Thermostat classifier.ipynb

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   "source": [
    "import pandas as pd\n",
    "from sklearn import svm\n",
    "import random"
   ]
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      "0.818330605565\n"
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   "source": [
    "# import climate control log\n",
    "data = pd.read_csv('climate_control_log.csv')\n",
    "\n",
    "#randomize order and reindex\n",
    "index = random.sample(range(0, len(data)), len(data))\n",
    "data = data.ix[index]\n",
    "data.index = np.arange(0,len(data))\n",
    "\n",
    "#set up features and classes\n",
    "#features = data[['temp','hum','outside_temp','outside_hum','temp_set_point','hum_set_point']]\n",
    "#features = data[['temp_set_point']]\n",
    "features = pd.DataFrame(np.ones(len(data)))\n",
    "\n",
    "features = features.values\n",
    "\n",
    "classification = data.state.values\n",
    "\n",
    "#slice out training set\n",
    "training_features = features[:len(features)/2]\n",
    "training_classification = classification[:len(classification)/2]\n",
    "\n",
    "#initialize classifier algo\n",
    "clf = svm.SVC()\n",
    "clf.fit(training_features,training_classification) \n",
    "\n",
    "#test algo\n",
    "test = clf.predict(features[len(features)/2:]) == classification[len(classification)/2:]\n",
    "print(sum(test)/len(test))\n",
    "del clf"
   ]
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