IndexError: index 10 is out of bounds for axis 0 with size 1

[vahid89]

Hello. I am posting for the first time on this site and I am asking my questions. I am implementing the following code and I will receive the error mentioned above. The original code is in MATLAB and I want to implement it in Python.Thank you for helping me to fix this error.

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import numpy as np from numpy import random        def fobj(X,Lb,Ub):           for s in range(3):         if X[s] > Ub[s]:             X[s] = Ub[s]         if X[s] < Lb[s]:             X[s] = Lb[s]     g = [[0 for m in range(1)]for b in range(4)]              g[0] =  1 - (X[1] **3 * X[2]) / (71785 * X[0] ** 4)     g[1] = (4 * X[1]** 2 - X[0] * X[1])/(12566 * (X[1] * X[0] ** 3 - X[0]**4)) + 1 / (51800 * X[0] ** 2) - 1     g[2] = 1 - (140.45 * X[0]) / (X[1] ** 2 * X[2])     g[3] = (X[0] + X[1]) / (1.5) - 1          # fit = np.zeros((1))     fit = (X[2] + 2) * X[1] * X[0] **2                   # print((X[2] + 2)) * X[1]                                                                                                    nou = 10 ** 9     penalty = 0     for n in range(4):         if g[n] > 0:             penalty = penalty + nou * g[n]    # print(X)                                         pfit = fit + penalty       return X,fit,pfit   def environment(TO,t):     envTO = np.zeros((20,3))     for i in range(20):                   c1 = random.randint(0,2,1)         c2 = random.randint(0,2,1)         c = c1 + c2 * ( 1 - t)         envTO[i,:] =(np.ones((1,nV)) - c *(np.random.random(nV)))* TO[i]     return envTO def Replacement(TO,Fit,PFit,TO_M,Fit_M,PFit_M):     nTO = 20     helpTO =np.concatenate((TO,TO_M),axis = 0)     helpFit = np.concatenate((Fit,Fit_M),axis = 1)     helpPFit = np.concatenate((PFit,PFit_M),axis = 1)     order = np.argsort(helpFit)     TO = np.take(helpTO, order)[0:nTO,:]     Fit= np.take(helpFit, order)[0:nTO]     PFit = np.take(helpPFit, order)[0:nTO]           return TO,Fit,PFit   def Memory(TO,Fit,PFit,TO_M,Fit_M,PFit_M):           nTO = 20     STO_M = 5     for i in range(nTO):         for j in range(STO_M):             if PFit[i] < PFit_M[j]:                 TO_M[j,:] = TO[i,:]                 Fit_M[j] = PFit[i]                 break         return  TO_M,Fit_M,PFit_M   def Newtonslaw(TO,envTO,Fit,PFit,p,t,Lb,Ub):     nTO = 20     nV = 3     order = np.argsort(PFit)     TO = np.take(TO, order)[:]     Fit = np.take(Fit, order)    # print(envTO[:,:])     PFit = np.take(PFit, order)     envTO = np.take(envTO, order)[:]     newTO = np.zeros((20,3))           for i in range(nTO):         beta = PFit[0,i] / PFit[0,nTO-1]                   if i <= nTO // 2:                          newTO[i,:]= envTO[nTO//2 + i,:] + (TO[i,:] -envTO[nTO // 2 + i,:]) * np.exp(beta * t)                           if np.random.random() < p:                 index = np.ceil(np.random.random() * nV)                 newTO[i,index] = Lb[index] + (Ub[index] - Lb[index]) * np.random.random()         else:             newTO[i,:] = envTO[ i - nTO // 2,:] + (TO[i,:] -envTO[i - nTO // 2,:] )* np.exp(-beta * t)             if np.random.random() < p:                 index = np.ceil(np.random.random() * nV)                 newTO[i,index] = Lb[index ] + (Ub[index] - Lb[index] ) * np.random.random()           return newTO                                   nV = 3 # Number of design variables. Lb = np.array( [0.05,0.25,2]) # Lower bounds of design variables. Ub= np.array([2,1.3,15]) # Upper bounds of design variables. nTO = 20 # Number of Thermal Objects. maxNFEs=20000 # Maximum Number of Objective Function Evaluations. p = 0.3 # With the probability of p a component of updated objects will be #regenerated randomly within the search space. STO_M = 5 # Size of the thermal objects memory.   TO = np.zeros((20,3)) zip_object = zip(Ub, Lb) difference = [] for Ub, Lb in zip_object:     difference.append(Ub-Lb) # append each difference to list Lb = np.array( [0.05,0.25,2]) Ub= np.array([2,1.3,15])       for j in range(nTO):     TO[j,:] += Lb + difference*np.random.rand(nV)           Fit =np.zeros((1,20)) PFit =np.zeros((1,20))   #X = np.zeros((1,3))   for k in range(nTO):     X,fit,pfit = fobj(TO[k,:],Lb,Ub)   #  print(fit)     TO[k,:] = X   #  print(X)     Fit[0,k] = fit   #  print(Fit)     PFit[0,k] = pfit  #   print(PFit)     #TO_M = np.zeros((5,3))    index = np.argsort(PFit,axis=1) TO_M = np.take(TO, index)[0:STO_M,:] Fit_M = np.take(Fit, index)[0:STO_M] PFit_M = np.take(PFit, index)[0:STO_M] NFEs = 0 NITs = 0 #Number of algorithm iterations maxNITs = maxNFEs / nTO   while NFEs < maxNFEs:     NITs += 1     t = NITs / maxNITs           envTO = environment(TO, t)     newTO = Newtonslaw (TO,envTO,Fit,PFit,p,t,Lb,Ub)     for i in range(nTO):         X,fit,pfit = fobj(newTO[i,:],Lb,Ub)         TO[i,:] = X         Fit[0,i] = fit         PFit[0,i] = pfit     NFEs = NFEs + nTO     TO,Fit,PFit=Replacement(TO,Fit,PFit,TO_M,Fit_M,PFit_M)     TO_M,Fit_M,PFit_M=Memory(TO,Fit,PFit,TO_M,Fit_M,PFit_M)     MinPFit=PFit_M[1]     MinFit=Fit_M[1]     bestTO=TO_M[1,:]

I get the error in this line of code:
newTO[i,:]= envTO[nTO//2 + i,:] + (TO[i,:] -envTO[nTO // 2 + i,:]) * np.exp(beta * t)

[deanhystad]

When posting questions about "Why do I get this error?" you should post the error. Not just the message, but the entire error trace with line numbers.

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for i in range(nTO):     beta = PFit[0,i] / PFit[0,nTO-1]                if i <= nTO // 2:                       newTO[i,:]= envTO[nTO//2 + i,:] + (TO[i,:] -envTO[nTO // 2 + i,:]) * np.exp(beta * t)

This code loops from i == 0 to i == 19.

When i == 10, your do this:

1	newTO[10,:]= envTO[10 + 10,:]...

But envTO was created to be this:

1	envTO = np.zeros((20,3))

20 is not a valid index. You can get envTO[19], but not env[20]

Maybe all you need to do is change your test:

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# from if i <= nTO // 2: # to if i < nTO // 2: