
]
<
bins
[
b
]
j
[
i
]
=
b
bo
=
b
break
end
end
end
return
j
end
const
pg
=
Distributions
.
Normal
(
0
,
1.0
)
const
pf
=
Distributions
.
Normal
(
0
,
1.0
)
const
p0
=
Distributions
.
Normal
(
0
,
2.0
)
n
=
7
m
=
2
p
=
2
T
=
randn
(
n
,
n
)
const
A
=
SMatrix
{
n
,
n
}(
T
*
diagm
(
linspace
(
0.5
,
0.99
,
n
))
/
T
)
const
B
=
@SMatrix
randn
(
n
,
m
)
const
C
=
@SMatrix
randn
(
p
,
n
)
function
f
(
x
,
xp
,
u
,
j
)
@inbounds
for
i
=
eachindex
(
x
)
x
[
i
]
=
A
*
xp
[
j
[
i
]]
+
B
*
u
+
rand
(
pf
)
end
end
function
f
(
x
,
u
)
x
[
i
]
=
A
*
xp
[
j
[
i
]]
+
B
*
u
+
rand
(
pf
)
end
function
g
(
w
,
y
::
Float64
,
x
)
@inbounds
for
i
=
1
:
length
(
w
)
w
[
i
]
-=
logpdf
(
pg
,
y
-
C
*
x
[
i
])
end
end
rms
(
x
)
=
sqrt
(
mean
(
abs2
,
x
))
function
run_test
()
particle_count
=
[
5
10
20
50
100
200
500
1000
10_000
]
time_steps
=
[
20
,
50
,
100
,
200
]
RMSE
=
zeros
(
length
(
particle_count
),
length
(
time_steps
))
# Store the RMS errors
propagated_particles
=
0
for
(
Ti
,
T
)
in
enumerate
(
time_steps
)
for
(
Ni
,
N
)
in
enumerate
(
particle_count
)
montecarlo_runs
=
maximum
(
particle_count
)
*
maximum
(
time_steps
)
/
T
/
N
# montecarlo_runs = 1
xp
=
Array
{
Float64
}(
N
,
T
)
w
=
Array
{
Float64
}(
N
,
T
)
x
=
Array
{
Float64
}(
T
)
y
=
Array
{
Float64
}(
T
)
E
=
@parallel
(
+
)
for
mc_iter
=
1
:
montecarlo_runs
x
[
1
]
=
σw
*
randn
()
y
[
1
]
=
σv
*
randn
()
@inbounds
for
t
=
1
:
T
-
1
x
[
t
+
1
]
=
f
(
x
[
t
],
u
)
+
σw
*
randn
()
y
[
t
+
1
]
=
0.05
x
[
t
+
1
]
^
2
+
σv
*
randn
()
end
# t
xh
=
pf!
(
x
,
xprev
,
w
,
u
,
y
,
g
,
f
)
rms
(
x
-
xh
)
end
# MC
RMSE
[
Ni
,
Ti
]
=
E
/
montecarlo_runs
propagated_particles
+=
montecarlo_runs
*
N
*
T
# figure();plot([x xh])
@show
N
end
# N
@show
T
end
# T
println
(
"Propagated
$
propagated_particles particles"
)
#
return
RMSE
end
@time
pf!
(
eye
(
4
),
eye
(
4
),
ones
(
4
),
4
,
g
,
f
,
σw
)
gc
()
Profile
.
clear
()
@time
RMSE
=
run_test
()
# Profile.print()
function
plotting
(
RMSE
)
time_steps
=
[
20
,
50
,
100
,
200
]
particle_count
=
[
5
,
10
,
20
,
50
,
100
,
200
,
500
,
1000
,
10_000
]
nT
=
length
(
time_steps
)
leg
=
reshape
([
"
$
(time_steps[i]) time steps"
for
i
=
1
:
nT
],
1
,
:
)
plot
(
particle_count
,
RMSE
,
xscale
=:
log10
,
ylabel
=
"RMS errors"
,
xlabel
=
" Number of particles"
,
lab
=
leg
)
end
plotting
(
RMSE
)
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