Listing db elements - java
I am getting some data from a db.For all data found in first table is compared with data from second table.So I am retrieving ingredients stored in one table and those ingredients are compared with users favorite ingredients stored in second table.
I want for each row that contains ingredients to list if there are the same ingredients as favorite ingredients.
Now I retrieve all data from db and the comparison is made for all ingredients, not for each one independently.
Here is my code:
Statement st1 = con.createStatement();
rs1 = st1.executeQuery("Select Nume from ingredientplacut where Id_user = '24'");
while( rs1.next())
{
String nrRet1 = rs1.getString("Nume");
secondList.add(nrRet1);
}
System.out.println(secondList);
Statement st = con.createStatement();
java.sql.PreparedStatement ps=con.prepareStatement("SELECT ID_reteta,Nume,Descriere,Ingrediente,Mod_preparare,Dificultate,Tip,Imagini,Total_grasimi,Total_carbohidrati,Total_proteine,Total_calorii FROM reteta");
rs=ps.executeQuery();
while( rs.next()){
String nrRet = rs.getString("Ingrediente");
firstList.add(nrRet);
System.out.println("----");
}
ArrayList<String> al3= new ArrayList<String>();
for (String temp : firstList)
{
boolean isTrue=false;
for(String temp2:secondList)
{
if(temp.contains(temp2))
{
isTrue=true;
break;
}
}
if(isTrue)
al3.add("YES");
else
al3.add("NO");
}
System.out.println(al3);
System.out.println(firstList);
ArrayList<Integer> al4= new ArrayList<Integer>();
for (String temp2 : secondList) {
boolean isTrue = false;
for (String temp : firstList) {
if (temp.contains(temp2)) {
isTrue = true;
break;
}
}
if (isTrue)
al4.add(1);
else
al4.add(0);
}
System.out.println(al4);
And the output is:
[afine, almette, alune, albus de ou de gaina, andive, mere]
----
----
[YES, YES, NO, NO, NO, NO, NO, NO, YES]
[200 grame fusilli cu legume (afine,alune);
200 grame smantana 12%;
50 grame iaurt;
50 grame cascaval afumat ;
1/2 lingurite mustar;
doi catei de usturoi sau o lingurita de usturoi deshidratat;
mere;
patrunjel;
piper;
sare., 250 grame dovleac ras;
170 grame fulgi fini de ovaz;
150 ml lapte dulce;
80 grame stafide;
un ou;
doua linguri faina integrala;
40 grame miere;
un plic de zahar vanilat;
½ lingurita scortisoara;
afine;
putina sare., 4 medalioane de somon proaspat (aproximativ 800 grame);
un ardei rosu mare;
o lamaie mica;
o rosie medie;
putin patrunjel;
4-5 catei de usturoi;
doua lingurite capere;
2-3 linguri ulei de masline;
piper;
boia dulc;
sare., 150 grame maioneza de casa;
300 grame telina (net);
300 grame piept sau pulpe dezosate;
1/3 capatani usturoi;
marar si patrunjel dupa gust;
aprox 1/2 borcan castraveti murati in otet;
o lingura boia dulce;
½ lingurite piper;
10-20 ml ulei masline.
[1, 0, 1, 0, 0, 1]
And I want the output be :
[200 grame fusilli cu legume (afine,alune);
200 grame smantana 12%;
50 grame iaurt;
50 grame cascaval afumat ;
1/2 lingurite mustar;
doi catei de usturoi sau o lingurita de usturoi deshidratat;
mere;
patrunjel;
piper;
sare.
[1, 0, 1, 0, 0, 1] //3 ingredients related
250 grame dovleac ras;
170 grame fulgi fini de ovaz;
150 ml lapte dulce;
80 grame stafide;
un ou;
doua linguri faina integrala;
40 grame miere;
un plic de zahar vanilat;
½ lingurita scortisoara;
afine;
putina sare.,
[0, 0, 0, 1, 0, 0] //one ingredient related
4 medalioane de somon proaspat (aproximativ 800 grame);
un ardei rosu mare;
o lamaie mica;
o rosie medie;
putin patrunjel;
4-5 catei de usturoi;
doua lingurite capere;
2-3 linguri ulei de masline;
piper;
boia dulc;
sare.
[0, 0, 0, 0, 0, 0] //0 Ingredients related
150 grame maioneza de casa;
300 grame telina (net);
300 grame piept sau pulpe dezosate;
1/3 capatani usturoi;
marar si patrunjel dupa gust;
aprox 1/2 borcan castraveti murati in otet;
o lingura boia dulce;
½ lingurite piper;
10-20 ml ulei masline.
[0, 0, 0, 0, 0, 0] //0 Ingredients related
Could anyone help me?
I am not 100% sure, if I get all the aspects fully right and my Romanian also is very bad, but here's what I got:
We have table called ingredientplacut which apparently list all ingredients, which the users like. We may assume
that the name of the ingredient is stored in attribute name there,
that the identifier of the user we are looking for is known
that the attribute user_id of ingredientplacut provides the identifier of the user and thus is part of the primary key in this table.
