kmeans_&_hierarchical_clustering.ipynb

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      " 0 1 2 1 3 0 0 3 2 0 0 0 3 3 1 0 1 1 1 3 0 0 0 0 1 3 0 2 1 0 2 3 0 3 3 2 1\n",
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     ]
    }
   ],
   "source": [
    "#creating random blobs\n",
    "\n",
    "from sklearn.datasets import make_blobs\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#create blobs\n",
    "x,y = make_blobs(n_samples=200, n_features=2, centers=4, cluster_std=1.6 , random_state=50)\n",
    "print(y)\n",
    "\n",
    "#create np array for data points\n",
    "points=x\n",
    "print(points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-15, 15)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create scatter plot\n",
    "plt.scatter(x[:,0],x[:,1],c=y,cmap='viridis')\n",
    "plt.xlim(-15,15)\n",
    "plt.ylim(-15,15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_x, temp_y = make_blobs(n_samples=10, n_features=2, cluster_std=1, centers=3,\n",
    "                            random_state=30)\n",
    "\n",
    "centerwise_indices=[ temp_y==x for x in np.unique(temp_y)]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 1 0 1 2 0 0 1 2 0]\n",
      "[[ 6.34878868 -1.44987282]\n",
      " [ 1.17706147 -5.78920377]\n",
      " [ 2.11882289 -3.16021872]\n",
      " [ 4.64480527 -5.96660039]\n",
      " [ 8.82914097 -2.0022811 ]\n",
      " [ 4.46109627 -2.27753226]\n",
      " [ 3.01716731 -3.49188567]\n",
      " [ 2.97531259 -6.188618  ]\n",
      " [ 9.24637828 -3.53424216]\n",
      " [ 1.15690829 -0.79993484]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[array([False, False,  True, False, False,  True,  True, False, False,\n",
       "         True]),\n",
       " array([False,  True, False,  True, False, False, False,  True, False,\n",
       "        False]),\n",
       " array([ True, False, False, False,  True, False, False, False,  True,\n",
       "        False])]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(temp_y)\n",
    "print(temp_x)\n",
    "centerwise_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "k=[np.mean(temp_x[idx],axis=0) for idx in centerwise_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([ 2.68849869, -2.43239287]),\n",
       " array([ 2.93239311, -5.98147405]),\n",
       " array([ 8.14143598, -2.32879869])]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.17706147, -5.78920377],\n",
       "       [ 4.64480527, -5.96660039],\n",
       "       [ 2.97531259, -6.188618  ]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_x[centerwise_indices[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.93239311"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(4.64480527+2.97531259+1.17706147)/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-2.40167949 10.17352695]\n",
      " [ 0.05161133 -5.35489826]\n",
      " [-5.56465793 -2.34988939]\n",
      " [-1.92101646  5.21673484]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "kmeans=KMeans(n_clusters=4)\n",
    "\n",
    "kmeans.fit(points)\n",
    "\n",
    "print(kmeans.cluster_centers_)\n",
    "\n",
    "y_km = kmeans.fit_predict(points)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method fit_predict in module sklearn.cluster.k_means_:\n",
      "\n",
      "fit_predict(X, y=None, sample_weight=None) method of sklearn.cluster.k_means_.KMeans instance\n",
      "    Compute cluster centers and predict cluster index for each sample.\n",
      "    \n",
      "    Convenience method; equivalent to calling fit(X) followed by\n",
      "    predict(X).\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    X : {array-like, sparse matrix}, shape = [n_samples, n_features]\n",
      "        New data to transform.\n",
      "    \n",
      "    y : Ignored\n",
      "        not used, present here for API consistency by convention.\n",
      "    \n",
      "    sample_weight : array-like, shape (n_samples,), optional\n",
      "        The weights for each observation in X. If None, all observations\n",
      "        are assigned equal weight (default: None)\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    labels : array, shape [n_samples,]\n",
      "        Index of the cluster each sample belongs to.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 1 0 2 3 1 2 1 1 3 3 2 3 0 3 0 2 2 3 3 3 0 2 2 2 0 2 1 0 2 2 3 3 1 1 3 3\n",
      " 3 0 0 2 3 0 0 1 1 2 0 0 2 1 3 0 2 0 0 1 3 3 0 1 1 2 1 1 2 1 0 2 2 1 3 2 1\n",
      " 0 0 3 0 0 1 3 0 3 3 3 3 2 1 2 3 1 2 1 0 0 3 1 0 1 0 3 1 0 2 0 1 0 1 3 2 1\n",
      " 3 2 2 3 0 2 2 0 1 2 0 2 2 1 2 2 0 0 1 3 1 1 0 2 0 1 2 2 3 2 0 0 1 3 2 1 0\n",
      " 1 3 2 3 0 1 1 0 2 1 1 1 0 0 1 1 3 3 3 0 1 1 1 1 3 0 1 0 3 1 2 0 1 0 0 2 3\n",
      " 1 3 2 0 1 0 0 3 0 1 2 3 0 3 2]\n"
     ]
    }
   ],
   "source": [
    "print(y_km)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x224e85596a0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(points[y_km==0,0],points[y_km==0,1],s=100,c='red')\n",
    "plt.scatter(points[y_km==1,0],points[y_km==1,1],s=100,c='yellow')\n",
    "plt.scatter(points[y_km==2,0],points[y_km==2,1],s=100,c='orange')\n",
    "plt.scatter(points[y_km==3,0],points[y_km==3,1],s=100,c='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function scatter in module matplotlib.pyplot:\n",
      "\n",
      "scatter(x, y, s=None, c=None, marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, verts=None, edgecolors=None, *, data=None, **kwargs)\n",
      "    A scatter plot of *y* vs *x* with varying marker size and/or color.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    x, y : array_like, shape (n, )\n",
      "        The data positions.\n",
      "    \n",
      "    s : scalar or array_like, shape (n, ), optional\n",
