update code & file naming

Author: Junwu0615

.github/workflows/clone.yml

@@ -44,7 +44,7 @@ jobs:
                 echo $gist_id | gh secret set GIST_ID
                 echo ::set-output name=GIST::$gist_id
                 cp NVDA-Price-Stock-Prediction_clone.json NVDA-Price-Stock-Prediction_clone_before.json
-                git rm --ignore-unmatch  Git_State/CLONE.md
+                git rm --ignore-unmatch  git_state/CLONE.md
               fi
           else
               echo "GIST_ID not found. Creating a gist..."
@@ -56,7 +56,7 @@ jobs:
 
       - name: update NVDA-Price-Stock-Prediction_clone.json
         run: |
-          curl https://raw.githubusercontent.com/Junwu0615/NVDA-Price-Stock-Prediction/main/Git_State/clone.py > clone.py
+          curl https://raw.githubusercontent.com/Junwu0615/NVDA-Price-Stock-Prediction/main/git_state/clone.py > clone.py
           python3 clone.py
 
       - name: Update gist with latest count
@@ -68,25 +68,25 @@ jobs:
             -H "Content-Type: application/json" \
             -d @NVDA-Price-Stock-Prediction_post_clone.json https://api.github.com/gists/${{ steps.set_id.outputs.GIST }} > /dev/null 2>&1
 
-          if [ ! -f Git_State/CLONE.md ]; then
+          if [ ! -f git_state/CLONE.md ]; then
             shields="https://img.shields.io/badge/dynamic/json?color=success&label=Clone&query=count_total&url="
             url="https://gist.githubusercontent.com/${{ github.actor }}/${{ steps.set_id.outputs.GIST }}/raw/NVDA-Price-Stock-Prediction_clone.json"
             repo="https://github.com/Junwu0615/NVDA-Price-Stock-Prediction"
-            echo ''> Git_State/CLONE.md
+            echo ''> git_state/CLONE.md
             echo '
             **Markdown**
 
-            ```markdown' >> Git_State/CLONE.md
-            echo "[![GitHub Clones]($shields$url&logo=github)]($repo)" >> Git_State/CLONE.md
+            ```markdown' >> git_state/CLONE.md
+            echo "[![GitHub Clones]($shields$url&logo=github)]($repo)" >> git_state/CLONE.md
             echo '
             ```
 
             **HTML**
-            ```html' >> Git_State/CLONE.md
-            echo "<a href='$repo'><img alt='GitHub Clones' src='$shields$url&logo=github'></a>" >> Git_State/CLONE.md
-            echo '```' >> Git_State/CLONE.md
+            ```html' >> git_state/CLONE.md
+            echo "<a href='$repo'><img alt='GitHub Clones' src='$shields$url&logo=github'></a>" >> git_state/CLONE.md
+            echo '```' >> git_state/CLONE.md
             
-            git add Git_State/CLONE.md
+            git add git_state/CLONE.md
             git config --global user.name "GitHub Action"
             git config --global user.email "action@github.com"
             git commit -m "create clone count badge"

.gitignore

@@ -2,4 +2,4 @@ __pycache__
 .idea
 .ipynb_checkpoints
 FMP-Get-NVDA-History.py
-Trained
+trained

Entry.py

@@ -4,8 +4,8 @@
 Update Time: 2024-11-24
 """
 
-from Depend.Model import Model
-from Depend.ArgumentParser import AP
+from package.Model import Model
+from package.ArgumentParser import AP
 
 class Entry:
     def __init__(self):

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 Ping Chun, Wu
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -48,13 +48,13 @@ python Entry.py -h
 ```py
 python Entry.py -open T -high T -low T -vol T -utm F
 ```
-- <img width='600' height='450' src="https://github.com/Junwu0615/NVDA-Price-Stock-Prediction/blob/main/Output/output_model_open_high_low_volume.png"/>
+- <img width='600' height='450' src="https://github.com/Junwu0615/NVDA-Price-Stock-Prediction/blob/main/sample/output_model_open_high_low_volume.png"/>
 
 - 只使用「交易量」此特徵來預測「收盤價」。
 ```py
 python Entry.py -open F -high F -low F -vol T -utm F
 ```
-- <img width='600' height='450' src="https://github.com/Junwu0615/NVDA-Price-Stock-Prediction/blob/main/Output/output_model_volume.png"/>
+- <img width='600' height='450' src="https://github.com/Junwu0615/NVDA-Price-Stock-Prediction/blob/main/sample/output_model_volume.png"/>
 
 - 使用已訓練過的模型進行預測。
 ```py
@@ -65,13 +65,13 @@ python Entry.py -open F -high F -low F -vol T -utm T
 
 ### A.　資料取得
 我透過 [Financial Modeling Prep](https://site.financialmodelingprep.com/) 取得我想預測的標的 NVIDIA ，它需要註冊會員以取得 API key，接著使用日線圖的 API 叫出該 Symbol 歷史資料。另外，我已先將其內容存儲成 json 格式 -> [`NVDA_history_20190225_20240223.json`](/dataset/NVDA_history_20190225_20240223.json)。
-- <img width='650' height='500' src="https://github.com/Junwu0615/NVDA-Price-Stock-Prediction/blob/main/Output/NVDA_History_Price.png"/>
+- <img width='650' height='500' src="https://github.com/Junwu0615/NVDA-Price-Stock-Prediction/blob/main/sample/NVDA_History_Price.png"/>
 
 ### B.　資料預處理
 - 原始資料如下圖所示。
-- <img width='400' height='450' src="https://github.com/Junwu0615/NVDA-Price-Stock-Prediction/blob/main/Sample/origin.jpg"/>
+- <img width='400' height='450' src="https://github.com/Junwu0615/NVDA-Price-Stock-Prediction/blob/main/sample/origin.jpg"/>
 - 將資料範圍大小，規範於 0 至 1 區間，標準化後的資料如下圖所示。
-- <img width='430' height='450' src="https://github.com/Junwu0615/NVDA-Price-Stock-Prediction/blob/main/Sample/norm.jpg"/>
+- <img width='430' height='450' src="https://github.com/Junwu0615/NVDA-Price-Stock-Prediction/blob/main/sample/norm.jpg"/>
 - 接著將資料切割成訓練集 ( 90% ) 和測試集 ( 10% )，任務目標是預測「收盤價」，而其餘特徵都是餵給機器的輸入。
   ```
   x_train = result[:int(number_train), :-1]

Dataset/NVDA_history_20190225_20240223.json renamed to datasets/NVDA_history_20190225_20240223.json

@@ -23,7 +23,7 @@ def __init__(self, obj):
         self.use_trained_model = obj.use_trained_model
         self.norm_check = ['T', 'True', 't']
 
-        file = "./Dataset/NVDA_history_20190225_20240223.json"
+        file = "./datasets/NVDA_history_20190225_20240223.json"
         self.loader = [json.loads(i) for i in open(file, "r")][0]
 
         gpus = tf.config.experimental.list_physical_devices("GPU")
@@ -87,7 +87,7 @@ def origin_status(self):
             ax2.legend(loc='upper right')
 
         ax1.tick_params(axis='x', labelrotation=90)
-        plt.savefig("./Output/NVDA_History_Price.png")
+        plt.savefig("./sample/NVDA_History_Price.png")
         plt.clf()
 
     def normalize(self) -> pd.DataFrame:
@@ -162,12 +162,12 @@ def make_img(self, denorm_pred, denorm_ytest, model_name):
         plt.plot(denorm_ytest, color='blue', label='Answer')
         plt.legend(loc='best')
         # plt.show()
-        plt.savefig(f"./Output/output_{model_name}.png")
+        plt.savefig(f"./sample/output_{model_name}.png")
         plt.clf()
 
     def main(self):
-        self.check_folder('./Output')
-        self.check_folder('./Trained')
+        self.check_folder('./sample')
+        self.check_folder('./trained')
 
         if self.use_trained_model in ['T', 'True', 't']:
             self.use_trained_model = True
@@ -202,10 +202,10 @@ def main(self):
 
         # 一個batch有128個instance，總共跑50個迭代
         if self.use_trained_model:
-            model = tf.keras.models.load_model(f"./Trained/{model_name}.keras")
+            model = tf.keras.models.load_model(f"./trained/{model_name}.keras")
         else:
             model.fit(x_train, y_train, batch_size=128, epochs=200, validation_split=0.1, verbose=1)
-            model.save(f"./Trained/{model_name}.keras")
+            model.save(f"./trained/{model_name}.keras")
 
         # 用訓練好的 LSTM 模型對測試資料集進行預測
         pred = model.predict(x_test)