Open Hub
Open Hub

 Settings |  Report Duplicate

0
I Use This!
Very Low Activity

Commits : Listings

Analyzed about 7 hours ago. based on code collected 1 day ago.
Nov 05, 2025 — Nov 05, 2026
Commit Message Contributor Files Modified Lines Added Lines Removed Code Location Date
Updated AsFer Design Document - Complement Text-Graph, Meaningfulness and Intrinsic Merit - Theorem 5.16 in https://tac.nist.gov/publications/2010/participant.papers/CMI_IIT.proceedings.pdf
Srinivasan Kannan
More... over 8 years ago
Updated AsFer Design Document - Complement Functions, Complement Graphs, Perfect Graph Theorem and Intrinsic Merit of Text Graphs
Srinivasan Kannan
More... over 8 years ago
Updated AsFer Design Document - Microsoft Concept Graph - Probase
Srinivasan Kannan
More... over 8 years ago
Updated AsFer Design Document - Complement Diophantine Factorization - Pell Equation Solver
Srinivasan Kannan
More... over 8 years ago
--------------------------------------------------------------------------------------------------------------- Complement Diophantine and Factorization - Pell Equation Solver Update 2 --------------------------------------------------------------------------------------------------------------- 1. Complement Function Implementation has been updated to invoke sympy Pell Equation solver for some N and D in x^2 - D*y^2 = N for D=1 2. Following are the factors p,q of N: x + y = p x - y = q for Pell equation solutions x and y. This is exponential in number of bits of N and equivalent to most Number Field Sieve algorithms for Factorization in vogue. Computational Geometric Factorization in NC implies x and y can be found in parallel polynomial time (which is quite surprising given the antiquity and notorious difficulty of this ~1500 year old problem). 3. Pell's Equation for Factoring and Prime polynomials (e.g Matiyasevich, Jones-Sato-Wada-Wiens) are thus complement diophantines representing the exact cover {Primes,Composites} of the Set of Natural Numbers
Srinivasan Kannan
More... over 8 years ago
--------------------------------------------------------------------------------------------------------------- Complement Diophantine and Factorization - Pell Equation Solver Update --------------------------------------------------------------------------------------------------------------- 1. Complement Function Implementation has been updated to invoke sympy Pell Equation solver for some N and D in x^2 - D*y^2 = N 2. Factorization reduces to integer solutions of Pell Equation but the converse is harder - factors must be known a priori as pq = N from some factoring algorithm. Then following equations can be solved: x + sqrt(D)*y = p x - sqrt(D)*y = q for x and y.
Srinivasan Kannan
More... over 8 years ago
Updated AsFer Design Document - Computational Geometric Factorization in NC - Parallel RAM Segment Tree References
Srinivasan Kannan
More... over 8 years ago
Updated AsFer Design Document - Chomsky-Norvig Debate, Gold Theorem
Srinivasan Kannan
More... over 8 years ago
Updated AsFer Design Document - Euclidean Ramsey Theory and Tiling/Exact Cover
Srinivasan Kannan
More... over 8 years ago
Updated AsFer Design Document - Various Social Network Intrinsic Fitness Models
Srinivasan Kannan
More... over 8 years ago
Updated AsFer Design Document - Packing/Tiling/Filling, Complement Diophantines, Exact Cover and MRDP theorem
Srinivasan Kannan
More... over 8 years ago
Updated AsFer Design Document - Pell Equation and Factoring
Srinivasan Kannan
More... over 8 years ago
Updated AsFer Design Document - Binary Quadratic Diophantine Equations and Factorization - References
Srinivasan Kannan
More... over 8 years ago
Updated Requirements.txt
Srinivasan Kannan
as K.Srinivasan
More... over 8 years ago
Updated Requirements.txt
Srinivasan Kannan
as Srinivasan Kannan (also known as: Ka.Shrinivaasan, Shrinivas Kannan)
More... over 8 years ago
Updated AsFer Design Document - Intrinsic Merit, Human Resource Analytics, Human Development Index, Intrinsic Performance Ratings
Srinivasan Kannan
More... over 8 years ago
