final version

yul19079 · Feb 9, 2024 · bd6d9f2 · bd6d9f2
1 parent 6137c1b
commit bd6d9f2
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Showing 211 changed files with 11,233 additions and 215 deletions.
diff --git a/GenDs.py b/GenDs.py
@@ -8,6 +8,7 @@ y_train = pd.read_pickle("./data/y_train.pkl")
 X_test = pd.read_pickle("./data/X_test.pkl")
 y_test = pd.read_pickle("./data/y_test.pkl")
 
+X_test_raw = X_test.copy()
 # preprocess data
 from sklearn.preprocessing import StandardScaler,MinMaxScaler
 std = StandardScaler()
@@ -63,4 +64,7 @@ class GenDs(object):
         return train_ds,std
     def gen_test(time_step = 48):
         test_ds = gen_test_ds(X_test,y_test,step_len=time_step)
-        return test_ds
+        return test_ds
+    def gen_raw_test(time_step = 48):
+        raw_test_ds = gen_test_ds(X_test_raw,y_test,step_len=time_step)
+        return raw_test_ds
diff --git a/GenDs2.py b/GenDs2.py
@@ -75,8 +75,9 @@ y_test = y_test.reshape(test_shape)
 X_train = X_train[:,1:,:].astype(np.float64)
 X_test = X_test[:,1:,:].astype(np.float64)
 
+X_test_raw = X_test.copy()
 X_train = apply_sin(X_train,2,24)
-X_train = apply_sin(X_test,2,24)
+X_test = apply_sin(X_test,2,24)
 
 
 X_train = X_train/X_train.max()
@@ -96,4 +97,7 @@ class GenDs(object):
         return train_ds,std
     def gen_test(time_step = 48):
         test_ds = gen_test_ds(X_test,y_test,step_len=time_step)
-        return test_ds
+        return test_ds
+    def gen_raw_test(time_step = 48):
+        test_raw_ds = gen_test_ds(X_test_raw,y_test,step_len=time_step)
+        return test_raw_ds
diff --git a/ModelPerformance.py b/ModelPerformance.py
@@ -1,6 +1,7 @@
 import numpy as np
 import  matplotlib.pyplot as plt
 from sklearn.metrics import mean_squared_error
+import math
 def std_inverse(std,data):
     original_shape = data.shape
     try:
@@ -22,15 +23,21 @@ def compare_result(predict,real):
     print(mean_squared_error(predict_flatten,real_flatten))
 
     return (mean_squared_error(predict_flatten,real_flatten))
-def plot_result(predict,real,batch =0,in_out = 0):
+def plot_result(predict,real,batch =0,in_out = 0,figure_name = None,time_c = None):
 
     # vmin = min(predict[batch].min(), real[batch].min())
     vmin = 0
-    vmax = max(predict[batch,in_out].max(), real[batch,in_out].max())
+    vmax = max(predict[:,in_out].max(), real[:,in_out].max())
 
     cmap = 'viridis'
     fig, axs = plt.subplots(1, 3)
+    if time_c !=None:
+        month = round(float(time_c[0,0]*30))
+        day = round(np.arcsin(time_c[1,0])/(2*np.pi)*30)
+        hour = np.arcsin(time_c[2,0])
+        minute = round(float(time_c[3,0])*30)
 
+        fig.suptitle("2019-{:02}-{:02}".format(month,day))
     axs[0].set_title("predict")
     predict_fig = axs[0].imshow(predict[batch,in_out,:,:],cmap = cmap,vmin = vmin,vmax = vmax)
     axs[1].set_title("real")
@@ -41,4 +48,6 @@ def plot_result(predict,real,batch =0,in_out = 0):
     plt.colorbar(predict_fig,ax=axs)
     # plt.colorbar(real_fig,ax = axs)
     print(diff_matrix.mean(),diff_matrix.shape)
+    if figure_name!=None:
+        plt.savefig(f"./figure/{figure_name}.png")
     plt.show()
diff --git a/__pycache__/GenDs2.cpython-311.pyc b/__pycache__/GenDs2.cpython-311.pyc
diff --git a/__pycache__/GenFeature.cpython-311.pyc b/__pycache__/GenFeature.cpython-311.pyc
diff --git a/__pycache__/ModelPerformance.cpython-311.pyc b/__pycache__/ModelPerformance.cpython-311.pyc
diff --git a/baseline_lstm.h5 b/baseline_lstm.h5
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diff --git a/figure/vi.ipynb b/figure/vi.ipynb
@@ -0,0 +1,63 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "import cv2\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "image_folder = './'\n",
+    "video_path = './.avi'\n",
+    "\n",
+    "images = [img for img in os.listdir(image_folder) if img.endswith(\".png\")]\n",
+    "images.sort(key=lambda x: int(x.split('.')[0])) \n",
+    "\n",
+    "\n",
+    "frame = cv2.imread(os.path.join(image_folder, images[0]))\n",
+    "height, width, layers = frame.shape\n",
+    "\n",
+    "\n",
+    "video = cv2.VideoWriter(video_path, cv2.VideoWriter_fourcc(*'DIVX'), 10, (width, height))\n",
+    "\n",
+    "for image in images:\n",
+    "    video.write(cv2.imread(os.path.join(image_folder, image)))\n",
+    "\n",
+    "cv2.destroyAllWindows()\n",
+    "video.release()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/gen_dataset.ipynb b/gen_dataset.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 320,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21,6 +21,26 @@
     "trip_avg_out = y_train.mean(axis=0)[1]"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1248, 2, 16, 8)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train.shape"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

diff --git a/lab.ipynb b/lab.ipynb
@@ -543,6 +543,13 @@
     "model.save(\"base.h5\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 428,

diff --git a/lstm_cnn_v6.h5 b/lstm_cnn_v6.h5
diff --git a/lstm_cnn_v8.h5 b/lstm_cnn_v8.h5
diff --git a/lstm_cnn_v9.keras b/lstm_cnn_v9.keras