aa=np.array([range(i,i+3) for i in [2,4,6]])

您给出的是一个Python代码，其功能是创建一个3行3列的二维数组，每一行都是从2开始的连续3个整数，即第一行为[2, 3, 4]，第二行为[4, 5, 6]，第三行为[6, 7, 8]。这个二维数组可以用NumPy库中的array函数创建，代码如下：

import numpy as np

aa = np.array([range(i,i+3) for i in [2,4,6]])
print(aa)

输出结果为：

[[2 3 4]
 [4 5 6]
 [6 7 8]]

相关问题

import pandas as pd import numpy as np # 计算用户对歌曲的播放比例 triplet_dataset_sub_song_merged_sum_df = triplet_dataset_sub_song_mergedpd[['user', 'listen_count']].groupby('user').sum().reset_index() triplet_dataset_sub_song_merged_sum_df.rename(columns={'listen_count': 'total_listen_count'}, inplace=True) triplet_dataset_sub_song_merged = pd.merge(triplet_dataset_sub_song_mergedpd, triplet_dataset_sub_song_merged_sum_df) triplet_dataset_sub_song_mergedpd['fractional_play_count'] = triplet_dataset_sub_song_mergedpd['listen_count'] / triplet_dataset_sub_song_merged['total_listen_count'] # 将用户和歌曲编码为数字 small_set = triplet_dataset_sub_song_mergedpd user_codes = small_set.user.drop_duplicates().reset_index() song_codes = small_set.song.drop_duplicates().reset_index() user_codes.rename(columns={'index': 'user_index'}, inplace=True) song_codes.rename(columns={'index': 'song_index'}, inplace=True) song_codes['so_index_value'] = list(song_codes.index) user_codes['us_index_value'] = list(user_codes.index) small_set = pd.merge(small_set, song_codes, how='left') small_set = pd.merge(small_set, user_codes, how='left') # 将数据转换为稀疏矩阵形式 from scipy.sparse import coo_matrix mat_candidate = small_set[['us_index_value', 'so_index_value', 'fractional_play_count']] data_array = mat_candidate.fractional_play_count.values row_array = mat_candidate.us_index_value.values col_array = mat_candidate.so_index_value.values data_sparse = coo_matrix((data_array, (row_array, col_array)), dtype=float) # 使用SVD方法进行矩阵分解并进行推荐 from scipy.sparse import csc_matrix from scipy.sparse.linalg import svds import math as mt def compute_svd(urm, K): U, s, Vt = svds(urm, K) dim = (len(s), len(s)) S = np.zeros(dim, dtype=np.float32) for i in range(0, len(s)): S[i, i] = mt.sqrt(s[i]) U = csc_matrix(U, dtype=np.float32) S = csc_matrix(S, dtype=np.float32) Vt = csc_matrix(Vt, dtype=np.float32) return U, S, Vt def compute_estimated_matrix(urm, U, S, Vt, uTest, K, test): rightTerm = S * Vt max_recommendation = 250 estimatedRatings = np.zeros(shape=(MAX_UID, MAX_PID), dtype=np.float16) recomendRatings = np.zeros(shape=(MAX_UID, max_recommendation), dtype=np.float16) for userTest in uTest: prod = U[userTest, :] * rightTerm estimatedRatings[userTest, :] = prod.todense() recomendRatings[userTest, :] = (-estimatedRatings[userTest, :]).argsort()[:max_recommendation] return recomendRatings K = 50 urm = data_sparse MAX_PID = urm.shape[1] MAX_UID = urm.shape[0] U, S, Vt = compute_svd(urm, K) uTest = [4, 5, 6, 7, 8, 73, 23] # uTest=[1b5bb32767963cbc215d27a24fef1aa01e933025] uTest_recommended_items = compute_estimated_matrix(urm, U, S, Vt 继续将这段代码输出完整

, 并进行解释：

import pandas as pd
import numpy as np

# 计算用户对歌曲的播放比例
triplet_dataset_sub_song_merged_sum_df = triplet_dataset_sub_song_merged[['user', 'listen_count']].groupby('user').sum().reset_index()
triplet_dataset_sub_song_merged_sum_df.rename(columns={'listen_count': 'total_listen_count'}, inplace=True)
triplet_dataset_sub_song_merged = pd.merge(triplet_dataset_sub_song_merged, triplet_dataset_sub_song_merged_sum_df)
triplet_dataset_sub_song_merged['fractional_play_count'] = triplet_dataset_sub_song_merged['listen_count'] / triplet_dataset_sub_song_merged['total_listen_count']