Least Squares SAT Solver Benchmarks - 1000 variables - Non-uniform choice 3 - different values of Alpha ----------------------------------------------------------------------------------------------------------------------------------------------- 1. SAT Solver (LSMR) has been benchmarked for 1000 variables and for varying values of Alpha as below. MAXSAT approximation ratio after atleast 20 iterations of random 3SAT for nonuniform_choice3() are: Alpha = 1 - Observed - MAXSAT-APPROXIMATION Ratio - Moving Average Percentage of Clauses per CNF satisfied so far: 94.3032258065 Alpha = 2 - Observed - MAXSAT-APPROXIMATION Ratio - Moving Average Percentage of Clauses per CNF satisfied so far: 92.2115384615 Alpha = 3 - Observed - MAXSAT-APPROXIMATION Ratio - Moving Average Percentage of Clauses per CNF satisfied so far: 91.1458333333 Alpha = 4.267 - Observed - MAXSAT-APPROXIMATION Ratio - Moving Average Percentage of Clauses per CNF satisfied so far: 90.196679346 Alpha = 5 - Observed - MAXSAT-APPROXIMATION Ratio - Moving Average Percentage of Clauses per CNF satisfied so far: 89.7233333333 Alpha = 6 - Observed - MAXSAT-APPROXIMATION Ratio - Moving Average Percentage of Clauses per CNF satisfied so far: 89.8702898551 Alpha = 7 - Observed - MAXSAT-APPROXIMATION Ratio - Moving Average Percentage of Clauses per CNF satisfied so far: 89.8266233766
Srinivasan Kannan
More... over 8 years ago
Updated AsFer Design Document - Complement Functions, Exact Cover, Dancing Links
Srinivasan Kannan
More... over 8 years ago
Updated Requirements.txt
Srinivasan Kannan
More... over 8 years ago
Updated AsFer Design Document - Recursive Lambda Function Growth - Simple Cycles and Rich Club Coefficient
Srinivasan Kannan
More... over 8 years ago
Recursive Lambda Function Growth - Simple Cycles and Rich Club Coefficient ----------------------------------------------------------------------------------------------------------------------------------------- 1.Recursive Lambda Function Growth implementation has been updated to loop through cycles in the definition graph of text and to choose between Cycles and Random Walks by a boolean flag ClosedPaths. 2.Closed Paths or Cycles simulate the meaning better and the tree lambda composition obtained from cycle vertices has been experimentally found to approximate meaning more closely. 3.Logs for this has been committed to testlogs/RecursiveLambdaFunctionGrowth.log.GraphTensorNeuronNetwork.27March2018 4.Rich Club Coefficients of the Definition Graph has been printed for each degree as a measure of connectivity of high degree vertices(rich club): Rich Club Coefficient of the Recursive Gloss Overlap Definition Graph: {0: 0.004865591072487624, 1: 0.016526610644257703, 2: 0.018738738738738738, 3: 0.02396259497369959, 4: 0.024154589371980676, 5: 0.023809523809523808, 6: 0.028985507246376812, 7: 0.032679738562091505, 8: 0.022222222222222223, 9: 0.0, 10: 0.0} 5.Top core number classes from Unsupervised Recursive Gloss Overlap Classifier: ============================================================================================================= Unsupervised Classification based on top percentile Core numbers of the definition graph(subgraph of WordNet) ============================================================================================================= This document belongs to class: incorporate ,core number= 4 This document belongs to class: environment ,core number= 4 This document belongs to class: regional ,core number= 4 This document belongs to class: `in ,core number= 4 This document belongs to class: component ,core number= 4 This document belongs to class: exploitation ,core number= 4 This document belongs to class: useful ,core number= 4 This document belongs to class: relating ,core number= 4 This document belongs to class: unit ,core number= 4 This document belongs to class: particular ,core number= 4 This document belongs to class: making ,core number= 4 This document belongs to class: process ,core number= 4 This document belongs to class: sphere ,core number= 4 This document belongs to class: something ,core number= 4 This document belongs to class: goal ,core number= 4 This document belongs to class: farm ,core number= 4 This document belongs to class: urbanization ,core number= 4 This document belongs to class: strategic ,core number= 4 This document belongs to class: ' ,core number= 4 This document belongs to class: increase ,core number= 4 This document belongs to class: comforts ,core number= 4 This document belongs to class: city ,core number= 4 This document belongs to class: district ,core number= 4 This document belongs to class: farming ,core number= 4 This document belongs to class: urban ,core number= 4 This document belongs to class: way ,core number= 4 This document belongs to class: plan ,core number= 4 This document belongs to class: do ,core number= 4 This document belongs to class: contain ,core number= 4 This document belongs to class: see ,core number= 4 This document belongs to class: state ,core number= 4 This document belongs to class: region ,core number= 4 This document belongs to class: proposal ,core number= 4 This document belongs to class: life ,core number= 4 This document belongs to class: decisiveness ,core number= 4 This document belongs to class: lives ,core number= 4 This document belongs to class: metropolitan ,core number= 4 ------------------------------------------------------------------------------------------- coincide reasonably well to the Maximum Merit Cycle as below and the meaning of the text can be inferred from the composition tree of the word chain and the composed tensor potential is printed for this maximum merit cycle: ------------------------------------------------------------------------------------------- Cycle : [u'particular', u'regional', u'region', u'something', u'see', u'make', u'plan', u'goal', u'state', u'city', u'urban', u'area', u'exploitation', u'land', u'farm', u'unit', u'assembly', u'parts', u'environment', u'sphere'] Cycle Composition Tree for this cycle : (u'particular((region(regional,(see(something,(plan(make,(state(goal,(urban(city,(exploitation(area,(farm(land,(assembly(unit,(environment(parts,sphere)))))))))))))))))))', 7.250082923612336) maximum_per_random_walk_graph_tensor_neuron_network_intrinsic_merit= (u'(regional(particular,(`in(region,(pronounce(way,(see(certain,(add(make,(important(increase,(plan(strategic,(state(goal,(urban(city,(administrative(relating,(unit(agency,(land(farm,(useful(exploitation,(proper(necessitate,(metropolitan(person,(environment(lives,sphere))))))))))))))))))))))))))))))))', 14.322488296017706) =================================================================== grow_lambda_function3(): Graph Tensor Neuron Network Intrinsic Merit for this text: 3804.34147246 6.NetworkX library has been upgraded to recently released version 2.1
Srinivasan Kannan
More... over 8 years ago
Updated AsFer Design Document - Graph Tensor Neuron Network - Word Chains and Chomsky Deep Structure
Srinivasan Kannan
More... over 8 years ago
Updated AsFer Design Document - MARA based Intrinsic Merit Ranking of Websites
Srinivasan Kannan
More... over 8 years ago
Recursive Lambda Function Growth - Graph Tensor Neuron Network Intrinsic Merit for Random Walks - Different Example
Srinivasan Kannan
More... over 8 years ago
Recursive Lambda Function Growth - Graph Tensor Neuron Network Intrinsic Merit for Random Walks - Analysis for different text -------------------------------------------------------------------------------------------------------------------------- 1. Text input for Recursive Lambda Function Growth has been updated 2. Graph Tensor Neuron Network Intrinsic Merit for Lambda Function Composition of Random Walks of Definition Graph have been captured in log testlogs/RecursiveLambdaFunctionGrowth.log.GraphTensorNeuronNetwork.26March2018 3. Maximum Graph Tensor Neuron Network Intrinsic Merit occurs for Random Walk Composition: grow_lambda_function3(): Maximum Per Random Walk Graph Tensor Neuron Network Intrinsic Merit : (u'(housing(protective,(certain(zone,(something(target,(part(include,(urban(area,(Chennai(city,formerly))))))))))))', 6.023684210526316) 4.Top percentile Unsupervised Classes of this text by Dense Subgraph Discovery (Core Numbers) are: ============================================================================================================= Unsupervised Classification based on top percentile Core numbers of