Funções de sensibilidade

soloalex1 · Nov 24, 2019 · 967f492 · 967f492
1 parent c698b7e
commit 967f492
Showing 1 changed file with 71 additions and 26 deletions.
diff --git a/dp_exponential/dp_exponential.ipynb b/dp_exponential/dp_exponential.ipynb
@@ -229,6 +229,8 @@
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
+    "from itertools import combinations\n",
+    "from scipy.stats import expon\n",
     "\n",
     "data = pd.read_csv(r'dados/movie_metadata.csv')\n",
     "budgets = [0.1, 1, 10]\n",
@@ -316,15 +318,16 @@
     "\n",
     "def score3(data, out):\n",
     "    data_countries = data.country.dropna().array\n",
-    "    countries, values = []\n",
+    "    countries = np.zeros(len(data_countries))\n",
+    "    values = []\n",
     "    for i in range(0, len(data_countries)):\n",
     "        if(data_countries[i] not in countries):\n",
-    "            countries[data_countries[i]] = 0\n",
-    "    for i in data:\n",
-    "        if(i.country != 'None'):\n",
-    "            countries[i.country] += 1\n",
-    "            if(i.country in saida):\n",
-    "                values.append(i.country)\n",
+    "            countries[i] = 0\n",
+    "    for i in range(0, len(data_countries)):\n",
+    "        if(data.iloc[i].country != 'None'):\n",
+    "            countries[i] += 1\n",
+    "            if(data.iloc[i].country in out):\n",
+    "                values.append(data.iloc[i].country)\n",
     "            else:\n",
     "                values.append(0)\n",
     "    return values"
@@ -339,18 +342,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\alexandre\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\ipykernel_launcher.py:29: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n"
+     ]
     }
    ],
    "source": [
@@ -365,7 +365,7 @@
     "        for j in range(0, len(mat) - 1):\n",
     "            for k in range(j + 1, len(mat)):\n",
     "                psens = abs(mat[i][j] - mat[i][k])\n",
-    "                if(sense <= psens):\n",
+    "                if(sense < psens):\n",
     "                    sense = psens\n",
     "    return sense\n",
     "\n",
@@ -379,36 +379,81 @@
     "        for j in range(0, len(mat) - 1):\n",
     "            for k in range(j + 1, len(mat)):\n",
     "                psens = abs(mat[i][j] - mat[i][k])\n",
-    "                if(sense <= psens):\n",
+    "                if(sense < psens):\n",
     "                    sense = psens\n",
     "    return sense\n",
     "\n",
     "def sense_q3(data):\n",
-    "    mat = []\n",
     "    sense = 0\n",
     "    country_list = []\n",
-    "    for i in data:\n",
-    "        if(i.country not in country_list and i.country != 'None'):\n",
-    "            country.list.append(i.country)\n",
-    "    return sense\n",
+    "    mat = []\n",
+    "    for i in range(0, len(data)):\n",
+    "        if(data.iloc[i].country not in country_list and data.iloc[i].country != 'None'):\n",
+    "            country_list.append(data.iloc[i].country)\n",
+    "    results = combinations(country_list, 3)\n",
+    "    \n",
+    "    for r in results:\n",
+    "        mat.append(score3(data, r))\n",
     "\n",
+    "    for i in range(0, len(mat)):\n",
+    "        for j in range(0, len(data)):\n",
+    "            for k in range(0, len(data)):\n",
+    "                psens = abs(mat[i][j] - mat[i][k])\n",
+    "                if(psens < sense):\n",
+    "                    sense = psens\n",
+    "    return sense\n",
     "\n",
-    "sense_q2(data)"
+    "sense_q3(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Mecanismo Exponencial"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def exponencial(data, sense, res, budget):\n",
+    "    return"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Randomização de consultas"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "# TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Execução"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "# TODO"
+   ]
   }
  ],
  "metadata": {