The table reteta apparently provides the ingredients for a given recipe and appears to be denormalized (#Bogdan: if this isn't already a view on another DB tables, kindly check your relational data model. Otherwise, you highly risk to run into delete and update anormalies). The name of the ingredient there is stored in attribute Ingrediente.
The list of ingredients of the recipes can then be easily checked, if they are "favorite ingredients" by issuing the following join:
SELECT r.ID_reteta, r.Ingrediente, fav.name IS NOT NULL as isFavorite
FROM retata as r
LEFT JOIN inredientplacut as fav ON r.Ingrediente = fav.name
WHERE fav.user_id = '24'
Given this statement, it is also easy to determine the recipe with the "most liked" ingredients:
SELECT ID_retata, count(*) as favoriteIngredients
FROM (
SELECT r.ID_reteta, r.Ingrediente, fav.name IS NOT NULL as isFavorite
FROM retata as r
LEFT JOIN inredientplacut as fav ON r.Ingrediente = fav.name
WHERE fav.user_id = '24'
) as favlist
WHERE favlist.isFavorite = true
ORDER BY favoriteIngredients DESC
GROUP BY ID_retata
Please note, however, that this is a very bad performance indicator, as there is no weighting considered for the ingredients (I may like pepper and onions, but this doesn't mean that I like all the dishes where both of them is in...)
For sure, you may implement such a LEFT JOIN also on PHP level by reading only the source tables via SQL. However, I would try to avoid this due to multiple reasons:
The DB optimizer is virtually not able to help you, so you need to be "better than the optimizer" - which I doubt that a interpreter-based language like PHP can be.
You need to retrieve all the recipes (which might become a huge table) from the DB into PHP. So, your calculation is transferred through multiple stacks, the data types need to be verified and so forth. In short, your processing is "farer away" from the data - this is known to be slower by architecture.
The coding is much worse to read and thus even worse to maintain. The SQL statement might not be too easy as well as it is more abstract, but by its descriptive nature, your intentions are visible in a more compact manner.
(NB: A seldom case, where more abstraction provides better performance - usually, mixing those two architectural concepts doesn't work out that well).
Related
Comparing rows with multiples keys that might or might not be present in 2 Excel sheets
I need to process 2 Excel files which I need to compare in order to get those registers that are not present in both tables. Each register is identified by 4 component: the 4 last digits of an account, the card number, the authorization and the amount of the transaction. The problem I'm facing is that while one of the files contains all of this component, the other one does not, leaving holes in the register, as such is not guaranteed for them to be unique. I'll include an example: File 1 with holes where I don't have data: ACCOUNT TC AUTO AMOUNT 456 0 98846 2302 0 4797 0 79268599 782 8415 38711 78636161 0 0 61658 199234 506 0 0 1029249 0 0 50724 33554244 782 0 12308 28755261 956 0 0 9392043 396 3238 0 2482527 614 4442 0 30846962 855 0 71143 61793724 File 2 with all the information: ACCOUNT TC AUTO AMOUNT 861 4797 19313 79268599 782 8415 38711 78636161 410 1022 61658 199234 506 6368 93040 1029249 785 2478 50724 33554244 782 4741 12308 28755261 956 2623 45025 9392043 396 3238 14196 2482527 614 4442 61568 30846962 855 5917 71143 61793724 As you can see in this case Id need to return this value: 456 0 98846 2302 Is there an efficient way I can do this either in SQL or using Java data structures? Is important to note that values might be missing from either one of the files. I was thinking about uploading them to a temporal data base, but that seems rather inefficient and I wanted to use a Map that supports the query operation I want with possible repeated values. Thank you for your help! I tried this query, but it isn't returning no information and I'm not sure about creating a temporal database table, is there a map implementation that supports values with several keys being some of them optional? select e1.id , e2.id from excel1 e1 left join excel2 e2 on ( e1.Cuenta = e2.Cuenta OR e1.TC = e2.TC OR e1.auto = e2.auto ) AND e1.monto = e2.monto where e1.id not in ( SELECT e1.id FROM excel1 e1 WHERE id NOT IN ( SELECT e1.id FROM excel1 e1 LEFT JOIN excel2 e2 ON ( e1.Cuenta = e2.Cuenta OR e1.TC = e2.TC OR e1.auto = e2.auto ) AND e1.monto = e2.monto GROUP BY e1.monto HAVING COUNT(*) = 1 ) ) AND e2.id = ( SELECT e2.id FROM excel1 e1 WHERE id NOT IN ( SELECT e1.id FROM excel1 e1 LEFT JOIN excel2 e2 ON ( e1.Cuenta = e2.Cuenta OR e1.TC = e2.TC OR e1.auto = e2.auto ) AND e1.monto = e2.monto GROUP BY e1.monto HAVING COUNT(*) = 1 ) )
Can not identify text in Spanish with Lingpipe
Some days ago, I am developing an java server to keep a bunch of data and identify its language, so I decided to use lingpipe for such task. But I have facing an issue, after training code and evaluating it with two languages(English and Spanish) by getting that I can't identify spanish text, but I got a successful result with english and french.
