1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
|
from .. import LinearSVC
from ...base import ClassifierMixin, RegressorMixin
from .base import SparseBaseLibSVM
from ...utils import deprecated
class SVC(SparseBaseLibSVM, ClassifierMixin):
"""SVC for sparse matrices (csr).
See :class:`sklearn.svm.SVC` for a complete list of parameters
Notes
-----
For best results, this accepts a matrix in csr format
(scipy.sparse.csr), but should be able to convert from any array-like
object (including other sparse representations).
Examples
--------
>>> import numpy as np
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
>>> y = np.array([1, 1, 2, 2])
>>> from sklearn.svm.sparse import SVC
>>> clf = SVC()
>>> clf.fit(X, y) #doctest: +NORMALIZE_WHITESPACE
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.5, kernel='rbf', probability=False, shrinking=True,
tol=0.001, verbose=False)
>>> print clf.predict([[-0.8, -1]])
[ 1.]
"""
def __init__(self, C=1.0, kernel='rbf', degree=3, gamma=0.0,
coef0=0.0, shrinking=True, probability=False,
tol=1e-3, cache_size=200, class_weight=None,
verbose=False):
super(SVC, self).__init__('c_svc', kernel, degree, gamma, coef0, tol,
C, 0., 0., shrinking, probability,
cache_size, class_weight, verbose)
class NuSVC(SparseBaseLibSVM, ClassifierMixin):
"""NuSVC for sparse matrices (csr).
See :class:`sklearn.svm.NuSVC` for a complete list of parameters
Notes
-----
For best results, this accepts a matrix in csr format
(scipy.sparse.csr), but should be able to convert from any array-like
object (including other sparse representations).
Examples
--------
>>> import numpy as np
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
>>> y = np.array([1, 1, 2, 2])
>>> from sklearn.svm.sparse import NuSVC
>>> clf = NuSVC()
>>> clf.fit(X, y) #doctest: +NORMALIZE_WHITESPACE
NuSVC(cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', nu=0.5, probability=False, shrinking=True, tol=0.001,
verbose=False)
>>> print clf.predict([[-0.8, -1]])
[ 1.]
"""
def __init__(self, nu=0.5, kernel='rbf', degree=3, gamma=0.0,
coef0=0.0, shrinking=True, probability=False,
tol=1e-3, cache_size=200, class_weight=None,
verbose=False):
super(NuSVC, self).__init__('nu_svc', kernel, degree, gamma, coef0,
tol, 0., nu, 0., shrinking, probability,
cache_size, class_weight, verbose)
class SVR(SparseBaseLibSVM, RegressorMixin):
"""SVR for sparse matrices (csr)
See :class:`sklearn.svm.SVR` for a complete list of parameters
Notes
-----
For best results, this accepts a matrix in csr format
(scipy.sparse.csr), but should be able to convert from any array-like
object (including other sparse representations).
Examples
--------
>>> from sklearn.svm.sparse import SVR
>>> import numpy as np
>>> n_samples, n_features = 10, 5
>>> np.random.seed(0)
>>> y = np.random.randn(n_samples)
>>> X = np.random.randn(n_samples, n_features)
>>> clf = SVR(C=1.0, epsilon=0.2)
>>> clf.fit(X, y)
SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.2, gamma=0.2,
kernel='rbf', probability=False, shrinking=True, tol=0.001,
verbose=False)
"""
def __init__(self, kernel='rbf', degree=3, gamma=0.0, coef0=0.0, tol=1e-3,
C=1.0, epsilon=0.1, shrinking=True, probability=False,
cache_size=200, verbose=False):
super(SVR, self).__init__('epsilon_svr', kernel, degree, gamma, coef0,
tol, C, 0., epsilon, shrinking, probability,
cache_size, None, verbose)
class NuSVR(SparseBaseLibSVM, RegressorMixin):
"""NuSVR for sparse matrices (csr)
See :class:`sklearn.svm.NuSVC` for a complete list of parameters
Notes
-----
For best results, this accepts a matrix in csr format
(scipy.sparse.csr), but should be able to convert from any array-like
object (including other sparse representations).
Examples
--------
>>> from sklearn.svm.sparse import NuSVR
>>> import numpy as np
>>> n_samples, n_features = 10, 5
>>> np.random.seed(0)
>>> y = np.random.randn(n_samples)
>>> X = np.random.randn(n_samples, n_features)
>>> clf = NuSVR(nu=0.1, C=1.0)
>>> clf.fit(X, y)
NuSVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma=0.2,
kernel='rbf', nu=0.1, probability=False, shrinking=True, tol=0.001,
verbose=False)
"""
def __init__(self, nu=0.5, C=1.0, kernel='rbf', degree=3, gamma=0.0,
coef0=0.0, shrinking=True, epsilon=0.1, probability=False,
tol=1e-3, cache_size=200, verbose=False):
super(NuSVR, self).__init__('nu_svr', kernel, degree, gamma, coef0,
tol, C, nu, epsilon, shrinking, probability, cache_size,
None, verbose)
class OneClassSVM(SparseBaseLibSVM):
"""OneClassSVM for sparse matrices (csr)
See :class:`sklearn.svm.OneClassSVM` for a complete list of parameters
Notes
-----
For best results, this accepts a matrix in csr format
(scipy.sparse.csr), but should be able to convert from any array-like
object (including other sparse representations).
"""
def __init__(self, kernel='rbf', degree=3, gamma=0.0, coef0=0.0, tol=1e-3,
nu=0.5, shrinking=True, probability=False, cache_size=200,
verbose=False):
super(OneClassSVM, self).__init__('one_class', kernel, degree, gamma,
coef0, tol, 0.0, nu, 0.0, shrinking, probability, cache_size,
None, verbose)
def fit(self, X, sample_weight=None):
super(OneClassSVM, self).fit(
X, [], sample_weight=sample_weight)
@deprecated("""to be removed in v0.12;
use sklearn.svm.LinearSVC instead""")
class LinearSVC(LinearSVC):
pass
|
