如何在每个for循环中向数组添加新行，以便在python中创建矩阵（m，n）？

我正在从信号数据库中创建特征矩阵

我想计算一些特征，以得到一个矩阵。每行对应于每个信号，4列对应于每个评估特征

我已经搜索过了，但我不知道如何在评估特性时，为每个信号、每个for循环正确插入或添加一行具有特性的新行

这是我遵循的代码：

.mat文件已附加到此链接

这将创建一个列表，如下所示：

这很不方便，因为我需要一个形状为（n，4）的数组，即n=a（信号数）

这是期望的结果：

总之,

-如何将计算列表创建为大小为（n，5）的float64数组？

-如何替换此行

calc.append（function（signal））

以将每一行添加到对应于每个for循环的评估特征数组中？

-或者正确添加每行的最有效方法是什么？

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PD：如果我尝试这种转换，它不起作用，会给我一个非常奇怪的float64数组，大小为（9,1,4）

只需创建一个空数组

features\u mat

，并通过迭代所有信号用你的特征填充它：

import numpy as np
import scipy.io as sio

mat = sio.loadmat('signal_1.mat')

# number of signals in .mat file
n = mat['signal_1'].shape[0]
# get the signals
signals = mat['signal_1'][:,0]

def get_features(signal):
    mu = np.mean(signal)                          
    mini  = np.min(signal)
    maxi = np.max(signal)
    ran = maxi-mini
    return  np.array([mu,mini,maxi,ran])

# pre-allocate memory without initializing it
features_mat = np.empty((n,4))
for i, signal in enumerate(signals):
    features_mat[i,:] = get_features(signal)


>>> np.array([[ 4.07850385e+00, -2.10251071e-01,  7.06541344e+00, 7.27566451e+00],
              [ 8.31759999e-02, -2.61125020e-03,  1.50838105e-01, 1.53449355e-01],
              [-5.55470935e+00, -5.81185396e+00, -5.17208787e+00, 6.39766089e-01],
              [-1.36478103e+01, -1.46263278e+02,  1.46379425e+02, 2.92642704e+02],
              [ 3.22094459e+00,  1.00760787e+00,  5.55007608e+00, 4.54246820e+00],
              [ 4.36753757e+01,  3.57114093e+01,  4.93010863e+01, 1.35896770e+01],
              [ 1.71242787e+00, -2.25392323e-01,  3.59933423e+00, 3.82472655e+00],
              [-1.73530851e+00, -2.00324815e+00, -1.35313746e+00, 6.50110688e-01],
              [-5.83099184e+00, -6.98125270e+00, -4.75522063e+00, 2.22603207e+00]])

输出具有所需的形状，并且似乎包含您要查找的功能。告诉我这是否有效

希望这有帮助

import numpy as np
import scipy.io as sio

mat = sio.loadmat('signal_1.mat')

# number of signals in .mat file
n = mat['signal_1'].shape[0]
# get the signals
signals = mat['signal_1'][:,0]

def get_features(signal):
    mu = np.mean(signal)                          
    mini  = np.min(signal)
    maxi = np.max(signal)
    ran = maxi-mini
    return  np.array([mu,mini,maxi,ran])

# pre-allocate memory without initializing it
features_mat = np.empty((n,4))
for i, signal in enumerate(signals):
    features_mat[i,:] = get_features(signal)


>>> np.array([[ 4.07850385e+00, -2.10251071e-01,  7.06541344e+00, 7.27566451e+00],
              [ 8.31759999e-02, -2.61125020e-03,  1.50838105e-01, 1.53449355e-01],
              [-5.55470935e+00, -5.81185396e+00, -5.17208787e+00, 6.39766089e-01],
              [-1.36478103e+01, -1.46263278e+02,  1.46379425e+02, 2.92642704e+02],
              [ 3.22094459e+00,  1.00760787e+00,  5.55007608e+00, 4.54246820e+00],
              [ 4.36753757e+01,  3.57114093e+01,  4.93010863e+01, 1.35896770e+01],
              [ 1.71242787e+00, -2.25392323e-01,  3.59933423e+00, 3.82472655e+00],
              [-1.73530851e+00, -2.00324815e+00, -1.35313746e+00, 6.50110688e-01],
              [-5.83099184e+00, -6.98125270e+00, -4.75522063e+00, 2.22603207e+00]])