Convert python code to matlab code

I need to convert following python codes to Matlab. Can anyone help me to convert following codes.

for i in range(0,PopSize):
            #pos[i,:]=checkBounds(pos[i,:],lb,ub)
            for j in range(dim):
                pos[i, j] = numpy.clip(pos[i,j], lb[j], ub[j])
            #Calculate objective function for each particle
            fitness=objf(pos[i,:])

Answer 1

[Ameer Hamza]

Something like this

pos(pos < lb) = lb;
pos(pos > ub) = ub;

fitness = zeros(1,PopSize);
for i=1:PopSize
    fitness(i)=objf(pos(i,:))
end

Also, see this basic course to get started in MATLAB: https://www.mathworks.com/learn/tutorials/matlab-onramp.html

[Usman Taya]

Hi Ameer,

It show following error, when i implement in matlab

Unable to perform assignment because the left and right sides have a different number of elements.

[Ameer Hamza]

Can you share your objective function objf and the matrix pos?

[Usman Taya]

It is full code.

def PSO(objf,lb,ub,dim,PopSize,iters):

# PSO parameters

dim=2

iters=200

Vmax=6

PopSize=50 #population size

wMax=0.9

wMin=0.2

c1=2

c2=2

lb=[-10 -10]

ub=[10 10]

s=solution()

vel=numpy.zeros((PopSize,dim))

pBestScore=numpy.zeros(PopSize)

pBestScore.fill(float("inf"))

pBest=numpy.zeros((PopSize,dim))

gBest=numpy.zeros(dim)

gBestScore=float("inf")

pos = numpy.zeros((PopSize, dim))

for i in range(dim):

pos[:, i] = numpy.random.uniform(0,1, PopSize) * (ub[i] - lb[i]) + lb[i]

convergence_curve=numpy.zeros(iters)

############################################

print("PSO is optimizing \""+objf.__name__+"\"")

timerStart=time.time()

s.startTime=time.strftime("%Y-%m-%d-%H-%M-%S")

for l in range(0,iters):

for i in range(0,PopSize):

for j in range(dim):

pos[i, j] = numpy.clip(pos[i,j], lb[j], ub[j])

#Calculate objective function for each particle

fitness=objf(pos[i,:])

if(pBestScore[i]>fitness):

pBestScore[i]=fitness

pBest[i,:]=pos[i,:].copy()

if(gBestScore>fitness):

gBestScore=fitness

gBest=pos[i,:].copy()

#Update the W of PSO

w=wMax-l*((wMax-wMin)/iters);