Develop Reference

Java Hash map / Array List Count distinct values

I am pretty new into programming and I have an assignment to make, but I got stuck.
I have to implement a program which will read a CSV file (1 million+ lines) and count how many clients ordered "x" distinct products on a specific day.
The CSV looks like this:
Product Name | Product ID | Client ID | Date
Name 544 86 10/12/2017
Name 545 86 10/12/2017
Name 644 87 10/12/2017
Name 644 87 10/12/2017
Name 9857 801 10/12/2017
Name 3022 801 10/12/2017
Name 3021 801 10/12/2017
The result from my code is:
801: 2 - incorrect
86: 2 - correct
87: 2 - incorrect
Desired output is:
Client 1 (801): 3 distinct products
Client 2 (86): 2 distinct products
Client 3 (87): 1 distinct product
Additionally,
If I want to know how many clients ordered 2 distinct products I would like a result to look like this:
Total: 1 client ordered 2 distinct products
If I want to know the maximum number of distinct products ordered in a day, I would like the result to look like this:
The maximum number of distinct products ordered is: 3
I tried to use a Hash Map and Multimap by Google Guava (my best guess here), but I couldn't wrap my head around it.
My code looks like this:
package Test;
import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.Map;
import com.google.common.collect.ArrayListMultimap;
import com.google.common.collect.HashMultimap;
public class Test {
public static void main(String[] args) {
//HashMultimap<String, String> myMultimap = HashMultimap.create();
Map<String, MutableInteger> map = new HashMap<String, MutableInteger>();
ArrayList<String> linesList = new ArrayList<>();
// Input of file which needs to be parsed
String csvFile = "file.csv";
BufferedReader csvReader;
// Data split by 'TAB' in CSV file
String csvSplitBy = "\t";
try {
// Read the CSV file into an ArrayList array for easy processing.
String line;
csvReader = new BufferedReader(new FileReader(csvFile));
while ((line = csvReader.readLine()) !=null) {
linesList.add(line);
}
csvReader.close();
} catch (IOException e) {
e.printStackTrace();
}
// Process each CSV file line which is now contained within
// the linesList list Array
for (int i = 0; i < linesList.size(); i++) {
String[] data = linesList.get(i).split(csvSplitBy);
String col2 = data[1];
String col3 = data[2];
String col4 = data[3];
// Determine if Column 4 has the desired date
// and count the values
if (col4.contains("10/12/2017")) {
String key = col3;
if (map.containsKey(key)) {
MutableInteger count = map.get(key);
count.set(count.get() + 1);
} else {
map.put(key, new MutableInteger(1));
}
}
}
for (final String k : map.keySet()) {
if (map.get(k).get() == 2) {
System.out.println(k + ": " + map.get(k).get());
}
}
}
}
Any advise or suggestion on how this can be implemented would be greatly appreciated.
Thank you in advance guys.

You could store a Setof productIds per clientId, and just take the size of that.
As a Set does not allow duplicate values, this will effectively give you the distinct number of productIds.
Also, I recommend that you give your variables meaningful name instead of col2, k, map... This will make your code more readable.
Map<String, Set<String>> distinctProductsPerClient = new HashMap<String, Set<String>>();
// Process each CSV file line which is now contained within
// the linesList list Array
// Start from 1 to skip the first line
for (int i = 1; i < linesList.size(); i++) {
String line = linesList.get(i);
String[] data = line.split(csvSplitBy);
String productId = data[1];
String clientId = data[2];
String date = data[3];
// Determine if Column 4 has the desired date
// and count the values
if (date.contains("10/12/2017")) {
if (!distinctProductsPerClient.containsKey(clientId)) {
distinctProductsPerClient.put(clientId, new HashSet<>());
}
distinctProductsPerClient.get(clientId).add(productId);
}
}
for (final String clientId : distinctProductsPerClient.keySet()) {
System.out.println(clientId + ": " + distinctProductsPerClient.get(clientId).size());
}
More advanced solution using Stream API (requires Java 9)
If you introduce the class OrderData(that represents a single line in the CSV) like this:
private static class OrderData {
private final String productName;
private final String productId;
private final String clientId;
private final String date;
public OrderData(String csvLine) {
String[] data = csvLine.split("\t");
this.productName = data[0];
this.productId = data[1];
this.clientId = data[2];
this.date = data[3];
}
public String getProductName() {
return productName;
}
public String getProductId() {
return productId;
}
public String getClientId() {
return clientId;
}
public String getDate() {
return date;
}
}
you can replace the for loop with this:
Map<String, Set<String>> distinctProductsPerClient2 = linesList.stream()
.skip(1)
.map(OrderData::new)
.collect(groupingBy(OrderData::getClientId, mapping(OrderData::getProductId, toSet())));
But I reckon this might be a little bit to complex if you're new into programming (although it might be a good exercise if you would try to understand what the above code does).

Use string values from an array as string variable to parse json - codenameone

I have a no. of checkboxes (20), what i did is if a user select any checkbox, its name is stored in an array (eg abc array below in code). The name of the string variable that stores the respective json is of the same name as of the checkbox. For eg if Checkbox "a" is clicked, string value "a" is stored in array and there is a string variable named "a" that stores the related json values. What I need is that if i pass the string value stored in array as InputStream is = new ByteArrayInputStream(abc.get(i).getBytes()), it should be used to parse the inputStream for json. But it gives NullPointerException since the string value "a" is not equal to string variable a. How can i solve this problem? I ran out of ideas here. Is there other ways to achieve what i want to do here?
code: String values of the selected checkboxes are stored in an array
String a = "[{\n"
+ "\t\t\t\"title\": \"title1\",\n"
+ "\t\t\t\"describ\": \"describ1\"\n"
+ "}]";
String b = "[{\n"
+ "\"title\": \"title2\",\n"
+ "\"describ\": \"describ2\"\n"
+ "}]";
String c = "[{\n"
+ "\t\t\t\"title\": \"title3\",\n"
+ "\t\t\t\"describ\": \"describ3\"\n"
+ "}]";
//and all jsons required are there
ArrayList<String> abc;
#Override
protected void beforeTestForApp(Form f) {
f.setTitle("abc");
abc = new ArrayList<>();
//I have stored "a" & "b" in the abc array here for simplicity, but it is dynamic,
//ie. if the user select checkbox c, c will be stored in abc array and so on
abc.add("a");
abc.add("b");
Button bb = new Button("go");
bb.addActionListener((e) -> {
showForm("TestForAppResult", null);
});
f.add(bb);
}
Form for json parser and displaying the values:
#Override
protected void beforeTestForAppResult(Form f) {
f.setLayout(new BoxLayout(BoxLayout.Y_AXIS));
InputStream is;
for (int i = 0; i < abc.size(); i++) {
Label heading = new Label(abc.get(i));
f.add(heading);
//this gives error since abc.get(i) gives string value, not string variable
is = new ByteArrayInputStream(abc.get(i).getBytes());
showDetails(is, f);
}
//if i do this instead of for loop jst above, i can get the result but since what value'll be stored in an array is not known,it is not possible
//is = new ByteArrayInputStream(a.getBytes());
//showDetails(is, f);
//is = new ByteArrayInputStream(b.getBytes());
//showDetails(is, f);
}
private void showDetails(InputStream is, Form f) {
JSONParser p = new JSONParser();
Hashtable<String, Object> test;
try {
test = p.parse(new InputStreamReader(is));
Vector aVector = (Vector) test.get("root");
for (int j = 0; j < aVector.size(); j++) {
Hashtable hm = (Hashtable) aVector.get(j);
String title = (String) hm.get("title");
String describ = (String) hm.get("describ");
Label z = new Label(title);
Label zz = new Label(describ);
f.add(z);
f.add(zz);
}
} catch (IOException ex) {
}
}

