day 92 - PCA.ipynb

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   "source": [
    "import numpy as np\n",
    "from bokeh.plotting import figure, show, output_notebook\n",
    "\n",
    "output_notebook()"
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   "source": [
    "## algorithm"
   ]
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   "source": [
    "def PCA(X, n_components):\n",
    "    # normalize to zero mean\n",
    "    mu = X.mean(axis=0)\n",
    "    X = X - mu\n",
    "    \n",
    "    # eigenvectors of covariance matrix\n",
    "    sigma = X.T @ X\n",
    "    eigvals, eigvecs = np.linalg.eig(sigma)\n",
    "    \n",
    "    # principal components\n",
    "    order = np.argsort(eigvals)[::-1]\n",
    "    components = eigvecs[:, order[:n_components]]\n",
    "    \n",
    "    # projection\n",
    "    Z = X @ components\n",
    "    \n",
    "    # result\n",
    "    return Z, components"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## 2D data & principal components"
   ]
  },
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   "source": [
    "# generate points\n",
    "x = np.linspace(0, 13, num=100)\n",
    "y = x + np.sin(x) - np.cos(x)\n",
    "\n",
    "# 2D data\n",
    "X = np.c_[x, y]"
   ]
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    {
     "data": {
      "text/plain": [
       "array([[ 0.72299657, -0.69085162],\n",
       "       [ 0.69085162,  0.72299657]])"
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   "source": [
    "# PCA\n",
    "projection, components = PCA(X, n_components=2)\n",
    "\n",
    "# principal components\n",
    "components"
   ]
  },
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    {
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      "text/plain": [
       "array([[ 2705.081,     0.   ],\n",
       "       [    0.   ,    47.716]])"
      ]
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   "source": [
    "# convariance matrix of projected data\n",
    "(projection.T @ projection).round(3)"
   ]
  },
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    "# prepare plot data\n",
    "mean = np.mean(X, axis=0)\n",
    "extent = projection.min(), projection.max()\n",
    "angle = np.arctan(components[1] / components[0]) + np.pi * (components[0] < 0)"
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       "            var render_items = [{\"docid\":\"723e036d-4b2f-4a8b-87e3-6395286aabc4\",\"elementid\":\"b1bb711e-abc4-4353-8cfd-869ebf78ed30\",\"modelid\":\"4b1955bd-92c5-429b-9041-30b9aabc5d90\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "          };\n",
       "          if (document.readyState != \"loading\") fn();\n",
       "          else document.addEventListener(\"DOMContentLoaded\", fn);\n",
       "        })();\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      \n",
       "      if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "        for (var i = 0; i < inline_js.length; i++) {\n",
       "          inline_js[i](window.Bokeh);\n",
       "        }if (force === true) {\n",
       "          display_loaded();\n",
       "        }} else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(run_inline_js, 100);\n",
       "      } else if (!window._bokeh_failed_load) {\n",
       "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "        window._bokeh_failed_load = true;\n",
       "      } else if (force !== true) {\n",
       "        var cell = $(document.getElementById(\"b1bb711e-abc4-4353-8cfd-869ebf78ed30\")).parents('.cell').data().cell;\n",
       "        cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "      }\n",
       "  \n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot original data & principal components\n",
    "plot = figure()\n",
    "\n",
    "plot.scatter(x, y)\n",
    "plot.ray(*mean, length=0, angle=angle[0], line_width=2, line_color='red')\n",
    "plot.ray(*mean, length=0, angle=angle[1], line_width=2, line_color='green')\n",
    "\n",
    "show(plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <div class=\"bk-plotdiv\" id=\"02c12ed0-e105-4be6-b6f6-9392f9fc53b8\"></div>\n",
       "    </div>\n",
       "<script type=\"text/javascript\">\n",
       "  \n",
       "  (function(global) {\n",
       "    function now() {\n",
       "      return new Date();\n",
       "    }\n",
       "  \n",
       "    var force = false;\n",
       "  \n",
       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "      window._bokeh_onload_callbacks = [];\n",
       "      window._bokeh_is_loading = undefined;\n",
       "    }\n",
       "  \n",
       "  \n",
       "    \n",
       "    if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "      window._bokeh_timeout = Date.now() + 0;\n",
       "      window._bokeh_failed_load = false;\n",
       "    }\n",
       "  \n",
       "    var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "       \"<div style='background-color: #fdd'>\\n\"+\n",
       "       \"<p>\\n\"+\n",
       "       \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "       \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "       \"</p>\\n\"+\n",
       "       \"<ul>\\n\"+\n",
       "       \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "       \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "       \"</ul>\\n\"+\n",
       "       \"<code>\\n\"+\n",
       "       \"from bokeh.resources import INLINE\\n\"+\n",
       "       \"output_notebook(resources=INLINE)\\n\"+\n",
       "       \"</code>\\n\"+\n",
       "       \"</div>\"}};\n",
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       "        setTimeout(display_loaded, 100)\n",
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       "    }\n",
       "  \n",
       "    function run_callbacks() {\n",
       "      window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      delete window._bokeh_onload_callbacks\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "  \n",
       "    function load_libs(js_urls, callback) {\n",
       "      window._bokeh_onload_callbacks.push(callback);\n",
       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "        return null;\n",
       "      }\n",
       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
       "        return null;\n",
       "      }\n",
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot projected data\n",
    "plot = figure(x_range=extent, y_range=extent)\n",
    "plot.scatter(projection[:, 0], projection[:, 1])\n",
    "show(plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## binary vectors & dimensionality reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# generate binary vectors\n",
    "X = np.random.rand(90, 30)\n",
    "X[:30, :] = X[:30, :] < ([.4] * 10 + [.1] * 10 + [.1] * 10)\n",
    "X[30:60, :] = X[30:60, :] < ([.1] * 10 + [.4] * 10 + [.1] * 10)\n",
    "X[60:, :] = X[60:, :] < ([.1] * 10 + [.1] * 10 + [.4] * 10)\n",
    "\n",
    "# define 3 classes\n",
    "Y = ['red'] * 30 + ['green'] * 30 + ['blue'] * 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# PCA\n",
    "projection, _ = PCA(X, n_components=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "       \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
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       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
       "          }\n",
       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"0d0671d4-4340-4a0f-aecb-b807d07c8427\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '0d0671d4-4340-4a0f-aecb-b807d07c8427' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        (function() {\n",
       "          var fn = function() {\n",
       "            var docs_json = 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       "            var render_items = [{\"docid\":\"ac85ef95-f742-42bb-9af1-cd04978af42d\",\"elementid\":\"0d0671d4-4340-4a0f-aecb-b807d07c8427\",\"modelid\":\"aa746f96-ab29-48c6-9748-ec6298b98562\"}];\n",
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       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "          };\n",
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       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      \n",
       "      if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "        for (var i = 0; i < inline_js.length; i++) {\n",
       "          inline_js[i](window.Bokeh);\n",
       "        }if (force === true) {\n",
       "          display_loaded();\n",
       "        }} else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(run_inline_js, 100);\n",
       "      } else if (!window._bokeh_failed_load) {\n",
       "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "        window._bokeh_failed_load = true;\n",
       "      } else if (force !== true) {\n",
       "        var cell = $(document.getElementById(\"0d0671d4-4340-4a0f-aecb-b807d07c8427\")).parents('.cell').data().cell;\n",
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       "      }\n",
       "  \n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot projected data: 30D -> 2D\n",
    "plot = figure()\n",
    "plot.scatter(projection[:, 0], projection[:, 1], color=Y)\n",
    "show(plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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