Open Hub
Open Hub

 Settings |  Report Duplicate

0
I Use This!
Very Low Activity

Commits : Listings

Analyzed about 9 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
(FEATURE and THEORY) Merit of Large Scale Visuals - Tensor Rank intrinsic merit of EventNet of a video
Srinivasan Kannan
More... almost 7 years ago
---------------------------------------------------------------------------------------------------------------------------------------------- 732. (FEATURE and THEORY) Merit of Large Scale Visuals - Tensor Rank intrinsic merit of EventNet of a video - 7 December 2019 - related to 588 ---------------------------------------------------------------------------------------------------------------------------------------------- 1.This commit upgrades ImageGraph_Keras_Theano.py to Python 3.6 from Python 2.7 and computes Tensor Rank of EventNet of a video by tensorly python library (tensorly requires Python 3) - autopep8 and 2to3 tools have been used for upgrade. Python 3.6 is many orders of magnitude faster than Python 2.7 and EventNet Tensor Product and Tensor Rank is computationally intensive which needs faster Python. 2.Following dependent files have been upgraded to Python 3.6 by two tools - autopep8 and 2to3 - which correct formatting issues to comply with PEP8 and convert a file written in python 2.7 to python 3.6 (Python 2.7 versions have been committed having .2.7 suffix): 2.1 RecursiveGlossOverlap_Classifier.py 2.2 WordNetPath.py 2.3 ImageGraph_Keras_Theano.py 3.Note: all python 2.7 source files in NeuronRain can be upgraded to Python 3.6 in two simple steps for better performance: 3.1 Execute autopep8 on the file for PEingP8 3.2 Execute 2to3-2.7 on PEP8 formatted file 4.ImageGraph_Keras_Theano.py - videograph_eventnet_tensor_product() has been modified for preprocessing (slicing 100*100 square) the tensor product of video EventNet and tensorly non_negative_parafac() is invoked which decomposes the video EventNet tensor. 5.logs have been committed to testlogs/ImageGraph_Keras_Theano.log.TensorRank1Decomposition.7December2019 6.Video EventNet Tensor is printed for debug purposes 7.Rank-1 tensor factors from non_negative_parafac() of Video EventNet Tensor are also printed
Srinivasan Kannan
More... almost 7 years ago
(THEORY) Category Theory formulation of EventNet - Objects, Morphisms, Functors - Tensor Decomposition of Logical Time (EventNet)
Srinivasan Kannan
More... almost 7 years ago
728. (THEORY and FEATURE) Set Partition to Tile Cover Reduction by Computational Geometric Factorization - Least Squares Approximate Solution for the underdetermined linear equations, Exact Money Changing Problem(MCP) Solution, Quadratic Programs, Pell Equation, Shell Turing Machines(Category of Topological Spaces)
Srinivasan Kannan
More... almost 7 years ago
(FEATURE) Astronomical Pattern Mining - Rule Search Script update for Maitreya Swiss Ephemeris text client maitreya8t
Srinivasan Kannan
More... almost 7 years ago
--------------------------------------------------------------------------------------------------------------------------------------------- 731. (FEATURE) Astronomical Pattern Mining - Rule Search Script update for Maitreya Swiss Ephemeris text client maitreya8t - 4 December 2019 --------------------------------------------------------------------------------------------------------------------------------------------- 1.python-src/MaitreyaEncHoro_RuleSearch.py has been changed for updated chart summary table of maitreya swiss ephemeris text client which truncates the names of signs and planets to first 3 alphabets. 2.New function prune_rule() has been defined to prune the class association rules to be matched from python-src/MinedClassAssociationRules.txt(which contains already learnt rules from SequenceMining.py for various weather datasets) 3.SearchOption=2 has been updated for redefined signs-planets defaultdict to populate truncated names. 4.Encoded chart concatenation has been changed for lookup of truncated names 5.Maitreya Text Client commandline has been changed to 64-bit deb package install /usr/bin/maitreya8t - text client is not built from Maitreya Dreams source. 6.An example celestial 2-body pattern "Mercury,Jupiter" is searched on 26/12/2019 when a rare 7 planet conjunction of Sun,Moon,Mercury,Jupiter,Saturn,Nodal Points,Pluto (8 planets including nodal axis) occurs. This 7+1 body planetary system in Sagittarius-Gemini is significant in the context of heightened correlation of weather events that might befall (seismic,oceanic and atmospheric). 7.logs for class association rule match are in python-src/testlogs/MaitreyaEncHoro_RuleSearch.maitreya8t.log.4December2019
