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Analyzed about 3 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
+1485. (THEORY and FEATURE) Set Partition Analytics - Set Partition Histogram to tile cover reduction - DDSketch Quantile Sketch analytics for EVMs - 21 April 2024 - related to 651,1213,1215,1217,1221,1226,1227,1484 and all sections on Set Partitions and Histogram Analytics, Merit of Large Scale Visuals, GIS Urban Sprawl Analytics - minimum rectangular bounding box of an urban sprawl, Computational Geometric Integer Factorization,Neuro Cryptocurrency Proof-Of-Work Hardness,Economic Merit,Poverty Alleviation,Money Changing Problem,Integer Linear Programming,Pell Diophantine for Factorization,Perfect Numbers,N-dimensional ball,Lebesgue-Borel measure,Chinese remaindering theorem,Kernel lifting,Shell Turing Machines
Srinivasan Kannan
More... over 2 years ago
-------------------------------------------------------------------------------------------------------------------------------------------- 1485. (THEORY and FEATURE) Set Partition Analytics - Set Partition Histogram to tile cover reduction - DDSketch Quantile Sketch analytics for EVMs - 21 April 2024 - related to 651,1213,1215,1217,1221,1226,1227,1484 and all sections on Set Partitions and Histogram Analytics, Merit of Large Scale Visuals, GIS Urban Sprawl Analytics - minimum rectangular bounding box of an urban sprawl, Computational Geometric Integer Factorization,Neuro Cryptocurrency Proof-Of-Work Hardness,Economic Merit,Poverty Alleviation,Money Changing Problem,Integer Linear Programming,Pell Diophantine for Factorization,Perfect Numbers,N-dimensional ball,Lebesgue-Borel measure,Chinese remaindering theorem,Kernel lifting,Shell Turing Machines -------------------------------------------------------------------------------------------------------------------------------------------- 1485.1 python-src/Streaming_SetPartitionAnalytics.py has been updated to import DDSketch Quantile sketch implementation from https://github.com/DataDog/sketches-py 1485.2 One DDSketch() object is instantiated per Electronic Voting Machine histogram 1485.3 Function def electronic_voting_machine(Voting_Machine_dict, QuantileSketch, candidatesdict, unique_id=None, voted_for="NOTA", Streaming_Analytics_Bertrand=False, onetimepassword=None) has been updated to include QuantileSketch object and candidates dictionary (maps candidate name to a numeric unique id sorted ascending) as arguments. 1485.4 Function def electronic_voting_analytics(QuantileSketches,Voting_Machine_dicts,quantiles=[10,20,30]) has been updated to include quantile array as argument and compute quantile sketch by DDSketch. 1485.5 Quantile values for each quantile is inferred from DDSketch.get_quantile_value() 1485.6 For 3 voter unique IDs,3 Candidates and 3 EVMs following is the JSON persisted for random voting: [{"CandidateA": 1, "CandidateB": 1}, {"CandidateA": 1}, {}] 1485.7 Quantile sketches for 3 EVMs earlier (which are histograms [0,1,1],[0,1,0],[0,0,0] for 3 candidates NOTA=1,CandidateA=2,CandidateB=3) is computed by DDSketch below for 3 quantiles [1,2,3]: EVM Histogram Quantiles: [2.974233423476717, None, None] EVM Histogram Quantiles: [1.993661701417351, None, None] EVM Histogram Quantiles: [None, None, None] 1485.8 It is worth observing that factorization and Chinese Remaindering Theorem isomorphically lift an one dimensional scalar respectively to (*) Lebesgue Hypervolume of a hyperrectangle of sides equal in length to factors (*) an arbitrary hyperrectangle of sides modulo divisors which is a special instance of Shell Turing Machines of varying Turing degrees defined over N-dimensional Spaces creating a Turing jump from a scalar in 1-Dimension to N-dimensions - Vide: Section 625 and 625.4 on Shell Turing Machines over N-dimension vector space and Hilbert Machines which are Turing machines over complex inner product spaces. Earlier 2 kinds of lifting a scalar to N-dimensional space (which could possibly be a Hilbert space of quantum superposition - for example factorization of integer N as N=p*q*r*s*t lifts to Lebesgue hyperrectangle of sides p,q,r,s,t on Hilbert space and sides could be proportional to amplitudes, and similarly Chinese remaindering produces a modulo tuple on Hilbert space corresponding to sides of hyperrectangle) make a classical-to-quantum transition theoretically possible though there is no experimental evidence as such (by decomposing a classical quantity into quantum superposition) while converse quantum-to-classical is known by decoherence and wave function collapse. 