邓凯洋
256 lines
36 KiB
Plaintext
256 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": "748ba6b0",
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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": 1,
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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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"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\n",
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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": 2,
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"id": "ce1c7ef6",
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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()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "74638a63",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"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": "3.10.9"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|