Srinivasan Kannan
More... almost 7 years ago
(THEORY and FEATURE) Drone Electronic Voting Machine - Streaming Voting Analytics - Bertrand Ballot Theorem - Approximation of Majority Voting, Streaming majority, Forecasts, Theoretical EVMs, Set Partitions
Srinivasan Kannan
More... almost 7 years ago
-------------------------------------------------------------------------------------------------------------------------------------------- 730. (THEORY and FEATURE) Drone Electronic Voting Machine - Streaming Voting Analytics - Bertrand Ballot Theorem - Approximation of Majority Voting, Streaming majority, Forecasts, Theoretical EVMs, Set Partitions - related to 666 - 28 November 2019 -------------------------------------------------------------------------------------------------------------------------------------------- 1.This commit implements the Betrand Ballot Theorem - probability p-q/p+q that one of the candidate might win of two rival candidates getting p and q votes respectively during the process of counting - as a live election analytics measure within electronic_voting_machine() function of Streaming_SetPartitionAnalytics.py 2.It assumes live counting of voting - votes are immediately counted after they are cast - is allowed as against deferred counting convention usually followed in elections because secrecy might be compromised. 3.But this measure is also useful for post-poll analytics during the process of counting itself for extrapolating the result - before election is called. 4.Random Majority voting is simulated on 3 fictitious candidates and probabilistic trend is printed while voting is underway. 5.logs are at testlogs/Streaming_SetPartitionAnalytics.EVM.log.28November2019 6.This measure applies only to binary election of 2 candidates - EVM dictionary is sorted and votes of top 2 rivals are compared dynamically (after each vote is cast), to print the Bertrand probability.
Srinivasan Kannan
More... almost 7 years ago
(THEORY and FEATURE) Drone Electronic Voting Machine - Streaming Boyer-Moore Majority voting
Srinivasan Kannan
More... almost 7 years ago
-------------------------------------------------------------------------------------------------------------------------- 729. (THEORY and FEATURE) Drone Electronic Voting Machine - Streaming Boyer-Moore Majority voting - 27 November 2019 -------------------------------------------------------------------------------------------------------------------------- 1.This commit updates already existing streaming majority implementation of Boyer-Moore algorithm to accept arbitrary datasources (majority_voting() in Streaming_BoyerMoore_MajorityVoting.py) 2.Drone electronic voting machine JSON persistence datastorage written by Streaming SetPartitionAnalytics electronic_voting_analytics() function (Streaming_SetPartitionAnalytics.py) is passed on to Boyer-Moore function as DictionaryHistogramPartition datasource. 3.Persisted EVMs are looped through and majority candidate bucket per EVM is printed.
Srinivasan Kannan
More... almost 7 years ago
(THEORY) Debug Analytics,Software Analytics,Automated Debugging - Section 166 rewritten
Srinivasan Kannan
More... almost 7 years ago
Computational Geometric Factorization - Parallel Planar Point Location of factors in Monotone Subdivision formed by Hyperbolic Arc Pixel Polygons with no necessity for Ray Shooting Queries, Van Der Waerden Ramsey Coloring of Sequences - Rasterization of Conic Sections
Srinivasan Kannan
More... almost 7 years ago
(THEORY and FEATURE) Bounded Error Factorization (Approximate Factors), Ray Shooting Queries, Transition from Quantum to Classical Computation, Derandomization - Proof of Ambainis-Aaronson conjecture - reference
Srinivasan Kannan
More... almost 7 years ago
(THEORY and FEATURE) Set Partition to Tile Cover Reduction by Computational Geometric Factorization - Least Squares Approximate Solution for the underdetermined linear equations, Exact Money Changing Problem(MCP) Solution, Quadratic Programs - References - Frobenius, Postage Stamp, Optimal Currency Denomination Problems
Srinivasan Kannan
More... almost 7 years ago
(THEORY and FEATURE) Set Partition to Tile Cover Reduction by Computational Geometric Factorization - Least Squares Approximate Solution for the underdetermined linear equations, Exact Money Changing Problem(MCP) Solution, Quadratic Programs
Srinivasan Kannan
More... almost 7 years ago
(THEORY and FEATURE) Set Partition to Tile Cover Reduction by Computational Geometric Factorization - Least Squares Approximate Solution for the underdetermined linear equations - example changed for square root of tiles
Srinivasan Kannan
