Python 为什么每次运行循环时都得不到相同的结果？

我在做一些信号处理，我在Python中有一个循环，我试图在参数alpha上优化结果ln（B）。当我运行它时，我得到了一个最佳的alpha值-0.8，但是当我在常规代码中使用它时，它给了我一个不同于循环给我的结果。为了清楚起见，常规代码的结果是~4，而alpha=-0.8的循环结果是~5

我觉得这很奇怪，所以在反复检查每种情况下的过程都是相同的之后，我在alpha=-0.8的一个实例上运行了循环代码，得到了与常规代码4相同的结果，而不是循环在某个范围内迭代时在alpha=-0.8给出的结果。然后，我在列表中迭代代码，

[-0.8，-0.8，-0.8，-0.8，-0.8]

，发现对于第一个-0.8，我得到了4，然后对于后续的每一个，我得到了5

对于第一项，循环会给我与其他项不同的答案或过程，这有什么原因吗？我盯着它看了好几个小时，却找不到我的错误。我想可能我没有重置变量，但差异只发生在第一次迭代和其余迭代之间，而不是每次迭代

这是我的循环：

alpharange = [-0.8,-0.8,-0.8, -0.8, -0.8, -0.8]

for alpha in alpharange:
  run += 1
  print("Run {0}, alpha = {1}".format(run, alpha))

  #find var_n
  denominator = 0 
  r = np.arange(1,16)
  for n in r:
    if n*fecho <= fmax: 
        denominator += (n*fecho)**(2-2*alpha)/(var_f[n*nf])**2
  var_n = numerator/denominator 
  print("var_n = ", var_n)

  #find lnB
  YB_ln = np.zeros(spec_H1.shape)
  B_t = np.arange(0,len(YB_ln[0,:]),1) #length of a row
  B_f = np.arange(0,len(YB_ln[:,0]),1) #length of a column 

  for nt in tzoomindx:
    for nf in fzoomindx:
      for n in r:
          if n*nf<realfreqs.size-1:
              f = realfreqs[n*nf]
              YB_ln[nf,nt] += (np.abs(2*np.real(spec_H1[n*nf,nt]*np.conj(spec_L1[n*nf,nt])) + np.abs(spec_H1[n*nf,nt])**2 + np.abs(spec_L1[n*nf,nt])**2 ))/(1/(var_n*(1/f**alpha)) + 1/var_f[n*nf]) - np.log(1 + (var_n*(1/f**alpha))/var_f[n*nf])    
  peak = np.max(YB_ln)
  peakcoord = np.where((YB_ln==peak))

  if peak > lnB_max:
    alpha_max = alpha
    lnB_max = peak
    time_max = t[peakcoord[1]]-tinterval
    freq_max = realfreqs[peakcoord[0]]
    var_n_max = var_n
    print("!!!!!NEW MAX:  alpha = {0} with peak {1} at time {2} and frequency {3}".format(alpha_max,lnB_max,time_max,freq_max))

  alphalist.append(alpha)
  lnBlist.append(peak)
  timelist.append(t[peakcoord[1]]-tinterval)
  freqlist.append(realfreqs[peakcoord[0]])
  print("Peak lnB = {0} at time {1} and frequency {2}. Time: {3} min {4} s".format(peak,t[peakcoord[1]]-tinterval,realfreqs[peakcoord[0]],(time.time()-start_time)//60,(time.time()-start_time)%60))

print("lnB is optimized at alpha = {0} with peak {1} at time {2} and frequency {3}, var_n = {4}".format(alpha_max,lnB_max,time_max,freq_max,var_n_max))
print("Run took {0} min, {1} s".format((time.time()-start_time)//60,(time.time()-start_time)%60)) 

（我忽略了这个错误，因为复杂值只有一个实部）

我很清楚，变量var_n在变化，这就是我的结果。我只是不知道为什么只有在第一次迭代中才会有所不同

我所有变量定义的完整代码如下所示，我只是不想把上面的定义弄得乱七八糟：

start_time = time.time()

#find the energy over the leading seconds

dt = t[1] - t[0]

inspiral = np.where((t-tinterval >= -2) & (t-tinterval <= 0))
Einspiral_sum = np.sum(E[inspiral])*dt

print("Summed E_inspiral = ", Einspiral_sum)

