I am new to Prolog
I am trying in Prolog a rule that gives me a given path from a node to another and also gives me the total weight of the path.
I have succeeded to get all the edges of the path but I am not able to show the weight of the path. I debbuged it and it is seen that variable S adds up to the whole weight of the path but in the way back, deletes all the elements. My idea is to add the total weight to P.
Code:
notIn(A,[]).
notIn(A,[H|T]):- A\==H,notIn(A,T).
path(X,X,_,[], S, P).
path(X,Y,[X|Cs], S, P) :-
path(X,Y,[X],Cs, S, P), P is S+W.
path(X,Y,Visited,[Z|Cs], S, P) :-
connection(X,Z,W),
notIn(Z,Visited),
path(Z,Y,[Z|Visited],Cs, S+W, P).
? path(ori, dest, X, 0, P).
Your predicate almost works. There are only two issues and some details I'd like to address. Firstly it would aid readability greatly to separate predicates with different arities. So let's put the one rule of path/5 in front of the two rules of path/6 like so:
path(X,Y,[X|Cs], S, P) :-
path(X,Y,[X],Cs, S, P),
P is S+W. % <-(1)
path(X,X,_,[], S, P).
path(X,Y,Visited,[Z|Cs], S, P) :-
connection(X,Z,W),
notIn(Z,Visited),
path(Z,Y,[Z|Visited],Cs, S+W, P). % <-(2)
Looking at your example query path/5 seems to be the predicate you want to call to find paths. In the second goal of its single rule (marked as % <-(1)) you are using the built-in is/2 with the expression S+W on the right hand side. The variable W appears here for the first time and thus is unbound. This leads to an instantiation error as illustrated by the following example:
?- X is 1+W.
ERROR!!
INSTANTIATION ERROR- in arithmetic: expected bound value
However, since you are only using path/5 to call path/6 there is no need for that goal. Secondly, in the second rule of path/6, in the last goal you are passing S+W as argument instead of evaluating it first. To see what happens, let's remove the goal marked % <-(1) from path/5 and add an example graph to your code:
connection(ori,a,2).
connection(a,b,5).
connection(b,a,4).
connection(b,dest,1).
Now consider your example query with an additional goal:
?- path(ori, dest, X, 0, P), Weight is P.
P = 0+2+5+1,
Weight = 8,
X = [ori,a,b,dest] ? ;
no
As you see the argument S+W leads to the final weight being an expression rather than a value. Consider adding a goal S1 is S+W before the recursive goal and pass S1 as an argument. Thirdly you are using the built-in (\==)/2 in your predicate notIn/2. This comparison succeeds or fails without side effect or unification. This is fine as long as both arguments are bound to values but are problematic when used with unbound variables. Consider the following queries:
?- X=Y, X\==Y.
no
fails as expected but:
?- X\==Y, X=Y.
X = Y
succeeds as X\==Y has no effect to the variables, so they can be unified in the next goal. It is a good idea to use dif/2 instead:
?- X=Y, dif(X,Y).
no
?- dif(X,Y), X=Y.
no
Lastly, two minor suggestions: First, since you are using the 4th argument of path/5 to pass 0 as a start-value for the weight, you might as well do that in the single goal of the rule, thereby simplifying the interface to path/4. Second, it would be nice to have a more descriptive name for the predicate that reflects its declarative nature, say start_end_path_weight/4. So your code would then look something like this:
notIn(A,[]).
notIn(A,[H|T]):-
dif(A,H),
notIn(A,T).
start_end_path_weight(X,Y,[X|Cs], P) :-
path(X,Y,[X],Cs, 0, P).
path(X,X,_,[], P, P).
path(X,Y,Visited,[Z|Cs], S, P) :-
connection(X,Z,W),
notIn(Z,Visited),
S1 is S+W,
path(Z,Y,[Z|Visited],Cs, S1, P).
With these modifications your example query looks like this:
?- start_end_path_weight(ori,dest,X,W).
W = 8,
X = [ori,a,b,dest] ? ;
no
Here's how to improve upon #tas's answer by using clpfd for arithmetics instead of (is)/2:
:- use_module(library(clpfd)).
start_end_path_weight(X,Y,[X|Cs], P) :-
path(X,Y,[X],Cs, 0, P).
path(X,X,_,[], P, P).
path(X,Y,Visited,[Z|Cs], S, P) :-
connection(X,Z,W),
notIn(Z,Visited)
maplist(dif(Z),Visited),
S1 is S+W
S1 #= S+W, S1 #=< P,
path(Z,Y,[Z|Visited],Cs, S1, P).
Limiting the maximum costs? Piece of cake!
Consider the following InterRail subset ...
... translated to Prolog ...
connection(X,Y,D) :- to_fro_dt(X,Y,D).
connection(X,Y,D) :- to_fro_dt(Y,X,D).
to_fro_dt(aberdeen,edinburgh,140). to_fro_dt(amsterdam,berlin,370). to_fro_dt(amsterdam,brussels,113). to_fro_dt(amsterdam,cologne,158). to_fro_dt(amsterdam,copenhagen,675). to_fro_dt(ancona,igoumenitsa,900). to_fro_dt(athens,patras,215). to_fro_dt(athens,/* for consistency */piraeus,5). to_fro_dt(athens,thessaloniki,265). to_fro_dt(bar,belgrade,572). to_fro_dt(barcelona,madrid,170). to_fro_dt(barcelona,marseille,280). to_fro_dt(barcelona,sevilla,330). to_fro_dt(barcelona,valencia,175). to_fro_dt(bari,igoumenitsa,570). to_fro_dt(bari,rome,240). to_fro_dt(belfast,dublin,240). to_fro_dt(belgrade,bucharest,730). to_fro_dt(belgrade,budapest,450). to_fro_dt(belgrade,sarajevo,540). to_fro_dt(belgrade,skopje,525). to_fro_dt(belgrade,sofia,485). to_fro_dt(bergen,oslo,405). to_fro_dt(berlin,cologne,260). to_fro_dt(berlin,hamburg,95). to_fro_dt(berlin,munich,345). to_fro_dt(berlin,prague,275). to_fro_dt(berlin,warsaw,365). to_fro_dt(bern,frankfurt,235). to_fro_dt(bern,lyon,230). to_fro_dt(bern,milan,240). to_fro_dt(birmingham,edinburgh,265). to_fro_dt(birmingham,holyhead,245). to_fro_dt(birmingham,london,105). to_fro_dt(bologna,florence,37). to_fro_dt(bologna,milan,60). to_fro_dt(bordeaux,lyon,375). to_fro_dt(bordeaux,madrid,660). to_fro_dt(bordeaux,paris,180). to_fro_dt(bristol,london,105). to_fro_dt(brussels,cologne,107). to_fro_dt(brussels,frankfurt,190). to_fro_dt(brussels,london,140). to_fro_dt(brussels,paris,85). to_fro_dt(bucharest,budapest,830). to_fro_dt(bucharest,sofia,540). to_fro_dt(bucharest,zagreb,365). to_fro_dt(budapest,ljubljana,540). to_fro_dt(budapest,vienna,165). to_fro_dt(budapest,warsaw,680). to_fro_dt(budapest,zagreb,365). to_fro_dt(catania,naples,450). to_fro_dt(cologne,frankfurt,82). to_fro_dt(copenhagen,hamburg,270). to_fro_dt(copenhagen,oslo,520). to_fro_dt(copenhagen,stockholm,315). to_fro_dt(cork,dublin,165). to_fro_dt(dublin,holyhead,195). to_fro_dt(dublin,westport,210). to_fro_dt(edinburgh,glasgow,50). to_fro_dt(faro,lisbon,230). to_fro_dt(florence,rome,95). to_fro_dt(florence,venice,123). to_fro_dt(frankfurt,hamburg,220). to_fro_dt(frankfurt,munich,190). to_fro_dt(frankfurt,paris,235). to_fro_dt(hamburg,munich,350). to_fro_dt(helsinki,rovaniemi,570). to_fro_dt(helsinki,turku,110). to_fro_dt(heraklion,piraeus,390). to_fro_dt(igoumenitsa,patras,360). to_fro_dt(istanbul,sofia,775). to_fro_dt(istanbul,thessaloniki,720). to_fro_dt(kiruna,stockholm,960). to_fro_dt(lisbon,madrid,610). to_fro_dt(lisbon,porto,165). to_fro_dt(ljubljana,venice,540). to_fro_dt(ljubljana,zagreb,140). to_fro_dt(london,paris,135). to_fro_dt(london,penzance,305). to_fro_dt(lyon,marseille,100). to_fro_dt(lyon,paris,115). to_fro_dt(madrid,'málaga',165). to_fro_dt(madrid,pamplona,180). to_fro_dt(madrid,santander,270). to_fro_dt(madrid,santiago,425). to_fro_dt(madrid,sevilla,155). to_fro_dt(madrid,valencia,105). to_fro_dt(marseille,montpellier,140). to_fro_dt(marseille,nice,155). to_fro_dt(milan,munich,465). to_fro_dt(milan,nice,310). to_fro_dt(milan,venice,155). to_fro_dt(munich,prague,365). to_fro_dt(munich,venice,425). to_fro_dt(munich,vienna,250). to_fro_dt(naples,rome,70). to_fro_dt(oslo,stockholm,380). to_fro_dt(paris,rennes,120). to_fro_dt(piraeus,rhodes,710). to_fro_dt(prague,vienna,270). to_fro_dt(prague,warsaw,520). to_fro_dt(sarajevo,zagreb,550). to_fro_dt(skopje,sofia,540). to_fro_dt(skopje,thessaloniki,240). to_fro_dt(sofia,thessaloniki,400). to_fro_dt(split,zagreb,335). to_fro_dt(stockholm,/* added by hand */turku,725). to_fro_dt(stockholm,'östersund',420). to_fro_dt(trondheim,'östersund',230). to_fro_dt(venice,vienna,440). to_fro_dt(vienna,warsaw,450).
