http://scikit-learn.org/stable/modules/svm.html
http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC
# !/usr/bin/env python
# -*- coding: utf-8 -*-
# __author__ = "abdata"
import time
import numpy as np
import pandas as pd
from sklearn import svm
# from sklearn import datasets
# import matplotlib.pyplot as plt
mdata = pd.read_csv('http://data.galaxystatistics.com/blog_data/regression/iris.csv')
X = mdata.iloc[:,2:6]
print(X.head())
print(X.shape)
print(type(X))
y = mdata.iloc[:,1]
t0 = time.time()
clf = svm.SVC()
clf.fit(X, y)
t = time.time() - t0
print(t)
print(clf.predict(X))
print(type(clf))
# get support vectors
print(clf.support_vectors_)
# get indices of support vectors
print(clf.support_)
# get number of support vectors for each class
print(clf.n_support_)
######
### 示例：
######
# from sklearn import svm
# X = [[0, 0], [1, 1]]
# y = [0, 1]
# clf = svm.SVC()
# clf.fit(X, y)
# # predict
# print(clf.predict([[2., 2.]]))
# print(clf.predict([[-0.8, -1]]))
# # get support vectors
# print(clf.support_vectors_)
# # get indices of support vectors
# print(clf.support_)
# # get number of support vectors for each class
# print(clf.n_support_)
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 0 5.1 3.5 1.4 0.2
## 1 4.9 3.0 1.4 0.2
## 2 4.7 3.2 1.3 0.2
## 3 4.6 3.1 1.5 0.2
## 4 5.0 3.6 1.4 0.2
## (150, 4)
## <class 'pandas.core.frame.DataFrame'>
## 0.001003265380859375
## ['setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa'
## 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa'
## 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa'
## 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa'
## 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa'
## 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa'
## 'setosa' 'setosa' 'versicolor' 'versicolor' 'versicolor' 'versicolor'
## 'versicolor' 'versicolor' 'versicolor' 'versicolor' 'versicolor'
## 'versicolor' 'versicolor' 'versicolor' 'versicolor' 'versicolor'
## 'versicolor' 'versicolor' 'versicolor' 'versicolor' 'versicolor'
## 'versicolor' 'versicolor' 'versicolor' 'versicolor' 'versicolor'
## 'versicolor' 'versicolor' 'versicolor' 'virginica' 'versicolor'
## 'versicolor' 'versicolor' 'versicolor' 'versicolor' 'virginica'
## 'versicolor' 'versicolor' 'versicolor' 'versicolor' 'versicolor'
## 'versicolor' 'versicolor' 'versicolor' 'versicolor' 'versicolor'
## 'versicolor' 'versicolor' 'versicolor' 'versicolor' 'versicolor'
## 'versicolor' 'virginica' 'virginica' 