- src.py: Brings in data, sorts data, and filters for non-eco rounds
- reader.py: Produces density map of pre and post A plant for entire data
- sim.py: simulates a round with attackers and victims color coded for each side (used for testing boxes)
- roles.py: main code for feature handling
- kmeans_final: code for determining clusters (AI stuff)
- Intial coding start date
- Finished Preplant heatmaps for offense and defense where they inflict damage and win the round
- End of First Semester Presentation (PPT)
- Planned Spring 2021 goals and modified code to reflect the aggressors in engagements ('att_side' == ...)
- Finished pre and postplant heatmaps where both sides are aggressors in engagements
- Finished pre and postplant heatmaps where both sides are victims in engagements
- Progress made on single round animation. Isolated 10 players in data for that single round
- Created method to track individual attacking player on heat map
- Created method to find individual player ID
- Created method to find IDs of all players and assign them to their respective teams
- 64 lines of code changed
- Created new heat maps that take a single round and display unique colors for CTs players and wether or not they are the victims or attackers in their engagements
- Finalized animation of single round, planning next steps
- Created a list of 23 features to track to determine player roles
- Began tracking player features and applied weights to each of these features to be used later for assigned player roles based on historical data
- Output visualization of sklearn clustering found on kmeans_final.py
- Experimented with Weka to create a Classifier tree of 14 tracked attributes (ClassiferTree.PNG) and output mean and SD for k=5 clusters
- Created AlgorithmComparison.py to compare the accuracy of various algorithms on training and testing data
- Added avg distance to A site for all players
- Tallied if a player got a kill in a mid box (see sim.py for mid boxes locations)
- Cleaned code in roles.py and sim.py.
- Added A_site box checks
- Statistics are now collectable on where certain types of players (obtained from clustering) get kills
- Since the focus will be on A-bombsite attacks, pre and post plant recommendations can be made to players based on statistical analysis
- of successful positions
- Ran rough PCA and KMeans analysis in kmeans_final.py
- SHOTGUN_kill, MACHINEGUN_kill These two features will be removed since these weapons are unlikely to appear in a NORMAL buy round
- Planned out coding of remaining features. Do trade-kill last since it is time dependent and annoying to code
- Added alone_death feature
- Noticed bug in average distance to A bomb kill where some values are negative
- Started coding distance_traveled feature
- Fixed average distance to A bomb kill bug (abs())
- Added total_distance_travel feature which considers time of kills, damage, and deaths in total calculation
- General unneccessary code removal
- Adjusted 'total_distance_traveled' to only calculate distance between previous and current loc when the player is at a new location
- Removed players whose K/D ratio is < 0.2 to prevent KMeans clustering for accounting for these outliers
- In a test sample of 8 games with 80 players, only 2 were removed for having a low K/D
- Fixed the removal of players with a KD < 0.2 There was an issue where some players had 0 deaths and we were dividing by 0
- Completed PCA analysis before computing KMeans clusters. The output is still confusing.
- Code from src.py class Writer was modified to rewrite file_to_rounds.txt to only include those files and rounds where 'A' bombsite is attacked
- In roles.py there was a serious issue causing the rounds loop to loop over all files in data imported from src.py main. This meant that the players_df output was not specific to rounds where A was attacked and players were being tracked for features over the entire course of the file ** Now, file_to_rounds servers a basis to select only those rounds where A is attacked.
- Features need to be relooked at to confirm their accuracy under this new paradigm
- The creation of a role heat map is in progress ** I realized that the data frame of labeled players was not overlapping with rows of data selected only for preplant A terrorists who attack in their engagements ** I am hoping that the rewritten players_df (once relabeled with role numbers) will include player IDs for those preplant attacking terrorists so that a heat map can be created of roles for attackers and the outcome of those positons
Pre and Postplants Heatmaps where both sides are victims in engagementsCreate simuation of single roundAdd Avg distance to A bombsiteMap boxes around vital areas of the map for A post plant- Interpret clustering output and find the best algorithm to group player behavior
- Apply statistics once clustering interpretion is finished
- Add columns for each box
- Continue to add features and clean code
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent