demo1.ipynb

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    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import ipywidgets as widgets\n",
    "\n",
    "np.random.seed(0)\n",
    "xmin = 0.01\n",
    "xmax = 1\n",
    "xres = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manual regression widget"
   ]
  },
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   "execution_count": 2,
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       "VBox(children=(HTML(value='<h1>Regression</h1>', layout=Layout(padding='0 0 0 50px')), HBox(children=(VBox(chi…"
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     "output_type": "display_data"
    }
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   "source": [
    "# build dataset\n",
    "X_data = np.linspace(xmin, xmax, xres)\n",
    "y_data = np.polynomial.polynomial.polyval(X_data, [0, 2, 5]) + np.sin(8*X_data) + 0.5 * np.random.normal(size=xres)\n",
    "\n",
    "# build widgets\n",
    "title = widgets.HTML(value='<h1>Regression</h1>', layout={'padding': '0 0 0 50px'})\n",
    "\n",
    "w0_slider = widgets.FloatSlider(min=0, max=10.0, step=1) \n",
    "w0_ui = widgets.HBox([\n",
    "    widgets.Label(value='$$w_0$$'), \n",
    "    w0_slider\n",
    "])\n",
    "w1_slider = widgets.FloatSlider(min=0, max=10.0, step=1)\n",
    "w1_ui = widgets.HBox([\n",
    "    widgets.Label(value='$$w_1$$'), \n",
    "    w1_slider\n",
    "])\n",
    "w2_slider = widgets.FloatSlider(min=0, max=10.0, step=1)\n",
    "w2_ui = widgets.HBox([\n",
    "    widgets.Label(value='$$w_2$$'), \n",
    "    w2_slider\n",
    "])\n",
    "\n",
    "sliders_ui = widgets.VBox([w0_ui, w1_ui, w2_ui], layout={'margin':'10px 100px 10px 10px'})\n",
    "\n",
    "equation_output = widgets.Label(\n",
    "    value=r'$$f(x)=w_0 \\times x+w_1 \\times x^2 + sin(w_2 \\times x)$$', \n",
    "    layout={\n",
    "        'width': '60%', \n",
    "        'height': '60%'\n",
    "    })\n",
    "weights_ui = widgets.HBox([sliders_ui, equation_output])\n",
    "\n",
    "plot_output = widgets.Output()\n",
    "\n",
    "ui = widgets.VBox([title, weights_ui, plot_output], layout={'border':'1px solid black'})\n",
    "\n",
    "# build events\n",
    "def update_plot(ev):\n",
    "    plot_output.clear_output(wait=True)\n",
    "    with plot_output:\n",
    "        X_reg = np.linspace(xmin, xmax, 100).astype(np.float)\n",
    "        y_reg = np.polynomial.polynomial.polyval(X_reg, [0, w0_slider.value, w1_slider.value]) + np.sin(w2_slider.value * X_reg)\n",
    "        plt.plot(X_data, y_data, \"+\", X_reg, y_reg)\n",
    "        plt.title('Manual regression')\n",
    "        plt.xlabel('x')\n",
    "        plt.ylabel('f(x)')\n",
    "        plt.show()\n",
    "\n",
    "# wire events together\n",
    "w0_slider.observe(update_plot, names='value')\n",
    "w1_slider.observe(update_plot, names='value')\n",
    "w2_slider.observe(update_plot, names='value')\n",
    "update_plot(None) # initialize widget\n",
    "\n",
    "display(ui)"
   ]
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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "fe9e915a2de04ab9a09097f1a6b6fe0f": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "FloatSliderModel",
      "state": {
       "layout": "IPY_MODEL_a74a9eeef2954bf8b0cd3ef0ea342b70",
       "max": 10,
       "style": "IPY_MODEL_765c3311ba24493fb600d5c2777793bc"
      }
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}