The tutorial that I have followed in order to complete this task is:
http://alias-i.com/lingpipe/demos/tutorial/langid/read-me.html
An the next steps I have made in order to complete the task:
Steps followed to train a Language Classifier
~1.First place and unpack the english and spanish metadata inside a folder named leipzig, as follow (Note: Metadata and Sentences are provided from http://wortschatz.uni-leipzig.de/en/download):
leipzig //Main folder
1M sentences //Folder with data of the last trial
eng_news_2015_1M
eng_news_2015_1M.tar.gz
spa-hn_web_2015_1M
spa-hn_web_2015_1M.tar.gz
ClassifyLang.java //Custom program to try the trained code
dist //Folder
eng_news_2015_300K.tar.gz //unpackaged english sentences
spa-hn_web_2015_300K.tar.gz //unpackaged spanish sentences
EvalLanguageId.java
langid-leipzig.classifier //trained code
lingpipe-4.1.2.jar
munged //Folder
eng //folder containing the sentences.txt for english
sentences.txt
spa //folder containing the sentences.txt for spanish
sentences.txt
Munge.java
TrainLanguageId.java
unpacked //Folder
eng_news_2015_300K //Folder with the english metadata
eng_news_2015_300K-co_n.txt
eng_news_2015_300K-co_s.txt
eng_news_2015_300K-import.sql
eng_news_2015_300K-inv_so.txt
eng_news_2015_300K-inv_w.txt
eng_news_2015_300K-sources.txt
eng_news_2015_300K-words.txt
sentences.txt
spa-hn_web_2015_300K //Folder with the spanish metadata
sentences.txt
spa-hn_web_2015_300K-co_n.txt
spa-hn_web_2015_300K-co_s.txt
spa-hn_web_2015_300K-import.sql
spa-hn_web_2015_300K-inv_so.txt
spa-hn_web_2015_300K-inv_w.txt
spa-hn_web_2015_300K-sources.txt
spa-hn_web_2015_300K-words.txt
~2.Second unpack the language metadata compressed into a unpack folder
unpacked //Folder
eng_news_2015_300K //Folder with the english metadata
eng_news_2015_300K-co_n.txt
eng_news_2015_300K-co_s.txt
eng_news_2015_300K-import.sql
eng_news_2015_300K-inv_so.txt
eng_news_2015_300K-inv_w.txt
eng_news_2015_300K-sources.txt
eng_news_2015_300K-words.txt
sentences.txt
spa-hn_web_2015_300K //Folder with the spanish metadata
sentences.txt
spa-hn_web_2015_300K-co_n.txt
spa-hn_web_2015_300K-co_s.txt
spa-hn_web_2015_300K-import.sql
spa-hn_web_2015_300K-inv_so.txt
spa-hn_web_2015_300K-inv_w.txt
spa-hn_web_2015_300K-sources.txt
spa-hn_web_2015_300K-words.txt
~3.Then Munge the sentences of each one in order to remove the line numbers, tabs and replacing line breaks with single space characters. The output is uniformly written using the UTF-8 unicode encoding (Note:the munge.java at Lingpipe site).
/-----------------Command line----------------------------------------------/
javac -cp lingpipe-4.1.2.jar: Munge.java
java -cp lingpipe-4.1.2.jar: Munge /home/samuel/leipzig/unpacked /home/samuel/leipzig/munged
----------------------------------------Results-----------------------------
spa
reading from=/home/samuel/leipzig/unpacked/spa-hn_web_2015_300K/sentences.txt charset=iso-8859-1
writing to=/home/samuel/leipzig/munged/spa/spa.txt charset=utf-8
total length=43267166
eng
reading from=/home/samuel/leipzig/unpacked/eng_news_2015_300K/sentences.txt charset=iso-8859-1
writing to=/home/samuel/leipzig/munged/eng/eng.txt charset=utf-8
total length=35847257
/---------------------------------------------------------------/
<---------------------------------Folder------------------------------------->
munged //Folder
eng //folder containing the sentences.txt for english
sentences.txt
spa //folder containing the sentences.txt for spanish
sentences.txt
<-------------------------------------------------------------------------->
~4.Next we start by training the language(Note:the TrainLanguageId.java at Lingpipe LanguageId tutorial).
/---------------Command line--------------------------------------------/
javac -cp lingpipe-4.1.2.jar: TrainLanguageId.java
java -cp lingpipe-4.1.2.jar: TrainLanguageId /home/samuel/leipzig/munged /home/samuel/leipzig/langid-leipzig.classifier 100000 5
-----------------------------------Results-----------------------------------
nGram=100000 numChars=5
Training category=eng
Training category=spa
Compiling model to file=/home/samuel/leipzig/langid-leipzig.classifier
/----------------------------------------------------------------------------/
~5. We evaluated our trained code with the next result, having some issues on the confusion matrix (Note:the EvalLanguageId.java at Lingpipe LanguageId tutorial).
