通过添加额外的列表扩展类似矩阵的numpy数组时出现的问题

问题描述

我编写了以下代码，该代码可以做到:(1)生成形状为(3, 16)的根矩阵，以及(2)生成1000个二元向量C，以便每个向量在根矩阵的末尾迭代地添加一个一个时间.

I wrote the following code that does: (1) Generate root matrix of shape (3, 16), and (2) Generate 1000 binary vectors C such that each vector is added at the end of the root matrix iteratively one at a time.

代码如下(python 2.7):

The code is the following (python 2.7):

# STEP1: Generate root matrix 
root = [random.randint(0, 2 ** 16 - 1) for _ in range(16 - 1)]
root = [('{0:0' + str(16) + 'b}').format(x) for x in root]
root = np.asarray([list(map(int, list(x))) for x in root], dtype=np.uint8)

# STEP2: Generate 1000 binary vectors
C = [random.randint(0, 2 ** 16 - 1) for _ in range(1000)]
C = list(set(C))
C = [('{0:0' + str(16) + 'b}').format(x) for x in C]
C = np.asarray([list(map(int, list(x))) for x in C], dtype=np.uint8)

# Step3: For each vector of C, append it at the end of root matrix
for i in range(1000):
    batch = root[i:(i + (len(root)))]
    batch = np.append(batch, C[i])
    print(batch)
    # Continue to process batch after being extended by adding a new vector

矩阵C看起来像这样:

[[0 1 1 ..., 0 1 1]
 [1 0 1 ..., 1 0 1]
 [0 1 0 ..., 1 1 0]
 ..., 
 [1 1 0 ..., 1 0 0]
 [0 0 1 ..., 1 1 0]
 [1 1 1 ..., 1 1 1]]

问题是np.append(batch, C[i])将所有向量合并为一个向量，但这不是我想要的.我的目标是通过迭代添加向量C[i]将root矩阵从(3, 16)扩展为(4,16).我该如何实现?

The problem is that np.append(batch, C[i])merges all vectors into a single one but this is not what I want. My goal is to extend the root matrix from (3, 16) to be (4,16) by just adding a vector C[i] iteratively. How can I achieve that?

谢谢

推荐答案

如果您可以交换以下内容:

If you can swap this:

batch = root[i:(i + (len(root)))]
batch = np.append(batch, C[i])

为此:

batch = np.append(batch, [C[i]], axis=0)

axis允许您在特定维度上附加两个矩阵.因此，我们将C[i]制成矩阵并将其附加到维度0中.

axis allows you to append two matrices on a particular dimension. So we make C[i] into a matrix and append it in dimension 0.

我不确定此batch = root[i:(i + (len(root)))]的意图是什么，但每次都会将batch缩短为一个根大小的矩阵，因此它不会增大大小.实际上，随着您在root末尾附近收缩.

I am not sure what the intention of this batch = root[i:(i + (len(root)))] is but it shortens batch to a matrix the size of root every time so it will not increase in size. It actually shrinks as you near the end of root.

C也不总是1000个向量.使其具有唯一性会删除一些内容.

Also C is not always 1000 vectors. Making them unique removes some.

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