SEDiL is an easy to use software platform created in the context of the ANR Marmota Project and the PASCAL pump-priming project: “Learning Stochastic Edit Distances from Structured Data”.
SEDiL aims at sharing many learning algorithms of probabilistic edit distances between structured data (sequences and trees).
Edit distances are used in many domains such as bio-informatic, image, sound and music recognition, WEB mining .
The Edit Distance represents the minimal number of necessary modifications (edit operations) to transform an input structured data into an output one.
In real world applications, the edit distances parameters are manually tuned. In domains where the level of knowledge is insufficient, it seems useful to automatically learn those parameters.
Because XML is becoming the new standard for information storage. Morever, trees provide more information than single sequences
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SEDiL Cracked 2022 Latest Version is an open source framework for building edit distance learning algorithms and tools.
The framework offers pre-trained models for some standard edit distances (Wang-Sum, Jaccard, Umeyama) as well as for some edit distances based on tree edit distance (pTET).
Available tools include web server for training, tools for inference and models of Edit distance algorithms for sequences.
The framework has the ability to process sequences and trees.
The architecture allows for different trade-offs between accuracy, speed and complexity.
Learning Approach
SEDiL algorithms are based on Stochastic Learning Algorithms for Edit Distance in Structured Data (SLADS).
SLADS are stochastic variants of the classical Levenshtein distance algorithm that can be used in noisy data with less restrictive assumptions on the target probability distribution.
It is based on the Stochastic Levenshtein learning algorithm, an algorithm to automatically learn the parameters of the Levenshtein distance that aims at minimizing the number of required operations.
Several SLADS have been evaluated in the literature:
The main theoretical and practical advantage of Stochastic Levenshtein Learning over the classical Levenshtein distance is that it can be used in the case where there is no target probability distribution available.
In contrast with the classic Levenshtein algorithm, Stochastic Levenshtein Learning can be used in the case of noisiness and the representation of the target probability distribution is to be approximate (by an unknown probability distribution).
SEDiL focuses on the case of binary representations.
The performance of SEDiL algorithms is first evaluated by evaluating the generalization error on data not used in the training.
Then it is compared to the performance of edit distance tools that provide the edit distance by following a greedy approach.
Finally, a practical comparison on benchmark sequences is provided.
Tool
The web server SEDiL is an open source project and is accessible at:
The tool is hosted on Github and it is available at:
SEDiL Tool Result:
See also
Probabilistic Wasserstein Distance
Unsupervised Feature Learning for Structured Data
Edit distance
Sequence
Tree
Edit distance (
SEDiL
As soon as a key is detected, the program performs a simple normalization of the pattern dictionary,
in order to eliminate the effect of keystrokes on the string.
Keystrokes are represented by a one-hot vector so that any combination of keys can be efficiently represented by a single vector.
Most of the time, the number of characters corresponding to each key is small (less than 5).
The output of the Keymacro macro is stored in a plain text file.
KEYMACRO syntax:
Keymacro data ” in target pattern “{| in target key “|}” {| out pattern “}
intorduce:
The macro defines a simple wrapper function for the unordered insertion of characters in a string.
From time to time, the recognition module introduces errors, which may occur due to a few circumstances.
To minimize their effect, we normalize the patterns by mean, and erase characters that occur with a low frequency.
LOSSY Descripition:
At the core of the module is a lossy compress function. This function can use any lossy compression algorithm,
and is useful if the dictionary is very large.
When a long input sequence contains duplicates, it is considered as a single entry and added to the dictionary,
subtracting the maximum length of the sequence from the total length of the dictionary.
The module uses the Trie to cache the patterns, and a histogram that is used to count the frequency of character occurrences.
To gain efficiency, the compress function calls the compress_pattern function.
The latter returns a compressed pattern that can be efficiently stored and loaded with better space and time complexity.
Using this method, the module avoids compression everytime the dictionary is expanded,
which improves the performance.
More efficiency can be achieved by storing only the histogram, and the compressed_pattern.
To limit the memory consumption of the function, we use a variable-length representation,
which is able to represent compressed patterns of a fixed length with a variable number of bytes.
To avoid the creation of a dictionary, the histogram is created during the training phase.
HISTOGRAM Description:
This is the dictionary used by the compress function to store character occurrences.
Each entry represents a pair of characters (the keys), and a frequency.
As the dictionary is big, a dictionary to string conversion is used.
HISTOGRAM syntax:
histogram data ” string ”
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The SEDiL platform consists of the following components:
A plugin (python-xml) which provides access to the xml format.
An XML schema specification and libraries
A set of learning algorithms to learn parameters (edit distances) of known edit operations
A python based programming interface to apply those algorithms on a training corpus or a sequence pair.
A Learning Algorithm
The goal of SEDiL is to learn unknown edit distances parameters from a training corpus.
The learning algorithm is based on a stochastic optimization approach.
It consists in building an inventory of permutations and scoring them according to edit distances.
In order to cope with large number of combinations, many permutations are sampled and scores are assigned with a Monte-Carlo approach.
The goal of the software is to use that inventory of permutations to find the combination that optimizes the edit distance between two input sequences.
As the learning algorithm is stochastic, the output is a probability distribution over edit distances.
See also
Probabilistic edit distance
References
External links
Category:Bioinformatics
Category:Information retrieval techniques
Category:Sequence alignment softwareQ:
Unable to run my React code with Babel and Webpack
I’m trying to build a React application that uses Webpack, Babel and React Router.
However, I’m getting this error:
ERROR in./src/index.js Module not found: Error: Can’t resolve ‘../index.js’ in ‘/home/user/tests/test-app/src’
@./src/index.js 4:0-26
@ multi./src/index.js./src/App.js./src/About.js
Why does it look for./index.js?
This is my code:
index.js:
import React, { Component } from’react’;
import ReactDOM from’react-dom’;
import ‘./App.css’;
class App extends Component {
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ReactDOM.render(
What’s New In SEDiL?
The main concepts of the SEDiL software:
– the edit distance between sequences/trees
– the continuous/discrete model
– the distance learning algorithms
– the calibration of the model (it takes in account different variables of the source and the target)
PASCAL EDIT is a database of structured data together with a library of algorithms for its analysis.
The PASCAL EDIT source database has been mainly created by the Projet de recherche en Informatique d’information structurée (PRIS) and the Agence Nationale de la Recherche (ANR), from 2008 to 2011.
It contains more than 16000 XML documents of biomedical, genomics and clinical interest.
The PASCAL EDIT library aims at providing the easiest access to these algorithms.
Two research directions are being explored for the next months:
– Enhancing the XML data and providing data comparison between XMLs of the same file or of different documents
– Automatic analysis of the documents:
– Learning of the edit distance parameters
– Calibration of the models (the learning results can vary according to the parameters)
PASCAL EDIT is also an application designed for use in education.
The graphical user interface allows the interactive use of the PASCAL EDIT data.
As an example, a student can learn a generic edit distance by observing the variation of the edit distance of a given sequence with the ones of the documents of the database.
PASCAL SEDiL is built with Delphi 7.
The XML databases are quite long and use advanced tree manipulations.
PASCAL SEDiL is built with Lazarus (
Features:
– XML databases of sequences/trees
– Calibration of the models (it takes in account different variables of the source and the target)
– Learning of the edit distance parameters (a generic learning is available, or a generic learning based on a set of documents from a given domain)
– Graphic display of the parameters
Some basic parameters can be learned:
– Length of the sequences/trees
– Number of the sequences/trees
– Minimum number of edit operations
– Number of the documents per file
– Average number of edit operations
– Range of the edit distance
The edit distance learning function does not work properly with the generic PASCAL EDIT.
The library is better to use directly.
The next PASCAL SEDiL development will be more directly related to the methods of the LearnXML’s project (
Please, notice that the GUI is not working in PASCAL EDIT:
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