ML_Maschinelles_Lernen_Grundlagen/Exercise_2
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k-nn gekürzt

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paul-loedige 2021-12-02 01:14:54 +01:00
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exercise.ipynb
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@ -23,7 +23,7 @@
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@ -522,9 +522,15 @@
" :return k-nearest x values [k x input_dimension], k-nearest y values [k x target_dimension]\n",
" \"\"\"\n",
"\n",
" distance = []\n",
" distances = []\n",
" for i in range(x_data.shape[0]):\n",
" distance.append(np.linalg.norm(x_data[i]-query_point))\n",
" distances.append(np.linalg.norm(x_data[i]-query_point))\n",
" \n",
" data = np.column_stack((x_data,y_data,distances))\n",
" data = data[data[:,-1].argsort()]\n",
" nearest_x = data[0:k]\n",
" nearest_y = data[0:k,-2]\n",
" return nearest_x, nearest_y\n",
"# point = np.array([x_data[i], y_data[i]])\n",
"# dist = 0\n",
"# for j in range(len(point[0])):\n",
@ -537,15 +543,15 @@
" #idx = np.argpartition(distance, k)\n",
" #idx = idx[:k]\n",
"\n",
" idx = sorted(range(len(distance)), key=lambda i: distance[i])[:k]\n",
"\n",
" nearest_x = []\n",
" nearest_y = []\n",
" for j in range(len(idx)):\n",
" nearest_x.append(x_data[idx[j]])\n",
" nearest_y.append(y_data[idx[j]])\n",
"\n",
" return nearest_x, nearest_y\n",
"# idx = sorted(range(len(distance)), key=lambda i: distance[i])[:k]\n",
"#\n",
"# nearest_x = []\n",
"# nearest_y = []\n",
"# for j in range(len(idx)):\n",
"# nearest_x.append(x_data[idx[j]])\n",
"# nearest_y.append(y_data[idx[j]])\n",
"#\n",
"# return nearest_x, nearest_y\n",
"\n",
"\n",
"\n",
@ -566,17 +572,9 @@
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 1.0\n"
]
}
],
"outputs": [],
"source": [
"k = 5\n",
"predictions = np.zeros(test_features.shape[0])\n",
@ -1321,7 +1319,7 @@
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