📝 Change the Name, Fix Relation, Improve Readme

.vscode/settings.json

@@ -1,5 +1,9 @@
 {
   "python.analysis.extraPaths": [
+    "./model",
+    "./functions/matrix_generator",
+    "./functions",
+    "./functions/data_extractor"
   ],
   "git.autofetch": "all",
   "git.blame.editorDecoration.enabled": true,

Readme.md

@@ -42,6 +42,28 @@ On this main class, it located function `fit_with_validation` for a training. Th
 
 ```
 
-For the element-level weight, we pass it to the `back_propagation` function. To make the element-level learnin from the previoused experincanced m
+### Model-Element
 
-© 2024, Prince of Songkla University under Inteligent Automation Engineering Center
+For the element-level weight, we pass it to the `back_propagate` function. To make the element-level learning from the previous iteration error. It adjust their weight and biases using gradient descent-liked algorithm as same as the model-level weight and bias. After the `back_propagate` function is called (in file `network.py`) it will forward into the layer inside the model element (`material_fc_layer.py`,`capital_fc_layer.py`, and `employee_fc_layer.py`).
+
+The model is not fully constructed is file `tdce_model.py` because it depends on the input. We will construt it before we use.
+
+## Experiment
+
+On the Artifical Neural Network-Like for Manufacturing Cost Estimation, we evaluate the model with the experiment inside the folder `experiment`. We using 4 set of datasets, 3 from simulation and 1 from the actual dataset. The actual dataset is prohibit to display and open. Three simulation datasets consist of **Simple Dataset**, **Complicated Dataset** (High Variation), and **Extended Random Dataset** (High data dimension).
+
+### Data Gathering
+
+The three set of data (Simple,Complicated,Actual) is created by upload the based data into IAEC Manufacturing ERP System "E-Manufac" and retrieve the data using REST API. The Code of data retrival can be located at `functions/data_extractor` directory, while the `emanufac_tdabc_extractor_class.py` is the main code. After the data was retrieved, it saved into the CSV file and the code in `adjust_data.py` was employed to intial preprocessing.
+
+For testing or validate our model, without access to the E-Manufac system. The simulation dataset can be downloaded from HuggingFace Dataset (the actual dataset will not provided).
+
+- [Simple Dataset](https://huggingface.co/datasets/theethawats98/tdce-example-simple-dataset)
+- [Complicated Dataset](https://huggingface.co/datasets/theethawats98/tdce-example-complicated-dataset)
+- [Extended Random Dataset](https://huggingface.co/datasets/theethawats98/tdce-example-extended-random)
+
+Go to tab **Files and Version** and download their files. File names are initial with the name `generated`. If you want to use our experiment script, please create the directory named `dataset` and then create folder for each set, and put all files of each set insided.
+
+### Construct Experiment
+
+© 2024, Prince of Songkla University under the Inteligent Automation Engineering Center, Faculty of Engineering.

experiment/batch_experiment_script.py

@@ -2,7 +2,7 @@ import importlib
 import sys
 import getopt
 import os
-import rsg_breakpoint_experiment as rbe
+import experiment.experiment_script as rbe
 importlib.reload(rbe)
 
 learning_rate = [0.005, 0.01, 0.05, 0.1, 0.5]

experiment/{rsg_breakpoint_experiment.py → experiment_script.py}

@@ -6,15 +6,14 @@ import time
 import os
 
 # fmt:off
-sys.path.append('../14-New-Final-Model/model')
-sys.path.append('../14-New-Final-Model/matrix_generator')
-sys.path.append('../14-New-Final-Model')
-sys.path.append('../14-New-Final-Model/extractor')
+sys.path.append('../model')
+sys.path.append('../functions/matrix_generator')
+sys.path.append('../functions/data_extractor')
 
 import tdce_model as tdce
 import material_fc_layer as mfl
 import employee_fc_layer as efl
-import captial_fc_layer as cfl
+import capital_fc_layer as cfl
 import loss
 import cost_matrix_class as cmc
 import display_input_variation as diva