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KindXiaoming · Apr 29, 2024 · bfe4e84 · bfe4e84
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diff --git a/.gitignore b/.gitignore
@@ -3,3 +3,4 @@ __pycache__/
 docs/_build/
 docs/_static/
 docs/_templates
+folder
diff --git a/docs/API_demo/.ipynb_checkpoints/API_9_video-checkpoint.ipynb b/docs/API_demo/.ipynb_checkpoints/API_9_video-checkpoint.ipynb
@@ -20,9 +20,35 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "train loss: 6.39e-03 | test loss: 6.40e-03 | reg: 7.91e+00 : 100%|██| 50/50 [01:30<00:00,  1.81s/it]\n"
+      "train loss: 5.89e-03 | test loss: 5.99e-03 | reg: 7.89e+00 : 100%|██| 50/50 [01:36<00:00,  1.92s/it]\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "from kan import KAN, create_dataset\n",
+    "import torch\n",
+    "\n",
+    "# create a KAN: 2D inputs, 1D output, and 5 hidden neurons. cubic spline (k=3), 5 grid intervals (grid=5).\n",
+    "model = KAN(width=[4,2,1,1], grid=3, k=3, seed=0)\n",
+    "f = lambda x: torch.exp((torch.sin(torch.pi*(x[:,[0]]**2+x[:,[1]]**2))+torch.sin(torch.pi*(x[:,[2]]**2+x[:,[3]]**2)))/2)\n",
+    "dataset = create_dataset(f, n_var=4, train_num=3000)\n",
+    "\n",
+    "image_folder = 'video_img'\n",
+    "\n",
+    "# train the model\n",
+    "#model.train(dataset, opt=\"LBFGS\", steps=20, lamb=1e-3, lamb_entropy=2.);\n",
+    "model.train(dataset, opt=\"LBFGS\", steps=50, lamb=5e-5, lamb_entropy=2., save_fig=True, beta=10, \n",
+    "            in_vars=[r'$x_1$', r'$x_2$', r'$x_3$', r'$x_4$'],\n",
+    "            out_vars=[r'${\\rm exp}({\\rm sin}(x_1^2+x_2^2)+{\\rm sin}(x_3^2+x_4^2))$'],\n",
+    "            img_folder=image_folder);\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c18245a3",
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
@@ -49,21 +75,35 @@
     }
    ],
    "source": [
-    "from kan import KAN, create_dataset\n",
-    "import torch\n",
+    "import os\n",
+    "import numpy as np\n",
+    "import moviepy.video.io.ImageSequenceClip # moviepy == 1.0.3\n",
     "\n",
-    "# create a KAN: 2D inputs, 1D output, and 5 hidden neurons. cubic spline (k=3), 5 grid intervals (grid=5).\n",
-    "model = KAN(width=[4,2,1,1], grid=3, k=3, seed=0)\n",
-    "f = lambda x: torch.exp((torch.sin(torch.pi*(x[:,[0]]**2+x[:,[1]]**2))+torch.sin(torch.pi*(x[:,[2]]**2+x[:,[3]]**2)))/2)\n",
-    "dataset = create_dataset(f, n_var=4, train_num=3000)\n",
+    "video_name='video'\n",
+    "fps=5\n",
     "\n",
-    "# train the model\n",
-    "#model.train(dataset, opt=\"LBFGS\", steps=20, lamb=1e-3, lamb_entropy=2.);\n",
-    "model.train(dataset, opt=\"LBFGS\", steps=50, lamb=5e-5, lamb_entropy=2., save_video=True, beta=10, \n",
-    "            in_vars=[r'$x_1$', r'$x_2$', r'$x_3$', r'$x_4$'],\n",
-    "            out_vars=[r'${\\rm exp}({\\rm sin}(x_1^2+x_2^2)+{\\rm sin}(x_3^2+x_4^2))$'],\n",
-    "            video_name='video', fps=5);"
+    "fps = fps\n",
+    "files = os.listdir(image_folder)\n",
+    "train_index = []\n",
+    "for file in files:\n",
+    "    if file[0].isdigit() and file.endswith('.jpg'):\n",
+    "        train_index.append(int(file[:-4]))\n",
+    "\n",
+    "train_index = np.sort(train_index)\n",
+    "\n",
+    "image_files = [image_folder+'/'+str(train_index[index])+'.jpg' for index in train_index]\n",
+    "\n",
+    "clip = moviepy.video.io.ImageSequenceClip.ImageSequenceClip(image_files, fps=fps)\n",
+    "clip.write_videofile(video_name+'.mp4')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "88d0d737",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