'virginica' 'virginica' 'virginica'
## 'virginica' 'virginica' 'virginica' 'virginica' 'virginica' 'virginica'
## 'virginica' 'virginica' 'virginica' 'virginica' 'virginica' 'virginica'
## 'virginica' 'virginica' 'virginica' 'virginica' 'virginica' 'virginica'
## 'virginica' 'virginica' 'virginica' 'virginica' 'virginica' 'virginica'
## 'virginica' 'virginica' 'virginica' 'virginica' 'virginica' 'virginica'
## 'virginica' 'virginica' 'virginica' 'virginica' 'virginica' 'virginica'
## 'virginica' 'virginica' 'virginica' 'virginica' 'virginica' 'virginica'
## 'virginica' 'virginica' 'virginica']
## <class 'sklearn.svm.classes.SVC'>
## [[ 4.3 3. 1.1 0.1]
## [ 5.7 4.4 1.5 0.4]
## [ 5.7 3.8 1.7 0.3]
## [ 5.1 3.3 1.7 0.5]
## [ 4.8 3.4 1.9 0.2]
## [ 4.5 2.3 1.3 0.3]
## [ 5.1 3.8 1.9 0.4]
## [ 7. 3.2 4.7 1.4]
## [ 6.9 3.1 4.9 1.5]
## [ 6.5 2.8 4.6 1.5]
## [ 6.3 3.3 4.7 1.6]
## [ 4.9 2.4 3.3 1. ]
## [ 5. 2. 3.5 1. ]
## [ 6.1 2.9 4.7 1.4]
## [ 5.6 3. 4.5 1.5]
## [ 6.2 2.2 4.5 1.5]
## [ 5.9 3.2 4.8 1.8]
## [ 6.3 2.5 4.9 1.5]
## [ 6.8 2.8 4.8 1.4]
## [ 6.7 3. 5. 1.7]
## [ 6. 2.9 4.5 1.5]
## [ 6. 2.7 5.1 1.6]
## [ 5.4 3. 4.5 1.5]
## [ 6. 3.4 4.5 1.6]
## [ 6.7 3.1 4.7 1.5]
## [ 5.1 2.5 3. 1.1]
## [ 6.3 3.3 6. 2.5]
## [ 4.9 2.5 4.5 1.7]
## [ 6.5 3.2 5.1 2. ]
## [ 7.7 2.6 6.9 2.3]
## [ 6. 2.2 5. 1.5]
## [ 5.6 2.8 4.9 2. ]
## [ 6.3 2.7 4.9 1.8]
## [ 6.2 2.8 4.8 1.8]
## [ 6.1 3. 4.9 1.8]
## [ 7.2 3. 5.8 1.6]
## [ 7.9 3.8 6.4 2. ]
## [ 6.3 2.8 5.1 1.5]
## [ 6.1 2.6 5.6 1.4]
## [ 6. 3. 4.8 1.8]
## [ 6.9 3.1 5.1 2.3]
## [ 5.8 2.7 5.1 1.9]
## [ 6.3 2.5 5. 1.9]
## [ 6.5 3. 5.2 2. ]
## [ 5.9 3. 5.1 1.8]]
## [ 13 15 18 23 24 41 44 50 52 54 56 57 60 63 66 68 70 72
## 76 77 78 83 84 85 86 98 100 106 110 118 119 121 123 126 127 129
## 131 133 134 138 141 142 146 147 149]
## [ 7 19 19]http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html#sklearn.svm.SVR
# !/usr/bin/env python
# -*- coding: utf-8 -*-
# __author__ = "abdata"
import time
import numpy as np
import pandas as pd
from sklearn import svm
# from sklearn import datasets
# import matplotlib.pyplot as plt
mdata = pd.read_csv('http://data.galaxystatistics.com/blog_data/regression/iris.csv')
X = mdata.iloc[:,3:6]
print(X.head())
print(X.shape)
print(type(X))
y = mdata.iloc[:,2]
t0 = time.time()
clf = svm.SVR()
clf.fit(X, y)
t = time.time() - t0
print(t)
pre = clf.predict(X)
print(pre)
print(pre.shape)
print(type(clf))
# get support vectors
print(clf.support_vectors_)
