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TrieHard | generic compact Trie implementation in Java | Natural Language Processing library

 by   ClickerMonkey Java Version: 1.0 License: OSL-3.0

 by   ClickerMonkey Java Version: 1.0 License: OSL-3.0

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kandi X-RAY | TrieHard Summary

TrieHard is a Java library typically used in Artificial Intelligence, Natural Language Processing applications. TrieHard has no bugs, it has no vulnerabilities, it has build file available, it has a Weak Copyleft License and it has low support. You can download it from GitHub.
A generic [Trie](https://en.wikipedia.org/wiki/Trie) implementation in Java. TrieHard comes ready to create Tries of many types: String, char[], byte[], int[], short[], long[], and java.nio.ByteBuffer.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • TrieHard has a low active ecosystem.
  • It has 39 star(s) with 8 fork(s). There are 4 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 0 open issues and 1 have been closed. On average issues are closed in 162 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of TrieHard is 1.0
TrieHard Support
Best in #Natural Language Processing
Average in #Natural Language Processing
TrieHard Support
Best in #Natural Language Processing
Average in #Natural Language Processing

quality kandi Quality

  • TrieHard has 0 bugs and 0 code smells.
TrieHard Quality
Best in #Natural Language Processing
Average in #Natural Language Processing
TrieHard Quality
Best in #Natural Language Processing
Average in #Natural Language Processing

securitySecurity

  • TrieHard has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • TrieHard code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
TrieHard Security
Best in #Natural Language Processing
Average in #Natural Language Processing
TrieHard Security
Best in #Natural Language Processing
Average in #Natural Language Processing

license License

  • TrieHard is licensed under the OSL-3.0 License. This license is Weak Copyleft.
  • Weak Copyleft licenses have some restrictions, but you can use them in commercial projects.
TrieHard License
Best in #Natural Language Processing
Average in #Natural Language Processing
TrieHard License
Best in #Natural Language Processing
Average in #Natural Language Processing

buildReuse

  • TrieHard releases are available to install and integrate.
  • Build file is available. You can build the component from source.
  • Installation instructions are not available. Examples and code snippets are available.
TrieHard Reuse
Best in #Natural Language Processing
Average in #Natural Language Processing
TrieHard Reuse
Best in #Natural Language Processing
Average in #Natural Language Processing
Top functions reviewed by kandi - BETA

kandi has reviewed TrieHard and discovered the below as its top functions. This is intended to give you an instant insight into TrieHard implemented functionality, and help decide if they suit your requirements.

  • Searches for a node in a tree .
    • Put a value in the trie
      • Adds all values in the trie .
        • Calculate the size of the trie
          • Retains the trie .
            • Updates the previous value .
              • Returns true if the sequenceA matches the sequenceA
                • Checks if the specified collection contains all of the elements in the trie .
                  • Returns the total number of values in the map .
                    • Prepend spaces to the values .

                      Get all kandi verified functions for this library.

                      Get all kandi verified functions for this library.

                      TrieHard Key Features

                      A generic compact Trie implementation in Java. Built for high-performance applications.

                      Code Example

                      copy iconCopydownload iconDownload
                      Trie<String, Boolean> t = Tries.forStrings();
                      
                      // Include
                      t.put( "java.lang.", true );
                      t.put( "java.io.", true );
                      t.put( "java.util.concurrent.", true );
                      
                      // Exclude
                      t.put( "java.util.", false );
                      t.put( "java.lang.Boolean", false );
                      
                      assertTrue( t.get( "java.lang.Integer" ) );
                      assertTrue( t.get( "java.lang.Long" ) );
                      assertFalse( t.get( "java.lang.Boolean" ) );
                      assertTrue( t.get( "java.io.InputStream" ) );
                      assertFalse( t.get( "java.util.ArrayList" ) );
                      assertTrue( t.get( "java.util.concurrent.ConcurrentHashMap" ) );

                      Use Cases

                      copy iconCopydownload iconDownload
                      Trie<String, Integer> t = Tries.forInsensitiveStrings();
                      t.setDefaultMatch( TrieMatch.PARTIAL );
                      // Add all available values to the Trie
                      t.put( "world", 23 );
                      t.put( "worm", 45 );
                      t.put( "worry", 76 );
                      t.put( "why", -89 );
                      t.put( "women", 123 );
                      ...
                      // Given user input, what are possible keys & values?
                      String userInput = "WO";
                      Set<Entry<String, Integer>> possible = t.nodes( userInput );
                      // possible = { world=>23, worm=>45, worry=>76, women=>123 }
                      ...
                      // Use possible to display full keys and their values.