diff --git a/docs/API_demo/API_9_video.ipynb b/docs/API_demo/API_9_video.ipynb
@@ -20,9 +20,35 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "train loss: 6.39e-03 | test loss: 6.40e-03 | reg: 7.91e+00 : 100%|██| 50/50 [01:30<00:00,  1.81s/it]\n"
+      "train loss: 5.89e-03 | test loss: 5.99e-03 | reg: 7.89e+00 : 100%|██| 50/50 [01:36<00:00,  1.92s/it]\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "from kan import KAN, create_dataset\n",
+    "import torch\n",
+    "\n",
+    "# create a KAN: 2D inputs, 1D output, and 5 hidden neurons. cubic spline (k=3), 5 grid intervals (grid=5).\n",
+    "model = KAN(width=[4,2,1,1], grid=3, k=3, seed=0)\n",
+    "f = lambda x: torch.exp((torch.sin(torch.pi*(x[:,[0]]**2+x[:,[1]]**2))+torch.sin(torch.pi*(x[:,[2]]**2+x[:,[3]]**2)))/2)\n",
+    "dataset = create_dataset(f, n_var=4, train_num=3000)\n",
+    "\n",
+    "image_folder = 'video_img'\n",
+    "\n",
+    "# train the model\n",
+    "#model.train(dataset, opt=\"LBFGS\", steps=20, lamb=1e-3, lamb_entropy=2.);\n",
+    "model.train(dataset, opt=\"LBFGS\", steps=50, lamb=5e-5, lamb_entropy=2., save_fig=True, beta=10, \n",
+    "            in_vars=[r'$x_1$', r'$x_2$', r'$x_3$', r'$x_4$'],\n",
+    "            out_vars=[r'${\\rm exp}({\\rm sin}(x_1^2+x_2^2)+{\\rm sin}(x_3^2+x_4^2))$'],\n",
+    "            img_folder=image_folder);\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c18245a3",
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
@@ -49,21 +75,35 @@
     }
    ],
    "source": [
-    "from kan import KAN, create_dataset\n",
-    "import torch\n",
+    "import os\n",
+    "import numpy as np\n",
+    "import moviepy.video.io.ImageSequenceClip # moviepy == 1.0.3\n",
     "\n",
-    "# create a KAN: 2D inputs, 1D output, and 5 hidden neurons. cubic spline (k=3), 5 grid intervals (grid=5).\n",
-    "model = KAN(width=[4,2,1,1], grid=3, k=3, seed=0)\n",
-    "f = lambda x: torch.exp((torch.sin(torch.pi*(x[:,[0]]**2+x[:,[1]]**2))+torch.sin(torch.pi*(x[:,[2]]**2+x[:,[3]]**2)))/2)\n",
-    "dataset = create_dataset(f, n_var=4, train_num=3000)\n",
+    "video_name='video'\n",
+    "fps=5\n",
     "\n",
-    "# train the model\n",
-    "#model.train(dataset, opt=\"LBFGS\", steps=20, lamb=1e-3, lamb_entropy=2.);\n",
-    "model.train(dataset, opt=\"LBFGS\", steps=50, lamb=5e-5, lamb_entropy=2., save_video=True, beta=10, \n",
-    "            in_vars=[r'$x_1$', r'$x_2$', r'$x_3$', r'$x_4$'],\n",
-    "            out_vars=[r'${\\rm exp}({\\rm sin}(x_1^2+x_2^2)+{\\rm sin}(x_3^2+x_4^2))$'],\n",
-    "            video_name='video', fps=5);"
+    "fps = fps\n",
+    "files = os.listdir(image_folder)\n",
+    "train_index = []\n",
+    "for file in files:\n",
+    "    if file[0].isdigit() and file.endswith('.jpg'):\n",
+    "        train_index.append(int(file[:-4]))\n",
+    "\n",
+    "train_index = np.sort(train_index)\n",
+    "\n",
+    "image_files = [image_folder+'/'+str(train_index[index])+'.jpg' for index in train_index]\n",
+    "\n",
+    "clip = moviepy.video.io.ImageSequenceClip.ImageSequenceClip(image_files, fps=fps)\n",
+    "clip.write_videofile(video_name+'.mp4')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "88d0d737",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

diff --git a/docs/API_demo/figures/sp_0_0_0.png b/docs/API_demo/figures/sp_0_0_0.png
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diff --git a/docs/API_demo/figures/sp_0_3_1.png b/docs/API_demo/figures/sp_0_3_1.png
diff --git a/docs/API_demo/figures/sp_1_0_0.png b/docs/API_demo/figures/sp_1_0_0.png
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diff --git a/docs/API_demo/figures/sp_1_1_1.png b/docs/API_demo/figures/sp_1_1_1.png
diff --git a/docs/API_demo/figures/sp_1_2_0.png b/docs/API_demo/figures/sp_1_2_0.png
diff --git a/docs/API_demo/figures/sp_1_2_1.png b/docs/API_demo/figures/sp_1_2_1.png
diff --git a/docs/API_demo/figures/sp_2_0_0.png b/docs/API_demo/figures/sp_2_0_0.png
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diff --git a/...pynb_checkpoints/PDE_3D_-checkpoint.ipynb → ...pynb_checkpoints/PDE_3D_-checkpoint.ipynb b/...pynb_checkpoints/PDE_3D_-checkpoint.ipynb → ...pynb_checkpoints/PDE_3D_-checkpoint.ipynb
diff --git a/...nb_checkpoints/symmetry_-checkpoint.ipynb → ...nb_checkpoints/symmetry_-checkpoint.ipynb b/...nb_checkpoints/symmetry_-checkpoint.ipynb → ...nb_checkpoints/symmetry_-checkpoint.ipynb
diff --git a/docs/PDE_3D_.ipynb → docs/Examples/PDE_3D_.ipynb b/docs/PDE_3D_.ipynb → docs/Examples/PDE_3D_.ipynb
diff --git a/docs/symmetry_.ipynb → docs/Examples/symmetry_.ipynb b/docs/symmetry_.ipynb → docs/Examples/symmetry_.ipynb
diff --git a/docs/example_files/example_1_1.png b/docs/example_files/example_1_1.png
diff --git a/docs/model_ckpt/ckpt1 b/docs/model_ckpt/ckpt1
diff --git a/docs/model_ckpt/ckpt2 b/docs/model_ckpt/ckpt2
diff --git a/docs/model_ckpt/ckpt3 b/docs/model_ckpt/ckpt3
diff --git a/docs/video.mp4 b/docs/video.mp4
diff --git a/docs/video/0.jpg b/docs/video/0.jpg
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diff --git a/docs/video/9.jpg b/docs/video/9.jpg
diff --git a/docs/video/sp_0_0_0.png b/docs/video/sp_0_0_0.png
diff --git a/docs/video/sp_0_0_1.png b/docs/video/sp_0_0_1.png
diff --git a/docs/video/sp_0_1_0.png b/docs/video/sp_0_1_0.png
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diff --git a/docs/video/sp_0_2_0.png b/docs/video/sp_0_2_0.png
diff --git a/docs/video/sp_0_2_1.png b/docs/video/sp_0_2_1.png
diff --git a/docs/video/sp_0_3_0.png b/docs/video/sp_0_3_0.png
diff --git a/docs/video/sp_0_3_1.png b/docs/video/sp_0_3_1.png
diff --git a/docs/video/sp_1_0_0.png b/docs/video/sp_1_0_0.png
diff --git a/docs/video/sp_1_1_0.png b/docs/video/sp_1_1_0.png
diff --git a/docs/video/sp_2_0_0.png b/docs/video/sp_2_0_0.png
diff --git a/figures/sp_0_0_0.png b/figures/sp_0_0_0.png
diff --git a/figures/sp_0_0_1.png b/figures/sp_0_0_1.png
diff --git a/figures/sp_0_0_2.png b/figures/sp_0_0_2.png
diff --git a/figures/sp_0_0_3.png b/figures/sp_0_0_3.png
diff --git a/figures/sp_0_0_4.png b/figures/sp_0_0_4.png
diff --git a/figures/sp_0_1_0.png b/figures/sp_0_1_0.png
diff --git a/figures/sp_0_1_1.png b/figures/sp_0_1_1.png
diff --git a/figures/sp_0_1_2.png b/figures/sp_0_1_2.png
diff --git a/figures/sp_0_1_3.png b/figures/sp_0_1_3.png
diff --git a/figures/sp_0_1_4.png b/figures/sp_0_1_4.png
diff --git a/figures/sp_1_0_0.png b/figures/sp_1_0_0.png
diff --git a/figures/sp_1_1_0.png b/figures/sp_1_1_0.png
diff --git a/figures/sp_1_2_0.png b/figures/sp_1_2_0.png
diff --git a/figures/sp_1_3_0.png b/figures/sp_1_3_0.png
diff --git a/figures/sp_1_4_0.png b/figures/sp_1_4_0.png
diff --git a/kan/KAN.py b/kan/KAN.py
@@ -10,7 +10,6 @@
 from tqdm import tqdm
 import random
 import copy
-import moviepy.video.io.ImageSequenceClip
 
 
 class KAN(nn.Module):
@@ -773,7 +772,7 @@ def score2alpha(score):
 
 
 
-    def train(self, dataset, opt="LBFGS", steps=100, log=1, lamb=0., lamb_l1 = 1., lamb_entropy = 2., lamb_coef = 0., lamb_coefdiff=0., update_grid=True, grid_update_num=10, loss_fn = None, lr=1., stop_grid_update_step=50, batch=-1, small_mag_threshold=1e-16, small_reg_factor=1., metrics=None, sglr_avoid=False, save_video=False, in_vars=None, out_vars=None, video_folder='./video', beta=3, save_fig_freq=1, video_name='video', fps=1, device='cpu'):
+    def train(self, dataset, opt="LBFGS", steps=100, log=1, lamb=0., lamb_l1 = 1., lamb_entropy = 2., lamb_coef = 0., lamb_coefdiff=0., update_grid=True, grid_update_num=10, loss_fn = None, lr=1., stop_grid_update_step=50, batch=-1, small_mag_threshold=1e-16, small_reg_factor=1., metrics=None, sglr_avoid=False, save_fig=False, in_vars=None, out_vars=None, beta=3, save_fig_freq=1, img_folder='./video', device='cpu'):
         '''
         training
 
@@ -813,10 +812,6 @@ def train(self, dataset, opt="LBFGS", steps=100, log=1, lamb=0., lamb_l1 = 1., l
                 device   
             save_fig_freq : int
                 save figure every (save_fig_freq) step
-            video_name : str
-                the name of the video, default 'video'
-            fps : int
-                frame per second, default fps=1
 
         Returns:
         --------
@@ -905,9 +900,9 @@ def closure():
             objective.backward()
             return objective
 
-        if save_video:
-            if not os.path.exists(video_folder):
-                os.makedirs(video_folder)
+        if save_fig:
+            if not os.path.exists(img_folder):
+                os.makedirs(img_folder)
 
         for _ in pbar:
 
@@ -950,31 +945,13 @@ def closure():
             results['test_loss'].append(torch.sqrt(test_loss).cpu().detach().numpy())
             results['reg'].append(reg_.cpu().detach().numpy())
 
-            if save_video and _ % save_fig_freq == 0:
+            if save_fig and _ % save_fig_freq == 0:
 
-                self.plot(folder=video_folder, in_vars=in_vars, out_vars=out_vars, title="Step {}".format(_), beta=beta)
-                plt.savefig(video_folder+'/'+str(_)+'.jpg', bbox_inches='tight', dpi=200)
+                self.plot(folder=img_folder, in_vars=in_vars, out_vars=out_vars, title="Step {}".format(_), beta=beta)
+                plt.savefig(img_folder+'/'+str(_)+'.jpg', bbox_inches='tight', dpi=200)
                 plt.close()
 
 
-        if save_video:
-            image_folder = video_folder
-            #video_name = 'haha'
-            fps = fps
-            files = os.listdir(image_folder)
-            train_index = []
-            for file in files:
-                if file[0].isdigit() and file.endswith('.jpg'):
-                    train_index.append(int(file[:-4]))
-
-            train_index = np.sort(train_index)
-
-            image_files = [image_folder+'/'+str(train_index[index])+'.jpg' for index in train_index]
-
-            clip = moviepy.video.io.ImageSequenceClip.ImageSequenceClip(image_files, fps=fps)
-            clip.write_videofile(video_name+'.mp4')
-            #print('saving video to',video_name+'.mp4')
-
         return results
 
 

diff --git a/pykan.egg-info/PKG-INFO b/pykan.egg-info/PKG-INFO
@@ -9,3 +9,52 @@ Classifier: License :: OSI Approved :: MIT License
 Classifier: Operating System :: OS Independent
 Requires-Python: >=3.6
 Description-Content-Type: text/markdown
+License-File: LICENSE
+
+<img width="600" alt="kan_plot" src="https://github.com/KindXiaoming/pykan/assets/23551623/a2d2d225-b4d2-4c1e-823e-bc45c7ea96f9">
+
+# Kolmogorov-Arnold Newtworks (KANs)
+
+This the github repo for the paper "KAN: Kolmogorov-Arnold Networks" [link]. The documentation can be found here [link].
+
+Kolmogorov-Arnold Networks (KANs) are promising alternatives of Multi-Layer Perceptrons (MLPs). KANs have strong mathematical foundations just like MLPs: MLPs are based on the [universal approximation theorem](https://en.wikipedia.org/wiki/Universal_approximation_theorem), while KANs are based on [Kolmogorov-Arnold representation theorem](https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Arnold_representation_theorem). KANs and MLPs are dual: KANs have activation functions on edges, while MLPs have activation functions on nodes. This simple change makes KANs better (sometimes much better!) than MLPs in terms of both model accuracy and interpretability. 
+
+<img width="1163" alt="mlp_kan_compare" src="https://github.com/KindXiaoming/pykan/assets/23551623/695adc2d-0d0b-4e4b-bcff-db2c8070f841">
+
+## Installation
+There are two ways to install pykan, through pypi or github.
+
+**Installation via github**
+
+```python
+git clone https://github.com/KindXiaoming/pykan.git
+cd pykan
+pip install -e .
+```
+
+**Installation via pypi (soon)**
+
+```python
+pip install pykan
+```
+
+
+To install requirements:
+```python
+pip install -r requirements.txt
+```
+
+## Documentation
+The documenation can be found here [].
+
+## Tutorials
+
+**Quickstart**
+
+Get started with [hellokan.ipynb](./hellokan.ipynb) notebook
+
+**More demos**
+
+Jupyter Notebooks in [docs/Examples](./docs/Examples) and [docs/API_demo](./docs/API\_demo) are ready to play. You may also find these examples in documentation.
+
+
diff --git a/pykan.egg-info/SOURCES.txt b/pykan.egg-info/SOURCES.txt
@@ -1,3 +1,4 @@
+LICENSE
 README.md
 setup.py
 kan/KAN.py

diff --git a/requirements.txt b/requirements.txt
@@ -1,9 +1,7 @@
 matplotlib==3.6.2
-moviepy==1.0.3
 numpy==1.24.4
 scikit_learn==1.1.3
 setuptools==65.5.0
-sphinx_rtd_theme==2.0.0
 sympy==1.11.1
 torch==2.2.2
 tqdm==4.66.2