/------------------------Command line---------------------------------/
javac -cp lingpipe-4.1.2.jar: EvalLanguageId.java
java -cp lingpipe-4.1.2.jar: EvalLanguageId /home/samuel/leipzig/munged /home/samuel/leipzig/langid-leipzig.classifier 100000 50 1000
-------------------------------Results-------------------------------------
Reading classifier from file=/home/samuel/leipzig/langid-leipzig.classifier
Evaluating category=eng
Evaluating category=spa
TEST RESULTS
BASE CLASSIFIER EVALUATION
Categories=[eng, spa]
Total Count=2000
Total Correct=1000
Total Accuracy=0.5
95% Confidence Interval=0.5 +/- 0.02191346617949794
Confusion Matrix
reference \ response
,eng,spa
eng,1000,0 <---------- not diagonal sampling
spa,1000,0
Macro-averaged Precision=NaN
Macro-averaged Recall=0.5
Macro-averaged F=NaN
Micro-averaged Results
the following symmetries are expected:
TP=TN, FN=FP
PosRef=PosResp=NegRef=NegResp
Acc=Prec=Rec=F
Total=4000
True Positive=1000
False Negative=1000
False Positive=1000
True Negative=1000
Positive Reference=2000
Positive Response=2000
Negative Reference=2000
Negative Response=2000
Accuracy=0.5
Recall=0.5
Precision=0.5
Rejection Recall=0.5
Rejection Precision=0.5
F(1)=0.5
Fowlkes-Mallows=2000.0
Jaccard Coefficient=0.3333333333333333
Yule's Q=0.0
Yule's Y=0.0
Reference Likelihood=0.5
Response Likelihood=0.5
Random Accuracy=0.5
Random Accuracy Unbiased=0.5
kappa=0.0
kappa Unbiased=0.0
kappa No Prevalence=0.0
chi Squared=0.0
phi Squared=0.0
Accuracy Deviation=0.007905694150420948
Random Accuracy=0.5
Random Accuracy Unbiased=0.625
kappa=0.0
kappa Unbiased=-0.3333333333333333
kappa No Prevalence =0.0
Reference Entropy=1.0
Response Entropy=NaN
Cross Entropy=Infinity
Joint Entropy=1.0
Conditional Entropy=0.0
Mutual Information=0.0
Kullback-Liebler Divergence=Infinity
chi Squared=NaN
chi-Squared Degrees of Freedom=1
phi Squared=NaN
Cramer's V=NaN
lambda A=0.0
lambda B=NaN
ONE VERSUS ALL EVALUATIONS BY CATEGORY
CATEGORY[0]=eng VERSUS ALL
First-Best Precision/Recall Evaluation
Total=2000
True Positive=1000
False Negative=0
False Positive=1000
True Negative=0
Positive Reference=1000
Positive Response=2000
Negative Reference=1000
Negative Response=0
Accuracy=0.5
Recall=1.0
Precision=0.5
Rejection Recall=0.0
Rejection Precision=NaN
F(1)=0.6666666666666666
Fowlkes-Mallows=1414.2135623730949
Jaccard Coefficient=0.5
Yule's Q=NaN
Yule's Y=NaN
Reference Likelihood=0.5
Response Likelihood=1.0
Random Accuracy=0.5
Random Accuracy Unbiased=0.625
kappa=0.0
kappa Unbiased=-0.3333333333333333
kappa No Prevalence=0.0
chi Squared=NaN
phi Squared=NaN
Accuracy Deviation=0.011180339887498949
CATEGORY[1]=spa VERSUS ALL
First-Best Precision/Recall Evaluation
Total=2000
True Positive=0
False Negative=1000
False Positive=0
True Negative=1000
Positive Reference=1000
Positive Response=0
Negative Reference=1000
Negative Response=2000
Accuracy=0.5
Recall=0.0
Precision=NaN
Rejection Recall=1.0
Rejection Precision=0.5
F(1)=NaN
Fowlkes-Mallows=NaN
Jaccard Coefficient=0.0
Yule's Q=NaN
Yule's Y=NaN
Reference Likelihood=0.5
Response Likelihood=0.0
Random Accuracy=0.5
Random Accuracy Unbiased=0.625
kappa=0.0
kappa Unbiased=-0.3333333333333333
kappa No Prevalence=0.0
chi Squared=NaN
phi Squared=NaN
Accuracy Deviation=0.011180339887498949
/-----------------------------------------------------------------------/
~6.Then we tried to make a real evaluation with spanish text:
/-------------------Command line----------------------------------/
javac -cp lingpipe-4.1.2.jar: ClassifyLang.java
java -cp lingpipe-4.1.2.jar: ClassifyLang
/-------------------------------------------------------------------------/
<---------------------------------Result------------------------------------>
Text: Yo soy una persona increíble y muy inteligente, me admiro a mi mismo lo que me hace sentir ansiedad de lo que viene, por que es algo grandioso lleno de cosas buenas y de ahora en adelante estaré enfocado y optimista aunque tengo que aclarar que no lo haré por querer algo, sino por que es mi pasión.
Best Language: eng <------------- Wrong Result
<----------------------------------------------------------------------->
Code for ClassifyLang.java:
import com.aliasi.classify.Classification;
import com.aliasi.classify.Classified;
import com.aliasi.classify.ConfusionMatrix;
import com.aliasi.classify.DynamicLMClassifier;
import com.aliasi.classify.JointClassification;
import com.aliasi.classify.JointClassifier;
import com.aliasi.classify.JointClassifierEvaluator;
import com.aliasi.classify.LMClassifier;
import com.aliasi.lm.NGramProcessLM;
import com.aliasi.util.AbstractExternalizable;
import java.io.File;
import java.io.IOException;
import com.aliasi.util.Files;
public class ClassifyLang {
public static String text = "Yo soy una persona increíble y muy inteligente, me admiro a mi mismo"
+ " estoy ansioso de lo que viene, por que es algo grandioso lleno de cosas buenas"
+ " y de ahora en adelante estaré enfocado y optimista"
+ " aunque tengo que aclarar que no lo haré por querer algo, sino por que no es difícil serlo. ";
private static File MODEL_DIR
= new File("/home/samuel/leipzig/langid-leipzig.classifier");
public static void main(String[] args)
throws ClassNotFoundException, IOException {
System.out.println("Text: " + text);
LMClassifier classifier = null;
try {
classifier = (LMClassifier) AbstractExternalizable.readObject(MODEL_DIR);
} catch (IOException | ClassNotFoundException ex) {
// Handle exceptions
System.out.println("Problem with the Model");
}
Classification classification = classifier.classify(text);
String bestCategory = classification.bestCategory();
System.out.println("Best Language: " + bestCategory);
}
}
~7.I tried with a 1 million metadata file, but it got the same result and also changing the ngram number by getting the same results.
I will be so thankfull for your help.
Well, after days working in Natural Language Processing I found a way to determine the language of one text using OpenNLP. Here is the Sample Code: https://github.com/samuelchapas/languagePredictionOpenNLP/tree/master/TrainingLanguageDecOpenNLP and over here is the training Corpus for the model created to make language predictions. I decided to use OpenNLP for the issue described in this question, really this library has a complete stack of functionalities. Here is the sample for model training> https://mega.nz/#F!HHYHGJ4Q!PY2qfbZr-e0w8tg3cUgAXg
Is there any way to paint my plate numbers to black using JavaCV?
Hi I started using JavaCV two days ago,
I m trying to make an ANPR Open Source System at my git repository under Java SE and Maven.
I have detected my plate rectangle and now I'm trying to prepare a good image for OCR reading.
the original image :
Right now I have obtained this image :
,
Is there any way to turn my plate numbers black using JavaCv ? I don't have the slightest idea how to do so using javacv functions .
here I give you the methods that are producing this result :
first I call this after a blur
public void toB_A_W(JLabel jLabel){
Mat rgbImage = Imgcodecs.imread(original);
Mat destination = new Mat(rgbImage.rows(), rgbImage.cols(), rgbImage.type());
// l objectif et de corriger les erreur de la transformation en noire et blan
int dilation_size = 2;
// la matrice de la dilatation on cherche a dilater en forme de rectange ( Imgproc.MORPH_RECT )
Mat element1 = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(dilation_size + 1, dilation_size + 1));
// on dilate l image
Imgproc.dilate(rgbImage, destination, element1);
Mat labImage = new Mat();
cvtColor(destination, labImage, Imgproc.COLOR_BGR2GRAY);
Imgcodecs.imwrite(ocrReadFrom, labImage);
jLabel.setIcon(new ImageIcon(ocrReadFrom));
JOptionPane.showConfirmDialog(null, "");
}
then I call this :
public void toB_W(JLabel jLabelBlackAndWhiteImage) {
// cella est l image de l ocr
smouthedImage = opencv_imgcodecs.cvLoadImage(ocrReadFrom);
blackAndWhiteImageOCR = opencv_core.IplImage.create(smouthedImage.width(),
smouthedImage.height(), IPL_DEPTH_8U, 1);
// la fonction qui va executé la transformation en noire et blan
System.out.println("0");
//cvAdaptiveThreshold(smouthedImage, smouthedImage, 255, CV_ADAPTIVE_THRESH_GAUSSIAN_C, opencv_imgproc.CV_THRESH_MASK, 15, -2);
opencv_imgproc.cvSmooth(smouthedImage, smouthedImage);
System.out.println("1");
cvCvtColor(smouthedImage, blackAndWhiteImageOCR, CV_BGR2GRAY);
System.out.println("2");
cvAdaptiveThreshold(blackAndWhiteImageOCR, blackAndWhiteImageOCR, 255, CV_ADAPTIVE_THRESH_GAUSSIAN_C, CV_THRESH_BINARY_INV, 17, -4);
System.out.println("3");
opencv_imgproc.cvSmooth(blackAndWhiteImageOCR, blackAndWhiteImageOCR);
// fin de la transformation
cvSaveImage(ocrReadFrom, blackAndWhiteImageOCR);
...}
Thanks
You want to fill the numbers, you could have considered performing binary threshold rather than adaptive threshold. I chose a threshold level of 40 to make the numbers distinct.
How to read the contents of (.bib) file format using Java
I need to read .bib file and insert it tags into an objects of bib-entries
the file is big (almost 4000 lines) , so my first question is what to use (bufferrReader or FileReader)
the general format is
#ARTICLE{orleans01DJ,
author = {Doug Orleans and Karl Lieberherr},
title = {{{DJ}: {Dynamic} Adaptive Programming in {Java}}},
journal = {Metalevel Architectures and Separation of Crosscutting Concerns 3rd
Int'l Conf. (Reflection 2001), {LNCS} 2192},
year = {2001},
pages = {73--80},
month = sep,
editor = {A. Yonezawa and S. Matsuoka},
owner = {Administrator},
publisher = {Springer-Verlag},
timestamp = {2009.03.09}
}
#ARTICLE{Ossher:1995:SOCR,
author = {Harold Ossher and Matthew Kaplan and William Harrison and Alexander
Katz},
title = {{Subject-Oriented Composition Rules}},
journal = {ACM SIG{\-}PLAN Notices},
year = {1995},
volume = {30},
pages = {235--250},
number = {10},
month = oct,
acknowledgement = {Nelson H. F. Beebe, University of Utah, Department of Mathematics,
110 LCB, 155 S 1400 E RM 233, Salt Lake City, UT 84112-0090, USA,
Tel: +1 801 581 5254, FAX: +1 801 581 4148, e-mail: \path|beebe#math.utah.edu|,
\path|beebe#acm.org|, \path|beebe#computer.org| (Internet), URL:
\path|http://www.math.utah.edu/~beebe/|},
bibdate = {Fri Apr 30 12:33:10 MDT 1999},
coden = {SINODQ},
issn = {0362-1340},
keywords = {ACM; object-oriented programming systems; OOPSLA; programming languages;
SIGPLAN},
owner = {Administrator},
timestamp = {2009.02.26}
}
As you can see , there are some entries that have more than line, entries that end with }
entries that end with }, or }},
Also , some entries have {..},{..}.. in the middle
so , i am a little bit confused on how to start reading this file and how to get these entries and manipulate them.
Any help will be highly appreciated.
We currently discuss different options at JabRef. These are the current options: JBibTeX ANTLRv3 Grammar JabRef's BibtexParser.java
JsonParser ArrayIndexOutOfBoundsException when parsing json
The following code should work as a JsonParser but it is returning an ArrayIndexOutOfBoundsException even though I check that there are still elements to parse.
public void parseJson(String url) {
try {
InputStream is = new URL(url).openStream();
JsonParser parser = Json.createParser(is);
while (parser.hasNext()) {
Event e = parser.next();
if (e == Event.KEY_NAME) {
if (parser.getString().equals("name") && parser.hasNext()) {
parser.next();
writer = new BufferedWriter(new FileWriter("names.txt"));
writer.write(parser.getString());
}
}
}
} catch (MalformedURLException e) {
e.printStackTrace();
} catch (IOException e) {
e.printStackTrace();
}
}
The error produced is such:
Exception in thread "main" java.lang.ArrayIndexOutOfBoundsException: 4096
at org.glassfish.json.JsonTokenizer.readChar(JsonTokenizer.java:388)
at org.glassfish.json.JsonTokenizer.read(JsonTokenizer.java:98)
at org.glassfish.json.JsonTokenizer.readString(JsonTokenizer.java:124)
at org.glassfish.json.JsonTokenizer.nextToken(JsonTokenizer.java:310)
at org.glassfish.json.JsonParserImpl$StateIterator.nextToken(JsonParserImpl.java:157)
at org.glassfish.json.JsonParserImpl$StateIterator.next(JsonParserImpl.java:182)
at org.glassfish.json.JsonParserImpl.next(JsonParserImpl.java:150)
at proj.Proj2.parseJson(Proj2.java:43)
at proj.Proj1.main(Proj1.java:78)
It parses everything up to when the error is thrown.