... let's find paths that
start in Vienna
include at least 2 other cities
and have a cumulative travel time of 10 hours (or less)!
?- W #=< 600, Path = [_,_,_|_], start_end_path_weight(vienna, _, Path, W).
W = 530, Path = [vienna,budapest,zagreb] ;
W = 595, Path = [vienna,munich,berlin] ;
W = 440, Path = [vienna,munich,frankfurt] ;
W = 522, Path = [vienna,munich,frankfurt,cologne] ;
W = 600, Path = [vienna,munich,hamburg] ;
W = 545, Path = [vienna,prague,berlin] ;
W = 563, Path = [vienna,venice,florence] ;
W = 600, Path = [vienna,venice,florence,bologna] ;
W = 595, Path = [vienna,venice,milan] ;
false. % terminates universally fast
Related
I would like to use dask.array.map_overlap to deal with the scipy interpolation function. However, I keep meeting errors that I cannot understand and hoping someone can answer this to me.
Here is the error message I have received if I want to run .compute().
ValueError: could not broadcast input array from shape (1070,0) into shape (1045,0)
To resolve the issue, I started to use .to_delayed() to check each partition outputs, and this is what I found.
Following is my python code.
Step 1. Load netCDF file through Xarray, and then output to dask.array with chunk size (400,400)
df = xr.open_dataset('./Brazil Sentinal2 Tile/' + data_file +'.nc')
lon, lat = df['lon'].data, df['lat'].data
slon = da.from_array(df['lon'], chunks=(400,400))
slat = da.from_array(df['lat'], chunks=(400,400))
data = da.from_array(df.isel(band=0).__xarray_dataarray_variable__.data, chunks=(400,400))
Step 2. declare a function for da.map_overlap use
def sumsum2(lon,lat,data, hex_res=10):
hex_col = 'hex' + str(hex_res)
lon_max, lon_min = lon.max(), lon.min()
lat_max, lat_min = lat.max(), lat.min()
b = box(lon_min, lat_min, lon_max, lat_max, ccw=True)
b = transform(lambda x, y: (y, x), b)
b = mapping(b)
target_df = pd.DataFrame(h3.polyfill( b, hex_res), columns=[hex_col])
target_df['lat'] = target_df[hex_col].apply(lambda x: h3.h3_to_geo(x)[0])
target_df['lon'] = target_df[hex_col].apply(lambda x: h3.h3_to_geo(x)[1])
tlon, tlat = target_df[['lon','lat']].values.T
abc = lNDI(points=(lon.ravel(), lat.ravel()),
values= data.ravel())(tlon,tlat)
target_df['out'] = abc
print(np.stack([tlon, tlat, abc],axis=1).shape)
return np.stack([tlon, tlat, abc],axis=1)
Step 3. Apply the da.map_overlap
b = da.map_overlap(sumsum2, slon[:1200,:1200], slat[:1200,:1200], data[:1200,:1200], depth=10, trim=True, boundary=None, align_arrays=False, dtype='float64',
)
Step 4. Using to_delayed() to test output shape
print(b.to_delayed().flatten()[0].compute().shape, )
print(b.to_delayed().flatten()[1].compute().shape)
(1065, 3)
(1045, 0)
(1090, 3)
(1070, 0)
which is saying that the output from da.map_overlap is only outputting 1-D dimension ( which is (1045,0) and (1070,0) ), while in the da.map_overlap, the output I am preparing is 2-D dimension ( which is (1065,3) and (1090,3) ).