#find the 'constant'

noise = np.where((t-tinterval <= -5) & (t-tinterval >= -50))
Enoise = np.sum(E[noise])/(E[noise].size)

print("E_noise = ", Enoise)

#finding the variance 

var_f = var_f_same

fecho = 72 
nf = np.where((realfreqs==fecho))
nf = int(nf[0])
fmax = 300

tzoomindx = np.where((t-tinterval>=0.5)&(t-tinterval<=1.5))
tzoomindx = np.array(tzoomindx[0])
fzoomindx = np.where((realfreqs>=63)&(realfreqs<=92))
fzoomindx=np.array(fzoomindx[0])

upperbound = 0
lowerbound = -2
numerator = (Einspiral_sum - (Enoise*upperbound-Enoise*lowerbound))

run = 0
lnB_max = -1e5
lnBlist = []
alphalist = []
timelist = []
freqlist = []

#alpharange = np.arange(-1.5,1,0.1)
#alpharange = np.array([-0.5,-0.4,-0.3,-0.2,-0.1,0,0.1,0.2,0.3,0.4])
alpharange = [-0.8,-0.8,-0.8, -0.8, -0.8, -0.8]

for alpha in alpharange:
  run += 1
  print("Run {0}, alpha = {1}".format(run, alpha))

  #find var_n
  denominator = 0 
  r = np.arange(1,16)
  for n in r:
    if n*fecho <= fmax: 
        denominator += (n*fecho)**(2-2*alpha)/(var_f[n*nf])**2
  var_n = numerator/denominator #for Einspiral = sum

  #find lnB
  YB_ln = np.zeros(spec_H1.shape)
  B_t = np.arange(0,len(YB_ln[0,:]),1) #length of a row
  B_f = np.arange(0,len(YB_ln[:,0]),1) #length of a column 

  for nt in tzoomindx:
    for nf in fzoomindx:
      for n in r:
          if n*nf<realfreqs.size-1:
              f = realfreqs[n*nf]
              YB_ln[nf,nt] += (np.abs(2*np.real(spec_H1[n*nf,nt]*np.conj(spec_L1[n*nf,nt])) + np.abs(spec_H1[n*nf,nt])**2 + np.abs(spec_L1[n*nf,nt])**2 ))/(1/(var_n*(1/f**alpha)) + 1/var_f[n*nf]) - np.log(1 + (var_n*(1/f**alpha))/var_f[n*nf])    
  peak = np.max(YB_ln)
  peakcoord = np.where((YB_ln==peak))

  if peak > lnB_max:
    alpha_max = alpha
    lnB_max = peak
    time_max = t[peakcoord[1]]-tinterval
    freq_max = realfreqs[peakcoord[0]]
    var_n_max = var_n
    print("!!!!!NEW MAX:  alpha = {0} with peak {1} at time {2} and frequency {3}".format(alpha_max,lnB_max,time_max,freq_max))

  alphalist.append(alpha)
  lnBlist.append(peak)
  timelist.append(t[peakcoord[1]]-tinterval)
  freqlist.append(realfreqs[peakcoord[0]])
  print("Peak lnB = {0} at time {1} and frequency {2}. Time: {3} min {4} s".format(peak,t[peakcoord[1]]-tinterval,realfreqs[peakcoord[0]],(time.time()-start_time)//60,(time.time()-start_time)%60))

print("lnB is optimized at alpha = {0} with peak {1} at time {2} and frequency {3}, var_n = {4}".format(alpha_max,lnB_max,time_max,freq_max,var_n_max))
print("Run took {0} min, {1} s".format((time.time()-start_time)//60,(time.time()-start_time)%60))                 

start\u time=time.time（）
#找出前几秒的能量
dt=t[1]-t[0]
inspiral=np。其中（（t-tinterval>=-2）和（t-tinterval=0.5）和（t-tinterval=63）和（realfreqs可能，当您在fzoomindx中迭代
nf时，您会更改
nf
，该值用于计算
分母+=（n*fecho）**（2-2*alpha）/（var\u f[n*nf]）**2
..
当您在fzoomindx中迭代
nf时，可能会更改
nf
，该值用于计算
分母+=（n*fecho）**（2-2*alpha）/（var_f[n*nf]）**2
…
由于
变量
来自
分子
和
分母
，请尝试打印它们，并查看哪些正在更改。您正在重用常量“nf”作为for循环的变量并覆盖它。哦，您也看到了：）@michael Butscherth这是一个很好的代码量，你能提供一个吗？既然
var\u n
来自
分子
和
分母
，试着打印它们，看看哪一个在变化。你将常数“nf”重新用作for循环的变量并覆盖它。哦，你也看到了：）@michael Butscherth这是一个很好的代码量，你能提供一个吗？
start_time = time.time()

