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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "Y_pred_gauss = KernelRegression.ker_reg(XXX, YYY, bw_manual, 'gauss')\n", - "Y_pred_epanechnikov = KernelRegression.ker_reg(XXX, YYY, bw_manual, 'epanechnikov')\n", - "\n", - "fig = px.scatter(x=XXX,y=YYY)\n", - "fig.add_trace(go.Scatter(x=XXX, y=np.array(Y_pred_gauss), name='Gauss', mode='lines'))\n", - "fig.add_trace(go.Scatter(x=XXX, y=np.array(Y_pred_epanechnikov), name='Epanechnikov', mode='lines'))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, + "execution_count": 11, "id": "loved-clinton", "metadata": {}, "outputs": [], @@ -7256,6 +114,11 @@ "dropdown_bw = widgets.Dropdown(options=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10], value=1, description='Szerokość okna')\n", "\n", "def interactive_kernel(bw_manual):\n", + " fig = px.scatter(x=XXX,y=YYY)\n", + " for i in range(0, len(XXX), bw_manual):\n", + " fig.add_vline(x=i)\n", + " fig.show()\n", + " \n", " Y_pred_gauss = KernelRegression.ker_reg(XXX, YYY, bw_manual, 'gauss')\n", " Y_pred_epanechnikov = KernelRegression.ker_reg(XXX, YYY, bw_manual, 'epanechnikov')\n", "\n", @@ -7267,14 +130,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 12, "id": "injured-english", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4b896162441e4a38b22a271f999cbaf3", + "model_id": "a38d1ceed30b42d19a56951cf88e16e6", "version_major": 2, "version_minor": 0 }, @@ -7291,7 +154,7 @@ "" ] }, - "execution_count": 7, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }