removed unecessary fiels

.gitignore

@@ -2,6 +2,11 @@
 # Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider
 # Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
 
+.env
+__pycache__/
+.RData
+.Rhistory
+
 # User-specific stuff
 .idea/
 

code/new_realtime/.Rhistory

@@ -1,11 +0,0 @@
-install.package("cpm")
-install.packages("cpm")
-install.packages("cpm")
-install.packages("signal", "zoo", "pracma")
-install.packages("signal", "zoo", "pracma")
-install.packages("signal", "pracma")
-install.packages("signal")
-install.packages("signal")
-install.packages("signal", "zoo", "pracma")
-install.packages("zoo", "pracma")
-install.packages("pracma")

code/new_realtime/.env

@@ -1,3 +0,0 @@
-LOG_DIR=/home/alex/projects/datascience-analysis/notebooks/logs
-LMDB_ROOT_DIR=/home/alex/projects/datascience-analysis/notebooks/lmdb_datasets
-RESULTS_ROOT_DIR=/home/alex/projects/datascience-analysis/notebooks/results

thesis/sections/methodology.tex

@@ -39,6 +39,23 @@ pregnancies, which would make the prediction task ambiguous.
 %TODO: show that for initial testing and fine tuning the dataset was reduced to 10% of size to speed up early impressions of performance
 %TODO: show that initial feature set with only temperature as observable and only pregnancy "chance" as target
 % did not show promise on small 10% dataset, so the features got extended
+% show the modifications that were made to account for multi target prediction
+% show, the mothod of circumventing the non-padding-implementation by using a dedicated feature
+% show, that a different loss / temporal loss wheighting or position aware loss could help
+% show, the problem of the model using its past targets, even though they are not observable and how I dealt with this -> explain and use exogenous
+% introduce change of quantiles to single variable output and why, quantiles loss other than .5 was almost always just 0 or 1, so no significant predicitve quality in this, and this only makes predictions harder for model
+% explain why class imablances are there, and what I did to counteract this, also explain, why special weighting is useful for something like the pregnancy risk prediction
+% show the work that needed to be done for inference -> padding values for future observables
+
+
+Notes for training:
+ - one head 512 hidden size make very "smoothed" out curves, which result in ver conservative predictions
+
+Intermediate training results:
+- first full training:
+ - first feature config with ov over and fertility did not show much promise, as it detected the ov too late, essentially when it was already over
+ - either there is no patterns detectable in advance, or there are not enough features yet
+ - soft of "soft ceiling" at 0.3 loss, many models get there quickly and then "bounce" around
 
 \subsection{Time-Series Modeling Approach}\label{subsec:time-series_modeling_approach}
 \subsubsection{Limitations of Traditional Time-Series Models}\label{subsubsec:limitations_of_traditional_time-series_models}