#find the energy over the leading seconds

dt = t[1] - t[0]

inspiral = np.where((t-tinterval >= -2) & (t-tinterval <= 0))
Einspiral_sum = np.sum(E[inspiral])*dt

print("Summed E_inspiral = ", Einspiral_sum)

#find the 'constant'

noise = np.where((t-tinterval <= -5) & (t-tinterval >= -50))
Enoise = np.sum(E[noise])/(E[noise].size)

print("E_noise = ", Enoise)

#finding the variance 

var_f = var_f_same

fecho = 72 
nf = np.where((realfreqs==fecho))
nf = int(nf[0])
fmax = 300

tzoomindx = np.where((t-tinterval>=0.5)&(t-tinterval<=1.5))
tzoomindx = np.array(tzoomindx[0])
fzoomindx = np.where((realfreqs>=63)&(realfreqs<=92))
fzoomindx=np.array(fzoomindx[0])

upperbound = 0
lowerbound = -2
numerator = (Einspiral_sum - (Enoise*upperbound-Enoise*lowerbound))

run = 0
lnB_max = -1e5
lnBlist = []
alphalist = []
timelist = []
freqlist = []

#alpharange = np.arange(-1.5,1,0.1)
#alpharange = np.array([-0.5,-0.4,-0.3,-0.2,-0.1,0,0.1,0.2,0.3,0.4])
alpharange = [-0.8,-0.8,-0.8, -0.8, -0.8, -0.8]

for alpha in alpharange:
  run += 1
  print("Run {0}, alpha = {1}".format(run, alpha))

  #find var_n
  denominator = 0 
  r = np.arange(1,16)
  for n in r:
    if n*fecho <= fmax: 
        denominator += (n*fecho)**(2-2*alpha)/(var_f[n*nf])**2
  var_n = numerator/denominator #for Einspiral = sum

  #find lnB
  YB_ln = np.zeros(spec_H1.shape)
  B_t = np.arange(0,len(YB_ln[0,:]),1) #length of a row
  B_f = np.arange(0,len(YB_ln[:,0]),1) #length of a column 

  for nt in tzoomindx:
    for nf in fzoomindx:
      for n in r:
          if n*nf<realfreqs.size-1:
              f = realfreqs[n*nf]
              YB_ln[nf,nt] += (np.abs(2*np.real(spec_H1[n*nf,nt]*np.conj(spec_L1[n*nf,nt])) + np.abs(spec_H1[n*nf,nt])**2 + np.abs(spec_L1[n*nf,nt])**2 ))/(1/(var_n*(1/f**alpha)) + 1/var_f[n*nf]) - np.log(1 + (var_n*(1/f**alpha))/var_f[n*nf])    
  peak = np.max(YB_ln)
  peakcoord = np.where((YB_ln==peak))

  if peak > lnB_max:
    alpha_max = alpha
    lnB_max = peak
    time_max = t[peakcoord[1]]-tinterval
    freq_max = realfreqs[peakcoord[0]]
    var_n_max = var_n
    print("!!!!!NEW MAX:  alpha = {0} with peak {1} at time {2} and frequency {3}".format(alpha_max,lnB_max,time_max,freq_max))

  alphalist.append(alpha)
  lnBlist.append(peak)
  timelist.append(t[peakcoord[1]]-tinterval)
  freqlist.append(realfreqs[peakcoord[0]])
  print("Peak lnB = {0} at time {1} and frequency {2}. Time: {3} min {4} s".format(peak,t[peakcoord[1]]-tinterval,realfreqs[peakcoord[0]],(time.time()-start_time)//60,(time.time()-start_time)%60))

print("lnB is optimized at alpha = {0} with peak {1} at time {2} and frequency {3}, var_n = {4}".format(alpha_max,lnB_max,time_max,freq_max,var_n_max))
print("Run took {0} min, {1} s".format((time.time()-start_time)//60,(time.time()-start_time)%60))