TODO: upon week completion

• Explore "big" data-visualizers such as wandb.ai and tableau
• Visualizer
  • Adjustable initial conditions
  • Adjustable timestep in the visualizer

Day 18 Thursday July 1st, 2021 7hr

• Read more of wandb.ai's documentation
• Created prototype random_sampler, random_walker, and hill_climber
• Used wandb with the hill_climber to verify it worked properly
  • Ran 100 instances of hill_climber and logged the results to wandb
  • Most networks had a fitness close to zero, but one had as high as 0.811

Day 17 Wednesday June 30th, 2021 2hr

• Worked on the visualizer
  • Adjusted parameter range for the visualizer
  • Normalized the step axis so it matches what we do elsewhere
  • Added axis labels to the network graph
  • Fixed the graphs ignoring the 0th step
  • Added ability to choose the initial conditions

Day 16 Tuesday June 29th, 2021 1hr

• Experimented with wandb.ai's sweep analysis which allowed me to:
  • Create a parallel coordinates plot
  • Analyze the results to generate a importance/correlation chart

Day 15 Monday June 28th, 2021 7hr

• Searched for time tracking methods (time-warrior seems to work well for me)
• Began looking into wandb.ai
  • Read through documentation
  • Viewed featured projects for direction
  • Made a sample oscillation trial
  • Link to project

TODO: upon week completion

• Wrap up visualizer
• Strip down Dr. Yoder's ctrnn python library to bare-bones
  • Base ctrnn functionality
  • Evaluator abstract class for creating different training metrics
  • abstract class for creating different learning methods
• Different learning algorithms
  • Random sampling
  • Random walker
  • Random hillclimber

Day 14 Sunday June 27th, 2021 3hr

• Finished writing the Oscillator Evaluator method with some tests
• Began planning the abstract class to allow for different learning algorithms

Day 13 Friday June 25th, 2021 5hr

• Planned out structure for the Evaluator abstract class
  • Any class extending Evaluator would have 5 abstract methods: pre_transient, step_transient, pre_evaluation, step_evaluation, and generate_report. By overriding these, we can configure different desired network behaviors, as opposed to oscillation
• Began and completed the Evaluator abstract class
• Planned and started Oscillator class, implementing Evaluator

Day 12 Thursday June 24th, 2021 4hr

• Created checklist for the ctrnn python library
• Started working on the ctrnn python library
  • Completed the base ctrnn functionality
  • Included some test cases for safety, but I plan to add more later

Day 11 Tuesday June 22th, 2021 4hr

• Fixed phase portrait's inverted y-axis
• Began implementing a timestep slider (still WIP)
• Began writing an interface for different fitness landscape paths

Day 10 Monday June 21th, 2021 8hr

• Implemented a path plot to the visualizer's phase portrait
• Used this plot to determine what was wrong with the vector field
  • In the end it was me not taking into account the biases when converting from regular space to sigmoid space (phase portrait issue only)
• Skimmed a few of the papers related to the Microbial Genetic Algorithm paper
• Began taking a deeper look into Dr. Yoder's ctrnn_bio_rl code

TODO: upon week completion

• Provide a visualization of the library for demonstration purposes
• Write a PhasePortrait React component
• Replace live neuron view with phase portrait
• Begin reading about Microbial Genetic Algorithms

Day 9 Friday June 18th, 2021 4hr

• Read through The Microbial Genetic Algorithm multiple times to verify my understanding of the concept before putting it to use.

Day 8 Thursday June 17th, 2021 10hr

• Rewrote code to accept new external node activation array system
• Wrote a React component that renders a phase portrait using HTML canvas
• Made visualizer iterate over a 20x20 grid of starting activations, and graph their outputs from the ctrnn on the phase portrait
• Made the phase portrait update in realtime on changes to the parameters

Day 7 Wednesday June 16th, 2021 9hr

• Began rewriting ctrnn library to use an external node activation array
  • This is preferable as it allows for an easy way to test what the outcome would be from all possible states.
• Began using this new external node activation array for the phase portrait
• Began working on the visuals of the phase portrait
• (now there is an empty space on the visualizer, perhaps we can put some other statistic/view there in the future)

Day 6 Monday June 14th, 2021 10hr

• Provided a realtime view of the oscillatory behavior in the visualizer
• Created a parameters panel that allows the user to configure the network
• Created a static view of the first seconds of simulation of the network
  • Smooth continuous updates when the user alters a parameter
• Expose at a public url cooperuser.dev/ctrnn-visualizer
  • Secure with an SSL certificate (HTTPS protocol)

TODO: upon week completion

• Grasp a deeper understanding of the research project’s underlying concepts
• Develop a CTRNN library in JavaScript/TypeScript
• Provide a visualization of the library for demonstration purposes

Day 5 Friday June 11th, 2021 7hr

• Succeeded in having ctrnn.js mimic the behavior of madvn/CTRNN
  • Network now allows for sinusoidal behavior instead of steady state
• Started integrating the graph/histogram for ctrnn-visualizer
• Read/skimmed through the next 2 resources

Day 4 Thursday June 10th, 2021 5hr

• Read/skimmed through the first 5 resources supplied by Dr. Yoder
• Analyzed the madvn/CTRNN python library
  • Started using the readme's example as a model for ctrnn.js

Day 3 Wednesday June 9th, 2021 7hr

• Organized journal to log my progress
  • Time tracking is still done manually, I hope to make it more streamline
• Began brainstorming a rl-ctrnn library that runs on top of ctrnn.js
• Skimmed through some of the references Dr. Yoder linked to for the research
• Continued work on the interface for ctrnn-visualizer
  • I want to get a working version first that does supports basic non-reinforcement learning CTRNN models
  • I plan to incorporate the oscillating weights RL model after this prototype

Day 2 Tuesday June 8th, 2021 6hr

• Looked into how "common" CTRNN libraries allow the user to interface
• Created a custom ctrnn.js library outline tailored to our needs
  • Not fully completed yet, and still needs more test cases
• Started work on ctrnn-visualizer to go along with ctrnn.js
  • Mainly waiting for the completion of the backend

Day 1 Monday June 7th, 2021 7hr

• Read through research proposal document
• Read documentation on existing CTRNN implementations in JS
  • Decided to write a library from scratch as no existing libraries fit our needs without a decent amount of integration
• Began brainstorming different ways of displaying information to the user for the CTRNN visualizer
• Began brainstorming which aspects/parameters the user should be able to control in the visualizer