1 files changed, 0 insertions, 202 deletions
diff --git a/src/notebooks/07-try-gtn.ipynb b/src/notebooks/07-try-gtn.ipynb
deleted file mode 100644
index 4ef444b..0000000
--- a/src/notebooks/07-try-gtn.ipynb
+++ /dev/null
@@ -1,202 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import gtn\n",
-    "from IPython.display import display, Image"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Make some graphs:\n",
-    "g1 = gtn.Graph()\n",
-    "g1.add_node(True)  # Add a start node\n",
-    "g1.add_node()  # Add an internal node\n",
-    "g1.add_node(False, True)  # Add an accepting node\n",
-    "\n",
-    "\n",
-    "# Add arcs with (src node, dst node, label):\n",
-    "g1.add_arc(0, 1, 1)\n",
-    "g1.add_arc(0, 1, 2)\n",
-    "g1.add_arc(1, 2, 1)\n",
-    "g1.add_arc(1, 2, 0)\n",
-    "\n",
-    "\n",
-    "g2 = gtn.Graph()\n",
-    "g2.add_node(True, True)\n",
-    "g2.add_arc(0, 0, 1)\n",
-    "g2.add_arc(0, 0, 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gtn.draw(g1, \"g1.png\")\n",
-    "gtn.draw(g2, \"g2.png\")\n",
-    "display(Image(\"g1.png\"), Image(\"g2.png\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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BgWlpadDv19DQAADQ19fHrq+urpa6eV1dXfGpPQAAPB5vwoQJUisPPQ74FtbT02tsbMRf81l+5paWFgCAdJ0b+H//L/6sA11dXQBAeXm5FNI0NTXhH/1m2hsKwEz0PB7vypUrqampNBrNx8fH1tYWAIBtjVc4mpqa8F889Onq6gIAcLncCxcuJCYm6uvrz58/H5oWvj1bWlqIat5Hjx7xeLx//vOfUkuATyy+hXtocPzgEq4RSb1UT2xUAWzxockA8p/JNgXHAETM386dO6fYZUBCSEpKwn4RmUwmZGcd3MsjYwwQ7ITxaVR8fHwCAgLw13zWv1EolPb2dilSSkJsbGzu37/P4XCwbhp2psOGDZNCWmdnJ/yDxWItX75cOpUGiSVLlvTxLYqifD5fV1fXxcXl2rVrQUFBclOsbzo6OqhUanx8vKIV+Yyurq4ff/yxjwtUVFS6uroMDQ1dXV0vXbo0a9Ys7CsqlYo9J7KwZcsWLy+vgIAAWYRAxw8+U2VHR4fYaPEze6PT6e3t7VIPObCoAszeZIkqaG5uhn98/fXXc+bMkU6lQeKnn37qXoiiqEAgUFNT8/HxmTt37pQpU/74449r165Bd7n8lexOS0uLpqbmUGvMzs7OHu0NmhkcmS9cuNDT0/PBgweXLl1qbW3F0g7QaDTZpxtXr17V0NBYu3atjHKg4YgNd/HeHSDmL4EzUXwYzoAIDAzU1tbOyMjASu7du0ehUObPny+FNEwNPT096fSRD2QymUwmoyg6adKk+Pj49+/fJyYmzpgxQ0VFBUaB1tXVKVrH/1FXVzf0c1qTyWQSiUShUGbOnHnlypWGhoaEhISJEyeSSCQ4QeJwONjFDAZDxua9c+dOdXU13thgrKkUQE0MDAywkqamJrHp5Wf9m5WV1atXr8rLy3