      "        The marker size in points**2.\n",
      "        Default is ``rcParams['lines.markersize'] ** 2``.\n",
      "    \n",
      "    c : color, sequence, or sequence of color, optional\n",
      "        The marker color. Possible values:\n",
      "    \n",
      "        - A single color format string.\n",
      "        - A sequence of color specifications of length n.\n",
      "        - A sequence of n numbers to be mapped to colors using *cmap* and\n",
      "          *norm*.\n",
      "        - A 2-D array in which the rows are RGB or RGBA.\n",
      "    \n",
      "        Note that *c* should not be a single numeric RGB or RGBA sequence\n",
      "        because that is indistinguishable from an array of values to be\n",
      "        colormapped. If you want to specify the same RGB or RGBA value for\n",
      "        all points, use a 2-D array with a single row.  Otherwise, value-\n",
      "        matching will have precedence in case of a size matching with *x*\n",
      "        and *y*.\n",
      "    \n",
      "        Defaults to ``None``. In that case the marker color is determined\n",
      "        by the value of ``color``, ``facecolor`` or ``facecolors``. In case\n",
      "        those are not specified or ``None``, the marker color is determined\n",
      "        by the next color of the ``Axes``' current \"shape and fill\" color\n",
      "        cycle. This cycle defaults to :rc:`axes.prop_cycle`.\n",
      "    \n",
      "    marker : `~matplotlib.markers.MarkerStyle`, optional\n",
      "        The marker style. *marker* can be either an instance of the class\n",
      "        or the text shorthand for a particular marker.\n",
      "        Defaults to ``None``, in which case it takes the value of\n",
      "        :rc:`scatter.marker` = 'o'.\n",
      "        See `~matplotlib.markers` for more information about marker styles.\n",
      "    \n",
      "    cmap : `~matplotlib.colors.Colormap`, optional, default: None\n",
      "        A `.Colormap` instance or registered colormap name. *cmap* is only\n",
      "        used if *c* is an array of floats. If ``None``, defaults to rc\n",
      "        ``image.cmap``.\n",
      "    \n",
      "    norm : `~matplotlib.colors.Normalize`, optional, default: None\n",
      "        A `.Normalize` instance is used to scale luminance data to 0, 1.\n",
      "        *norm* is only used if *c* is an array of floats. If *None*, use\n",
      "        the default `.colors.Normalize`.\n",
      "    \n",
      "    vmin, vmax : scalar, optional, default: None\n",
      "        *vmin* and *vmax* are used in conjunction with *norm* to normalize\n",
      "        luminance data. If None, the respective min and max of the color\n",
      "        array is used. *vmin* and *vmax* are ignored if you pass a *norm*\n",
      "        instance.\n",
      "    \n",
      "    alpha : scalar, optional, default: None\n",
      "        The alpha blending value, between 0 (transparent) and 1 (opaque).\n",
      "    \n",
      "    linewidths : scalar or array_like, optional, default: None\n",
      "        The linewidth of the marker edges. Note: The default *edgecolors*\n",
      "        is 'face'. You may want to change this as well.\n",
      "        If *None*, defaults to rcParams ``lines.linewidth``.\n",
      "    \n",
      "    edgecolors : color or sequence of color, optional, default: 'face'\n",
      "        The edge color of the marker. Possible values:\n",
      "    \n",
      "        - 'face': The edge color will always be the same as the face color.\n",
      "        - 'none': No patch boundary will be drawn.\n",
      "        - A matplotib color.\n",
      "    \n",
      "        For non-filled markers, the *edgecolors* kwarg is ignored and\n",
      "        forced to 'face' internally.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    paths : `~matplotlib.collections.PathCollection`\n",
      "    \n",
      "    Other Parameters\n",
      "    ----------------\n",
      "    **kwargs : `~matplotlib.collections.Collection` properties\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    plot : To plot scatter plots when markers are identical in size and\n",
      "        color.\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    \n",
      "    * The `.plot` function will be faster for scatterplots where markers\n",
      "      don't vary in size or color.\n",
      "    \n",
      "    * Any or all of *x*, *y*, *s*, and *c* may be masked arrays, in which\n",
      "      case all masks will be combined and only unmasked points will be\n",
      "      plotted.\n",
      "    \n",
      "    * Fundamentally, scatter works with 1-D arrays; *x*, *y*, *s*, and *c*\n",
      "      may be input as 2-D arrays, but within scatter they will be\n",
      "      flattened. The exception is *c*, which will be flattened only if its\n",
      "      size matches the size of *x* and *y*.\n",
      "    \n",
      "    .. note::\n",
      "        In addition to the above described arguments, this function can take a\n",
      "        **data** keyword argument. If such a **data** argument is given, the\n",
      "        following arguments are replaced by **data[<arg>]**:\n",
      "    \n",
      "        * All arguments with the following names: 'c', 'color', 'edgecolors', 'facecolor', 'facecolors', 'linewidths', 's', 'x', 'y'.\n",
      "    \n",
      "        Objects passed as **data** must support item access (``data[<arg>]``) and\n",
      "        membership test (``<arg> in data``).\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(plt.scatter)"
   ]
  },
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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