08. EM & K-MEANS ALGORITHM (USING API).ipynb

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   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn.metrics as sm\n",
    "from sklearn import preprocessing\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.mixture import GaussianMixture"
   ]
  },
  {
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      "       [5.9, 3. , 5.1, 1.8]]), 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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      "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]), 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'), 'DESCR': '.. _iris_dataset:\\n\\nIris plants dataset\\n--------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 150 (50 in each of three classes)\\n    :Number of Attributes: 4 numeric, predictive attributes and the class\\n    :Attribute Information:\\n        - sepal length in cm\\n        - sepal width in cm\\n        - petal length in cm\\n        - petal width in cm\\n        - class:\\n                - Iris-Setosa\\n                - Iris-Versicolour\\n                - Iris-Virginica\\n                \\n    :Summary Statistics:\\n\\n    ============== ==== ==== ======= ===== ====================\\n                    Min  Max   Mean    SD   Class Correlation\\n    ============== ==== ==== ======= ===== ====================\\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\n    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\\n    ============== ==== ==== ======= ===== ====================\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: 33.3% for each of 3 classes.\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\\nfrom Fisher\\'s paper. Note that it\\'s the same as in R, but not as in the UCI\\nMachine Learning Repository, which has two wrong data points.\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\n.. topic:: References\\n\\n   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n     Mathematical Statistics\" (John Wiley, NY, 1950).\\n   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n     Structure and Classification Rule for Recognition in Partially Exposed\\n     Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n     on Information Theory, May 1972, 431-433.\\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n     conceptual clustering system finds 3 classes in the data.\\n   - Many, many more ...', 'feature_names': ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'], 'filename': '/home/akshayrb22/.local/lib/python3.7/site-packages/sklearn/datasets/data/iris.csv'}\n"
     ]
    }
   ],
   "source": [
    "dataset = load_iris()\n",
    "print(dataset)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
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    "ExecuteTime": {
     "end_time": "2019-10-18T05:57:20.540588Z",
     "start_time": "2019-10-18T05:57:20.532514Z"
    }
   },
   "outputs": [],
   "source": [
    "X = pd.DataFrame(dataset.data)\n",
    "X.columns = ['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width']\n",
    "y = pd.DataFrame(dataset.target)\n",
    "y.columns = ['Targets']\n",
    "#print(X)"
   ]
  },
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     "end_time": "2019-10-18T05:57:25.561350Z",
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       "Text(0.5, 1.0, 'GMM Classification')"
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\n",
      "text/plain": [
       "<Figure size 1008x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 7))\n",
    "colormap = np.array(['red', 'lime', 'black'])\n",
    "#REAL PLOT\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.scatter(X.Petal_Length, X.Petal_Width, c=colormap[y.Targets], s=40)\n",
    "plt.title('Real')\n",
    "#K-PLOT\n",
    "plt.subplot(1, 3, 2)\n",
    "model = KMeans(n_clusters=3)\n",
    "model.fit(X)\n",
    "predY = np.choose(model.labels_, [0, 1, 2]).astype(np.int64)\n",
    "plt.scatter(X.Petal_Length, X.Petal_Width, c=colormap[predY], s=40)\n",
    "plt.title('KMeans')\n",
    "#GMM PLOT\n",
    "scaler = preprocessing.StandardScaler()\n",
    "scaler.fit(X)\n",
    "xsa = scaler.transform(X)\n",
    "xs = pd.DataFrame(xsa, columns=X.columns)\n",
    "gmm = GaussianMixture(n_components=3)\n",
    "gmm.fit(xs)\n",
    "y_cluster_gmm = gmm.predict(xs)\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.scatter(X.Petal_Length, X.Petal_Width, c=colormap[y_cluster_gmm], s=40)\n",
    "plt.title('GMM Classification')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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