This is the repository for problem 1 of the 2018 BDAthlon programming contest.
It my solution for the problem on genetic technology mapping.
- docs
- Documentation generated by documentation generators
- Doxygen
- pydoc (for Python scripts): /usr/bin/pydoc
- Documentation generated by documentation generators
- notes
- Handout for the BDAthlon 2018 contest.
- Software licenses
- MIT License.
- Guidelines for collaborating on open source software and/or hardware
projects.
- Documentation about guidelines that I am following for my research, and for my research collaborators to know about.
- Project report in LaTeX
- Externalities list.
- Publicly available library, API, or framework that I have used as external components for my software.
- Some Python modules from the repository bibtex-analytics, which I developed.
My solution for genetic technology mapping, problem1_solution.py, takes in two input parameters, [input JSON netlist] and [output JSON technology mapping].
The first input parameter [input JSON netlist] is a JSON file that contains a structural netlist for genetic technology mapping.
The other input parameter [output JSON technology mapping] is the filename of an output JSON file that contains a genetic technology mapping for the input structural netlist ([input JSON netlist]).
The front-end of problem1_solution.py parses the input arguments, checks their validity, and parses the input structural netlist for genetic technology mapping. It also parses the genetic technology library genetic_gate_library.json, maps each genetic NOT gate into a genetic_not_gate object, and stores each object in a map of (id,objects); the id of the object is the id field of the genetic NOT gate, which is specified in the genetic technology library.
The genetic technology mapping engine of problem1_solution.py consists of a (set of) solution(s) to perform genetic technology mapping. A solution is an implementation of a known/new algorithm/heuristic for genetic technology mapping.
Dr. Nicholas Roehner and Dr. Curtis Madsen, I am running out of time, so I will sketch the outline of my solutions, and update them as I solve the problem.
Solution 1a performs brute force search to explore different options for genetic technology mapping.
- Explore each permutation of NOT gates.
- For each selection, store its alpha value and the permutation in a table
- Enumerate a table to find the largest alpha value, and select the corresponding permutation of NOT gates.
Solution 1b uses simulated annealing for discrete optimization.
- Pseudo-randomly select a permutation of NOT gates.
- Initialize temperature to be very hot
- While temperature is not 0, cool the temperature of the annealing process.
- As the temperature cools, slowly decrease the
Solution 1b uses a genetic algorithm for discrete optimization.
Other solutions considered:
- 0-1 integer linear programming (ILP)
- Can't formulate the objective function of the 0-1 ILP problem.
- pseudo-boolan optimization (PBO)
- Can't formulate the conjunctive normal form (CNF) boolean satisfiability formula for PBO.
The back-end of problem1_solution.py generates an output file containing the genetic technology mapping of the input genetic circuit, in JSON format.
To execute the software solution, try:
./problem1_solution.py [input JSON netlist] [output JSON technology mapping]
Input Parameters: +[input JSON netlist]: A JSON file that contains a structural netlist for genetic technology mapping.
- [output JSON technology mapping]: A filename of an output JSON file that contains a genetic technology mapping for the input structural netlist.
To use Doxygen to generate documentation for the Python software, try:
make doxygen
To view the Doxygen-generated documention, open the file docs/html/index.html in a Web browser.
The command make doxygeninit has already been used to generate a Doxygen configuration file named doxygen.config. DO NOT RUN THE COMMAND doxygen.config AGAIN!!!
To use pydoc to view or generate documentation for my software solution (Python code), try:
pydoc [*Python* package/module/class]
A Python package corresponds to a subdirectory of this repository, while a Python module/class corresponds to a Python source file in a subdirectory.
Refactor the class queue_ip_args in the Python module queue_ip_arguments.py, so that does not need the argument which_script for the static method set_input_arguments(list_of_ip_arguments,which_script). That is, refactor the static method to set_input_arguments(list_of_ip_arguments).
Using this refactored Python class has an impact on its corresponding Python class in the bibtex-analytics software. Use this refactored code to handle usage modes in the bibtex-analytics software, such as: obtaining a sorted list of keyphrases/keywords; obtaining a sorted list of publishers, organizations, and institutions; and a list of series of books or conference proceedings. These modes shall be specified by an input argument. If multiple modes are specified as input arguments, an order of precedance is used to specify which mode shall be selected for processing. The bibtex-analytics software can only process one mode per execution run.
Refactoring step has been completed.
References for generics programming for Python:
- https://www.quora.com/Can-you-provide-an-example-of-generics-in-Python
- https://www.quora.com/How-can-I-use-generic-in-Python
Instead of printing results from automated regression testing of the software to standard output (and standard error) stream(s) (typically directed to the Terminal application), write the regression testing results to an output file.
This allows us to avoid printing too much information (i.e., 5000 lines of text, as limited by Google Colab). When Google Colab detects that too much information has been printed to standard output (and standard error) stream(s), it stops printing to standard output (and standard error) stream(s).
Similarly, we write the simulation/experiment results to an output file. This also allows us to track research progress, and note the parameters selected during decision making in the design process.
Instead of creating a file object for an output file storing the results from automated regression testing and another file object for another output file storing the results from simulations and experiments, we choose to keep a copy of the file object in the main function and pass it to each called function in the test bench (or module in the software test suite).
The software test suite has multiple Python modules, such that each Python module in the software test suite corresponds to a Python module in the software design.
The main reason for doing this is to use the facade pattern \cite{Gamma1995} to simplify our software architecture. This reduces the amount of parameters that functions and class constructors have to manage and update, as people refactor the code. Consequently, it reduces coupling/dependencies and function calls between Python modules.
Citations/References that use the LaTeX/BibTeX notation are taken from my BibTeX database (set of BibTeX entries).
Additional references not found in the reference list shall be indicated below (TO BE UPDATED).
@misc{Roehner2018,
Address = {Boston, {MA}},
Author = {Nicholas Roehner and Curtis Madsen},
Howpublished = {Self-published},
Month = {July 31},
Publisher = {Boston University},
School = {Boston University},
Title = {BDAthlon 2018},
Year = {2018}}
... @book{Gamma1995, Address = {Upper Saddle River, {NJ}}, Author = {Gamma, Erich and Helm, Richard and Johnson, Ralph and Vlissides, John}, Keywords = {design patterns, GoF, Gang of 4, Gang of four}, Publisher = {Addison-Wesley}, Series = {Addison-Wesley Professional Computing Series}, Title = {Design Patterns: Elements of Reusable Object-Oriented Software}, Year = {1995}}
The MIT License (MIT)
Copyright (c) <2018> Zhiyang Ong
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Email address: echo "cukj -wb- 23wU4X5M589 TROJANS cqkH wiuz2y 0f Mw Stanford" | awk '{ sub("23wU4X5M589","F.d_c_b. ") sub("Stanford","d0mA1n"); print $5, $2, $8; for (i=1; i<=1; i++) print "6\b"; print $9, $7, $6 }' | sed y/kqcbuHwM62z/gnotrzadqmC/ | tr 'q' ' ' | tr -d [:cntrl:] | tr -d 'ir' | tr y "\n" Don't compromise my computing accounts. You have been warned.