Update in notebooks

1 files changed, 125 insertions, 1 deletions

diff --git a/notebooks/Untitled1.ipynb b/notebooks/Untitled1.ipynb
index 06129a3..a2d6168 100644
--- a/notebooks/Untitled1.ipynb
+++ b/notebooks/Untitled1.ipynb
@@ -536,8 +536,132 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "c0e733d8-c17d-46f5-b484-9c74e46d7308",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from typing import Union\n",
+    "\n",
+    "import torch\n",
+    "\n",
+    "\n",
+    "def first_appearance(x: torch.Tensor, element: Union[int, float], dim: int = 1) -> torch.Tensor:\n",
+    "    \"\"\"Return indices of first appearance of element in x, collapsing along dim.\n",
+    "\n",
+    "    Based on https://discuss.pytorch.org/t/first-nonzero-index/24769/9\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    x\n",
+    "        One or two-dimensional Tensor to search for element.\n",
+    "    element\n",
+    "        Item to search for inside x.\n",
+    "    dim\n",
+    "        Dimension of Tensor to collapse over.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    torch.Tensor\n",
+    "        Indices where element occurs in x. If element is not found,\n",
+    "        return length of x along dim. One dimension smaller than x.\n",
+    "\n",
+    "    Raises\n",
+    "    ------\n",
+    "    ValueError\n",
+    "        if x is not a 1 or 2 dimensional Tensor\n",
+    "\n",
+    "    Examples\n",
+    "    --------\n",
+    "    >>> first_appearance(torch.tensor([[1, 2, 3], [2, 3, 3], [1, 1, 1], [3, 1, 1]]), 3)\n",
+    "    tensor([2, 1, 3, 0])\n",
+    "    >>> first_appearance(torch.tensor([1, 2, 3]), 1, dim=0)\n",
+    "    tensor(0)\n",
+    "    \"\"\"\n",
+    "    if x.dim() > 2 or x.dim() == 0:\n",
+    "        raise ValueError(f\"only 1 or 2 dimensional Tensors allowed, got Tensor with dim {x.dim()}\")\n",
+    "    matches = x == element\n",
+    "    first_appearance_mask = (matches.cumsum(dim) == 1) & matches\n",
+    "    does_match, match_index = first_appearance_mask.max(dim)\n",
+    "    first_inds = torch.where(does_match, match_index, x.shape[dim])\n",
+    "    return first_inds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "26ff2314-b2df-408f-b83a-f5fc903145da",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([2, 3])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "first_appearance(torch.tensor([[1, 1, 3], [1, 1, 1]]), 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e8c9dd16-4917-40bc-8504-084035882ced",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([ True, False])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.any(torch.isin(torch.tensor([[1, 1, 3], [1, 1, 1]]), 3), 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "30eb1926-53d2-431a-b7c1-f95919887b84",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([2, 0])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.tensor([[1, 1, 3], [1, 1, 1]]).argmax(dim=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a02c61b3-c84f-4778-90d0-fe6aafa12ccc",
    "metadata": {},
    "outputs": [],
    "source": []