How could I translate this Python code to Matlab?

I have this python code that I would like to convert to Matlab code. Could anyone help me understand what is going on and help convert from one language to the other?

The code is as follows:

import random

N = 15
L = 10
sigma = 0.075
n_runs = 800
    for runs in range(n_runs):
        y = [random.uniform(0.0, L - 2 * N * sigma) for k in range(N)]
        y.sort()
        print [y[i] + (2 * i + 1) * sigma for 1 in range (N)]|

Much thanks to anyone who can assist me.

Update: I updated the code, if anyone can help, that would be great.

[Gaëtan Poirier]

Could anyone help with the new code? The distribution is not correct.

[Audumbar Dhage]

I have Python code i want to convert it into matlab

Answer 1

[Andrei Bobrov]

N = 15;
L = 10;
sigma = 0.075;
n_configs = 100;
rejections = 0     ; 
x = zeros(N,n_configs);
for config = 1:n_configs
    while 1
        x(:,config) = sort((L-2*sigma)*rand(N,1));
        if min(diff(x(:,config))) > 2*sigma
            break
        end
    end
end

or

LL = linspace(0,L,N+1)';
x = (L/N - 2*sigma)*rand(N,n_configs) + LL(1:end-1) + sigma;

[Gaëtan Poirier]

Thank you so much for the answer! You may recognize this as a solution to the probability distribution to the random clothes-pins on a clothes-line problem (I hope!). However, I'm not getting the distribution I was hoping for. Both outputs are different.

[Audumbar Dhage]

I have to convert python code to matlab

Answer 2

[harshi yaduvanshi]

i have this question i want to convert this python code to matlab.

import random;
import pandas as pd
import numpy as np

def sqrt_sum(a,b): return round(np.sqrt(np.sum((np.array(a)-np.array(b))**2)),5)

def weighted(W,d): return (np.multiply(W,d))

def partition(X,N,L,R,W):

    #compute distances
    p=[[] for i in range(N)]
    w_dist=[]

    for i in range(N):
        for j in range(L):
            dj=sqrt_sum(X.ix[i].tolist(),R[j])

            p[i].append(dj)
        wj=sum(weighted(W,p[i]))
        w_dist.append(wj)

    max_d=max(w_dist)
    min_d=min(w_dist)
    interval_length=(max_d - min_d)/L

#find ranges ranges=[] ranges.append(min_d) #find all ranges for j in range(L): rangej=min_d + interval_length min_d=rangej ranges.append(rangej)

    X=X.values.tolist()
    #now put the elements into range intancess
    #############################################
    pj=[[] for i in range(L)]
    for i in range(N):
        for j in range(len(ranges)-1):
            if w_dist[i]>=ranges[j] and w_dist[i]<=ranges[j+1]:
                pj[j].append(X[i])
    return ranges,pj

def Search_phase(X,N,L,R,W,K,Q,ranges,pj): dq=0 knn=[] for j in range(L): d=sqrt_sum(Q,R[j]) dq=dq+weighted(W[j],d) for i in range(len(ranges)-1): if dq>=ranges[i] and dq<=ranges[i+1]: distance=[] for j in range(len(pj[i])): distance.append(float(sqrt_sum(Q,pj[i][j]))) distance=np.asarray(distance) lists=distance.argsort()[:5] for x in lists: knn.append(pj[i][x])

    return knn

def fetching_data(): X=pd.read_csv("IRIS(2).csv",header=None) N=len(X) L=4 R = X.sample(L)

    R=R.values.tolist()
    W=[0.1,0.3,0.8,0.2]

    K=5
    Q=[0.196667,0.166667,0.389831,0.375000]
  
    return X,N,L,R,W,K,Q

X,N,L,R,W,K,Q=fetching_data() ranges,pj=partition(X,N,L,R,W) KNN=Search_phase(X,N,L,R,W,K,Q,ranges,pj) print("top k-nn are",KNN)