# 将用户和歌曲编码为数字
small_set = triplet_dataset_sub_song_merged
user_codes = small_set.user.drop_duplicates().reset_index()
song_codes = small_set.song.drop_duplicates().reset_index()
user_codes.rename(columns={'index': 'user_index'}, inplace=True)
song_codes.rename(columns={'index': 'song_index'}, inplace=True)
song_codes['so_index_value'] = list(song_codes.index)
user_codes['us_index_value'] = list(user_codes.index)
small_set = pd.merge(small_set, song_codes, how='left')
small_set = pd.merge(small_set, user_codes, how='left')

# 将数据转换为稀疏矩阵形式
from scipy.sparse import coo_matrix
mat_candidate = small_set[['us_index_value', 'so_index_value', 'fractional_play_count']]
data_array = mat_candidate.fractional_play_count.values
row_array = mat_candidate.us_index_value.values
col_array = mat_candidate.so_index_value.values
data_sparse = coo_matrix((data_array, (row_array, col_array)), dtype=float)

# 使用SVD方法进行矩阵分解并进行推荐
from scipy.sparse import csc_matrix
from scipy.sparse.linalg import svds
import math as mt

def compute_svd(urm, K):
    U, s, Vt = svds(urm, K)
    dim = (len(s), len(s))
    S = np.zeros(dim, dtype=np.float32)
    for i in range(0, len(s)):
        S[i, i] = mt.sqrt(s[i])
    U = csc_matrix(U, dtype=np.float32)
    S = csc_matrix(S, dtype=np.float32)
    Vt = csc_matrix(Vt, dtype=np.float32)
    return U, S, Vt

def compute_estimated_matrix(urm, U, S, Vt, uTest, K, test):
    rightTerm = S * Vt
    max_recommendation = 250
    estimatedRatings = np.zeros(shape=(MAX_UID, MAX_PID), dtype=np.float16)
    recomendRatings = np.zeros(shape=(MAX_UID, max_recommendation), dtype=np.float16)
    for userTest in uTest:
        prod = U[userTest, :] * rightTerm
        estimatedRatings[userTest, :] = prod.todense()
        recomendRatings[userTest, :] = (-estimatedRatings[userTest, :]).argsort()[:max_recommendation]
    return recomendRatings

K = 50
urm = data_sparse
MAX_PID = urm.shape[1]
MAX_UID = urm.shape[0]
U, S, Vt = compute_svd(urm, K)
uTest = [4, 5, 6, 7, 8, 73, 23] # uTest=[1b5bb32767963cbc215d27a24fef1aa01e933025]
uTest_recommended_items = compute_estimated_matrix(urm, U, S, Vt, uTest, K, test)

这段代码实现了一个基于SVD方法的推荐系统，具体步骤如下：

1. 读入数据，计算每个用户对每首歌曲的播放比例。

2. 将用户和歌曲编码为数字，转换为稀疏矩阵形式。

3. 使用SVD方法进行矩阵分解，得到用户和歌曲的隐向量。

4. 对于给定的测试用户，使用隐向量和分解后的矩阵计算出该用户对每首歌曲的预测评分。

5. 根据预测评分，为该用户推荐最高的250首歌曲。

其中，SVD方法是一种矩阵分解的方法，可以将一个大矩阵分解为多个小矩阵，这些小矩阵可以表示出原始矩阵中的潜在特征（即隐向量）。通过计算用户和歌曲的隐向量，可以获得它们之间的相似度，从而进行推荐。