1485.9 Logs for this update are at python-src/testlogs/Streaming_SetPartitionAnalytics.QuantileSketch.log.21April2024
Srinivasan Kannan
More... over 2 years ago
Section 1471 updated
Srinivasan Kannan
More... over 2 years ago
+1484. (JIRA,THEORY and FEATURE) Set Partition Analytics - Set Partition Histogram to tile cover reduction - Lebesge-Borel hypervolume and Perfect numbers - 18 April 2024 - related to 651,1213,1215,1217,1221,1226,1227 and all sections on Set Partitions and Histogram Analytics, Merit of Large Scale Visuals, GIS Urban Sprawl Analytics - minimum rectangular bounding box of an urban sprawl, Computational Geometric Integer Factorization,Neuro Cryptocurrency Proof-Of-Work Hardness,Economic Merit,Poverty Alleviation,Money Changing Problem,Integer Linear Programming,Pell Diophantine for Factorization,Perfect Numbers,N-dimensional ball,Lebesgue-Borel measure,Chinese remaindering theorem,Kernel lifting
Srinivasan Kannan
More... over 2 years ago
-------------------------------------------------------------------------------------------------------------------------------------------- 1484. (JIRA,THEORY and FEATURE) Set Partition Analytics - Set Partition Histogram to tile cover reduction - Lebesge-Borel hypervolume and Perfect numbers - 18 April 2024 - related to 651,1213,1215,1217,1221,1226,1227 and all sections on Set Partitions and Histogram Analytics, Merit of Large Scale Visuals, GIS Urban Sprawl Analytics - minimum rectangular bounding box of an urban sprawl, Computational Geometric Integer Factorization,Neuro Cryptocurrency Proof-Of-Work Hardness,Economic Merit,Poverty Alleviation,Money Changing Problem,Integer Linear Programming,Pell Diophantine for Factorization,Perfect Numbers,N-dimensional ball,Lebesgue-Borel measure,Chinese remaindering theorem,Kernel lifting -------------------------------------------------------------------------------------------------------------------------------------------- 1484.1 python-src/Streaming_AbstractGenerator.py has been updated for an error related to json import 1484.2 python-src/Streaming_SetPartitionAnalytics.py has been updated to enable fileIO in Neuro Cryptocurrency Watermarking. 1484.3 New function is_perfect_partition_LebesgueBorel(factors,number_to_factorize) has been defined for checking if a set partition is perfect (sum of lengths of bins in histogram partition = product of the lengths of bins) and print it as a Lebesgue-Borel hypervolume measure of a hypercube. Lebesgue-Borel measure - https://en.wikipedia.org/wiki/Lebesgue_measure - generalizes usual measures of length,area,volume in 1D-2D-3D spaces respectively to N-dimensional spaces - ".... For any rectangular cuboid {\displaystyle C}{\displaystyle C} which is a product {\displaystyle C=I_{1}\times \cdots \times I_{n}}{\displaystyle C=I_{1}\times \cdots \times I_{n}} of open intervals, let {\displaystyle \operatorname {vol} (C)=\ell (I_{1})\times \cdots \times \ell (I_{n})}{\displaystyle \operatorname {vol} (C)=\ell (I_{1})\times \cdots \times \ell (I_{n})} denote its volume.....". Perfect number in its divisor product form is a Lebesgue hypervolume of a hyperrectangle of sides with lengths equal to k divisors and therefore lifts 1D quantity to k-dimensional space - in general any factorization is a Borel-Lebesgue hypervolume of a hyperrectangle. Similar lifting from 1D to k-dimensional space through Chinese remaindering has been described in Section 651 which is a ring isomorphism between x mod N <=> (x mod n1,x mod n2,x mod n3,....,x mod nk) for N=n1*n2*n3*...