tbh i didnt get your problem concretely but i still try to give you some shots so you can try out.
If i understand correctly you have 20 objects which contains values underlying?
So then you have a JSONArray, just iterate trough it and grab that JSONObject.
now just use parseJSON instead of parse as it is deprecated...
here is a short snippet of my code
JSONArray jsonTasks = new JSONArray(responseString);
for (int index = 0; index < jsonTasks.length(); index++) {
JSONObject jsonObject = (JSONObject) jsonTasks.get(index);
if (jsonObject != null) {
Map jsonMap = new JSONParser().parseJSON(new InputStreamReader(new ByteArrayInputStream(jsonObject.toString().getBytes(UTF8)), UTF8));
System.out.println(jsonMap.get("date"));

Print out prediction with WEKA in Java

I am trying to make a prediction with Weka in Java, using the Naive Bayes Classifier, with the following code:
JAVA
public class Run {
public static void main(String[] args) throws Exception {
ConverterUtils.DataSource source1 = new ConverterUtils.DataSource("./data/train.arff");
Instances train = source1.getDataSet();
// setting class attribute if the data format does not provide this information
// For example, the XRFF format saves the class attribute information as well
if (train.classIndex() == -1)
train.setClassIndex(train.numAttributes() - 1);
ConverterUtils.DataSource source2 = new ConverterUtils.DataSource("./data/test.arff");
Instances test = source2.getDataSet();
// setting class attribute if the data format does not provide this information
// For example, the XRFF format saves the class attribute information as well
if (test.classIndex() == -1)
test.setClassIndex(train.numAttributes() - 1);
// model
NaiveBayes naiveBayes = new NaiveBayes();
naiveBayes.buildClassifier(train);
Evaluation evaluation = new Evaluation(train);
evaluation.evaluateModel(naiveBayes, test);
}
}
TRAIN
#relation weather
#attribute outlook {sunny, overcast, rainy}
#attribute temperature real
#attribute humidity real
#attribute windy {TRUE, FALSE}
#attribute play {yes, no}
#data
sunny,85,85,FALSE,no
sunny,80,90,TRUE,no
...
PREDICT
#relation weather
#attribute outlook {sunny, overcast, rainy}
#attribute temperature real
#attribute humidity real
#attribute windy {TRUE, FALSE}
#attribute play {yes, no}
#data
sunny,85,85,FALSE,?
In the GUI the predicted output is
=== Predictions on test split ===
inst#, actual, predicted, error, probability distribution
1 ? 2:no + 0.145 *0.855
How can I get this output with Java? Which method do I need to use to get this?

public class Run {
public static void main(String[] args) throws Exception {
ConverterUtils.DataSource source1 = new ConverterUtils.DataSource("./data/train.arff");
Instances train = source1.getDataSet();
// setting class attribute if the data format does not provide this information
// For example, the XRFF format saves the class attribute information as well
if (train.classIndex() == -1)
train.setClassIndex(train.numAttributes() - 1);
ConverterUtils.DataSource source2 = new ConverterUtils.DataSource("./data/test.arff");
Instances test = source2.getDataSet();
// setting class attribute if the data format does not provide this information
// For example, the XRFF format saves the class attribute information as well
if (test.classIndex() == -1)
test.setClassIndex(train.numAttributes() - 1);
// model
NaiveBayes naiveBayes = new NaiveBayes();
naiveBayes.buildClassifier(train);
// this does the trick
double label = naiveBayes.classifyInstance(test.instance(0));
test.instance(0).setClassValue(label);
System.out.println(test.instance(0).stringValue(4));
}
}