In addition, if I turn off the trim argument, which is
c = da.map_overlap(sumsum2,
slon[:1200,:1200],
slat[:1200,:1200],
data[:1200,:1200],
depth=10,
trim=False,
boundary=None,
align_arrays=False,
dtype='float64',
)
print(c.to_delayed().flatten()[0].compute().shape, )
print(c.to_delayed().flatten()[1].compute().shape)
The output becomes
(1065, 3)
(1065, 3)
(1090, 3)
(1090, 3)
This is saying that when trim=True, I cut out everything?
because...
#-- print out the values
b.to_delayed().flatten()[0].compute()[:10,:]
(1065, 3)
array([], shape=(1045, 0), dtype=float64)
while...
#-- print out the values
c.to_delayed().flatten()[0].compute()[:10,:]
array([[ -47.83683837, -18.98359832, 1395.01848583],
[ -47.8482856 , -18.99038681, 2663.68391094],
[ -47.82800624, -18.99207069, 1465.56517187],
[ -47.81897323, -18.97919009, 2769.91556363],
[ -47.82066663, -19.00712956, 1607.85927095],
[ -47.82696896, -18.97167714, 2110.7516765 ],
[ -47.81562653, -18.98302933, 2662.72112163],
[ -47.82176881, -18.98594465, 2201.83205114],
[ -47.84567 , -18.97512514, 1283.20631652],
[ -47.84343568, -18.97270783, 1282.92117225]])
Any thoughts for this?
Thank You.
I guess I got the answer. Please let me if I am wrong.
I am not allowing to use trim=True is because I change the shape of output array (after surfing the internet, I notice that the shape of output array should be the same with the shape of input array). Since I change the shape, the dask has no idea how to deal with it so it returns the empty array to me (weird).
Instead of using trim=False, since I didn't ask cutting-out the buffer zone, it is now okay to output the return values. (although I still don't know why the dask cannot concat the chunked array, but believe is also related to shape)
The solution is using delayed function on da.concatenate, which is
delayed(da.concatenate)([e.to_delayed().flatten()[idx] for idx in range(len(e.to_delayed().flatten()))])
In this case, we are not relying on the concat function in map_overlap but use our own concat to combine the outputs we want.
I am trying to use reinforcement learning in julia to teach a car that is constantly being accelerated backwards (but with a positive initial velocity) to apply brakes so that it gets as close to a target distance as possible before moving backwards.
To do this, I am making use of POMDPs.jl and crux.jl which has many solvers (I'm using DQN). I will list what I believe to be the relevant parts of the script first, and then more of it towards the end.
To define the MDP, I set the initial position, velocity, and force from the brakes as a uniform distribution over some values.
#with_kw struct SliderMDP <: MDP{Array{Float32}, Array{Float32}}
x0 = Distributions.Uniform(0., 80.)# Distribution to sample initial position
v0 = Distributions.Uniform(0., 25.) # Distribution to sample initial velocity
d0 = Distributions.Uniform(0., 2.) # Distribution to sample brake force
...
end
My state holds the values of (position, velocity, brake force), and the initial state is given as:
function POMDPs.initialstate(mdp::SliderMDP)
ImplicitDistribution((rng) -> Float32.([rand(rng, mdp.x0), rand(rng, mdp.v0), rand(rng, mdp.d0)]))
end
Then, I set up my DQN solver using crux.jl and called a function to solve for the policy
solver_dqn = DQN(π=Q_network(), S=s, N=30000)
policy_dqn = solve(solver_dqn, mdp)
calling solve() gives me the error MethodError: no method matching logpdf(::Distributions.Categorical{Float64, Vector{Float64}}, ::Nothing). I am quite sure that this comes from the initial state sampling, but I am not sure why or how to fix it. I have only been learning RL from various books and online lectures for a very short time, so any help regarding the error or my the model I set up (or anything else I'm oblivious to) would be appreciated.
More comprehensive code:
Packages:
using POMDPs
using POMDPModelTools
using POMDPPolicies
using POMDPSimulators
using Parameters
using Random
using Crux
using Flux
using Distributions
Rest of it:
#with_kw struct SliderMDP <: MDP{Array{Float32}, Array{Float32}}
x0 = Distributions.Uniform(0., 80.)# Distribution to sample initial position
v0 = Distributions.Uniform(0., 25.) # Distribution to sample initial velocity
d0 = Distributions.Uniform(0., 2.) # Distribution to sample brake force
m::Float64 = 1.
tension::Float64 = 3.
dmax::Float64 = 2.
target::Float64 = 80.
dt::Float64 = .05
γ::Float32 = 1.
actions::Vector{Float64} = [-.1, 0., .1]
end
function POMDPs.gen(env::SliderMDP, s, a, rng::AbstractRNG = Random.GLOBAL_RNG)
x, ẋ, d = s
if x >= env.target
a = .1
end
if d+a >= env.dmax || d+a <= 0
a = 0.
end
force = (d + env.tension) * -1
ẍ = force/env.m
# Simulation
x_ = x + env.dt * ẋ
ẋ_ = ẋ + env.dt * ẍ
d_ = d + a
sp = vcat(x_, ẋ_, d_)
reward = abs(env.target - x) * -1
return (sp=sp, r=reward)
end
function POMDPs.initialstate(mdp::SliderMDP)
ImplicitDistribution((rng) -> Float32.([rand(rng, mdp.x0), rand(rng, mdp.v0), rand(rng, mdp.d0)]))
end
POMDPs.isterminal(mdp::SliderMDP, s) = s[2] <= 0
POMDPs.discount(mdp::SliderMDP) = mdp.γ
mdp = SliderMDP();
s = state_space(mdp); # Using Crux.jl
function Q_network()
layer1 = Dense(3, 64, relu)
layer2 = Dense(64, 64, relu)
layer3 = Dense(64, length(3))
return DiscreteNetwork(Chain(layer1, layer2, layer3), [-.1, 0, .1])
end
solver_dqn = DQN(π=Q_network(), S=s, N=30000) # Using Crux.jl
policy_dqn = solve(solver_dqn, mdp) # Error comes here
Stacktrace:
policy_dqn
MethodError: no method matching logpdf(::Distributions.Categorical{Float64, Vector{Float64}}, ::Nothing)
Closest candidates are:
logpdf(::Distributions.DiscreteNonParametric, !Matched::Real) at C:\Users\name\.julia\packages\Distributions\Xrm9e\src\univariate\discrete\discretenonparametric.jl:106
logpdf(::Distributions.UnivariateDistribution{S} where S<:Distributions.ValueSupport, !Matched::AbstractArray) at deprecated.jl:70
logpdf(!Matched::POMDPPolicies.PlaybackPolicy, ::Any) at C:\Users\name\.julia\packages\POMDPPolicies\wMOK3\src\playback.jl:34
...
logpdf(::Crux.ObjectCategorical, ::Float32)#utils.jl:16
logpdf(::Crux.DistributionPolicy, ::Vector{Float64}, ::Float32)#policies.jl:305
var"#exploration#133"(::Base.Iterators.Pairs{Union{}, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, ::typeof(Crux.exploration), ::Crux.DistributionPolicy, ::Vector{Float64})#policies.jl:302
exploration#policies.jl:297[inlined]
action(::Crux.DistributionPolicy, ::Vector{Float64})#policies.jl:294
var"#exploration#136"(::Crux.DiscreteNetwork, ::Int64, ::typeof(Crux.exploration), ::Crux.MixedPolicy, ::Vector{Float64})#policies.jl:326
var"#step!#173"(::Bool, ::Int64, ::typeof(Crux.step!), ::Dict{Symbol, Array}, ::Int64, ::Crux.Sampler{Main.workspace#2.SliderMDP, Vector{Float32}, Crux.DiscreteNetwork, Crux.ContinuousSpace{Tuple{Int64}}, Crux.DiscreteSpace})#sampler.jl:55
var"#steps!#174"(::Int64, ::Bool, ::Int64, ::Bool, ::Bool, ::Bool, ::typeof(Crux.steps!), ::Crux.Sampler{Main.workspace#2.SliderMDP, Vector{Float32}, Crux.DiscreteNetwork, Crux.ContinuousSpace{Tuple{Int64}}, Crux.DiscreteSpace})#sampler.jl:108
var"#fillto!#177"(::Int64, ::Bool, ::typeof(Crux.fillto!), ::Crux.ExperienceBuffer{Array}, ::Crux.Sampler{Main.workspace#2.SliderMDP, Vector{Float32}, Crux.DiscreteNetwork, Crux.ContinuousSpace{Tuple{Int64}}, Crux.DiscreteSpace}, ::Int64)#sampler.jl:156
solve(::Crux.OffPolicySolver, ::Main.workspace#2.SliderMDP)#off_policy.jl:86
top-level scope#Local: 1[inlined]
Short answer:
Change your output vector to Float32 i.e. Float32[-.1, 0, .1].
