I have the following code
# Random Strategy Selection: ramdomly choose a strategy within each player's strategy profile for each game in the input list
def RandomStrategySelection(): # Return the vectors with the selected
P = pool_of_games
j=10 #number of iterations
s=1 #current round
random_strategy=[] #combination of randomly chosen strategies for each player
random_pool=[] #pool of selected random strategies for the input vector games
rp=random_pool
while s<=j:
for game in range (0, len(P)):
p1=random.choice(P[game][0][0:3]) #random choice within p1(row)'s strategy profile
p2=random.choice(P[game][0][4:8]) #random choice within p2(column)'s strategy profile
random_strategy=[p1,p2]
random_pool.append(random_strategy)
s=s+1
return(rp)
def FitnessEvaluation(): # Return the rank of fitness of all evaluated games
for game in range (0,len(rp)):
pf1=rp[game][0]
pf2=rp[game][1]
fitness=payoff1+payoff2
return(fitness)
#fitness: f(G)=(F(G)*j+s)/j - F(G)=pf1+pf2
RandomStrategySelection generates a list of objects, such as
[[0,2][3,1]]
FitnessEvaluation is supposed to use that list, but I cant get it running. FitnessEvaluation doesn't seem to recognize the list created, even after I stored it in the rp variable. Any thoughts? Thanks!
You stored it in a local rp variable. This is distinct from the local rp variable in RandomStrategySelection.
The standard way to handle this is to save the return value (in the calling program) and pass it to the next one, such as:
pool = RandomStrategySelection()
how_fit = FitnessEvaluation(pool)
... and give the second function a signature that declares the parameter:
FitnessEvaluation(rp):
for game in range (0,len(rp)):
....
Related
I'm trying to pass a geopandas.overlay() to multiprocessing to speed it up.
I have used custom functions and functools to partially fill function inputs and then pass the iterative component to the function to produce a series of dataframes that I then concat into one.
def taska(id, points, crs):
return make_break_points((vms_points[points.ID == id]).reset_index(drop=True), crs)
points_gdf = geodataframe of points with an id field
grid_gdf = geodataframe polygon grid
partialA = functools.partial(taska, points=points_gdf, crs=grid_gdf.crs)
partialA_results =[]
with Pool(cpu_count()-4) as pool:
for results in pool.map(partialA, list(points_gdf.ID.unique())):
partialA_results.append(results)
bpts_gdf = pd.concat(partialA_results)
In the example above I use the list of unique values to subset the df and pass it to a processor to perform the function and return the results. In the end all the results are combined using pd.concat.
When I apply the same approach to a list of dataframes created using numpy.array_split() the process starts with a number of processors, then they all close and everything hangs with no indication that work is being done or that it will ever exit.
def taskc(tracks, grid):
return gpd.overlay(tracks, grid, how='union').explode().reset_index(drop=True)
tracks_gdf = geodataframe of points with an id field
dfs = np.array_split(tracks_gdf, (cpu_count()-4))
grid_gdf = geodataframe polygon grid
partialC_results = []
partialC = functools.partial(taskc, grid=grid_gdf)
with Pool(cpu_count() - 4) as pool:
for results in pool.map(partialC, dfs):
partialC_results.append(results)
results_df = pd.concat(partialC_results)
I tried using with get_context('spawn').Pool(cpu_count() - 4) as pool: based on the information here https://pythonspeed.com/articles/python-multiprocessing/ with no change in behavior.
Additionally, if I simply run geopandas.overlay(tracks_gdf, grid_gdf) the process is successful and the script carries on to the end with expected results.
Why does the partial function approach work on a list of items but not a list of dataframes?
Is the numpy.array_split() not an iterable object like a list?
How can I pass a single df into geopandas.overlay() in chunks to utilize multiprocessing capabilities and get back a single dataframe or a series of dataframes to concat?
This is my work around but am also interested if there is a better way to perform this and similar tasks. Essentially, modified the partial function so the df split is moved to the partial function then I create a list of values from range() as my iteral.
def taskc(num, tracks, grid):
return gpd.overlay(np.array_split(tracks, cpu_count()-4)[num], grid, how='union').explode().reset_index(drop=True)
partialC = functools.partial(taskc, tracks=tracks_gdf, grid=grid_gdf)
dfrange = list(range(0, cpu_count() - 4))
partialC_results = []
with get_context('spawn').Pool(cpu_count() - 4) as pool:
for results in pool.map(partialC, dfrange):
partialC_results.append(results)
results_gdf = pd.concat(partialC_results)
I wrote a function that acts on each combination of columns in an input matrix. It uses multiple for loops and is very slow, so I am trying to parallelize it to use the maximum number of threads on my computer.
