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"import numpy as np\n",
"import plotly.express as px\n",
"from statsmodels.nonparametric.kernel_regression import KernelReg\n",
"import plotly.graph_objs as go\n",
"import pandas as pd \n",
"import KernelRegression"
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"num_points = x.shape[0]\n",
"residuals = np.random.normal(scale=2.0, size=[num_points])# We will be using fun_y to generate y values all the way through\n",
"fun_y = lambda x: -(x*x) + residuals"
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"num_points = x.shape[0]\n",
"residuals = np.random.normal(scale=2.0, size=[num_points])# We will be using fun_y to generate y values all the way through\n",
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"kernel_x = np.arange(-20,20,0.1)\n",
"## Plotting gaussian for all input x points \n",
"kernel_fns = {'kernel_x': kernel_x}\n",
"for input_x in new_x: \n",
" input_string= 'x_value_{}'.format(np.round(input_x,2)) \n",
" kernel_fns[input_string] = epanechnikov_list(bw_manual, kernel_x, input_x)\n",
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