Long answer:
Crux creates a Distribution over your network's output values, and at some point (policies.jl:298) samples a random value from it. It then converts this value to a Float32. Later (utils.jl:15) it does a findfirst to find the index of this value in the original output array (stored as objs within the distribution), but because the original array is still Float64, this fails and returns a nothing. Hence the error.
I believe this (converting the sampled value but not the objs array and/or not using approximate equality check i.e. findfirst(isapprox(x), d.objs)) to be a bug in the package, and would encourage you to raise this as an issue on Github.
Using SWI-Prolog I have made this simple predicate that relates a time that is in hh:mm format into a time term.
time_string(time(H,M), String) :-
number_string(H,Hour),
number_string(M,Min),
string_concat(Hour,":",S),
string_concat(S,Min,String).
The predicate though can only work in one direction.
time_string(time(10,30),String).
String = "10:30". % This is perfect.
Unfortunately this query fails.
time_string(Time,"10:30").
ERROR: Arguments are not sufficiently instantiated
ERROR: In:
ERROR: [11] number_string(_8690,_8692)
ERROR: [10] time_string(time(_8722,_8724),"10:30") at /tmp/prolcompDJBcEE.pl:74
ERROR: [9] toplevel_call(user:user: ...) at /usr/local/logic/lib/swipl/boot/toplevel.pl:1107
It would be really nice if I didn't have to write a whole new predicate to answer this query. Is there a way I could do this?
Well, going from the structured term time(H,M) to the string String is easier than going from the unstructured String the term time(H,M).
Your predicate works in the "generation" direction.
For the other direction, you want to parse the String. In this case, this is computationally easy and can be done without search/backtracking, which is nice!
Use Prolog's "Definite Clause Grammar" syntax which are "just" a nice way to write predicates that process a "list of stuff". In this case the list of stuff is a list of characters (atoms of length 1). (For the relevant page from SWI-Prolog, see here)
With some luck, the DCG code can run backwards/forwards, but this is generally not the case. Real code meeting some demands of efficiency or causality may force it so that under the hood of a single predicate, you first branch by "processing direction", and then run through rather different code structures to deliver the goods.
So here. The code immediately "decays" into the parse and generate branches. Prolog does not yet manage to behave fully constraint-based. You just have to do some things before others.
Anyway, let's do this:
:- use_module(library(dcg/basics)).
% ---
% "Generate" direction; note that String may be bound to something
% in which case this clause also verifies whether generating "HH:MM"
% from time(H,M) indeed yields (whatever is denoted by) String.
% ---
process_time(time(H,M),String) :-
integer(H), % Demand that H,M are valid integers inside limits
integer(M),
between(0,23,H),
between(0,59,M),
!, % Guard passed, commit to this code branch
phrase(time_g(H,M),Chars,[]), % Build Codes from time/2 Term
string_chars(String,Chars). % Merge Codes into a string, unify with String
% ---
% "Parse" direction.
% ---
process_time(time(H,M),String) :-
string(String), % Demand that String be a valid string; no demands on H,M
!, % Guard passed, commit to this code branch
string_chars(String,Chars), % Explode String into characters
phrase(time_p(H,M),Chars,[]). % Parse "Codes" into H and M
% ---
% "Generate" DCG
% ---
time_g(H,M) --> hour_g(H), [':'], minute_g(M).
hour_g(H) --> { divmod(H,10,V1,V2), digit_int(D1,V1), digit_int(D2,V2) }, digit(D1), digit(D2).
minute_g(M) --> { divmod(M,10,V1,V2), digit_int(D1,V1), digit_int(D2,V2) }, digit(D1), digit(D2).
% ---
% "Parse" DCG
% ---
time_p(H,M) --> hour_p(H), [':'], minute_p(M).
hour_p(H) --> digit(D1), digit(D2), { digit_int(D1,V1), digit_int(D2,V2), H is V1*10+V2, between(0,23,H) }.
minute_p(M) --> digit(D1), digit(D2), { digit_int(D1,V1), digit_int(D2,V2), M is V1*10+V2, between(0,59,M) }.
% ---
% Do I really have to code this? Oh well!