Sample JSON that produces error:
[{"id":774,"na":"Hungry Leaf","cs_phone":"919-234-7755","rds_info":{"id":125,"name":"Triangle Food Guy","logo":""},"services":{"deliver":{"time":1200,"mino":100,"can":0}},"allow_tip":1,"allow_asap":1,"cu":["American","Breakfast","Sandwiches"],"addr":"2608 Erwin Rd, Durham, NC","full_addr":{"addr":"2608 Erwin Rd","addr2":"","city":"Durham","state":"NC","postal_code":"27705-3843"},"city":"Durham","latitude":36.008621,"longitude":-78.94474,"del":1200,"mino":100,"is_delivering":0},{"id":780,"na":"Armadillo Grill","cs_phone":"919-234-7755","rds_info":{"id":125,"name":"Triangle Food Guy","logo":""},"services":{"deliver":{"time":1080,"mino":100,"can":0}},"allow_tip":1,"allow_asap":1,"cu":["Mexican","Tex-Mex"],"addr":"439 Glenwood Ave, Raleigh, NC","full_addr":{"addr":"439 Glenwood Ave","addr2":"","city":"Raleigh","state":"NC","postal_code":"27603-1219"},"city":"Raleigh","latitude":35.785754,"longitude":-78.647293,"del":1080,"mino":100,"is_delivering":0},{"id":783,"na":"Chef Mario's Bistro","cs_phone":"919-234-7755","rds_info":{"id":125,"name":"Triangle Food Guy","logo":""},"services":{"deliver":{"time":2880,"mino":150,"can":0}},"allow_tip":1,"allow_asap":1,"cu":["American","Sandwiches"],"addr":"2610 Wycliff Rd, Raleigh, NC","full_addr":{"addr":"2610 Wycliff Rd","addr2":"","city":"Raleigh","state":"NC","postal_code":"27607-3060"},"city":"Raleigh","latitude":35.818683,"longitude":-78.692145,"del":2880,"mino":150,"is_delivering":0},{"id":784,"na":"Chef Mario's Catering","cs_phone":"919-234-7755","rds_info":{"id":125,"name":"Triangle Food Guy","logo":""},"services":{"deliver":{"time":4320,"mino":150,"can":0}},"allow_tip":1,"allow_asap":1,"cu":["American","Breakfast","Catering","Italian","Sandwiches"],"addr":"2610 Wycliff Rd, Raleigh, NC","full_addr":{"addr":"2610 Wycliff Rd","addr2":"","city":"Raleigh","state":"NC","postal_code":"27607-3060"},"city":"Raleigh","latitude":35.818683,"longitude":-78.692145,"del":4320,"mino":150,"is_delivering":0},{"id":786,"na":"D'Nardys Caterers","cs_phone":"919-234-7755","rds_info":{"id":125,"name":"Triangle Food Guy","logo":""},"services":{"deliver":{"time":1320,"mino":6.55,"can":0}},"allow_tip":1,"allow_asap":1,"cu":["American","Catering","Italian","Sandwiches"],"addr":"314 Madison Grove Pl, Cary, NC","full_addr":{"addr":"314 Madison Grove Pl","addr2":"","city":"Cary","state":"NC","postal_code":"27519-8161"},"city":"Cary","latitude":35.82135,"longitude":-78.854469,"del":1320,"mino":6.55,"is_delivering":0},{"id":787,"na":"Danny's Bar-B-Que","cs_phone":"919-234-7755","rds_info":{"id":125,"name":"Triangle Food Guy","logo":""},"services":{"deliver":{"time":2640,"mino":100,"can":1}},"allow_tip":1,"allow_asap":1,"cu":["American","Barbecue"],"addr":"9561 Chapel Hill Rd, Morrisville, NC","full_addr":{"addr":"9561 Chapel Hill Rd","addr2":"","city":"Morrisville","state":"NC","postal_code":"27560-7359"},"city":"Morrisville","latitude":35.805972,"longitude":-78.809315,"del":2640,"mino":100,"is_delivering":1},{"id":792,"na":"Flying Biscuit","cs_phone":"919-234-7755","rds_info":{"id":125,"name":"Triangle Food Guy","logo":""},"services":{"deliver":{"time":1440,"mino":150,"can":1}},"allow_tip":1,"allow_asap":1,"cu":["Breakfast","Sandwiches"],"addr":"2016 Clark Ave, Raleigh, NC","full_addr":{"addr":"2016 Clark Ave","addr2":"","city":"Raleigh","state":"NC","postal_code":"27605-1604"},"city":"Raleigh","latitude":35.788996,"longitude":-78.659642,"del":1440,"mino":150,"is_delivering":1},{"id":793,"na":"Gateway Restaurant","cs_phone":"919-234-7755","rds_info":{"id":125,"name":"Triangle Food Guy","logo":""},"services":{"deliver":{"time":1320,"mino":50,"can":0}},"allow_tip":1,"allow_asap":1,"cu":["American","Breakfast"],"addr":"2411 Crabtree Blvd, Raleigh, NC","full_addr":{"addr":"2411 Crabtree Blvd","addr2":"","city":"Raleigh","state":"NC","postal_code":"27604-2232"},"city":"Raleigh","latitude":35.805222,"longitude":-78.611873,"del":1320,"mino":50,"is_delivering":0},{"id":796,"na":"Hibachi Xpress","cs_phone":"919-234-7755","rds_info":{"id":125,"name":"Triangle Food Guy","logo":""},"services":{"deliver":{"time":720,"mino":150,"can":0}},"allow_tip":1,"allow_asap":1,"addr":"2470 Walnut St, Cary, NC","full_addr":{"addr":"2470 Walnut St","addr2":"","city":"Cary","state":"NC","postal_code":"27518-9212"},"city":"Cary","latitude":35.75113,"longitude":-78.741205,"del":720,"mino":150,"is_delivering":0},{"id":798,"na":"HoneyBaked Ham","cs_phone":"919-234-7755","rds_info":{"id":125,"name":"Triangle