又菜又爱玩
257 lines
36 KiB
Plaintext
257 lines
36 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "b24327aa",
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn import linear_model #导入线性模型\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"from sklearn.datasets import load_iris #导入金盏花数据集"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "815e1ba7",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[5.1, 3.5, 1.4, 0.2],\n",
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" [4.9, 3. , 1.4, 0.2],\n",
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" [4.7, 3.2, 1.3, 0.2],\n",
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" [4.6, 3.1, 1.5, 0.2],\n",
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" [5. , 3.6, 1.4, 0.2],\n",
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" [5.4, 3.9, 1.7, 0.4],\n",
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" [4.6, 3.4, 1.4, 0.3],\n",
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" [5. , 3.4, 1.5, 0.2],\n",
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" [4.4, 2.9, 1.4, 0.2],\n",
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" [4.9, 3.1, 1.5, 0.1],\n",
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" [5.4, 3.7, 1.5, 0.2],\n",
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" [4.8, 3.4, 1.6, 0.2],\n",
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" [4.8, 3. , 1.4, 0.1],\n",
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" [4.3, 3. , 1.1, 0.1],\n",
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" [5.8, 4. , 1.2, 0.2],\n",
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" [5.7, 4.4, 1.5, 0.4],\n",
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" [5.4, 3.9, 1.3, 0.4],\n",
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" [5.1, 3.5, 1.4, 0.3],\n",
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" [5.7, 3.8, 1.7, 0.3],\n",
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" [5.1, 3.8, 1.5, 0.3],\n",
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" [5.4, 3.4, 1.7, 0.2],\n",
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" [5.1, 3.7, 1.5, 0.4],\n",
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" [4.6, 3.6, 1. , 0.2],\n",
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" [5.1, 3.3, 1.7, 0.5],\n",
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" [4.8, 3.4, 1.9, 0.2],\n",
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" [5. , 3. , 1.6, 0.2],\n",
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" [5. , 3.4, 1.6, 0.4],\n",
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" [5.2, 3.5, 1.5, 0.2],\n",
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" [5.2, 3.4, 1.4, 0.2],\n",
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" [4.7, 3.2, 1.6, 0.2],\n",
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" [4.8, 3.1, 1.6, 0.2],\n",
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" [5.4, 3.4, 1.5, 0.4],\n",
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" [5.2, 4.1, 1.5, 0.1],\n",
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" [5.5, 4.2, 1.4, 0.2],\n",
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" [4.9, 3.1, 1.5, 0.2],\n",
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" [5. , 3.2, 1.2, 0.2],\n",
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" [5.5, 3.5, 1.3, 0.2],\n",
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" [4.9, 3.6, 1.4, 0.1],\n",
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" [4.4, 3. , 1.3, 0.2],\n",
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" [5.1, 3.4, 1.5, 0.2],\n",
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" [5. , 3.5, 1.3, 0.3],\n",
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" [4.5, 2.3, 1.3, 0.3],\n",
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" [4.4, 3.2, 1.3, 0.2],\n",
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" [5. , 3.5, 1.6, 0.6],\n",
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" [5.1, 3.8, 1.9, 0.4],\n",
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" [4.8, 3. , 1.4, 0.3],\n",
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" [5.1, 3.8, 1.6, 0.2],\n",
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" [4.6, 3.2, 1.4, 0.2],\n",
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" [5.3, 3.7, 1.5, 0.2],\n",
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" [5. , 3.3, 1.4, 0.2],\n",
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" [7. , 3.2, 4.7, 1.4],\n",
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" [6.4, 3.2, 4.5, 1.5],\n",
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" [6.9, 3.1, 4.9, 1.5],\n",
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" [5.5, 2.3, 4. , 1.3],\n",
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" [6.5, 2.8, 4.6, 1.5],\n",
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" [5.7, 2.8, 4.5, 1.3],\n",
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" [6.3, 3.3, 4.7, 1.6],\n",
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" [4.9, 2.4, 3.3, 1. ],\n",
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" [6.6, 2.9, 4.6, 1.3],\n",
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" [5.2, 2.7, 3.9, 1.4],\n",
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" [5. , 2. , 3.5, 1. ],\n",
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" [5.9, 3. , 4.2, 1.5],\n",
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" [6. , 2.2, 4. , 1. ],\n",
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" [6.1, 2.9, 4.7, 1.4],\n",
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" [5.6, 2.9, 3.6, 1.3],\n",
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" [6.7, 3.1, 4.4, 1.4],\n",
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" [5.6, 3. , 4.5, 1.5],\n",
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" [5.8, 2.7, 4.1, 1. ],\n",
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" [6.2, 2.2, 4.5, 1.5],\n",
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" [5.6, 2.5, 3.9, 1.1],\n",
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" [5.9, 3.2, 4.8, 1.8],\n",
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" [6.1, 2.8, 4. , 1.3],\n",
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" [6.3, 2.5, 4.9, 1.5],\n",
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" [6.1, 2.8, 4.7, 1.2],\n",
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" [6.4, 2.9, 4.3, 1.3],\n",
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" [6.6, 3. , 4.4, 1.4],\n",
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" [6.8, 2.8, 4.8, 1.4],\n",
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" [6.7, 3. , 5. , 1.7],\n",
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" [6. , 2.9, 4.5, 1.5],\n",
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" [5.7, 2.6, 3.5, 1. ],\n",
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" [5.5, 2.4, 3.8, 1.1],\n",