# get indices of support vectors
print(clf.support_)
# get number of support vectors for each class
print(clf.n_support_)
######
### 示例：
######
# from sklearn import svm
# X = [[0, 0], [2, 2]]
# y = [0.5, 2.5]
# clf = svm.SVR()
# clf.fit(X, y)
# clf.predict([[1, 1]])
# # get support vectors
# print(clf.support_vectors_)
# # get indices of support vectors
# print(clf.support_)
# # get number of support vectors for each class
# print(clf.n_support_)
## Sepal.Width Petal.Length Petal.Width
## 0 3.5 1.4 0.2
## 1 3.0 1.4 0.2
## 2 3.2 1.3 0.2
## 3 3.1 1.5 0.2
## 4 3.6 1.4 0.2
## (150, 3)
## <class 'pandas.core.frame.DataFrame'>
## 0.0015039443969726562
## [ 5.02748142 4.73571387 4.83276105 4.79627722 5.09416934 5.32995468
## 4.96908089 4.97250071 4.69244634 4.79968913 5.16895611 4.98557028
## 4.7390479 4.72930067 5.35479204 5.59995925 5.31240281 5.0348059
## 5.25867121 5.2421861 5.00118991 5.18788009 5.08808426 4.96486226
## 5.03920079 4.7609706 5.00021578 5.03701049 4.96225877 4.86543867
## 4.81077704 4.98967371 5.41670896 5.47748792 4.79627722 4.82935651
## 5.02081771 5.09156899 4.72893682 4.97250071 5.02900754 4.59986775
## 4.83276105 5.0999624 5.28886301 4.73974046 5.24290436 4.84014774
## 5.16895611 4.89948204 6.29171381 6.14052008 6.33963545 5.59518138
## 6.07085889 6.08496073 6.24281764 5.13579423 6.17753533 5.61836942
## 5.21179418 5.89598283 5.62878367 6.19998607 5.50034062 6.08668269
## 6.07839915 5.85097543 5.84465931 5.60891455 6.18399633 5.74617055
## 6.13790387 6.23313919 5.99008472 6.05482318 6.22251065 6.28557429
## 6.04559245 5.33893509 5.50007799 5.43319504 5.65699213 6.28057434
## 6.07839915 6.15674178 6.22552705 5.87154703 5.88788015 5.64739823
## 5.98732144 6.17484483 5.69977498 5.11246758 5.85476108 5.98281105
## 5.92251641 5.99008472 4.99995752 5.81803186 6.97918012 6.15696378
## 6.93669716 6.66597325 6.79406571 7.61622237 5.84140648 7.33001711
## 6.66488807 7.10001532 6.28442978 6.30033011 6.51299432 5.98630492
## 5.98077803 6.30856387 6.61650816 7.60004206 7.56834266 6.09822927
## 6.70783629 6.0189936 7.59995932 6.07714296 6.81015631 7.15475405
## 6.05280882 6.17909737 6.53529588 6.91166792 7.09886549 7.4749038
## 6.50040881 6.35366987 6.59917562 7.10283689 6.60440559 6.65242786
## 6.11974352 6.45329028 6.52793286 6.11616648 6.15696378 6.9326115
## 6.64442026 6.16071834 6.02838773 6.29246693 6.45286309 6.30748264]
## (150,)
## <class 'sklearn.svm.classes.SVR'>
## [[ 3. 