*nk and all ni are coprime (For perfect number N, N=n1 + n2 + n3 + ... + nk = n1*n2*n3*...*nk). 1484.4 Function setpartition_to_tilecover(histogram_partition=None, number_to_factorize=1, solution="ILP", Neuro_Crypto_ProofOfWork=False,sparkcommandline="/home/ksrinivasan/spark-3.3.0-bin-hadoop3/bin/spark-submit") has been updated to remove hardcoded Spark commandline and make it function argument. 1484.5 Datatype mismatches between str() and int() for factors JSON read in have been resolved by int() cast. 1484.6 This update resolves JIRA issue - https://krishnairesearch.atlassian.net/browse/NEUR-38
Srinivasan Kannan
More... over 2 years ago
Section 1236 updated
Srinivasan Kannan
More... over 2 years ago
section 1480 updated
Srinivasan Kannan
More... over 2 years ago
Section 1480 updated
Srinivasan Kannan
More... over 2 years ago
Section 1426 updated
K.Srinivasan
More... over 2 years ago
Section 135 updated
K.Srinivasan
More... over 2 years ago
Updated index.rst
K.Srinivasan
More... over 2 years ago
Section 1483 updated
K.Srinivasan
More... over 2 years ago
Section 1479 updated
K.Srinivasan
More... over 2 years ago
Section 1471 updated
K.Srinivasan
More... over 2 years ago
Section 1438 updated
K.Srinivasan
More... over 2 years ago
Section 1483 updated
Srinivasan Kannan
More... over 2 years ago
Updated Requirements.txt
Srinivasan Kannan
More... over 2 years ago
+1483. (THEORY and FEATURE) Social network analysis and People analytics - Support for flair NLP framework sentiment analysis - related to Section 588,1380,1406,1407,1408,1409,1410,1411,1430 and all sections on Majority Voting, Approximate Majority,Election analytics-Psephology-Opinion polls,Reservoir Sampling,Topic detection and tracking,Social network analysis and People analytics from Twitter-Facebook-LinkedIn-PIPL streams, Large Scale Visuals-Analyzing Audio-Visuals for sentiment (Movies,YouTube) by LIWC-Empath, Drone Electronic Voting Machines-Multipolar voting, KLL Sketch-Quantile and Histogram Approximations, Opinion poll sampling
Srinivasan Kannan
More... over 2 years ago
-------------------------------------------------------------------------------------------------------------------------------------------- 1483. (THEORY and FEATURE) Social network analysis and People analytics - Support for flair NLP framework sentiment analysis - related to Section 588,1380,1406,1407,1408,1409,1410,1411,1430 and all sections on Majority Voting, Approximate Majority,Election analytics-Psephology-Opinion polls,Reservoir Sampling,Topic detection and tracking,Social network analysis and People analytics from Twitter-Facebook-LinkedIn-PIPL streams, Large Scale Visuals-Analyzing Audio-Visuals for sentiment (Movies,YouTube) by LIWC-Empath, Drone Electronic Voting Machines-Multipolar voting, KLL Sketch-Quantile and Histogram Approximations, Opinion poll sampling -------------------------------------------------------------------------------------------------------------------------------------------- 1483.1 python-src/SocialNetworkAnalysis_OpinionMining.py has been updated to add support for flair NLP sentiment analysis - https://github.com/flairNLP/flair which is based on Python3.9 + PyTorch 1483.2 Function def opinion_mining(query,fromdate,todate,fulltextortitle="title",maxpages=10,maxarticles=100,articlefraction=0.2,UseNeuronRainSentimentAnalyzer=False) has been updated to choose between fulltext or headline of a news article for sentiment analysis based on argument fulltextortitle. 1483.3 Flag UseNeuronRainSentimentAnalyzer is being checked in more places to avoid intensive NeuronRain textgraph graphical model sentiment analysis. 1483.4 Flair sentiment analyzer scores in addition to VADER,TextBlob and Empath are logged to testlogs/SocialNetworkAnalysis_OpinionMining.log.10April2024 for query "GDP" which is strewn with rate limit and URL errors internal to newspaper3k on Python 3.9.
