Update new version 2.0.0

Author: thieu1995

readme.md

@@ -1,24 +1,23 @@
 # Efficient Time-series Forecasting using Neural Network and Opposition-based Coral Reefs Optimization
 
-## Publications
-* If you see our work is useful, please cite us as follows:
-```code
-   @article{Nguyen2019,
-  title={Efficient Time-Series Forecasting Using Neural Network and Opposition-Based Coral Reefs Optimization},
-  author={Thieu Nguyen and Tu Nguyen and Binh Minh Nguyen and Giang Nguyen},
-  year={2019},
-  journal={International Journal of Computational Intelligence Systems},
-  volume={12},
-  issue={2},
-  pages={1144-1161},
-  issn={1875-6883},
-  url={https://doi.org/10.2991/ijcis.d.190930.003},
-  doi={https://doi.org/10.2991/ijcis.d.190930.003}
-}
-```
+[![GitHub release](https://img.shields.io/badge/release-2.0.0-yellow.svg)]()
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3742045.svg)](https://doi.org/10.5281/zenodo.3742045)
+[![License](https://img.shields.io/badge/License-Apache%202.0-green.svg)](https://opensource.org/licenses/Apache-2.0)
 
-* Link full PDF:
-	=> [PDF](https://download.atlantis-press.com/article/125921354.pdf)
+## Dear friends and followers
+* I updated to repository to the newest version (version 2.0.0), which is very easy to read and reproduce. 
+* All of our optimizers are (meta-heuristics) now deleted and taken the new one from my newest library: 
+
+        https://pypi.org/project/mealpy/
+    
+* If you use my code or library in your project, I would appreciate the cites:
+    * Nguyen, T., Nguyen, T., Nguyen, B. M., & Nguyen, G. (2019). Efficient Time-Series Forecasting Using Neural Network and Opposition-Based Coral Reefs Optimization. International Journal of Computational Intelligence Systems, 12(2), 1144-1161.
+	
+	* Nguyen, T., Nguyen, B. M., & Nguyen, G. (2019, April). Building Resource Auto-scaler with Functional-Link Neural Network and Adaptive Bacterial Foraging Optimization. In International Conference on Theory and Applications of Models of Computation (pp. 501-517). Springer, Cham.
+	
+    * Nguyen, T., Tran, N., Nguyen, B. M., & Nguyen, G. (2018, November). A Resource Usage Prediction System Using Functional-Link and Genetic Algorithm Neural Network for Multivariate Cloud Metrics. In 2018 IEEE 11th Conference on Service-Oriented Computing and Applications (SOCA) (pp. 49-56). IEEE.
+
+* If you want to know more about code, or want a pdf of both above paper, contact me: [email protected]
 
 
 ## How to read my repository
@@ -28,15 +27,11 @@
     * results: forecasting results of all models (3 folders inside) 
         * final: final forecasting results (runs on server)
         * stability: final stability results(runs on server)
-4. model: (4 folders) 
+4. model: (2 folders) 
     * root: (want to understand the code, read this classes first)
         * root_base.py: root for all models (traditional, hybrid and variants...) 
-        * root_algo.py: root for all optimization algorithms
         * traditional: root for all traditional models (inherit: root_base)
         * hybrid: root for all hybrid models (inherit: root_base)
-    * optimizer: (this classes inherit: root_algo.py)
-        * evolutionary: include algorithms related to evolution algorithm such as GA, DE,..
-        * swarm: include algorithms related to swarm optimization such as PSO, CSO, BFO, ...
     * main: (final models)
         * this classes will use those optimizer above and those root (traditional, hybrid) above 
         * the running files (outside with the orginial folder: cro_mlnn_script.py, ...) will call this classes
@@ -78,18 +73,19 @@ For example:
     6. ABFO-MLNN 	=> abfo_mlnn_script.py
     7. CRO-MLNN 	=> cro_mlnn_script.py
     8. OCRO-MLNN 	=> ocro_mlnn_script.py
-```
 
-3. In paper/results/final model includes folder's name represent the data such as 
-```code
-cpu: input model would be cpu, output model would be cpu 
-ram: same as cpu
-multi_cpu : input model would be cpu and ram, output model would be cpu 
-multi_ram : input model would be cpu and ram, output model would be ram
-multi : input model would be cpu and ram, output model would be cpu and ram
+
+6. Multi-output meaning?
+
+    cpu: input model would be cpu, output model would be cpu 
+    ram: same as cpu
+    multi_cpu : input model would be cpu and ram, output model would be cpu 
+    multi_ram : input model would be cpu and ram, output model would be ram
+    multi : input model would be cpu and ram, output model would be cpu and ram
 ```
 
-4. How to change model's parameters?
+### How to change model's parameters?
+
 ```code 
 You can change the model's parameters in file: utils/SettingPaper.py 
 
@@ -100,10 +96,10 @@ For example:
 ####: MLNN-1HL
 mlnn1hl_paras_final = {
     "sliding": [2, 5, 10],
-    "hidden_sizes" : [[5] ],
+    "hidden_sizes" : [(10, True) ],
     "activations": [("elu", "elu")],  # 0: elu, 1:relu, 2:tanh, 3:sigmoid
     "learning_rate": [0.0001],
-    "epoch": [5000],
+    "epoch": [1000],
     "batch_size": [128],
     "optimizer": ["adam"],   # GradientDescentOptimizer, AdamOptimizer, AdagradOptimizer, AdadeltaOptimizer
     "loss": ["mse"]
@@ -112,7 +108,7 @@ mlnn1hl_paras_final = {
 - If you want to tune the parameters, you can adding more value in each parameters like this:
 