More... almost 7 years ago
(THEORY and FEATURE) Set Partition to Tile Cover Reduction by Computational Geometric Factorization - Least Squares Approximate Solution for the underdetermined linear equations - example
Srinivasan Kannan
More... almost 7 years ago
(THEORY and FEATURE) Set Partition to Tile Cover Reduction by Computational Geometric Factorization - Least Squares Approximate Solution for the underdetermined linear equations
Srinivasan Kannan
More... almost 7 years ago
--------------------------------------------------------------------------------------------------------------------------------------------- 728. (THEORY and FEATURE) Set Partition to Tile Cover Reduction by Computational Geometric Factorization - Least Squares Approximate Solution for the underdetermined linear equations - related to 651 - 19 November 2019 --------------------------------------------------------------------------------------------------------------------------------------------- 1.A random set is partitioned by SymPy set partition API and a partition of the set is chosen at random. Random set partition is mapped to a histogram by map(len,) and setpartition_to_tilecover() is invoked. New parameter for number to factorize has been added in setpartition_to_tilecover(). 2.An integer is factorized by Spark Computational Geometric PRAM factorization by subprocess.call() invocation and factors of N=pq are read from JSON persistence. 3.Following underdetermined system of equations is solved approximately by LSMR least squares for finding arrangement of tiles on the periphery of the rectangle and the fractional solution vector is rounded off to 1 or 0 based on a threshold: #c1*x1 + c2*x2 + ... + ck*xk + ... + cn*xn = p #d1*x1 + d2*x2 + ... + dk*xk + ... + dn*xn = q #solve AX=B: #X = [c1 c2 ... cn] - unknowns (boolean include or exclude of a tile) #A = [[x1 x2 ... xn] - knowns (sides of the square tiles) # permutation_of[x1 x2 ... xn]] #B = [p q] - factors - pq=N 4.Solving underdetermined equations by most libraries including SciPy, NumPy, SymPy etc., yield only approximate solutions because of Rouche-Capelli theorem limitation. In the previous system of equations coefficient matrix A has two rows of square tile sides from Lagrange four square theorem and one of the rows is permuted for randomness (reverse() is invoked) and unknown tile choice vector (d1,d2,...,dn) should also be some permutation of (c1,c2,...,cn). 5.This commit approximately solves the previous underdetermined linear equations by lsmr() function of scipy.sparse.linalg and rounds the real solution vector to binary vector. Mutual exclusion of tiles is by an if-else clause which chooses 1 tiles for side1 and 0 tiles for side2 of rectangle. But product of sum of sides won't be equalling N always because of error allowed in LSMR and rounding. Sophisticated PRAM GEPP implementations might do better at finding the tile arrangment vector (c1,c2,...,cn) 6.logs for this commit are at testlogs/Streaming_SetPartitionAnalytics.log.19November2019
Srinivasan Kannan
More... almost 7 years ago
(THEORY and FEATURE) Reduction from Set Partition to Exact Square Tiles Cover of Rectangular Region, Lagrange Four Square Theorem, Complementary Sets, Computational Geometric Factorization, Space Filling, Tile Cover, Packing, Set Partition/Histogram Analytics, LSH Partitions, Separate Chaining Hashtables - Underdetermined System of Equations, GEPP, P-Completeness, Quadratic Programs - References
Srinivasan Kannan
More... almost 7 years ago
(THEORY and FEATURE) Reduction from Set Partition to Exact Square Tiles Cover of Rectangular Region, Lagrange Four Square Theorem, Complementary Sets, Computational Geometric Factorization, Space Filling, Tile Cover, Packing, Set Partition/Histogram Analytics, LSH Partitions, Separate Chaining Hashtables - Underdetermined System of Equations, GEPP, P-Completeness, Quadratic Programs - update
Srinivasan Kannan
More... almost 7 years ago
727. (THEORY) Fame-Merit Equilibrium and Converging Markov Random Walk on World Wide Web, Condorcet Jury Theorem, Bipartite Decomposition of World Wide Web, Margulis-Ruzzo Threshold - Conjecture - 12 November 2019 - related to 572 and all sections on Intrinsic Merit, Fame and Majority Voting
Srinivasan Kannan
More... almost 7 years ago
(THEORY and FEATURE) Drone Electronic Voting Machine - Pseudorandom Majority Voting Balls-Bins Simulation and Voting Analytics
Srinivasan Kannan
More... about 7 years ago