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" [5.5, 2.4, 3.7, 1. ],\n",
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" [5.8, 2.7, 3.9, 1.2],\n",
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" [6. , 2.7, 5.1, 1.6],\n",
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" [5.4, 3. , 4.5, 1.5],\n",
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" [6. , 3.4, 4.5, 1.6],\n",
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" [6.7, 3.1, 4.7, 1.5],\n",
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" [6.3, 2.3, 4.4, 1.3],\n",
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" [5.6, 3. , 4.1, 1.3],\n",
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" [5.5, 2.5, 4. , 1.3],\n",
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" [5.5, 2.6, 4.4, 1.2],\n",
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" [6.1, 3. , 4.6, 1.4],\n",
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" [5.8, 2.6, 4. , 1.2],\n",
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" [5. , 2.3, 3.3, 1. ],\n",
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" [5.6, 2.7, 4.2, 1.3],\n",
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" [5.7, 3. , 4.2, 1.2],\n",
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" [5.7, 2.9, 4.2, 1.3],\n",
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" [6.2, 2.9, 4.3, 1.3],\n",
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" [5.1, 2.5, 3. , 1.1],\n",
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" [5.7, 2.8, 4.1, 1.3],\n",
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" [6.3, 3.3, 6. , 2.5],\n",
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" [5.8, 2.7, 5.1, 1.9],\n",
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" [7.1, 3. , 5.9, 2.1],\n",
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" [6.3, 2.9, 5.6, 1.8],\n",
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" [6.5, 3. , 5.8, 2.2],\n",
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" [7.6, 3. , 6.6, 2.1],\n",
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" [4.9, 2.5, 4.5, 1.7],\n",
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" [7.3, 2.9, 6.3, 1.8],\n",
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" [6.7, 2.5, 5.8, 1.8],\n",
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" [7.2, 3.6, 6.1, 2.5],\n",
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" [6.5, 3.2, 5.1, 2. ],\n",
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" [6.4, 2.7, 5.3, 1.9],\n",
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" [6.8, 3. , 5.5, 2.1],\n",
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" [5.7, 2.5, 5. , 2. ],\n",
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" [5.8, 2.8, 5.1, 2.4],\n",
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" [6.4, 3.2, 5.3, 2.3],\n",
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" [6.5, 3. , 5.5, 1.8],\n",
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" [7.7, 3.8, 6.7, 2.2],\n",
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" [7.7, 2.6, 6.9, 2.3],\n",
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" [6. , 2.2, 5. , 1.5],\n",
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" [6.9, 3.2, 5.7, 2.3],\n",
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" [5.6, 2.8, 4.9, 2. ],\n",
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" [7.7, 2.8, 6.7, 2. ],\n",
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" [6.3, 2.7, 4.9, 1.8],\n",
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" [6.7, 3.3, 5.7, 2.1],\n",
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" [7.2, 3.2, 6. , 1.8],\n",
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" [6.2, 2.8, 4.8, 1.8],\n",
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" [6.1, 3. , 4.9, 1.8],\n",
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" [6.4, 2.8, 5.6, 2.1],\n",
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" [7.2, 3. , 5.8, 1.6],\n",
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" [7.4, 2.8, 6.1, 1.9],\n",
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" [7.9, 3.8, 6.4, 2. ],\n",
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" [6.4, 2.8, 5.6, 2.2],\n",
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" [6.3, 2.8, 5.1, 1.5],\n",
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" [6.1, 2.6, 5.6, 1.4],\n",
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" [7.7, 3. , 6.1, 2.3],\n",
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" [6.3, 3.4, 5.6, 2.4],\n",
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" [6.4, 3.1, 5.5, 1.8],\n",
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" [6. , 3. , 4.8, 1.8],\n",
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" [6.9, 3.1, 5.4, 2.1],\n",
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" [6.7, 3.1, 5.6, 2.4],\n",
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" [6.9, 3.1, 5.1, 2.3],\n",
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" [5.8, 2.7, 5.1, 1.9],\n",
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" [6.8, 3.2, 5.9, 2.3],\n",
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" [6.7, 3.3, 5.7, 2.5],\n",
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" [6.7, 3. , 5.2, 2.3],\n",
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" [6.3, 2.5, 5. , 1.9],\n",
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" [6.5, 3. , 5.2, 2. ],\n",
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" [6.2, 3.4, 5.4, 2.3],\n",
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" [5.9, 3. , 5.1, 1.8]])"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"iris = load_iris()\n",
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"iris.data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "ec3cbc26",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"y = -0.06188x + 3.41895\n"
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]
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},
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{
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"data": {
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"## 选取第０，１列数据，做线性回归分析\n",
|
|
"#获取花瓣的长和宽\n",
|
|
"x = [n[0] for n in iris.data]\n",
|
|
"y = [n[1] for n in iris.data]\n",
|
|
"x = np.array(x).reshape(len(x),1)\n",
|
|
"y = np.array(y).reshape(len(y),1)\n",
|
|
"\n",
|
|
"lr = linear_model.LinearRegression()\n",
|
|
"lr.fit(x,y)\n",
|
|
"pred = lr.predict(x)\n",
|
|
"# 打印截距和系数\n",
|
|
"intercept = lr.intercept_\n",
|
|
"coef = lr.coef_\n",
|
|
"# 以公式的形式打印出来\n",
|
|
"print(\"y = %10.5fx + %10.5f\" % (coef, intercept))\n",
|
|
"plt.scatter(x,y,s=50)\n",
|
|
"plt.plot(x,pred,\"r\",linewidth=2)\n",
|
|
"for idx, m in enumerate(x):\n",
|
|
" plt.plot([m,m],[y[idx],pred[idx]], 'g')\n",
|
|
"plt.show()"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.9.13"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|