Search element in arraylist

How can I search element in arraylist and display it? Example is the user wants to search the code A25 Then it will print the whole content on that arraylist that he search only and the output is A25 CS 212 Data Structures 3.
Subject CS212 = new Subject("A25","\t\tCS 212","\t\tData Structures\t\t\t\t",units);
Subject IT312 = new Subject("A26","\t\tIT 312","\t\tData Base Management System 2\t\t",units);
Subject IT313 = new Subject("A27","\t\tIT 312","\t\tData Base Management System 2\t\t",units);
Subject CS313 = new Subject("A29","\t\tCS 313","\t\tDigital Designt\t\t\t\t",units);
Subject Disc = new Subject("A30","\t\tIT 212","\t\tDiscrete Structurest\t\t",units);
Subject A31 = new Subject("A31","\t\tIT 212","\t\tDiscrete Structurest\t\t",units);
Subject Engl3 = new Subject("984","\t\tEngl 3","\t\tSpeech and oral Communicationt\t\t",units);
Subject Theo3 = new Subject("582","\t\tTheo 3","\t\tChrist and Sacramentst\t\t",units);
Subject Stat = new Subject("470","\t\tStata1","\t\tProbablility and Statisticst\t\t",units);
Subject Dota = new Subject("999","\t\tDota 2","\t\tDota Guide\t\t\t\t",units);
ArrayList<Subject> arrList = new ArrayList<Subject>();
arrList.add(CS212);
arrList.add(IT312);
arrList.add(IT313);
arrList.add(CS313);
arrList.add(Disc);
arrList.add(A31);
arrList.add(Engl3);
arrList.add(Theo3);
arrList.add(Stat);
arrList.add(Dota);
//User input that he wants to search
for(int i = 0; i < 3; i++,num++)
{
System.out.print("\t\t"+num +". ");
codeNo[i] = scan.next();
String output = Character.toUpperCase(codeNo[i].charAt(0)) + codeNo[i].substring(1);
codeNo[i] = output;
}
// This is what I tried but it doesn't work Idk why
for (Subject s : arrList) {
for(int i =0; i < codeNo.length; i++)
if (s.equals(codeNo[i])) {
System.out.println("\t\t\t"+s);
}
}
public Subject(String codeNo, String subjectID, String title , int unit)
{
//Constructor . .
}
//Desired output
Code to search
A25
A26
A27
output
A25 CS 212 Data Structures 3
A26 IT 312 Data Base Management System 2 3
A27 IT 312 Data Base Management System 2 3

You are trying to search an arraylist of subjects, you need to write a small function to compare the code string to the corresponding string of the class. You can do this by adding this to your subject class.
Example :
#Override
public boolean equals(String code) {
return code.equals(this.<compare to member>);
}
and change the compare to member that needs to match the code that you match.
EDIT : Easier way to do is to just change your existing code to :
if (s.code.equals(codeNo[i])) //assuming your code class member is a public string