---------------------------------------------------------------------------------------------------------------------------------------------- 726. (THEORY and FEATURE) Drone Electronic Voting Machine - Pseudorandom Majority Voting Balls-Bins Simulation and Voting Analytics - 3 November 2019 - related to 620,725 ---------------------------------------------------------------------------------------------------------------------------------------------- 726.1 electronic_voting_machine() function in Streaming_SetPartitionAnalytics.py has been updated to import random, sklearn packages for histogram analytics. 726.2 evm_histograms dictionary of EVM dictionaries has been made a list of EVM dictionaries which is dumped to JSON persistence. EVM stream is read from Streaming Abstract Generator - DictionaryHistogram JSON datasource testlogs/Streaming_SetPartitionAnalytics.EVMs.json and ARI(adjusted rand index), AMI(adjusted mutual information index) and EMD(earth mover distance) histogram distance functions are invoked from sklearn and SciPy for every consecutive pair of EVM histogram set partitions. 726.3 Exceptions for dissimilar or empty histogram shapes in ARI,AMI and EMD have been handled. 726.4 Majority Voting is simulated by a poll loop of 10 voters, 3 unique id contexts, 3 candidates and 3 electronic voting machines - only 3 votes are allowed and others are discarded as "already voted". 726.5 Majority Voting simulation is by python pseudorandom number function random() for randomly choosing candidate to vote for - bounds for the balls and bins coupon collector problem described in 620 apply. 726.6 These 3 EVMs are analyzed pairwise by electronic_voting_analytics() for ARI, AMI and EMD-Wasserstein distance. 726.7 Logs and EVMs JSON for previous simulated election are at Streaming_SetPartitionAnalytics.EVMs.json and Streaming_SetPartitionAnalytics.EVM.log.3November2019 726.8 calcEMD2() is not available on OpenCV2 Python (error: no name found) though recently released OpenCV3 Gold has its C++ counterpart - OpenCV2 histogram comparison function compareHist() requires UMat format.
Srinivasan Kannan
More... about 7 years ago
(THEORY) Drone Electronic Voting Machine, Voting Analytics and Forecasts, Bertrand Ballot Theorem, Money Changing Problem, Set Partitions, Depth-2 Boolean Majority Circuit
Srinivasan Kannan
More... about 7 years ago
723. (THEORY) Update 2 - Drone Electronic Voting Machine - Voting Analytics - Set Partition Distance Measures - 2 November 2019 - related to 722 724. (FEATURE) Update 3 - Drone Electronic Voting Machine - JSON persistence of EVMs - Datasource for Voting Analytics - 2 November 2019 - related to 723
Srinivasan Kannan
More... about 7 years ago
-------------------------------------------------------------------------------------------------------- 723. (THEORY) Update 2 - Drone Electronic Voting Machine - Voting Analytics - Set Partition Distance Measures - 2 November 2019 - related to 722 -------------------------------------------------------------------------------------------------------- 723.1 Drone Electronic Voting machine partial implementation (depends on drone navigation) in NeuronRain is an obfuscated set partition of electorate (voters identity per candidate bucket is encrypted) 723.2 Analytics inferences can be drawn from a stream of previous encrypted set partition EVMs without compromising voter privacy. 723.3 Variety of distance measures are available for comparing set partitions: 723.3.1 Earth Mover Distance or Wasserstein Distance (OpenCV2 and SciPy) - which is amount of work necessary to move a mass of one shape to another - shape is a multidimensional object and two set partition EVMs are of different shapes - https://docs.opencv.org/2.4/modules/imgproc/doc/histograms.html?highlight=emd#cv.CalcEMD2 and https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.wasserstein_distance.html - EMD2 in OpenCV2 is a sophisticated version of EMD for comparing two visuals by mapping images to histogram probability distributions while EMD in SciPy is wasserstein distance 723.3.2 OpenCV2 provides many other measures for comparing image histograms - https://docs.opencv.org/2.4/modules/imgproc/doc/histograms.html - cvCompareHist() by Correlation, Chi-Square, Intersection,Bhattacharyya,Hellinger distance measures
Srinivasan Kannan
More... about 7 years ago
(THEORY) Category Theory formulation of EventNet - Objects, Morphisms, Functors - EventNet Tensor Decomposition and Graph Partition, Retrocausality
Srinivasan Kannan
More... about 7 years ago
(THEORY and FEATURE) Update - Drone Electronic Voting Machine NeuronRainApps Usecase
Srinivasan Kannan
More... about 7 years ago
(THEORY and FEATURE) People Analytics - Handwriting Recognition - Inner Product Space of Contour Interpolated Polynomials
Srinivasan Kannan
More... about 7 years ago