%
matplotlib inline
# 支持向量机SVM的核函数
import
numpy
as
np
import
matplotlib
.
pyplot
as
plt
from
sklearn
import
svm
from
sklearn
.
datasets
import
make_blobs
X
,
y
=
make_blobs
(
n_samples
=
50
,
centers
=
2
,
random_state
=
6
)
print
(
'X:\n'
,
X
,
'\n'
)
print
(
'y:\n'
,
y
,
'\n'
)
X:
[[ 6.45519089 -9.46356669]
[ 8.49142837 -2.54974889]
[ 6.87151089 -10.18071547]
[ 9.49649411 -3.7902975 ]
[ 7.67619643 -2.82620437]
[ 6.3883927 -9.25691447]
[ 9.24223825 -3.88003098]
[ 5.95313618 -6.82945967]
[ 6.86866543 -10.02289012]
[ 7.52132141 -2.12266605]
[ 7.29573215 -4.39392379]
[ 6.85086785 -9.92422452]
[ 4.29225906 -8.99220442]
[ 8.21597398 -2.28672255]
[ 7.9683312 -3.23125265]
[ 8.68185687 -4.53683537]
[ 6.77811308 -9.80940478]
[ 7.93333064 -3.51553205]
[ 7.73046665 -4.72901672]
[ 7.37578372 -8.7241701 ]
[ 6.95292352 -8.22624269]
[ 8.07502382 -4.25949569]
[ 7.39169472 -3.1266933 ]
[ 6.59823581 -10.20150177]
[ 7.27059007 -4.84225716]
[ 8.71445065 -2.41730491]
[ 5.73005848 -4.19481136]
[ 9.42169269 -2.6476988 ]
[ 6.26221548 -8.43925752]
[ 7.89359985 -7.41655113]
[ 8.98426675 -4.87449712]
[ 10.48848359 -2.75858164]
[ 5.45644482 -8.99900075]
[ 6.50072722 -3.82403586]
[ 7.07705089 -2.4047943 ]
[ 9.07568367 -4.21790533]
[ 7.92736799 -9.7615272 ]
[ 7.29885085 -9.90563956]
[ 6.6008728 -8.07144707]
[ 5.94709536 -9.05353781]
[ 5.88397542 -8.37284513]
[ 5.37042238 -2.44715237]
[ 8.21201164 -1.54781358]
[ 6.40500112 -7.50322463]
[ 6.94752781 -9.75794397]
[ 6.04907774 -8.76969991]
[ 8.32932478 -8.47191434]
[ 6.37734541 -10.61510727]
[ 4.29810787 -8.41461865]
[ 7.97164446 -3.38236058]]
y:
[1 0 1 0 0 1 0 1 1 0 0 1 1 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 1 1 0 0 1 0 0 0 1
1 1 1 1 0 0 1 1 1 1 1 1 0]
clf
=
svm
.
SVC
(
kernel
=
'linear'
,
C
=
1000
)
clf
.
fit
(
X
,
y
)
plt
.
scatter
(
X
[
:
,
0
]
,
X
[
:
,
1
]
,
c
=
y
,
s
=
30
,
cmap
=
plt
.
cm
.
Paired
)
ax
=
plt
.
gca
(
)
print
(
'打印ax:\n'
,
ax
,
'\n'
)
print
(
'ax的类型：\n'
,
type
(
ax
)
,
'\n'
)
xlim
=
ax
.
get_xlim
(
)
print
(
'xlim:\n'
,
xlim
,
'\n'
)
print
(
'xlim的类型:\n'
,
type
(
xlim
)
,
'\n'
)
ylim
=
ax
.
get_ylim
(
)
print
(
'ylim:\n'
,
ylim
,
'\n'
)
打印ax:
AxesSubplot(0.125,0.125;0.775x0.755)
ax的类型：
xlim:
(0.0, 1.0)
xlim的类型:
ylim:
(0.0, 1.0)
xx
=
np
.
linspace
(
xlim
[
0
]
,
xlim
[
1
]
,
30
)
yy
=
np
.
linspace
(
ylim
[
0
]
,
ylim
[
1
]
,
30
)
print
(
'打印xx:\n'
,
xx
,
'\n'
)
print
(
'xx的类型:\n'
,
type
(
xx
)
,
'\n'
)
xx_
=
np
.
arange
(
xlim
[
0
]
,
xlim
[
1
]
,
0.1
)
print
(
'打印xx_:\n'
,
xx_
,
'\n'
)
print
(
'打印xx.shape'
,
xx
.
shape
,
'\n'
)
print
(
'xx_的类型:\n'
,
type
(
xx_
)
,
'\n'
)
YY
,
XX
=
np
.
meshgrid
(
yy
,
xx
)
print
(
'打印XX:\n'
,
XX
,
'\n'
)
xy
=
np
.
vstack
(
[
XX
.
ravel
(
)
,
YY
.
ravel
(
)
]
)
.