k8HoVCGWh9Ojo669ati42NXbp0KY1Ga2lpOX78+IYNG/AeS8nBlgGGjj8dD4IgJBJJJBJ5eHgEBgb6+PhgDh4MS0tLFEVLSkqkONgBD5wG93Y4oOSw2eyh2ZgAAARBYJNOnTp1wYIFM2bM6D5Jhp7z8vJybK2IyWSKOdwHxN27d3fu3Dl79uwjR44AAEQiUVlZmYaGhnT54dlsNoqimF9dJBKVl5eLefs/szdbW9vLly/z+fxXr17BLUwDZc2aNfr6+qGhoSNGjCgpKYmMjOxx6CUJWAjlEHxEUBR1cnJauHChv79/Hz0GhUIxNzcvLCz08/OTuq7MzMzk5GQAQEVFxa5du7y9vfGJdwfEy5cvsTQZQwoymezm5rZw4cLZs2dra2v3dpm2tjaDwWCz2ZMnT4Yltra2qamp0lWanZ09c+bMtra2e/fuYYUIgsBISCl4+fLlyJEjsY6qpqamh9VXvPPk8uXLsFAhO5HFgAnf6XS6orZp9oEkkVyQoKAgd3f3wdRFUuDiqtSReoOHQCAQy1jcB+7u7j/99BP28caNGwiC1NbWDo5qA8PV1RWvG0xKIpbf8bP523fffQf9xTC2RYF8/PgR7iByd3cn4BBXopHc4+rp6fn48eOhsISYnp5OoVBcXV0VrYg4JBIJn9y+b8aPH3///n3so5ubG4qi6enpg6PaAGhubn769CmLxcJKMjMzbWxs8KvzQMxfoq2tDf8fd+/eVewsPyUlBU5Xpk+frkA1ZAdm8rh06ZKiFQHJycne3t4EHv2uEFgsFpvNxkKOaDTaxIkTz58/r1itAACpqakIgnh7e2MlGRkZnp6e4teJ9YknTpyA5TExMfLog3sB5qKkUCiSH74zZPHx8fH09FSsDu/eveseOysdfe+3Gux8r1wuV01N7dixY1hJcnIyiqIKH1KyWCxfX1/sY11dHZlM7n72k7i9cTgceKbjsGHD8JEi8gQbzfr5+SlEAWKBc4y8vDwF6rB27VpDQ0PZE1f3u99KDvleAwIC8Ps429raGAwGgalupABuw7t58yZWsnfvXk1Nze6JlnvIz7V69Wr4uBMSiDRQhELh+PHjoQI5OTnyV4BwhEKho6OjAt8dHz9+pNPpsm+ClmS/lRzyvV6/fh1BkLdv32IlO3bs0NLSkvzQQ8KZNWuWk5MTvsTBwaHH9HY92Nu7d+/g0oeOjg6xp+FIApZAZdq0aXKuevC4fPkygiBZWVkKqR3m2W9ubpZRTr9ZXOWT77Wrq2vEiBE///wzVtLU1MRgMMLCwmQXLgX37t1DEAR/kAAM+eieLEjUW35lLEra399/sNTsCSwAgkKhvHr1Sp5VDzbTp0+3s7OT/xD9yZMnJBLp7NmzsovqN4ur3PK9Hjp0SFVVFS8/Pj6eTCbLf0DU0dFha2srlnfMy8urtxl7z/bW0dGBLeHj56aDikAgwPaqyuEEGTlTVlZGp9NXrFghz0qbmposLS2trKwIGWv1ncVVnvlz29rajIyMwsPDsRKhUOjl5cVkMmXvxgdEWFiYpqYmPh8U7Nx6O/231/M64El2AABVVdXuqSkGg5UrV0Jjc3R0VJSrZlBJTk5GEIQQJ6EkCAQCX19ffX19fX19AwOD2NhYGXNg9r3fSm75XiFxcXEoiuLTH717947BYPj7+w/GKYU9cu7cObF/aFdX16hRoyZPntzbLX2d/3bw4EFoAFpaWoPtXoMjfljXmzdvBrUuBRIeHk6lUvs+ko8oVqxYQaVSMzMzW1paoqKiqFSqra0t3oc2UPrO4jp4+V45HE5xcfGDBw9SU1MPHz4cFRUVGhr6ww8/0Gg0NTU1fCwUjI9fuXKllL9wINy9e5dKpUZEROALY2Ji1NTU8L4cMfo533TVqlXQDGg02iCFAgmFwqioKFgLhUIZggFHBCIQCObOnUun0wfV5IRCYWRkJIlEgqM7yOvXr6dMmQIAmDNnjnRvtAMHDgAA8G/eZ8+eQS8Ij8fT09PDfAZ9XNlvLfv27YNZgEaNGmVoaIgdr4s9IaqqqliMIoqiYkf4pqSkkEiktWvXDurZHXfv3qXT6QEBAfi+NCcnh0ql4r2y3enH3gQCAUx1DAeWsbGxxOj7/zQ1Nfn7+2NNSfixb0OQzs7OuXPnUqnUQRpYdnR0LFq0SEVFpUcfSVpamrW1NYqiixcvHmgW2j6yuBKY7zUmJkY8JqMnEASJjY2FR/iKTZYSEhJUVFSCgoIG6ai95ORkKpUaEBCAl9/Y2Nj9cOPu9GNvIpFIIBCEh4djv9Pf35+o8yPv37+P7caj0Wi3bt0iROzQBzYpgiArVqwgdqZaWlrq6Oioqal548aNPmq/cOGClZWViopKYGBgH4Of7vSWxZXAfK9NTU19b8+F23ZOnz4Nr58zZ46urm5RURFeyPXr1+l0upOT04B+Xb90dHSEhYUBACIiIvB21dHR4enpaWJi0u/6Wf/2Bjl48CCVSoU/WFtb+8CBA7IEK1RUVAQGBiIIAgWOHDlSseEXCiE1NVVbW5vJZBIyhO7q6tq/fz+dTndwcJBkuMjj8eLi4kxNTalUalhYmOR9XfcsrrLnzxVj1apVYsNIvLGhKHrx4kXs4ra2NldX1+HDh1dWVuKFlJeXOzs7q6urR0VFEdLRZWZm2tnZ0el0sdg0gUDg7++vpaUlydGqktqbSCTKycnBb+YxNjaOjo6WfCcF5MmTJ0FBQfjWnD9/vjyPUB1SlJWVff/99wiCzJ49+9mzZ9IJ6erqOnv2rI2NjZqa2pYtWwbUYXZ2dv773/8ePnw4mUyeNWtWenq6Qo4sFKOqqqrHvEYkEolKpXbvuhsaGuzt7c3NzdlsNr68vb198+bNqqqqtra2iYmJUrtnc3NzYdKUGTNmiL2YOjo6/P39JffhD8DeoPRt27bhY8zJZLK3t/fu3bufPXvW21ukoaHhxo0bkZGRYnvvmEzmX2cM2QdpaWkwPpvFYsXHx0u+gvTmzZuoqChzc3MURQc6MsQjEAiuXLkyceJEBEGsrKx27twJ4x7lT2Vl5f79+11dXXV0dMS6ODKZrKqqij8QA8+HDx+cnZ0NDAy6H0xVWlq6YMECFEXNzc03b96MP4i0b5qamk6fPg232Dg5OXXP893Y2MhisbS0tCRfMBuYvUHq6uoiIyO75/0jk8kjR4787rvvpk2bNmfOnIkTJ3777bf4dA54Szt9+jQ2vVYiEolu3rw5e/ZsKpWqoqLi5ua2cePG8+fP5+fn19XVwfzeDQ0NlZWVWVlZcXFxISEhcOprbGy8atUqomYpBQUFwcHB6urqWlpay5cvf/DggXy6u6KiopiYGFdXVwRB9PX1Q0JCjh8/jn9gUBSl0Wg9RkhhtLS0TJkyhUqlHjlypPu3paWlERER8OhcJpMZEhISFxd3//79yspKGAzQ2tpaV1eXl5eXkpKyYcMGNzc3FRUVKpXq6+vb4yJKTk6OhYXFsGHDJBlGYkhjb5CmpqaTJ08OaD+otrb2okWL0tPT5bYi+afj06dPiYmJS5YsgV7EHpuRRqO5uLisX7/+7t27g3GM46dPn3bt2mVvbw8AMDU1jYyMzMrKIryi9+/fX758OTQ0FKYlNjAwWLRo0bVr17C3MNxLCo1NR0dH7OCLHuHz+Zs3byaTyX5+fj3OdPh8fnp6+rp161xcXOA+mO6gKGptbR0cHJyUlNRjXA6Px4uJiaFSqZMmTRpogDEiEokktJbeaG5uvn///qNHj9hsNpvN/vDhA5fL5XK5Ojo6GhoaJiYmNjY2dnZ27u7uY8eO/QLyjcoNHo9XVlZWX18P21NbW5tGow0fPly6/EtSUFhYmJycnJqaWlJSoqur6+3tPW7cOCcnJ0dHxwFlvIU0NTWx2eyCgoLs7Ozs7Gw2m40giIODw7Rp06ZPn+7s7Ewifbb7+erVqzNmzEBRVE9PLysrS/I0NhkZGUuWLGloaNiyZUtoaGhvrhcAQHV1NTzjgcPhaGho0Gg0BoMxcuTIPm7JzMwMCwsrKyvbtGnTmjVrxHTuFwLsTckXT0lJydWrV9PT03Nycj5+/IiiqL29vbW1tbGxsYmJiZGRkaGhIeZtBgAIBIJPnz59/Pjx06dP79+/f/PmTXFxMUygoqGh4ezs7Orq6uLi4uLigs9+L4ZIJGIymTweLysrC/aBktPe3h4dHb1z504jI6OIiIilS5dK8YIQ4+HDh9HR0VevXvXy8jp8+LCNjY0UQpT2pmRglJWV5eTk5Obmvn37tq6urrq6ur