% ---
digit_int('0',0).
digit_int('1',1).
digit_int('2',2).
digit_int('3',3).
digit_int('4',4).
digit_int('5',5).
digit_int('6',6).
digit_int('7',7).
digit_int('8',8).
digit_int('9',9).
% ---
% Let's add plunit tests!
% ---
:- begin_tests(hhmm).
test("parse 1", true(T == time(0,0))) :- process_time(T,"00:00").
test("parse 2", true(T == time(12,13))) :- process_time(T,"12:13").
test("parse 1", true(T == time(23,59))) :- process_time(T,"23:59").
test("generate", true(S == "12:13")) :- process_time(time(12,13),S).
test("verify", true) :- process_time(time(12,13),"12:13").
test("complete", true(H == 12)) :- process_time(time(H,13),"12:13").
test("bad parse", fail) :- process_time(_,"66:66").
test("bad generate", fail) :- process_time(time(66,66),_).
:- end_tests(hhmm).
That's a lot of code.
Does it work?
?- run_tests.
% PL-Unit: hhmm ........ done
% All 8 tests passed
true.
Given the simplicity of the pattern, a DCG could be deemeed overkill, but actually it provides us an easy access to the atomics ingredients that we can feed into some declarative arithmetic library. For instance
:- module(hh_mm_bi,
[hh_mm_bi/2
,hh_mm_bi//1
]).
:- use_module(library(dcg/basics)).
:- use_module(library(clpfd)).
hh_mm_bi(T,S) :- phrase(hh_mm_bi(T),S).
hh_mm_bi(time(H,M)) --> n2(H,23),":",n2(M,59).
n2(V,U) --> d(A),d(B), {V#=A*10+B,V#>=0,V#=<U}.
d(V) --> digit(D), {V#=D-0'0}.
Some tests
?- hh_mm_bi(T,`23:30`).
T = time(23, 30).
?- hh_mm_bi(T,`24:30`).
false.
?- phrase(hh_mm_bi(T),S).
T = time(0, 0),
S = [48, 48, 58, 48, 48] ;
T = time(0, 1),
S = [48, 48, 58, 48, 49] ;
...
edit
library(clpfd) is not the only choice we have for declarative arithmetic. Here is another shot, using library(clpBNR), but it requires you install the appropriate pack, using ?- pack_install(clpBNR). After this is done, another solution functionally equivalent to the one above could be
:- module(hh_mm_bnr,
[hh_mm_bnr/2
,hh_mm_bnr//1
]).
:- use_module(library(dcg/basics)).
:- use_module(library(clpBNR)).
hh_mm_bnr(T,S) :- phrase(hh_mm_bnr(T),S).
hh_mm_bnr(time(H,M)) --> n2(H,23),":",n2(M,59).
n2(V,U) --> d(A),d(B), {V::integer(0,U),{V==A*10+B}}.
d(V) --> digit(D), {{V==D-0'0}}.
edit
The comment (now removed) by #DavidTonhofer has made me think that a far simpler approach is available, moving the 'generation power' into d//1:
:- module(hh_mm,
[hh_mm/2
,hh_mm//1
]).
hh_mm(T,S) :- phrase(hh_mm(T),S).
hh_mm(time(H,M)) --> n2(H,23),":",n2(M,59).
n2(V,U) --> d(A),d(B), { V is A*10+B, V>=0, V=<U }.
d(V) --> [C], { member(V,[0,1,2,3,4,5,6,7,8,9]), C is V+0'0 }.
time_string(time(H,M),String)
:-
hour(H) ,
minute(M) ,
number_string(H,Hs) ,
number_string(M,Ms) ,
string_concat(Hs,":",S) ,
string_concat(S,Ms,String)
.
hour(H) :- between(0,11,H) .
minute(M) :- between(0,59,M) .
/*
?- time_string(time(10,30),B).
B = "10:30".
?- time_string(time(H,M),"10:30").
H = 10,
M = 30 ;
false.
?- time_string(time(H,M),S).
H = M, M = 0,
S = "0:0" ;
H = 0,
M = 1,
S = "0:1" ;
H = 0,
M = 2,
S = "0:2" ;
H = 0,
M = 3,
S = "0:3" %etc.
*/
Yet another answer, avoiding DCGs as overkill for this task. Or rather, the two separate tasks involved here: Not every relation can be expressed in a single Prolog predicate, especially not every relation on something as extra-logical as SWI-Prolog's strings.
So here is the solution for one of the tasks, computing strings from times (this is your code renamed):
time_string_(time(H,M), String) :-
number_string(H,Hour),
number_string(M,Min),
string_concat(Hour,":",S),
string_concat(S,Min,String).
