C&C++——班级学生档案管理系统源代码：
实现功能：
一、管理系统
1、输入学生信息；2、显示学生信息；3、修改学生信息；4、追加一个学生信息；5、删除一个学生信息；0、返回；
二、查询系统
根据输入要查询人的信息，显示学生号、姓名、职务等；
三、通讯录
1、输入通讯录信息；2、查询通讯录信息；3、修改通讯录信息；0、退出通讯录系统；
四、退出系统
以上功能，诚实有效！！！！！亲测有效！！！！！！！
2021-09-26 11:01:45
277KB
1
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
from math import sqrt
critics={'Lisa Rose': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.5,
'Just My Luck': 3.0, 'Superman Returns': 3.5, 'You, Me and Dupree': 2.5,
'The Night Listener': 3.0},
'Gene Seymour': {'Lady in the Water': 3.0, 'Snakes on a Plane': 3.5,
'Just My Luck': 1.5, 'Superman Returns': 5.0, 'The Night Listener': 3.0,
'You, Me and Dupree': 3.5},
'Michael Phillips': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.0,
'Superman Returns': 3.5, 'The Night Listener': 4.0},
'Claudia Puig': {'Snakes on a Plane': 3.5, 'Just My Luck': 3.0,
'The Night Listener': 4.5, 'Superman Returns': 4.0,
'You, Me and Dupree': 2.5},
'Mick LaSalle': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0,
'Just My Luck': 2.0, 'Superman Returns': 3.0, 'The Night Listener': 3.0,
'You, Me and Dupree': 2.0},
'Jack Matthews': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0,
'The Night Listener': 3.0, 'Superman Returns': 5.0, 'You, Me and Dupree': 3.5},
'Toby': {'Snakes on a Plane':4.5,'You, Me and Dupree':1.0,'Superman Returns':4.0}}
df_critics=pd.DataFrame(critics)
##欧氏距离
def sim_distance(prefs,person1,person2):
si={}
for item in prefs[person1]:
if item in prefs[person2]:
si[item]=1
if len(si)==0:
return 0
sum_of_squares=sum([pow(prefs[person1][item]-prefs[person2][item],2) for item in prefs[person1] if item in prefs[person2]])
return 1/(1+sqrt(sum_of_squares))
##numpy pandas 方法
def sim_distance2(prefs,person1,person2):
return 1/(1+np.linalg.norm(prefs[person1]-prefs[person2]))
##皮尔逊相关系数
def sim_pearson(prefs,p1,p2):
si={}
for item in prefs[p1]:
if item in prefs[p2]:
si[item]=1
n=len(si)
if n==0:
return 1
##对所有偏好求和
sum1=sum([prefs[p1][it] for it in si])
sum2=sum([prefs[p2][it] for it in si])
##求平方和
sum1Sq=sum([pow(prefs[p1][it]
2021-08-15 01:58:10
37.75MB
共10个章节，由浅入深介绍数据挖掘算法，含配套数据源和源代码
10个章节信息如下：
精品 干货 数据挖掘数据分析配套纯英文版教程课件 chap1_intro
精品 干货 数据挖掘数据分析配套纯英文版教程课件 chap2_data
精品 干货 数据挖掘数据分析配套纯英文版教程课件 chap3_data_exploration
精品 干货 数据挖掘数据分析配套纯英文版教程课件 chap4_basic_classification
精品 干货 数据挖掘数据分析配套纯英文版教程课件 chap5_alternative_classification
精品 干货 数据挖掘数据分析配套纯英文版教程课件 chap6_basic_association_analysis
精品 干货 数据挖掘数据分析配套纯英文版教程课件 chap7_extended_association_analysis
精品 干货 数据挖掘数据分析配套纯英文版教程课件 chap8_basic_cluster_analysis
精品 干货 数据挖掘数据分析配套纯英文版教程课件 chap9_advanced_cluster_analysis
精品 干货 数据挖掘数据分析配套纯英文版教程课件 chap10_anomaly_detection
2021-08-13 09:15:32
10.55MB
单片机电动自行车设计（keil完整源项目+源c代码+proteus源仿真电路图）
2021-08-08 21:06:23
550KB
基于At89c51的病房呼叫系统Proteus仿真+程序（keil完整源项目+源c代码+proteus源仿真电路图）
2021-08-08 21:06:22
2.04MB
# -*- coding: utf-8 -*-
import sys
import nltk
import json
# Load in output from blogs_and_nlp__get_feed.py
BLOG_DATA = sys.argv[1]
blog_data = json.loads(open(BLOG_DATA).read())
for post in blog_data:
sentences = nltk.tokenize.sent_tokenize(post['content'])
tokens = [nltk.tokenize.word_tokenize(s) for s in sentences]
pos_tagged_tokens = [nltk.pos_tag(t) for t in tokens]
# Flatten the list since we're not using sentence structure
# and sentences are guaranteed to be separated by a special
# POS tuple such as ('.', '.')
pos_tagged_tokens = [token for sent in pos_tagged_tokens for token in sent]
all_entity_chunks = []
previous_pos = None
current_entity_chunk = []
for (token, pos) in pos_tagged_tokens:
if pos == previous_pos and pos.startswith('NN'):
current_entity_chunk.append(token)
elif pos.startswith('NN'):
if current_entity_chunk != []:
# Note that current_entity_chunk could be a duplicate when appended,
# so frequency analysis again becomes a consideration
all_entity_chunks.append((' '.join(current_entity_chunk), pos))
current_entity_chunk = [token]
previous_pos = pos
# Store the chunks as an index for the document
# and account for frequency while we're at it...
post['entities'] = {}
for c in all_entity_chunks:
post['entities'][c] = post['entities'].get(c, 0) + 1
# For example, we could display just the title-cased entities
print post['title']
print '-' * len(post['title'])
proper_nouns = []
for (entity, pos) in post['entities']:
if entity.istitle():
print '\t%s (%s)' % (entity, post['entities'][(entity, pos)])
print
2021-08-08 17:09:28
803KB