Classify and Predict Multiple Attributes with Weka

I need to input 6 attributes and classify/predict 3 attributes from that input using Java/Weka programmatically. I've figured out how to predict 1 (the last) attribute, but how can I change this to train and predict the last 3 at the same time?
The numbers in the .arff files correspond to movie objects in a database.
Here is my Java code:
import java.io.BufferedReader;
import java.io.FileReader;
import weka.classifiers.meta.FilteredClassifier;
import weka.classifiers.trees.DecisionStump;
import weka.classifiers.trees.J48;
import weka.classifiers.trees.RandomForest;
import weka.classifiers.trees.RandomTree;
import weka.core.Instances;
import weka.filters.unsupervised.attribute.Remove;
public class WekaTrial {
/**
* #param args
* #throws Exception
*/
public static void main(String[] args) throws Exception {
// Create training data instance
Instances training_data = new Instances(
new BufferedReader(
new FileReader(
"C:/Users/Me/Desktop/File_Project/src/movie_training.arff")));
training_data.setClassIndex(training_data.numAttributes() - 1);
// Create testing data instance
Instances testing_data = new Instances(
new BufferedReader(
new FileReader(
"C:/Users/Me/Desktop/FileProject/src/movie_testing.arff")));
testing_data.setClassIndex(training_data.numAttributes() - 1);
// Print initial data summary
String summary = training_data.toSummaryString();
int number_samples = training_data.numInstances();
int number_attributes_per_sample = training_data.numAttributes();
System.out.println("Number of attributes in model = "
+ number_attributes_per_sample);
System.out.println("Number of samples = " + number_samples);
System.out.println("Summary: " + summary);
System.out.println();
// a classifier for decision trees:
J48 j48 = new J48();
// filter for removing samples:
Remove rm = new Remove();
rm.setAttributeIndices("1"); // remove 1st attribute
// filtered classifier
FilteredClassifier fc = new FilteredClassifier();
fc.setFilter(rm);
fc.setClassifier(j48);
// Create counters and print values
float correct = 0;
float incorrect = 0;
// train using stock_training_data.arff:
fc.buildClassifier(training_data);
// test using stock_testing_data.arff:
for (int i = 0; i < testing_data.numInstances(); i++) {
double pred = fc.classifyInstance(testing_data.instance(i));
System.out.print("Expected values: "
+ testing_data.classAttribute().value(
(int) testing_data.instance(i).classValue()));
System.out.println(", Predicted values: "
+ testing_data.classAttribute().value((int) pred));
// Increment correct/incorrect values
if (testing_data.classAttribute().value(
(int) testing_data.instance(i).classValue()) == testing_data.classAttribute().value((int) pred)) {
correct += 1;
} else {
incorrect += 1;
}
}
// Print correct/incorrect
float percent_correct = correct/(correct+incorrect)*100;
System.out.println("Number correct: " + correct + "\nNumber incorrect: " + incorrect + "\nPercent correct: " +
percent_correct + "%");
}
}
This is my .arff training file (with excess rows removed):
#relation movie_data
#attribute movie1_one {28,12,16,35,80,105,99,18,82,2916,10751,10750,14,10753,10769,36,10595,27,10756,10402,22,9648,10754,1115,10749,878,10755,9805,10758,10757,10748,10770,53,10752,37}
#attribute movie1_two {28,12,16,35,80,105,99,18,82,2916,10751,10750,14,10753,10769,36,10595,27,10756,10402,22,9648,10754,1115,10749,878,10755,9805,10758,10757,10748,10770,53,10752,37}
#attribute movie1_three {28,12,16,35,80,105,99,18,82,2916,10751,10750,14,10753,10769,36,10595,27,10756,10402,22,9648,10754,1115,10749,878,10755,9805,10758,10757,10748,10770,53,10752,37}
#attribute movie2_one {28,12,16,35,80,105,99,18,82,2916,10751,10750,14,10753,10769,36,10595,27,10756,10402,22,9648,10754,1115,10749,878,10755,9805,10758,10757,10748,10770,53,10752,37}