1.4 0.2]
## [ 3.2 1.3 0.2]
## [ 3.1 1.5 0.2]
## [ 3.4 1.4 0.3]
## [ 2.9 1.4 0.2]
## [ 3.1 1.5 0.1]
## [ 3.7 1.5 0.2]
## [ 3.4 1.6 0.2]
## [ 3. 1.1 0.1]
## [ 4. 1.2 0.2]
## [ 4.4 1.5 0.4]
## [ 3.8 1.7 0.3]
## [ 3.8 1.5 0.3]
## [ 3.4 1.7 0.2]
## [ 3.6 1. 0.2]
## [ 3.3 1.7 0.5]
## [ 3.4 1.9 0.2]
## [ 3. 1.6 0.2]
## [ 3.5 1.5 0.2]
## [ 3.4 1.4 0.2]
## [ 3.2 1.6 0.2]
## [ 3.4 1.5 0.4]
## [ 4.1 1.5 0.1]
## [ 3.1 1.5 0.2]
## [ 3.2 1.2 0.2]
## [ 3.5 1.3 0.2]
## [ 3.6 1.4 0.1]
## [ 3. 1.3 0.2]
## [ 3.4 1.5 0.2]
## [ 2.3 1.3 0.3]
## [ 3.2 1.3 0.2]
## [ 3.5 1.6 0.6]
## [ 3.8 1.9 0.4]
## [ 3.8 1.6 0.2]
## [ 3.2 1.4 0.2]
## [ 3.7 1.5 0.2]
## [ 3.3 1.4 0.2]
## [ 3.2 4.7 1.4]
## [ 3.2 4.5 1.5]
## [ 3.1 4.9 1.5]
## [ 2.8 4.6 1.5]
## [ 2.8 4.5 1.3]
## [ 2.4 3.3 1. ]
## [ 2.9 4.6 1.3]
## [ 2.7 3.9 1.4]
## [ 2. 3.5 1. ]
## [ 2.2 4. 1. ]
## [ 2.9 4.7 1.4]
## [ 2.9 3.6 1.3]
## [ 3.1 4.4 1.4]
## [ 3. 4.5 1.5]
## [ 2.2 4.5 1.5]
## [ 3.2 4.8 1.8]
## [ 2.8 4. 1.3]
## [ 2.5 4.9 1.5]
## [ 2.8 4.7 1.2]
## [ 2.9 4.3 1.3]
## [ 3. 4.4 1.4]
## [ 2.8 4.8 1.4]
## [ 3. 5. 1.7]
## [ 2.6 3.5 1. ]
## [ 2.7 3.9 1.2]
## [ 2.7 5.1 1.6]
## [ 3. 4.5 1.5]
## [ 3.4 4.5 1.6]
## [ 3.1 4.7 1.5]
## [ 2.3 4.4 1.3]
## [ 3. 4.1 1.3]
## [ 2.5 4. 1.3]
## [ 2.6 4.4 1.2]
## [ 2.6 4. 1.2]
## [ 2.3 3.3 1. ]
## [ 2.7 4.2 1.3]
## [ 3. 4.2 1.2]
## [ 2.9 4.2 1.3]
## [ 2.9 4.3 1.3]
## [ 2.5 3. 1.1]
## [ 2.8 4.1 1.3]
## [ 3.3 6. 2.5]
## [ 2.7 5.1 1.9]
## [ 3. 5.9 2.1]
## [ 2.9 5.6 1.8]
## [ 3. 5.8 2.2]
## [ 2.5 4.5 1.7]
## [ 3.6 6.1 2.5]
## [ 3.2 5.1 2. ]
## [ 2.7 5.3 1.9]
## [ 3. 5.5 2.1]
## [ 2.5 5. 2. ]
## [ 2.8 5.1 2.4]
## [ 3. 5.5 1.8]
## [ 3.8 6.7 2.2]
## [ 2.6 6.9 2.3]
## [ 3.2 5.7 2.3]
## [ 2.8 4.9 2. ]
## [ 2.8 6.7 2. ]
## [ 2.7 4.9 1.8]
## [ 3.3 5.7 2.1]
## [ 2.8 4.8 1.8]
## [ 2.8 5.6 2.1]
## [ 3. 5.8 1.6]
## [ 2.8 6.1 1.9]
## [ 3.8 6.4 2. ]
## [ 2.8 5.6 2.2]
## [ 2.6 5.6 1.4]
## [ 3. 6.1 2.3]
## [ 3.4 5.6 2.4]
## [ 3.1 5.5 1.8]
## [ 3. 4.8 1.8]
## [ 3.1 5.4 2.1]
## [ 3.1 5.6 2.4]
## [ 3.1 5.1 2.3]
## [ 2.7 5.1 1.9]
## [ 3.2 5.9 2.3]
## [ 3. 5.2 2.3]
## [ 2.5 5. 1.9]
## [ 3. 5.2 2. ]
## [ 3.4 5.4 2.3]
## [ 3. 5.1 1.8]]
## [ 1 2 3 6 8 9 10 11 13 14 15 18 19 20 22 23 24 25
## 27 28 29 31 32 34 35 36 37 38 39 41 42 43 44 46 47 48
## 49 50 51 52 54 55 57 58 59 60 62 63 64 65 66 68 70 71
## 72 73 74 75 76 77 79 82 83 84 85 86 87 88 89 90 92 93
## 94 95 96 97 98 99 100 101 102 103 104 106 109 110 111 112 113 114
## 116 117 118 120 121 122 123 124 126 128 129 130 131 132 134 135 136 137
## 138 139 140 141 142 143 145 146 147 148 149]
## [1141240072 676]