I am having difficulty finding the correct syntax to set this up. I'm using the Parallel package in octave, and have tried several ways to set up the calls. Here are two of them, in a simplified form, as well as a non-parallel version that I believe works:
function A = parallelExample(M)
pkg load parallel;
# Get total count of columns
ct = columns(M);
# Generate column pairs
I = nchoosek([1:ct],2);
ops = rows(I);
slice = ones(1, ops);
Ic = mat2cell(I, slice, 2);
## # Non-parallel
## A = zeros(1, ops);
## for i = 1:ops
## A(i) = cmbtest(Ic{i}, M);
## endfor
# Parallelized call v1
A = parcellfun(nproc, #cmbtest, Ic, {M});
## # Parallelized call v2
## afun = #(x) cmbtest(x, M);
## A = parcellfun(nproc, afun, Ic);
endfunction
# function to apply
function P = cmbtest(indices, matrix)
colset = matrix(:,indices);
product = colset(:,1) .* colset(:,2);
P = sum(product);
endfunction
For both of these examples I generate every combination of two columns and convert those pairs into a cell array that the parcellfun function should split up. In the first, I attempt to convert the input matrix M into a 1x1 cell array so it goes to each parallel instance in the same form. I get the error 'C must be a cell array' but this must be internal to the parcellfun function. In the second, I attempt to define an anonymous function that includes the matrix. The error I get here specifies that 'cmbtest' is undefined.
(Naturally, the actual function I'm trying to apply is far more complex than cmbtest here)
Other things I have tried:
Put M into a global variable so it doesn't need to be passed. Seemed to be impossible to put a global variable in a function file, though I may just be having syntax issues.
Make cmbtest a nested function so it can access M (parcellfun doesn't support that)
I'm out of ideas at this point and could use help figuring out how to get this to work.
Converting my comments above to an answer.
When performing parallel operations, it is useful to think of each parallel worker that will result as separate and independent octave instances, which need to have appropriate access to all functions and variables they will require in order to do their independent work.
Therefore, do not rely on subfunctions when calling parcellfun from a main function, since this might lead to errors if the worker is unable to access the subfunction directly under the hood.
In this case, separating the subfunction into its own file fixed the problem.
Say that I have a list (or array) that links Suppliers with the materials they supply. For example, an array of the form
[[Supplier_1, Material_a], [Supplier_2, Material_a], [Supplier_3, Material_a], [Supplier_1, Material_b], [Supplier_2, Material_c], [Supplier_3, Material_b], ...]
I am interested in finding the the list of suppliers that supply at least k materials that a particular supplier say Supplier_1 supplies.
One way that I can think of is to pair all suppliers with Supplier_1 for each material Supplier_1 supplies
[[Supplier_1, Supplier_2, Material_a], [Supplier_1, Supplier_3, Material_a], [Supplier_1, Supplier_3, Material_b]...]
and then count the number of times each pair is present
[[Supplier_1, Supplier_2, 1], [Supplier_1, Supplier_3, 2]...]
The problem is that this approach can be very time consuming since the list provided can be quite long. I was wondering if there is a better way to do this.
You would put the materials of Supplier_1 in a hash set, so that you can verify for any material whether it is supplied by Supplier_1 in constant time.
Once you have that you can iterate the data again, and in a dictionary (hash map) keep a count per supplier which you increment each time the material is in the above mentioned set.
In Python it would look like this:
def getsuppliers(pairs, selected_supplier, k):
materialset = set()
countmap = {} # a dictionary with <key=supplier, value=count> pairs
for supplier, material in pairs:
if supplier == selected_supplier:
materialset.add(material)
countmap[supplier] = 0
# An optional quick exit: if the selected provider does not have k materials,
# there is no use in continuing...
if countmap[selected_supplier] < k:
return [] # no supplier meets the requirement
for supplier, material in pairs:
if material in materialset:
countmap[supplier] = countmap[supplier]+1
result = []
for supplier, count in countmap.items():
if count >= k:
result.append(supplier)
return result
NB: this would include the selected supplier also, provided it has at least k materials.
All operations within each individual loop body, have a constant time complexity, so the overall time complexity is O(n), where n is the size of the input list (pairs).
I am relatively new to Modelica (Dymola-environment) and I am getting very desperate/upset that I cannot solve such a simple problem as a random number generation in Modelica and I hope that you can help me out.
The simple function random produces a random number between 0 and 1 with an input seed seedIn[3] and produces the output seed seedOut[3] for the next time step or event. The call
(z,seedOut) = random(seedIn);
works perfectly fine.
The problem is that I cannot find a way in Modelica to compute this assignment over time by using the seedOut[3] as the next seedIn[3], which is very frustrating.
My simple program looks like this:
*model Randomgenerator
Real z;
Integer seedIn[3]( start={1,23,131},fixed=true), seedOut[3];
equation
(z,seedOut) = random(seedIn);
algorithm
seedIn := seedOut;
end Randomgenerator;*
I have tried nearly all possibilities with algorithm assignments, initial conditions and equations but none of them works. I just simply want to use seedOut in the next time step. One problem seems to be that when entering into the algorithm section, neither the initial conditions nor the values from the equation section are used.
Using the 'sample' and 'reinit' functions the code below will calculate a new random number at the frequency specified in 'sample'. Note the way of defining the "start value" of seedIn.
model Randomgenerator
Real seedIn[3] = {1,23,131};
Real z;
Real[3] seedOut;
equation
(z,seedOut) = random(seedIn);
when sample(1,1) then
reinit(seedIn,pre(seedOut));
end when;
end Randomgenerator;
The 'pre' function allows the use of the previous value of the variable. If this was not used, the output 'z' would have returned a constant value. Two things regarding the 'reinint' function, it requires use of 'when' and requires 'Real' variables/expressions hence seedIn and seedOut are now defined as 'Real'.
