我正在尝试为我的Android APP（SMS检索器API）创建一个哈希键，我点击了此链接
但是在运行关键工具命令时，出现以下错误：
xxd：术语“ xxd”未被识别为cmdlet，函数，脚本文件或可运行程序的名称。检查 拼写名称，或者如果包含路径，请验证路径是否正确，然后重试。 在第1行：char：57“
答案 0 :(得分：2)
xxd是用于进行hexdump或进行相反操作的linux命令，有时与import pandas as pd
f1 = pd.read_csv('/home/kopal/Desktop/jobs.csv')
f1.head()
df = pd.DataFrame(f1)
df.head()
df.loc[df['id'] == 13]
df.loc[df['location'] == 'Mumbai']
f2 = pd.read_csv('/home/kopal/Desktop/candidate_details.csv')
f2.head()
df1 = pd.DataFrame(f2)
df1.head()
df2=df1[['candidateName','skillSet','relevantExperiance','qualification','location','industryType']]
df2
a=df2.head(1000)
a
df3=df[['job_title','keyskills','location']]
df3
df3.loc[df['id'] == 13 ]
import requests
import matplotlib.pyplot as plt ##Visualization
import nltk ##NLP
from textblob import TextBlob
from textblob.sentiments import NaiveBayesAnalyzer ##MACHINE LEARNING
import re, math
from collections import Counter
import numpy as np
blob = TextBlob(df3.keyskills[4], analyzer=NaiveBayesAnalyzer())
blob1= TextBlob(df3.location[4], analyzer=NaiveBayesAnalyzer())
df4=pd.DataFrame()
df4['skills']=a.skillSet
df4['location_prefer']=a.location
df4
text1 = df3.keySkills[4]
text4 = df3.location[4]
class Similarity():
def compute_cosine_similarity(self, string1, string2):
# intersects the words that are common
# in the set of the two words
intersection = set(string1.keys()) & set(string2.keys())
# dot matrix of vec1 and vec2
numerator = sum([string1[x] * string2[x] for x in intersection])
# sum of the squares of each vector
# sum1 is the sum of text1 and same for sum2 for text2
sum1 = sum([string1[x]**2 for x in string1.keys()])
sum2 = sum([string2[x]**2 for x in string2.keys()])
# product of the square root of both sum(s)
denominator = math.sqrt(sum1) * math.sqrt(sum2)
if not denominator:
return 0.0
else:
return round(numerator/float(denominator),4)
def text_to_vector(self,text):
WORD = re.compile(r'\w+')
words = WORD.findall(text)
return Counter(words)
# Jaccard Similarity
def tokenize(self,string):
return string.lower().split(" ")
def jaccard_similarity(self, string1, string2):
intersection = set(string1).intersection(set(string2))
union = set(string1).union(set(string2))
return len(intersection)/float(len(union))
cosine=[]
for text2 in a.skillSet:
#print(i)
similarity = Similarity()
vector1 = similarity.text_to_vector(text1)
vector2 = similarity.text_to_vector(text2)
token1 = similarity.tokenize(text1)
token2 = similarity.tokenize(text2)
cosine.append(similarity.compute_cosine_similarity(vector1, vector2))
class Similarity():
def compute_cosine_similarity(self, string1, string2):
# intersects the words that are common
# in the set of the two words
intersection = set(string1.keys()) & set(string2.keys())
# dot matrix of vec1 and vec2
numerator = sum([string1[x] * string2[x] for x in intersection])
# sum of the squares of each vector
# sum1 is the sum of text1 and same for sum2 for text2
sum1 = sum([string1[x]**2 for x in string1.keys()])
sum2 = sum([string2[x]**2 for x in string2.keys()])
# product of the square root of both sum(s)
denominator = math.sqrt(sum1) * math.sqrt(sum2)
if not denominator:
return 0.0
else:
return round(numerator/float(denominator),4)
def text_to_vector(self,text):
WORD = re.compile(r'\w+')
words = WORD.findall(text)
return Counter(words)
cosine1=[]
for text3 in str(a.location):
similarity1 = Similarity()
vector4 = similarity1.text_to_vector(text4)
vector3 = similarity1.text_to_vector(text3)
token4 = similarity1.tokenize(text4)
token3 = similarity1.tokenize(text3)
cosine1.append(similarity1.compute_cosine_similarity(vector4, vector3))
print(str(a.location))
df4['similarity_for_skills']=cosine
se = pd.Series(cosine1)
df4['similarity_for_location'] = se
#df4.insert(loc=0, column='simlarity_for_location', value=se)
#df4['similarity_for_location']=str(cosine1)
df4
df5 = pd.concat([a.candidateName,df4], axis=1)
result=df5.sort_values('similarity_for_skills',ascending=False)
df6=pd.DataFrame(result)
df6
一起提供。
根据您的操作系统，尝试安装vim或xxd，它可能会解决此问题。