Question

我试图理解为什么我的算法的准确性突然发生了巨大变化。当我发现在标准化我的4维列车/测试集时我只使用了3个索引时，我做了一个小改动，我添加了一个:。现在我很好奇 - 下面的旧/新代码会不会这样做？如果没有，如何仅使用3个索引索引到4维数组？

旧

   # standardize all non-binary variables
   channels = 14 # int(X.shape[1])
   mu_f     = np.zeros(shape=channels)
   sigma_f  = np.zeros(shape=channels)

   for i in range(channels):
      mu_f[i]    = np.mean(X_train[:,i,:])
      sigma_f[i] = np.std(X_train[:,i,:])   

   for i in range(channels):
      X_train[:, i, :]  -= mu_f[i]   
      X_test[:, i, :]   -= mu_f[i]

      if (sigma_f[i] != 0):
         X_train[:, i, :]  /= sigma_f[i]
         X_test[:, i, :]   /= sigma_f[i]

新

   # standardize all non-binary variables
   channels = 14
   mu_f     = np.zeros(shape=channels)
   sigma_f  = np.zeros(shape=channels)

   for i in range(channels):
      mu_f[i]    = np.mean(X_train[:,i,:,:])
      sigma_f[i] = np.std(X_train[:,i,:,:])   

   for i in range(channels):
      X_train[:, i, :, :]  -= mu_f[i]   
      X_test[:, i, :, :]   -= mu_f[i]

      if (sigma_f[i] != 0):
         X_train[:, i, :, :]  /= sigma_f[i]
         X_test[:, i, :, :]   /= sigma_f[i]

Answer 1

我不明白为什么额外的:会产生影响。当我对简单的np.mean(X[:,1]) v np.mean(X,1,:,:]等进行时间测试时，它不会发生。

至于plonser's建议您可以对整个事物进行矢量化，关键是要意识到mean和std需要添加一些参数。检查他们的文档并使用样本数组。

Xmean = np.mean(X,axis=(0,2,3),keepdims=True)
X -= Xmean
X /= Xmean

使用3个索引切片4维数组时会发生什么？

1 个答案: