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‎README.md

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### [Kmeans和Kmeans++的实现](https://github.com/SmallVagetable/machine_learning_python/tree/master/kmeans)
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### [EM GMM高斯混合和GMM+LASSO的实现](https://github.com/SmallVagetable/machine_learning_python/tree/master/em)
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### [实现朴素贝叶斯的基本算法和高斯混合朴素贝叶斯算法](https://github.com/SmallVagetable/machine_learning_python/tree/master/naive_bayes)
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### [实现决策树的基本算法](https://github.com/SmallVagetable/machine_learning_python/tree/master/decision_tree)
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### [实现决策树的基本算法](https://github.com/SmallVagetable/machine_learning_python/tree/master/decision_tree)
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### [实现adaboost基本算法](https://github.com/SmallVagetable/machine_learning_python/tree/master/adaboost)
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### [实现svm基本算法](https://github.com/SmallVagetable/machine_learning_python/tree/master/support_vector_machine)
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### [实现逻辑回归基本算法](https://github.com/SmallVagetable/machine_learning_python/tree/master/logistic_regression)

‎adaboost/.ipynb_checkpoints/Adaboost-checkpoint.ipynb

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{
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"import os\n",
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"searchPath=os.path.abspath('..')\n",
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"sys.path.append(searchPath)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from adaboost.AdaBoost import AdaBoost\n",
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"from sklearn.ensemble import AdaBoostClassifier\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"from sklearn.datasets import load_iris\n",
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"from sklearn.model_selection import train_test_split\n",
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"np.random.seed(10)\n",
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"\n",
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"%matplotlib inline"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"def create_data():\n",
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" iris = load_iris()\n",
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" df = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
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" df['label'] = iris.target\n",
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" df.columns = ['sepal length', 'sepal width', 'petal length', 'petal width', 'label']\n",
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" data = np.array(df.iloc[:100, [0, 1, -1]])\n",
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" for i in range(len(data)):\n",
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" if data[i,-1] == 0:\n",
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" data[i,-1] = -1\n",
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" return data[:,:2], data[:,-1]"
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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": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"X, y = create_data()\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
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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": 5,
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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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"<matplotlib.legend.Legend at 0x10fb7ee48>"
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]
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},
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"execution_count": 5,
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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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"data": {
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"image/png": 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\n",
80+
"text/plain": [
81+
"<matplotlib.figure.Figure at 0x10adf0898>"
82+
]
83+
},
84+
"metadata": {},
85+
"output_type": "display_data"
86+
}
87+
],
88+
"source": [
89+
"plt.scatter(X[:50,0],X[:50,1], label='0')\n",
90+
"plt.scatter(X[50:,0],X[50:,1], label='1')\n",
91+
"plt.legend()"
92+
]
93+
},
94+
{
95+
"cell_type": "code",
96+
"execution_count": 6,
97+
"metadata": {},
98+
"outputs": [
99+
{
100+
"data": {
101+
"text/plain": [
102+
"0.65"
103+
]
104+
},
105+
"execution_count": 6,
106+
"metadata": {},
107+
"output_type": "execute_result"
108+
}
109+
],
110+
"source": [
111+
"clf = AdaBoost(n_estimators=10, learning_rate=0.2)\n",
112+
"clf.fit(X_train, y_train)\n",
113+
"clf.score(X_test, y_test)"
114+
]
115+
},
116+
{
117+
"cell_type": "code",
118+
"execution_count": 7,
119+
"metadata": {},
120+
"outputs": [
121+
{
122+
"data": {
123+
"text/plain": [
124+
"AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,\n",
125+
" learning_rate=0.5, n_estimators=100, random_state=None)"
126+
]
127+
},
128+
"execution_count": 7,
129+
"metadata": {},
130+
"output_type": "execute_result"
131+
}
132+
],
133+
"source": [
134+
"clf = AdaBoostClassifier(n_estimators=100, learning_rate=0.5)\n",
135+
"clf.fit(X_train, y_train)"
136+
]
137+
},
138+
{
139+
"cell_type": "code",
140+
"execution_count": 8,
141+
"metadata": {},
142+
"outputs": [
143+
{
144+
"data": {
145+
"text/plain": [
146+
"0.9"
147+
]
148+
},
149+
"execution_count": 8,
150+
"metadata": {},
151+
"output_type": "execute_result"
152+
}
153+
],
154+
"source": [
155+
"clf.score(X_test, y_test)"
156+
]
157+
}
158+
],
159+
"metadata": {
160+
"kernelspec": {
161+
"display_name": "Python 3",
162+
"language": "python",
163+
"name": "python3"
164+
},
165+
"language_info": {
166+
"codemirror_mode": {
167+
"name": "ipython",
168+
"version": 3
169+
},
170+
"file_extension": ".py",
171+
"mimetype": "text/x-python",
172+
"name": "python",
173+
"nbconvert_exporter": "python",
174+
"pygments_lexer": "ipython3",
175+
"version": "3.6.4"
176+
}
177+
},
178+
"nbformat": 4,
179+
"nbformat_minor": 2
180+
}

‎adaboost/AdaBoost.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -2,7 +2,7 @@
22

33
from utils.data_generater import *
44

5-
class AdaBoost:
5+
class AdaBoost(object):
66
def __init__(self, n_estimators=50, learning_rate=1.0):
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self.clf_num = n_estimators
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self.learning_rate = learning_rate

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