                      How do I create my own Trie type?

                      copy iconCopydownload iconDownload
                      public interface TrieSequencer<S>
                      {
                         public int matches(S sequenceA, int indexA, S sequenceB, int indexB, int count);
                         public int lengthOf(S sequence);
                         public int hashOf(S sequence, int index);
                      }

                      Community Discussions

                      Trending Discussions on Natural Language Processing
                      • number of matches for keywords in specified categories
                      • Apple's Natural Language API returns unexpected results
                      • Tokenize text but keep compund hyphenated words together
                      • Create new boolean fields based on specific bigrams appearing in a tokenized pandas dataframe
                      • ModuleNotFoundError: No module named 'milvus'
                      • Which model/technique to use for specific sentence extraction?
                      • Assigning True/False if a token is present in a data-frame
                      • How to calculate perplexity of a sentence using huggingface masked language models?
                      • Mapping values from a dictionary's list to a string in Python
                      • What are differences between AutoModelForSequenceClassification vs AutoModel
                      Trending Discussions on Natural Language Processing

                      QUESTION

                      number of matches for keywords in specified categories

                      Asked 2022-Apr-14 at 13:32

                      For a large scale text analysis problem, I have a data frame containing words that fall into different categories, and a data frame containing a column with strings and (empty) counting columns for each category. I now want to take each individual string, check which of the defined words appear, and count them within the appropriate category.

                      As a simplified example, given the two data frames below, i want to count how many of each animal type appear in the text cell.

                      df_texts <- tibble(
                        text=c("the ape and the fox", "the tortoise and the hare", "the owl and the the 
                        grasshopper"),
                        mammals=NA,
                        reptiles=NA,
                        birds=NA,
                        insects=NA
                      )
                      
                      df_animals <- tibble(animals=c("ape", "fox", "tortoise", "hare", "owl", "grasshopper"),
                                 type=c("mammal", "mammal", "reptile", "mammal", "bird", "insect"))
                      

                      So my desired result would be:

                      df_result <- tibble(
                        text=c("the ape and the fox", "the tortoise and the hare", "the owl and the the 
                        grasshopper"),
                        mammals=c(2,1,0),
                        reptiles=c(0,1,0),
                        birds=c(0,0,1),
                        insects=c(0,0,1)
                      )
                      

                      Is there a straightforward way to achieve this keyword-matching-and-counting that would be applicable to a much larger dataset?

                      Thanks in advance!

                      ANSWER

                      Answered 2022-Apr-14 at 13:32

                      Here's a way do to it in the tidyverse. First look at whether strings in df_texts$text contain animals, then count them and sum by text and type.

                      library(tidyverse)
                      
                      cbind(df_texts[, 1], sapply(df_animals$animals, grepl, df_texts$text)) %>% 
                        pivot_longer(-text, names_to = "animals") %>% 
                        left_join(df_animals) %>% 
                        group_by(text, type) %>% 
                        summarise(sum = sum(value)) %>% 
                        pivot_wider(id_cols = text, names_from = type, values_from = sum)
                      
                        text                                   bird insect mammal reptile
                        <chr>                                 <int>  <int>  <int>   <int>
                      1 "the ape and the fox"                     0      0      2       0
                      2 "the owl and the the \n  grasshopper"     1      0      0       0
                      3 "the tortoise and the hare"               0      0      1       1
                      

                      To account for the several occurrences per text:

                      cbind(df_texts[, 1], t(sapply(df_texts$text, str_count, df_animals$animals, USE.NAMES = F))) %>% 
                        setNames(c("text", df_animals$animals)) %>% 
                        pivot_longer(-text, names_to = "animals") %>% 
                        left_join(df_animals) %>% 
                        group_by(text, type) %>% 
                        summarise(sum = sum(value)) %>% 
                        pivot_wider(id_cols = text, names_from = type, values_from = sum)
                      

                      Source https://stackoverflow.com/questions/71871613

                      Community Discussions, Code Snippets contain sources that include Stack Exchange Network

                      Vulnerabilities

                      No vulnerabilities reported

                      Install TrieHard

                      You can download it from GitHub.
                      You can use TrieHard like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the TrieHard component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

                      Support

                      For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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