cambios 18-5-24

centroides.ipynb

@@ -0,0 +1,186 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Carlos\\anaconda3\\envs\\pytorch\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def binary_distance(X, Y):\n",
+    "    \"\"\"Compute distance matrix between  rows of X, Y.\n",
+    "\n",
+    "    d(x_i, y_j) = 1 if x_i == y_j, 0 in other case.\n",
+    "\n",
+    "    for all rows x_i in X, y_j in Y\n",
+    "\n",
+    "    \"\"\"\n",
+    "    return (X.unsqueeze(1) == Y.unsqueeze(0)).all(-1).float()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_centroids(X, y):\n",
+    "    label_matrix = binary_distance(y.unique().unsqueeze(1), y.unsqueeze(1))\n",
+    "    label_matrix = torch.div(label_matrix, torch.sum(label_matrix, dim=1).unsqueeze(1))\n",
+    "    return torch.matmul(label_matrix, X), y.unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prueba = torch.tensor([2,3,2,2,1,2,3,4], dtype=torch.float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "etiquetas = prueba.unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "matriz = binary_distance(etiquetas.unsqueeze(1), prueba.unsqueeze(1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[0., 0., 0., 0., 1., 0., 0., 0.],\n",
+       "        [1., 0., 1., 1., 0., 1., 0., 0.],\n",
+       "        [0., 1., 0., 0., 0., 0., 1., 0.],\n",
+       "        [0., 0., 0., 0., 0., 0., 0., 1.]])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "matriz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = torch.rand((5,6), dtype=torch.float)\n",
+    "y = torch.tensor([2,3,2,2,1], dtype=torch.float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[0.8297, 0.3224, 0.5171, 0.4091, 0.2864, 0.7082],\n",
+       "        [0.8326, 0.8648, 0.8250, 0.7273, 0.6693, 0.8729],\n",
+       "        [0.8528, 0.2973, 0.4668, 0.0865, 0.9033, 0.1066],\n",
+       "        [0.0826, 0.5666, 0.1821, 0.4346, 0.9174, 0.1333],\n",
+       "        [0.2146, 0.4583, 0.8526, 0.2099, 0.6207, 0.7287]])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(tensor([[0.2146, 0.4583, 0.8526, 0.2099, 0.6207, 0.7287],\n",
+       "         [0.5884, 0.3954, 0.3887, 0.3101, 0.7024, 0.3160],\n",
+       "         [0.8326, 0.8648, 0.8250, 0.7273, 0.6693, 0.8729]]),\n",
+       " tensor([1., 2., 3.]))"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "compute_centroids(X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pytorch",
+   "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": "3.8.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}