如何根据自己的类型对 3D 数组的特定维度中的特征进行归一化

Question

我有一个 3D 数组 (1883,100,68) 作为 (batch,step,features)。

68个特征是energy和mfcc等完全不同的特征

我希望根据自己的类型规范化特征。

scaler = StandardScaler()
X_train = scaler.fit_transform(X_train.reshape(X_train.shape[0], -1)).reshape(X_train.shape)
X_test = scaler.transform(X_test.reshape(X_test.shape[0], -1)).reshape(X_test.shape)
print(X_train.shape)
print(max(X_train[0][0]))
print(min(X_train[0][0]))

显然，将其转换为二维数组是行不通的，因为每个特征都针对所有 6800 个特征进行了归一化。这导致 all 100 步中的多个特征变为零。

我要找的例如，特征[0]是能量。对于一个批次，由于有 100 个步骤，因此有 100 个能量值。我希望这 100 个能量值在它们自身内归一化。

所以应该在[1,1,0],[1,2,0],[1,3,0]...[1,100,0]之间进行归一化。所有其他功能相同。

我该如何处理？

更新：

以下代码是在 sai 的帮助下生成的。

def feature_normalization(x):
    batches_unrolled = np.expand_dims(np.reshape(x, (-1, x.shape[2])), axis=0)

    x_normalized = (x - np.mean(batches_unrolled, axis=1, keepdims=True)) / np.std(batches_unrolled, axis=1, keepdims=True)

    np.testing.assert_allclose(x_normalized[0, :, 0], (x[0, :, 0] - np.mean(x[:, :, 0])) / np.std(x[:, :, 0]))
    return x_normalized

def testset_normalization(X_train,X_test):
    batches_unrolled = np.expand_dims(np.reshape(X_train, (-1, x.shape[2])), axis=0)
    fitted_mean = np.mean(batches_unrolled, axis=1, keepdims=True)
    fitted_std = np.std(batches_unrolled, axis=1, keepdims=True)
    X_test_normalized = (X_test - fitted_mean) / fitted_std
    return X_test_normalized

Answer 1

批量对所有样本的特征进行独立归一化-

展开批处理样本以获得 [10(时间步长)*batch_size] x [40 个特征] 矩阵
获取每个特征的均值和标准差
对实际批处理的样本执行逐元素归一化

import numpy as np

x = np.random.random((20, 10, 40))

batches_unrolled = np.expand_dims(np.reshape(x, (-1, 40)), axis=0)

x_normalized = (x - np.mean(batches_unrolled, axis=1, keepdims=True)) / np.std(batches_unrolled, axis=1, keepdims=True)

np.testing.assert_allclose(x_normalized[0, :, 0], (x[0, :, 0] - np.mean(x[:, :, 0])) / np.std(x[:, :, 0]))

如何根据自己的类型对 3D 数组的特定维度中的特征进行归一化

How to normalize features in a specific dimension of a 3D array with respect to their own type

python

arrays

numpy

machine-learning

normalization