6+vntObhRFsdDNkSNHWltb29ra2trampmZSd4nXL9+fcyYMdLl5wYAVFRUxMTEnDp1Sltbe968eQsXLnRwcBiokLq6upSUlISEhPz8fBaLtWHDhh6yJEiM0t6UEIDYKTAIgvSR1k7O1NbWxsXFJSYmvnnzxsLCgsVisVgsR0dHfO46MaqqqoqKirKysjIyMnJzc2k0mp+fX3Bw8Lhx42RURmlvSv4qZGdn37x58969e0+fPuXxeCiKmpmZ6enp0el0bW1tLpcLJ3JlZWVcLhcAYG1t7enp6eXlNW3aNNmHoxClvSn5y9He3s5ms0tKSthsNofDaW1tbWxs1NDQoNPpmpqaZmZmNjY23VPZEYLS3pQokR8D82YqUaJEFpT2pkSJ/FDamxIl8uP/AH3eepn8qwoBAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "intersect = gtn.intersect(g1, g2)\n",
-    "gtn.draw(intersect, \"intersect.png\")\n",
-    "Image(\"intersect.png\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1.0, 0.0, 1.0, 0.0]\n"
-     ]
-    }
-   ],
-   "source": [
-    "score = gtn.viterbi_score(intersect)\n",
-    "gtn.backward(score)\n",
-    "\n",
-    "# print gradients of arc weights \n",
-    "print(g1.grad().weights_to_list()) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1.0, 0.0, 0.5, 0.5]\n"
-     ]
-    }
-   ],
-   "source": [
-    "import gtn\n",
-    "\n",
-    "# Make some graphs:\n",
-    "g1 = gtn.Graph()\n",
-    "g1.add_node(True)  # Add a start node\n",
-    "g1.add_node()  # Add an internal node\n",
-    "g1.add_node(False, True)  # Add an accepting node\n",
-    "\n",
-    "# Add arcs with (src node, dst node, label):\n",
-    "g1.add_arc(0, 1, 1)\n",
-    "g1.add_arc(0, 1, 2)\n",
-    "g1.add_arc(1, 2, 1)\n",
-    "g1.add_arc(1, 2, 0)\n",
-    "\n",
-    "g2 = gtn.Graph()\n",
-    "g2.add_node(True, True)\n",
-    "g2.add_arc(0, 0, 1)\n",
-    "g2.add_arc(0, 0, 0)\n",
-    "\n",
-    "# Compute a function of the graphs:\n",
-    "intersection = gtn.intersect(g1, g2)\n",
-    "score = gtn.forward_score(intersection)\n",
-    "\n",
-    "# Visualize the intersected graph:\n",
-    "gtn.draw(intersection, \"intersection.pdf\")\n",
-    "\n",
-    "# Backprop:\n",
-    "gtn.backward(score)\n",
-    "\n",
-    "# Print gradients of arc weights \n",
-    "print(g1.grad().weights_to_list()) # [1.0, 0.0, 1.0, 0.0]"
-   ]
-  },
-  {
-   "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": "3.8.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}