Srinivasan Kannan
More... over 2 years ago
Section 1408 updated
Srinivasan Kannan
More... over 2 years ago
Section 1438 updated
Srinivasan Kannan
More... over 2 years ago
Section 1482 updated
Srinivasan Kannan
More... over 2 years ago
Section 1482 updated
K.Srinivasan
More... over 2 years ago
Section 1463 updated
K.Srinivasan
More... over 2 years ago
Section 1482 updated
K.Srinivasan
More... over 2 years ago
Section 1482 updated
K.Srinivasan
More... over 2 years ago
Updated Requirements.txt
K.Srinivasan
More... over 2 years ago
1482. (THEORY and FEATURE) Computational Geometric Integer Factorization - Python 3.12 + PySpark 3.5.1 Upgrade - Single node clustering benchmark and Euler Sigma Function implementation for Perfect Numbers - related to 498,601,1226,1259,1303,1319,1321,1323,1324,1325,1355,1365,1366,1369,1379,1381,1384,1385,1401,1419,1454 and all sections on Integer Factorization (DMRC MapReduce PySpark and NC HPC Rust implementations),Computational Geometry,Set Partitions-to-Tile Cover reduction by Lagranges Four Square Theorem and ILP,Primality Testing,RSA Semiprimes,Almost-Square Bounding Boxes in Large Scale Visual Recognition,Pell's Equation,Brahmagupta's Chakravala,Approximate Factoring by Newton-Raphson Rayshooting queries,Algebraic Geometry of Computational Geometric Factorization,Quantum computation and Derandomization of BQP Factorization by Condorcet Jury Theorem Majority-BQPVoterSAT boolean composition - 5 April 2024
K.Srinivasan
More... over 2 years ago
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 1482. (THEORY and FEATURE) Computational Geometric Integer Factorization - Python 3.12 + PySpark 3.5.1 Upgrade - Single node clustering benchmark and Euler Sigma Function implementation for Perfect Numbers - related to 498,601,1226,1259,1303,1319,1321,1323,1324,1325,1355,1365,1366,1369,1379,1381,1384,1385,1401,1419,1454 and all sections on Integer Factorization (DMRC MapReduce PySpark and NC HPC Rust implementations),Computational Geometry,Set Partitions-to-Tile Cover reduction by Lagranges Four Square Theorem and ILP,Primality Testing,RSA Semiprimes,Almost-Square Bounding Boxes in Large Scale Visual Recognition,Pell's Equation,Brahmagupta's Chakravala,Approximate Factoring by Newton-Raphson Rayshooting queries,Algebraic Geometry of Computational Geometric Factorization,Quantum computation and Derandomization of BQP Factorization by Condorcet Jury Theorem Majority-BQPVoterSAT boolean composition - 5 April 2024 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 1482.1 Python version has been bumped to 3.12 and Spark version to 3.5.1 by following commandlines to install a virtual environment (venv) for Spark Python 3.12: 1482.1.1 apt update && apt upgrade -y 1482.1.2 apt install software-properties-common -y 1482.1.3 add-apt-repository ppa:deadsnakes/ppa 1482.1.4 apt update 1482.1.5 apt install python3.12 1482.1.6 apt install python3.12-venv 1482.1.7 python3.12 -m venv $VENVDIR/venv 1482.1.8 $VENVDIR/venv/bin/pip3.12 install pyspark 1482.1.9 export PYSPARK_PYTHON=$VENVDIR/venv/bin/python3.12 1482.1.10 export PYSPARK_DRIVER_PYTHON=$VENVDIR/venv/bin/python3.12 1482.2 Computational Geometric Parallel Planar Point Location factorization implementation DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.py has been updated to define Euler sigma Function for Perfect Numbers (function is_perfect_number_Euler_Sigma_Function(factors,number_to_factorize)) which returns true if aliquot sum (sum of proper divisors or factors of integer) equals the number itself and false otherwise. In other words for perfect numbers, unique factorization product equals sum of factors. All perfect numbers found so far are even. There is no known odd perfect number so far. 1482.3 Perfect Number 33550336 is factorized as (interrupted) and all factors are printed: venv/bin/python3.12 DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.py 33550336 3 False False all False 2>&1 > testlogs/DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.Python3.12Upgrade.PerfectNumbers.log.5April2024 1482.4 Perfect Number 8128 is factorized as: venv/bin/python3.12 DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.py 8128 3 False False all False 2>&1 > testlogs/DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.Python3.12Upgrade.PerfectNumbers.log2.5April2024 and verified as Perfect Number.
K.Srinivasan
More... over 2 years ago
Section 1481 updated
K.Srinivasan
More... over 2 years ago