d3

D3/TP/TP_SETI_MLP/sinc_dim2_input.csv

@@ -0,0 +1,201 @@
+x1;x2
+0.0625;0.0775
+0.3475;0.1425
+0.8325;0.5825
+0.1025;0.8875
+0.5225;0.1525
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D3/TP/TP_SETI_MLP/test.ipynb

@@ -0,0 +1,30 @@
+{
+ "cells": [],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.10 64-bit",
+   "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": "3.8.10"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

D3/TP/test.ipynb

@@ -1,94 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import scipy.spatial"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "clusters = 3\n",
-    "mean = np.random.randint(5, size=clusters)\n",
-    "sd = [0.25, 0.25, 0.3]\n",
-    "dim = 2\n",
-    "nb = 50\n",
-    "K= clusters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def gen_points(mean=1,sd=0.5, nb=100, dim=2, clusters=2):\n",
-    "    size = []\n",
-    "    # for i in range(0,dim):\n",
-    "    size.append(nb)\n",
-    "    size.append(dim)\n",
-    "    points = np.random.normal(mean[0],sd[0],size=size)\n",
-    "    for i in range(1,clusters):\n",
-    "        points = np.concatenate((points,np.random.normal(mean[i],sd[i],size=size)),axis=0)\n",
-    "    \n",
-    "    return points"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def distance(points,Pc):   \n",
-    "    return scipy.spatial.distance.cdist(points[:,:], Pc[:,:])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "points = gen_points(mean,sd,nb,dim,clusters)\n",
-    "dist = distance(points,points)\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.8.10 64-bit",
-   "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": "3.8.10"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}