clc,clear; load('b.mat'); Q=vp(10); pp=vp(2); global space space = zeros(1e7,12); global ans_num ans_num = 0; global DB DB = vp(12); for v1 = 1:8 for v2 = v1+1:9 L1 = v2 - v1 - 1; L2 = 12 - v2; L = L1 + 2*L2; al = 2^L; for i=1:al aa = de2bi(i-1,L,2,'left-msb'); E = zeros(2,12); E(1,v1) = 1; E(2,v2) = 1; for j=1:L1 E(1,v1+j) = aa(j); end for j=1:L2 E(1,v2+j) = aa(L1+j); E(2,v2+j) = aa(L1+L2+j); end pe = E(1,:) | E(2,:); penum = 0; for j=v2+1:12 penum = penum + pe(j); end if(penum > 9-v2) continue; end re = mod(pp * E(:,:),2); w = [v1 v2]; s = zeros(1,12); d = [1 2 3 4 5 6 7 8 9 10 11 12]; d(w) = []; s(v1) = 0; s(v2) = 0; t0 = 0; t1 = 0; T0 = zeros(1024,12); T1 = zeros(1024,12); for t = 1:1024 s(d) = Q(t,:); h=[]; for ii=1:4 rre = 0; for jj=1:12 rre = rre * 2 + mod(s(jj)+re(ii,jj),2); end h = [h rre]; end if(b(h(1)+1) == b(h(2)+1) && b(h(2)+1) == b(h(3)+1) && b(h(3)+1) == b(h(4)+1)) if(b(h(1)+1) == 0) t0 = t0+1; T0(t0,:) = s(:); else t1 = t1+1; T1(t1,:) = s(:); end end end combine_2(T0,T1,E,2,t0,t1); end end end

这是一段MATLAB代码，我可以将其转换为Python代码。以下是转换后的代码：

import numpy as np
from numpy.matlib import repmat
from scipy.io import loadmat

b_data = loadmat('b.mat')
vp_data = loadmat('vp.mat')

b = b_data['b'][0]
Q = vp_data['vp'][9]
pp = vp_data['vp'][1]
DB = vp_data['vp'][11]

space = np.zeros((1e7, 12))
ans_num = 0


def de2bi(num, n, order, msb):
    if order == 2:
        b_str = bin(num)[2:].zfill(n)
    else:
        b_str = np.base_repr(num, base=order, padding=n)
    if msb == 'left-msb':
        b = np.array([int(b_str[i]) for i in range(n)])
    else:
        b = np.array([int(b_str[n - i - 1]) for i in range(n)])
    return b


def combine_2(T0, T1, E, num, t0, t1):
    global space
    for i in range(1, t0 + 1):
        for j in range(1, t1 + 1):
            if np.all(T0[i, :] == T1[j, :]):
                continue
            temp = T0[i, :] + T1[j, :]
            if np.any(temp > 1):
                continue
            temp = np.append(temp, np.zeros((1,)))
            if np.any(temp[E == 1] == 0):
                continue
            temp = temp[:12]
            if np.any(temp > 1):
                continue
            if np.any(temp[E == 1] == 0):
                continue
            global ans_num
            ans_num += 1
            space[ans_num, :] = temp
            if ans_num % 100000 == 0:
                print(ans_num)


for v1 in range(1, 9):
    for v2 in range(v1 + 1, 10):
        L1 = v2 - v1 - 1
        L2 = 12 - v2
        L = L1 + 2 * L2
        al = 2 ** L
        for i in range(al):
            aa = de2bi(i - 1, L, 2, 'left-msb')
            E = np.zeros((2, 12))
            E[0, v1] = 1
            E[1, v2] = 1
            for j in range(L1):
                E[0, v1 + j + 1] = aa[j]
            for j in range(L2):
                E[0, v2 + j + 1] = aa[L1 + j]
                E[1, v2 + j + 1] = aa[L1 + L2 + j]
            pe = E[0, :] | E[1, :]
            penum = pe[v2:].sum()
            if penum > 9 - v2:
                continue
            re = pp * repmat(E, 2, 1)
            w = np.array([v1, v2])
            s = np.zeros((1, 12))
            d = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
            d[w - 1] = 0
            d = d[d != 0]
            s[0, v1 - 1] = 0
            s[0, v2 - 1] = 0
            t0 = 0
            t1 = 0
            T0 = np.zeros((1024, 12))
            T1 = np.zeros((1024, 12))
            for t in range(1, 1025):
                s[0, d - 1] = Q[t - 1, :]
                h = np.array([])
                for ii in range(4):
                    rre = 0
                    for jj in range(12):
                        rre = rre * 2 + np.mod(s[0, jj] + re[ii, jj], 2)
                    h = np.append(h, rre)
                if b[int(h[0])] == b[int(h[1])] == b[int(h[2])] == b[int(h[3])]:
                    if b[int(h[0])] == 0:
                        t0 += 1
                        T0[t0, :] = s
                    else:
                        t1 += 1
                        T1[t1, :] = s
            combine_2(T0, T1, E, 2, t0, t1)

请注意，由于Python的语法和MATLAB略有不同，因此在转换过程中可能需要进行一些修改。