The simple "random" generator I used was:
function random
input Real[3] seedIn;
output Real z;
output Real[3] seedOut;
algorithm
seedOut[1] :=seedIn[1] + 1;
seedOut[2] :=seedIn[2] + 5;
seedOut[3] :=seedIn[3] + 10;
z :=(0.1*seedIn[1] + 0.2*seedIn[2] + 0.3*seedIn[3])/(0.5*sum(seedIn));
end random;
Surely there are other ways depending on the application to perform this operation. At least this will give you something to start with. Hope it helps.
Alright, someone must know easier ways to do this than me.
I'm trying to write a random number generator using a fairly common formula.
--Random Number Generator
local X0=os.time()
local A1=710425941047
local B1=813633012810
local M1=711719770602
local X1=(((A1*X0)+B1)%M1)
local X2=(((A1*X1)+B1)%M1) --then I basically take the vaiable X1 and feed
--it back into itself.
print(X2)
local X3=(((A1*X2)+B1)%M1)
print(X3)
local X4=(((A1*X3)+B1)%M1)
print(X4)
local X5=(((A1*X4)+B1)%M1)
print(X5)
local X6=(((A1*X5)+B1)%M1)
print(X6)
local X7=(((A1*X6)+B1)%M1)
print(X7)
Etc Etc.
Does anybody know a faster way to do this?
I would love to be able to fit it into something along the lines of a:
for i=1,Number do
local X[loop count]=(((A1*X[Loop count-1])+B1)%M1)
math.randomseed(X[loop count])
local roll=math.random(1,20)
print("You rolled a "..roll)
end
io.read()
Type of string.
I'm using it to generate random numbers for pieces of track I'm making in a tabletop game.
Example hunk of code:
if trackclass == "S" then
for i=1,S do --Stated earlier that S=25
local roll=math.random(1,5)
local SP=math.random(1,3)
local Count= roll
if Count == 1 then
local Track = "Straightaway"
p(Track.." Of SP "..SP)
else
end
if Count == 2 then
local Track = "Curve"
p(Track.." of SP "..SP)
else
end
if Count == 3 then
local Track = "Hill"
p(Track.." of SP "..SP)
else
end
if Count == 4 then
local Track = "Water"
p(Track.." of SP "..SP)
else
end
if Count == 5 then
local Track = "Jump"
p(Track.." of SP "..SP)
else
end
end
end
Unfortunately this seems to generate a pretty poor set of random number distribution when I use it and I would really like it to work out better. Any possible assistance in fleshing out the variable loop cycle would be greatly appreciated.
Even something like a call so that every time math.random() is called, it adds one to the X[loop count]] so that every generated number is actually a better pseudo-random number distribution.
Please forgive my semi-rambling. My mind is not necessarily thinking in order right now.
Does anybody know a faster way to do this?
Each XN in the expression is always the previous X, so just restructure the code to use the previous X rather than creating new ones:
local X = os.time()
local A1 = 710425941047
local B1 = 813633012810
local M1 = 711719770602
function myrandomseed(val)
X = val
end
function myrandom()
X = (A1 * X + B1) % M1
return X
end
Now you can call myrandom to your heart's content:
for i=1,100 do
print(myrandom())
end
Another way of packaging it, to avoid static scope, would be generating random number generators as closures, which bind to their state variables:
function getrandom(seed)
local X = seed or os.time()
local A1 = 710425941047
local B1 = 813633012810
local M1 = 711719770602
return function()
X = (A1 * X + B1) % M1
return X
end
end
Now you call getrandom to get a random number generator for a given seed:
local rand = getrandom()
for i=1,100 do
print(rand())
end
I would love to be able to fit it into something along the lines of a:
math.randomseed(X[loop count])
local roll=math.random(1,20)
If you're calling randomseed every time you call random, you're not using Lua's (i.e. C's) random number generator at all. You can see why this is true by looking at myrandomseed above. Why are you funneling your numbers through Lua's random in the first place? Why not just use math.random and be done with it.
Just make to sure to call math.randomseed once rather than every time you call math.random and you'll be fine.
I'm using it to generate random numbers for pieces of track I'm making in a tabletop game. Example hunk of code:
When you see tons of nearly identical code, you should refactor it. When you see variables names like foo1, foo2, etc. you're either naming variables poorly or should be using a list. In your case you have a bunch of branches Count == 1, Count == 2, etc. when we could be using a list. For instance, this does the same thing as your code:
local trackTypes = { 'Straightaway', 'Curve', 'Hill', 'Water', 'Jump' }
for i=1,S do
local trackTypeIndex = math.random(1, #trackTypes)
local SP = math.random(1, 3)
p(trackTypes[trackTypeIndex]..' of SP '..SP)
end
Note that you can probably guess what trackTypes is just by reading the variable name. I have no idea what S and SP are. They are probably not good names.