为什么每次运行循环时都没有得到相同的结果？

我正在做一些信号处理，并且在Python中有一个循环，其中我试图通过参数alpha优化结果ln（B）。当我运行它时，我得到的最佳alpha值为-0.8，但是当我在常规代码中使用它时，它给出的结果与循环给出的结果不同。为了清楚起见，常规代码的结果为〜4，而alpha = -0.8的循环结果为〜5。

我认为这很奇怪，因此在两次检查过程都相同之后，我对一个alpha = -0.8实例运行了循环代码，并得到与常规代码4相同的结果，而不是循环的结果在一个范围内迭代时给我的alpha = -0.8。然后，我遍历列表中的代码[-0.8,-0.8,-0.8, -0.8, -0.8, -0.8]，发现对于第一个-0.8，我得到4，然后对于每个后续的我得到5。

是否有某些原因使循环会给我第一项与其他项不同的答案或过程？我一直盯着它看了好几个小时，却找不到我的错误。我以为也许我没有重置变量，但是差异只发生在第一次迭代和其余的迭代之间，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)) 

和输出：

Run 1, alpha = -0.8
var_n =  (1.1668471858083481e-14+0j)

/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:72: ComplexWarning: Casting complex values to real discards the imaginary part

!!!!!NEW MAX:  alpha = -0.8 with peak 4.115523906554817 at time [1.0625] and frequency [72.]
Peak lnB = 4.115523906554817 at time [1.0625] and frequency [72.]. Time: 0.0 min 0.3648514747619629 s
Run 2, alpha = -0.8
var_n =  (3.664163403845884e-14+0j)
!!!!!NEW MAX:  alpha = -0.8 with peak 5.330720524005124 at time [1.0625] and frequency [72.]
Peak lnB = 5.330720524005124 at time [1.0625] and frequency [72.]. Time: 0.0 min 0.702958345413208 s
Run 3, alpha = -0.8
var_n =  (3.664163403845884e-14+0j)
Peak lnB = 5.330720524005124 at time [1.0625] and frequency [72.]. Time: 0.0 min 1.0434083938598633 s
Run 4, alpha = -0.8
var_n =  (3.664163403845884e-14+0j)
Peak lnB = 5.330720524005124 at time [1.0625] and frequency [72.]. Time: 0.0 min 1.375929832458496 s
Run 5, alpha = -0.8
var_n =  (3.664163403845884e-14+0j)
Peak lnB = 5.330720524005124 at time [1.0625] and frequency [72.]. Time: 0.0 min 1.7248213291168213 s
Run 6, alpha = -0.8
var_n =  (3.664163403845884e-14+0j)
Peak lnB = 5.330720524005124 at time [1.0625] and frequency [72.]. Time: 0.0 min 2.0683481693267822 s
lnB is optimized at alpha = -0.8 with peak 5.330720524005124 at time [1.0625] and frequency [72.], var_n = (3.664163403845884e-14+0j)
Run took 0.0 min, 2.069751739501953 s

（我忽略了该错误，因为复数值仅包含实部）。

对我来说很明显，变量var_n发生了变化，而这正是我得出的结果。我只是不确定为什么它只在第一次迭代中有所不同。

我的所有变量定义的完整代码如下，我只是不想使上面的代码混乱：

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))                 

E是我的信号的能量，spec_H1 / spec_L1是我的信号的频谱图，总而言之，我试图找到在特定时间和频率范围内信号的贝叶斯因子。