For example:
?- time_string_(time(11, 59), String).
String = "11:59".
Here is a simple implementation of the opposite transformation:
string_time_(String, time(H, M)) :-
split_string(String, ":", "", [Hour, Minute]),
number_string(H, Hour),
number_string(M, Minute).
For example:
?- string_time_("11:59", Time).
Time = time(11, 59).
And here is a predicate that chooses which of these transformations to use, depending on which arguments are known. The exact condition will depend on the cases that can occur in your application, but it seems reasonable to say that if the string is indeed a string, we want to try to parse it:
time_string(Time, String) :-
( string(String)
-> % Try to parse the existing string.
string_time_(String, Time)
; % Hope that Time is a valid time term.
time_string_(Time, String) ).
This will translate both ways:
?- time_string(time(11, 59), String).
String = "11:59".
?- time_string(Time, "11:59").
Time = time(11, 59).
I have this code:
function createRect(x, y, w, h)
local rect = {
type = "rect",
x = x,
y = y,
w = w,
h = h,
translate = function(rect, vector)
assert(vector.type == "vector2d")
local rect = shapes.createRect(rect.x + vector.x, rect.y + vector.y, rect.w, rect.h)
end,
}
return rect
end
translate = function(rect, vector)
assert(vector.type == "vector2d")
local rect = shapes.createRect(rect.x + vector.x, rect.y + vector.y, rect.w, rect.h)
end
local o = createRect(2,3,4,5)
local q = createRect(2,3,4,5)
print(o.translate, q.translate, translate)
Which is some very easy code and is written to test factory functions in Lua and is very reminiscent of the JS module pattern. Something people usually complain about when talking about factory functions is the memory footprint.
Because o and q are just assigned, of course they have different translate() functions, I assumed.
However I was proven wrong:
function: 0x7fcdbe600d50 function: 0x7fcdbe600d50 function: 0x7fcdbe600d90
Why is this? How can this even be? I assumed to be o.translate and q.translate to be different functions, however they are the same...
How can this even be? I assumed to be o.translate and q.translate to be different functions, however they are the same...
Normally you are correct, however Lua 5.2 introduced an optimization where anonymous functions may be cached if certain conditions are met. Specifically, if the values it references doesn't change between construction then the first created instance of that anonymous function gets reused.
Running your example in repl.it, Lua 5.1, shows this as one possible output:
function: 0xb81f30 function: 0xb81f00 function: 0xb82ca0
But running it under melpon.org/wandbox, Lua 5.2+, shows:
function: 0x14f0650 function: 0x14f0650 function: 0x14efb40
In your example, createRect creates and returns a different rect table for every call but the field rect.translate is being assigned the same anonymous function as the lua value due to this optimization.
Also see
http://lua-users.org/lists/lua-l/2010-07/threads.html#00339
http://lua-users.org/lists/lua-l/2010-07/msg00862.html
http://lua-users.org/lists/lua-l/2010-05/threads.html#00617
I am trying to implement the Regularized Logistic Regression Algorithm, using the fminunc() function in Octave for minimising the cost function. As generally advised, I would like to plot the cost function as a function of iterations of the fminunc() function. The function call looks as follows -
[theta, J, exit_flag] = ...
fminunc(#(t)(costFunctionReg(t, X, y, lambda)), initial_theta, options);
with
options = optimset('GradObj', 'on', 'MaxIter', 400, 'OutputFcn',#showJ_history);
[showJ-history is the intended output function; I hope I have set the options parameter correctly].
But, I can't find good sources on the internet highlighting how to write this output function, specifically, what parameters are passed to it by the fminunc(), what it returns (if anything in particular required by the fminunc()).
Could someone please mention some helpful links or assist me in writing the output function.
I think you can refer to the source code. Consider also this example:
1;
function f = __rosenb (x)
# http://en.wikipedia.org/wiki/Rosenbrock_function
n = length (x);
f = sumsq (1 - x(1:n-1)) + 100 * sumsq (x(2:n) - x(1:n-1).^2);
endfunction
function bstop = showJ_history(x, optv, state)
plot(optv.iter, optv.fval, 'x')
# setting bstop to true stops optimization
bstop = false;
endfunction
opt = optimset('OutputFcn', #showJ_history);
figure()
xlabel("iteration")
ylabel("cost function")
hold on
[x, fval, info, out] = fminunc (#__rosenb, [5, -5], opt);