#attribute movie2_two {28,12,16,35,80,105,99,18,82,2916,10751,10750,14,10753,10769,36,10595,27,10756,10402,22,9648,10754,1115,10749,878,10755,9805,10758,10757,10748,10770,53,10752,37}
#attribute movie2_three {28,12,16,35,80,105,99,18,82,2916,10751,10750,14,10753,10769,36,10595,27,10756,10402,22,9648,10754,1115,10749,878,10755,9805,10758,10757,10748,10770,53,10752,37}
#attribute decision_one {28,12,16,35,80,105,99,18,82,2916,10751,10750,14,10753,10769,36,10595,27,10756,10402,22,9648,10754,1115,10749,878,10755,9805,10758,10757,10748,10770,53,10752,37}
#attribute decision_two {28,12,16,35,80,105,99,18,82,2916,10751,10750,14,10753,10769,36,10595,27,10756,10402,22,9648,10754,1115,10749,878,10755,9805,10758,10757,10748,10770,53,10752,37}
#attribute decision_three {28,12,16,35,80,105,99,18,82,2916,10751,10750,14,10753,10769,36,10595,27,10756,10402,22,9648,10754,1115,10749,878,10755,9805,10758,10757,10748,10770,53,10752,37}
#data
18,18,18,18,18,18,18,18,18
28,18,36,18,53,10769,18,53,10769
37,37,37,28,12,14,28,12,14
27,53,27,18,10749,10769,27,53,27
12,12,12,35,10751,35,12,12,12
35,18,10749,18,18,18,35,18,10749
28,12,878,53,53,53,53,53,53
18,18,18,28,37,10769,18,18,18
18,53,18,28,12,35,18,53,18
28,80,53,80,18,10749,28,80,53
18,10749,18,18,10756,18,18,10756,18
18,10749,10769,28,12,878,18,10749,10769
18,10756,18,16,35,10751,16,35,10751
35,18,10751,35,18,10752,35,18,10751
And the .arff testing file:
#relation movie_data
#attribute movie1_one {28,12,16,35,80,105,99,18,82,2916,10751,10750,14,10753,10769,36,10595,27,10756,10402,22,9648,10754,1115,10749,878,10755,9805,10758,10757,10748,10770,53,10752,37}
#attribute movie1_two {28,12,16,35,80,105,99,18,82,2916,10751,10750,14,10753,10769,36,10595,27,10756,10402,22,9648,10754,1115,10749,878,10755,9805,10758,10757,10748,10770,53,10752,37}
#attribute movie1_three {28,12,16,35,80,105,99,18,82,2916,10751,10750,14,10753,10769,36,10595,27,10756,10402,22,9648,10754,1115,10749,878,10755,9805,10758,10757,10748,10770,53,10752,37}
#attribute movie2_one {28,12,16,35,80,105,99,18,82,2916,10751,10750,14,10753,10769,36,10595,27,10756,10402,22,9648,10754,1115,10749,878,10755,9805,10758,10757,10748,10770,53,10752,37}
#attribute movie2_two {28,12,16,35,80,105,99,18,82,2916,10751,10750,14,10753,10769,36,10595,27,10756,10402,22,9648,10754,1115,10749,878,10755,9805,10758,10757,10748,10770,53,10752,37}
#attribute movie2_three {28,12,16,35,80,105,99,18,82,2916,10751,10750,14,10753,10769,36,10595,27,10756,10402,22,9648,10754,1115,10749,878,10755,9805,10758,10757,10748,10770,53,10752,37}
#attribute decision_one {28,12,16,35,80,105,99,18,82,2916,10751,10750,14,10753,10769,36,10595,27,10756,10402,22,9648,10754,1115,10749,878,10755,9805,10758,10757,10748,10770,53,10752,37}
#attribute decision_two {28,12,16,35,80,105,99,18,82,2916,10751,10750,14,10753,10769,36,10595,27,10756,10402,22,9648,10754,1115,10749,878,10755,9805,10758,10757,10748,10770,53,10752,37}
#attribute decision_three {28,12,16,35,80,105,99,18,82,2916,10751,10750,14,10753,10769,36,10595,27,10756,10402,22,9648,10754,1115,10749,878,10755,9805,10758,10757,10748,10770,53,10752,37}
#data
18,27,53,18,53,10756,18,27,53
35,18,10749,18,10769,18,18,10769,18
16,878,53,16,18,16,16,18,16
35,10749,10757,18,18,18,18,18,18
80,18,10748,18,10749,18,18,10749,18
28,18,36,35,18,10751,28,18,36
18,10749,10769,35,18,10402,35,18,10402
28,12,878,18,10749,10769,18,10749,10769
35,10749,35,14,10402,10751,14,10402,10751

If I understood you correctly, you have a "Multi-Class" or "Multi-Target" problem.
You have several simple options to solve the problem:
Create a new target class which incorporates all 3 (concatenation of decision_one, decision_two and decision_three)
Train each target separately.

I think the simplest approach would be, as Bella said, to train three separate models, one for each class, possibly removing the rest of the class attribs (depending on whether or not you want the other two classes to influence your classification).