the definition graph(subgraph of WordNet) ============================================================================================================= This document belongs to class: arsenic ,core number= 16 This document belongs to class: Greenwich_Village ,core number= 14 This document belongs to class: state_of_matter ,core number= 14 This document belongs to class: order ,core number= 14 This document belongs to class: include ,core number= 10 This document belongs to class: part ,core number= 10 This document belongs to class: exploitation ,core number= 9 This document belongs to class: area ,core number= 9 This document belongs to class: three ,core number= 9 This document belongs to class: collector ,core number= 8 This document belongs to class: housing ,core number= 8 This document belongs to class: January ,core number= 8 This document belongs to class: trey ,core number= 8 This document belongs to class: Chennai ,core number= 7 This document belongs to class: urban ,core number= 7 This document belongs to class: planning ,core number= 6 This document belongs to class: zone ,core number= 6 This document belongs to class: free-base ,core number= 6 This document belongs to class: travel ,core number= 6 This document belongs to class: one ,core number= 6 This document belongs to class: target ,core number= 6 This document belongs to class: something ,core number= 6 This document belongs to class: republic ,core number= 6 This document belongs to class: government ,core number= 5 This document belongs to class: legal_power ,core number= 5 ---------------------------------------------------------------------- which reasonably coincide with the vertices of the maximum intrinsic merit random walk and capture the Legal/Gubernatorial/Urban planning nature of the text(though there are WordNet anomalies printing 'arsenic' etc., as classes. These anomalies are caused because usually Urban planning is related to exploitation and Solid Waste Management which in turn are connected to Toxic substances and Pollutants. WordNet contains all these relations. Some other Net like ConceptNet5 might probably do better which are word2vec based.
Srinivasan Kannan
More... over 8 years ago
Updated AsFer Design Document - Multiple Agent Resource Allocation(MARA) and Intrinsic Merit
Srinivasan Kannan
More... over 8 years ago
Updated AsFer Design Document - Unsorted Search - Streaming Abstract Generator File Datasource Support, Prefix-Suffix substrings hashtables
Srinivasan Kannan
More... over 8 years ago
Unsorted Search Update - Streaming Abstract Generator File Datasource Support, Prefix-Suffix substrings hashtables ----------------------------------------------------------------------------------------------------------------------------- 1. Hardcoded File name in Streaming Abstract Generator has been replaced by a file name variable and file contents are tokenized and stripped of leading and trailing whitespaces and yielded. 2. Padding of '#' in Unsorted Search has been replaced by '0'. Revised Unsorted Search Algorithm in https://github.com/shrinivaasanka/Grafit/blob/master/course_material/ComputerScienceMiscellaneous/ComputerScienceMiscellaneous_CourseNotes.txt has been implemented by creating hash tables for all integer-string prefixes and suffixes (though not for all substrings) than just for single digits. 3.New function to print contents of all hashtables has been added. 4.Function create_prefix_suffix_hashtables() initializes prefix and suffix hashtables for strings from Streaming Abstract Generator 5.search_number() function has been rewritten to search for all prefixes and suffixes hashtables and match True/False is written 6.New input file First100Primes.txt has been created. 7.Primes and Non-Primes are lookedup and logs has been committed to testlogs/UnsortedSearch.log.23March2018
Srinivasan Kannan
More... over 8 years ago
Updated AsFer Design Document - Hardy-Ramanujan Approximate Ray Shooting Queries for Factoring
Srinivasan Kannan
More... over 8 years ago
Hardy-Ramanujan Approximate Ray Shooting Queries for Factors - Optimization in Computational Geometric Factorization
Srinivasan Kannan
More... over 8 years ago