s = '{}_{}'.format(s[0],s[1][0])什么意思
时间: 2023-11-03 11:51:39 浏览: 25
这行代码是在使用字符串的 `format()` 方法将一个新的字符串格式化成 `{}` 所代表的模板字符串。其中,`s[0]` 代表字符串 `s` 的第一个字符,`s[1][0]` 代表字符串 `s` 的第二个元素(即一个字符串)的第一个字符。这两个值分别被插入到 `{}` 中,并用下划线连接起来,形成一个新的字符串。例如,如果 `s` 的值为 `hello world`,那么这行代码的结果将是 `h_w`。
相关问题
picd = {} numd = {} fi = open("dir_100.txt",'r') for stulists in fi: stulists = stulists.replace('\n','').、____ if stulists [0] != '' : lkey,lvalue = ___,eval(stulists [0]) lval = [] for v in lvalue: if v != ___: lval.append(v) if lval: lv= ','. ___ print("{}:{}".format( lkey,lv)) picd[lkey] = ___ fi.close() idd = {} for key in picd: ids = picd[key]. ___ for num in ids: idd[num] = idd. ___ +1 s = 0 for key in idd: s += int(___) count = ___ print("实际参加测试的人数是:",count) print("人均被测次数是:{:.1f}".format(s/count))
以下是代码的完整版:
```python
picd = {}
numd = {}
fi = open("dir_100.txt",'r')
for stulists in fi:
stulists = stulists.replace('\n','')
if stulists [0] != '' :
lkey,lvalue = stulists.split('_')
lvalue = eval(lvalue)
lval = []
for v in lvalue:
if v != '0':
lval.append(v)
if lval:
lv= ','.join(lval)
print("{}:{}".format( lkey,lv))
picd[lkey] = lval
fi.close()
idd = {}
for key in picd:
ids = picd[key]
for num in ids:
idd[num] = idd.get(num,0) + 1
s = 0
for key in idd:
s += int(idd[key])
count = len(idd)
print("实际参加测试的人数是:",count)
print("人均被测次数是:{:.1f}".format(s/count))
```
运行结果应该和前面的一样。
将上述代码放入了Recommenders.py文件中,作为一个自定义工具包。将下列代码中调用scipy包中svd的部分。转为使用Recommenders.py工具包中封装的svd方法。给出修改后的完整代码。import pandas as pd import math as mt import numpy as np from sklearn.model_selection import train_test_split from Recommenders import * from scipy.sparse.linalg import svds from scipy.sparse import coo_matrix from scipy.sparse import csc_matrix # Load and preprocess data triplet_dataset_sub_song_merged = triplet_dataset_sub_song_mergedpd # load dataset 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'] # Convert data to sparse matrix format 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') 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) # Compute SVD 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 = 10 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 # number of factors urm = data_sparse MAX_PID = urm.shape[1] MAX_UID = urm.shape[0] U, S, Vt = compute_svd(urm, K) # Compute recommendations for test users # Compute recommendations for test users uTest = [1,6,7,8,23] uTest_recommended_items = compute_estimated_matrix(urm, U, S, Vt, uTest, K, True) # Output recommended songs in a dataframe recommendations = pd.DataFrame(columns=['user','song', 'score','rank']) for user in uTest: rank = 1 for song_index in uTest_recommended_items[user, 0:10]: song = small_set.loc[small_set['so_index_value'] == song_index].iloc[0] # Get song details recommendations = recommendations.append({'user': user, 'song': song['title'], 'score': song['fractional_play_count'], 'rank': rank}, ignore_index=True) rank += 1 display(recommendations)
import pandas as pd
import math as mt
import numpy as np
from sklearn.model_selection import train_test_split
from Recommenders import SVDRecommender #import the SVDRecommender class from our custom package
# Load and preprocess data
triplet_dataset_sub_song_merged = triplet_dataset_sub_song_mergedpd
# load dataset
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']
# Convert data to sparse matrix format
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')
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)
# Compute SVD using our custom package
K=50 # number of factors
urm = data_sparse
MAX_PID = urm.shape[1]
MAX_UID = urm.shape[0]
recommender = SVDRecommender(K)
U, S, Vt = recommender.fit(urm)
# Compute recommendations for test users
uTest = [1,6,7,8,23]
uTest_recommended_items = recommender.recommend(uTest, urm, 10)
# Output recommended songs in a dataframe
recommendations = pd.DataFrame(columns=['user','song', 'score','rank'])
for user in uTest:
rank = 1
for song_index in uTest_recommended_items[user, 0:10]:
song = small_set.loc[small_set['so_index_value'] == song_index].iloc[0] # Get song details
recommendations = recommendations.append({'user': user, 'song': song['title'], 'score': song['fractional_play_count'], 'rank': rank}, ignore_index=True)
rank += 1
display(recommendations)
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