Food Guy","logo":""},"services":{"deliver":{"time":1080,"mino":60,"can":0}},"allow_tip":1,"allow_asap":1,"cu":["American","Sandwiches"],"addr":"5275 Six Forks Rd, Raleigh, NC","full_addr":{"addr":"5275 Six Forks Rd","addr2":"","city":"Raleigh","state":"NC","postal_code":"27609-4431"},"city":"Raleigh","latitude":35.85326,"longitude":-78.642593,"del":1080,"mino":60,"is_delivering":0},{"id":804,"na":"Moe's Southwest Grill","cs_phone":"919-234-7755","rds_info":{"id":125,"name":"Triangle Food Guy","logo":""},"services":{"deliver":{"time":1080,"mino":100,"can":0}},"allow_tip":1,"allow_asap":1,"cu":["Mexican","Tex-Mex"],"addr":"506 Daniels St, Raleigh, NC","full_addr":{"addr":"506 Daniels St","addr2":"","city":"Raleigh","state":"NC","postal_code":"27605-1317"},"city":"Raleigh","latitude":35.791061,"longitude":-78.66118,"del":1080,"mino":100,"is_delivering":0},{"id":806,"na":"Ole Time Barbecue","cs_phone":"919-234-7755","rds_info":{"id":125,"name":"Triangle Food Guy","logo":""},"services":{"deliver":{"time":1080,"mino":75,"can":0}},"allow_tip":1,"allow_asap":1,"cu":["Barbecue"],"addr":"6309 Hillsborough St, Raleigh, NC","full_addr":{"addr":"6309 Hillsborough St","addr2":"","city":"Raleigh","state":"NC","postal_code":"27606-1148"},"city":"Raleigh","latitude":35.787922,"longitude":-78.73851,"del":1080,"mino":75,"is_delivering":0},{"id":811,"na":"The Q Shack","cs_phone":"919-234-7755","rds_info":{"id":125,"name":"Triangle Food Guy","logo":""},"services":{"deliver":{"time":180,"mino":40,"can":0}},"allow_tip":1,"allow_asap":1,"cu":["American","Barbecue"],"addr":"North Hills, Raleigh, NC","full_addr":{"addr":"North Hills","addr2":"","city":"Raleigh","state":"NC","postal_code":"27609"},"city":"Raleigh","latitude":35.837521,"longitude":-78.64312,"del":180,"mino":40,"is_delivering":0},{"id":812,"na":"We Cook For You","cs_phone":"919-234-7755","rds_info":{"id":125,"name":"Triangle Food Guy","logo":""},"services":{"deliver":{"time":720,"mino":125,"can":0}},"allow_tip":1,"allow_asap":1,"cu":["American","Breakfast","Caribbean","Catering","Sandwiches"],"addr":"1125 Fuller St, Raleigh, NC","full_addr":{"addr":"1125 Fuller St","addr2":"","city":"Raleigh","state":"NC","postal_code":"27603-2217"},"city":"Raleigh","latitude":35.765381,"longitude":-78.650581,"del":720,"mino":125,"is_delivering":0},{"id":813,"na":"WhichWich Superior Sandwiches","cs_phone":"919-234-7755","rds_info":{"id":125,"name":"Triangle Food Guy","logo":""},"services":{"deliver":{"time":1080,"mino":75,"can":0}},"allow_tip":1,"allow_asap":1,"cu":["American","Sandwiches"],"addr":"4025 Lake Boone Trl, Raleigh, NC","full_addr":{"addr":"4025 Lake Boone Trl","addr2":"","city":"Raleigh","state":"NC","postal_code":"27607-2928"},"city":"Raleigh","latitude":35.815604,"longitude":-78.696541,"del":1080,"mino":75,"is_delivering":0},{"id":820,"na":"Greens","cs_phone":"919-234-7755","rds_info":{"id":125,"name":"Triangle Food Guy","logo":""},"services":{"deliver":{"time":1080,"mino":6.55,"can":0}},"allow_tip":1,"allow_asap":1,"cu":["American","Italian","Seafood"],"addr":"4120 Main at North Hills St, Raleigh, NC","full_addr":{"addr":"4120 Main at North 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NC","full_addr":{"addr":"421 Fayetteville St","addr2":"","city":"Raleigh","state":"NC","postal_code":"27601-1778"},"city":"Raleigh","latitude":35.77467,"longitude":-78.639389,"del":900,"mino":75,"is_delivering":0},{"id":3500,"na":"Artisan Sandwiches & Salads","cs_phone":"919-234-7755","rds_info":{"id":125,"name":"Triangle Food Guy","logo":""},"services":{"deliver":{"time":900,"mino":50,"can":0}},"allow_tip":1,"allow_asap":1,"cu":["American","Breakfast","Sandwiches"],"addr":"421 Fayetteville St, Raleigh, NC","full_addr":{"addr":"421 Fayetteville St","addr2":"","city":"Raleigh","state":"NC","postal_code":"27601-1778"},"city":"Raleigh","latitude":35.77467,"longitude":-78.639389,"del":900,"mino":50,"is_delivering":0},{"id":3795,"na":"Chef Mario's Catering Dinner","cs_phone":"919-234-7755","rds_info":{"id":125,"name":"Triangle Food 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NC","full_addr":{"addr":"3607 Falls River Ave","addr2":"","city":"Raleigh","state":"NC","postal_code":"27614-7359"},"city":"Raleigh","latitude":35.930141,"longitude":-78.564522,"del":720,"mino":125,"is_delivering":0}]
It seems this was a bug in version 1.0.1. Here's another thread that talks about it. The JsonTokenizer class has changed pretty dramatically since then. Upgrade to 1.0.2 and you will be fine.