 - sliding: [1, 2, 3, 4] or you want just 1 parameter: [12]
-- hidden_sizes: [ [5], [10], [100] ] or [ [14] ]
+- hidden_sizes: [ (5, True), (10, True), (1000, True) ] or [ (14, True) ]
 - activations: [ ("elu", "relu"), ("elu", "tanh") ] or just: [ ("elu", "elu") ]
 - learning_rate: [0.1, 0.01, 0.001] or just: [0.1]
 ....
@@ -123,12 +119,12 @@ mlnn1hl_paras_final = {
 #### : GA-MLNN
 ga_mlnn_paras_final = {
     "sliding": [2, 5, 10],
-    "hidden_size" : [5 ],
+    "hidden_size" : [(10, True) ],
     "activations": [(0, 0)],             # 0: elu, 1:relu, 2:tanh, 3:sigmoid
     "train_valid_rate": [(0.6, 0.4)],
 
-    "epoch": [700],
-    "pop_size": [200],                  # 100 -> 900
+    "epoch": [1000],
+    "pop_size": [100],                  # 100 -> 900
     "pc": [0.95],                       # 0.85 -> 0.97
     "pm": [0.025],                      # 0.005 -> 0.10
     "domain_range": [(-1, 1)]           # lower and upper bound
@@ -137,55 +133,50 @@ ga_mlnn_paras_final = {
 - Same as traditional models.
 ```
 
-5. Where is the results folder?
+### Where is the results folder?
 ```code 
-- Look at the running file, for example: ga_mlnn_script.py
+- Look at the running file, for example: ga_elm_script.py
 
-+) For normal runs (Only run 1 time for each model).
++) For 1-time runs (Only run 1 time for each model).
     _There are 3 type of results file include: model_name.csv file, model_name.png file and Error-model_name.csv file 
     _model_name.csv included: y_true and y_predict 
     _model_name.png is visualized of: y_true and y_predict (test dataset)
-    _Error-model_name.csv included errors of training dataset after epoch. 1st, 2nd column are: MSE, MAE errors
+    _Error-model_name.csv included errors of training dataset after epoch. 1st, 2nd column are: MSE errors
 
-=> All 3 type of files above is automatically generated in folder: paper/results/final/...
+=> All 3 type of files above is automatically generated in folder which you can set in SettingPaper
 
-+) For stability runs (Run each model 15 times with same parameters).
++) For stability runs (Run each model n-time with same parameters).
 Because in this test, we don't need to visualize the y_true and y_predict and also don't need to save y_true and y_predict
-So I just save 15 times running in the same csv files in folder: paper/results/stability/...
+So I just save n-time running in the same csv files in folder
 
 - Noted:
 
-+ In the training set we can use MSE or MAE error. But for the testing set, we can use so much more error like: R2, MAPE, ...
++ In the training set we use MSE. But for the testing set, we can use so much more error like: R2, MAPE, ...
 You can find the function code described it in file: 
     model/root/root_base.py 
-        + _save_results__: this is for normal runs
-        + _save_results_ntimes_run__: this is for stability runs.
-
-+ You want to change the directory to save results?. Simple.
-
-For example: Look at running file: ga_mlnn_script.py
-
-_Line 14: pathsave = "paper/results/final/"   --> This is general path to save file. 
-But for each dataset like: wc, uk, eu,... I need to save it in different folder right. So now look at 
-
-_Line 33:  "path_save_result": pathsave + requirement_variables[4].
-Here, the requirement_variables[4]: is inside file: utils/SettingPaper.py ("eu/", # path_save_result)
-
-So the final path to save the results for normal runs is: paper/results/final/eu/
+        + _save_results__: 
 ```
 
-4. Take a look at project_structure.md file.  Describe how the project was built.
-
+* Take a look at project_structure.md file.  Describe how the project was built.
 
 
-## Contact
-* If you want to know more about code, or want a pdf of both above paper, contact me: [email protected]
-
-* Take a look at this repos, the simplify code using python (numpy) for all algorithms above. (without neural networks)
-	
-	* https://github.com/thieunguyen5991/metaheuristics
 
-## License
+### Publications
+* If you see our work is useful, please cite us as follows:
+```code
+   @article{Nguyen2019,
+  title={Efficient Time-Series Forecasting Using Neural Network and Opposition-Based Coral Reefs Optimization},
+  author={Thieu Nguyen and Tu Nguyen and Binh Minh Nguyen and Giang Nguyen},
+  year={2019},
+  journal={International Journal of Computational Intelligence Systems},
+  volume={12},
+  issue={2},
+  pages={1144-1161},
+  issn={1875-6883},
+  url={https://doi.org/10.2991/ijcis.d.190930.003},
+  doi={https://doi.org/10.2991/ijcis.d.190930.003}
+}
+```
 
-[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
-  
+* Link full PDF:
+	=> [PDF](https://download.atlantis-press.com/article/125921354.pdf)