T
print
(
'打印XX.ravel():\n'
,
XX
.
ravel
(
)
,
'\n'
)
print
(
'打印np.vstack([XX.ravel(),YY.ravel()])\n'
,
np
.
vstack
(
[
XX
.
ravel
(
)
,
YY
.
ravel
(
)
]
)
,
'\n'
)
print
(
'打印xy:\n'
,
xy
,
'\n'
)
Z
=
clf
.
decision_function
(
xy
)
.
reshape
(
XX
.
shape
)
打印xx:
[0. 0.03448276 0.06896552 0.10344828 0.13793103 0.17241379
0.20689655 0.24137931 0.27586207 0.31034483 0.34482759 0.37931034
0.4137931 0.44827586 0.48275862 0.51724138 0.55172414 0.5862069
0.62068966 0.65517241 0.68965517 0.72413793 0.75862069 0.79310345
0.82758621 0.86206897 0.89655172 0.93103448 0.96551724 1. ]
xx的类型:
打印xx_:
[0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9]
打印xx.shape (30,)
xx_的类型:
打印XX:
[[0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. ]
[0.03448276 0.03448276 0.03448276 0.03448276 0.03448276 0.03448276
0.03448276 0.03448276 0.03448276 0.03448276 0.03448276 0.03448276
0.03448276 0.03448276 0.03448276 0.03448276 0.03448276 0.03448276
0.03448276 0.03448276 0.03448276 0.03448276 0.03448276 0.03448276
0.03448276 0.03448276 0.03448276 0.03448276 0.03448276 0.03448276]
[0.06896552 0.06896552 0.06896552 0.06896552 0.06896552 0.06896552
0.06896552 0.06896552 0.06896552 0.06896552 0.06896552 0.06896552
0.06896552 0.06896552 0.06896552 0.06896552 0.06896552 0.06896552
0.06896552 0.06896552 0.06896552 0.06896552 0.06896552 0.06896552
0.06896552 0.06896552 0.06896552 0.06896552 0.06896552 0.06896552]
[0.10344828 0.10344828 0.10344828 0.10344828 0.10344828 0.10344828
0.10344828 0.10344828 0.10344828 0.10344828 0.10344828 0.10344828
0.10344828 0.10344828 0.10344828 0.10344828 0.10344828 0.10344828
0.10344828 0.10344828 0.10344828 0.10344828 0.10344828 0.10344828
0.10344828 0.10344828 0.10344828 0.10344828 0.10344828 0.10344828]
[0.13793103 0.13793103 0.13793103 0.13793103 0.13793103 0.13793103
0.13793103 0.13793103 0.13793103 0.13793103 0.13793103 0.13793103
0.13793103 0.13793103 0.13793103 0.13793103 0.13793103 0.13793103
0.13793103 0.13793103 0.13793103 0.13793103 0.13793103 0.13793103
0.13793103 0.13793103 0.13793103 0.13793103 0.13793103 0.13793103]
[0.17241379 0.17241379 0.17241379 0.17241379 0.17241379 0.17241379
0.17241379 0.17241379 0.17241379 0.17241379 0.17241379 0.17241379
0.17241379 0.17241379 0.17241379 0.17241379 0.17241379 0.17241379
0.17241379 0.17241379 0.17241379 0.17241379 0.17241379 0.17241379
0.17241379 0.17241379 0.17241379 0.17241379 0.17241379 0.17241379]
[0.20689655 0.20689655 0.20689655 0.20689655 0.20689655 0.20689655
0.20689655 0.20689655 0.20689655 0.20689655 0.20689655 0.20689655
0.20689655 0.20689655 0.20689655 0.20689655 0.20689655 0.20689655
0.20689655 0.20689655 0.20689655 0.20689655 0.20689655 0.20689655
0.20689655 0.20689655 0.20689655 0.20689655 0.20689655 0.20689655]
[0.24137931 0.24137931 0.24137931 0.24137931 0.24137931 0.24137931
0.24137931 0.24137931 0.24137931 0.24137931 0.24137931 0.24137931
0.24137931 0.24137931 0.24137931 0.24137931 0.24137931 0.24137931
0.24137931 0.24137931 0.24137931 0.24137931 0.24137931 0.24137931
0.24137931 0.24137931 0.24137931 0.24137931 0.24137931 0.24137931]
[0.27586207 0.27586207 0.27586207 0.27586207 0.27586207 0.27586207
0.27586207 0.27586207 0.27586207 0.27586207 0.27586207 0.27586207
0.27586207 0.27586207 0.27586207 0.27586207 0.27586207 0.27586207
0.27586207 0.27586207 0.27586207 0.27586207 0.27586207 0.27586207
0.27586207 0.27586207 0.27586207 0.27586207 0.27586207 0.27586207]
[0.31034483 0.31034483 0.31034483 0.31034483 0.31034483 0.31034483
0.31034483 0.31034483 0.31034483 0.31034483 0.31034483 0.31034483
0.31034483 0.31034483 0.31034483 0.31034483 0.31034483 0.31034483
0.31034483 0.31034483 0.31034483 0.31034483 0.31034483 0.31034483
0.31034483 0.31034483 0.31034483 0.31034483 0.31034483 0.31034483]
[0.34482759 0.34482759 0.34482759 0.34482759 0.34482759 0.34482759
0.34482759 0.34482759 0.34482759 0.34482759 0.34482759 0.34482759
0.34482759 0.34482759 0.34482759 0.34482759 0.34482759 0.34482759
0.34482759 0.34482759 0.34482759 0.34482759 0.34482759 0.34482759
0.34482759 0.34482759 0.34482759 0.34482759 0.34482759 0.34482759]
[0.37931034 0.37931034 0.37931034 0.37931034 0.37931034 0.37931034
0.37931034 0.37931034 0.37931034 0.37931034 0.37931034 0.37931034
0.37931034 0.37931034 0.37931034 0.37931034 0.37931034 0.37931034
0.37931034 0.37931034 0.37931034 0.37931034 0.37931034 0.37931034
0.37931034 0.37931034 0.37931034 0.37931034 0.37931034 0.37931034]
[0.4137931 0.4137931 0.4137931 0.4137931 0.4137931 0.4137931
0.4137931 0.4137931 0.4137931 0.4137931 0.4137931 0.4137931
0.4137931 0.4137931 0.4137931 0.4137931 0.4137931 0.4137931
0.4137931 0.4137931 0.4137931 0.4137931 0.4137931 0.4137931
0.4137931 0.4137931 0.4137931 0.4137931 0.4137931 0.4137931 ]
[0.44827586 0.44827586 0.44827586 0.44827586 0.44827586 0.44827586
0.44827586 0.44827586 0.44827586 0.44827586 0.44827586 0.44827586
0.44827586 0.44827586 0.44827586 0.44827586 0.44827586 0.44827586
0.44827586 0.44827586 0.44827586 0.44827586 0.44827586 0.44827586
0.44827586 0.44827586 0.44827586 0.44827586 0.44827586 0.44827586]
[0.48275862 0.48275862 0.48275862 0.48275862 0.48275862 0.48275862
0.48275862 0.48275862 0.48275862 0.48275862 0.48275862 0.48275862
0.48275862 0.48275862 0.48275862 0.48275862 0.48275862 0.48275862
0.48275862 0.48275862 0.48275862 0.48275862 0.48275862 0.48275862
0.48275862 0.48275862 0.48275862 0.48275862 0.48275862 0.48275862]
[0.51724138 0.51724138 0.51724138 0.51724138 0.51724138 0.51724138
0.51724138 0.51724138 0.51724138 0.51724138 0.51724138 0.51724138
0.51724138 0.51724138 0.51724138 0.51724138 0.51724138 0.51724138
0.51724138 0.51724138 0.51724138 0.51724138 0.51724138 0.51724138
0.51724138 0.51724138 0.51724138 0.51724138 0.51724138 0.51724138]
[0.55172414 0.55172414 0.55172414 0.55172414 0.55172414 0.55172414
0.55172414 0.55172414 0.55172414 0.55172414 0.55172414 0.55172414
0.55172414 0.55172414 0.55172414 0.55172414 0.55172414 0.55172414
0.55172414 0.55172414 0.55172414 0.55172414 0.55172414 0.55172414
0.55172414 0.55172414 0.55172414 0.55172414 0.55172414 0.55172414]
[0.5862069 0.5862069 0.5862069 0.5862069 0.5862069 0.5862069
0.5862069 0.5862069 0.5862069 0.5862069 0.5862069 0.5862069
0.5862069 0.5862069 0.5862069 0.5862069 0.5862069 0.5862069
0.5862069 0.5862069 0.5862069 0.5862069 0.5862069 0.5862069
0.5862069 0.5862069 0.5862069 0.5862069 0.5862069 0.5862069 ]
[0.62068966 0.62068966 0.62068966 0.62068966 0.62068966 0.62068966
0.62068966 0.62068966 0.62068966 0.62068966 0.62068966 0.62068966
0.62068966 0.62068966 0.62068966 0.62068966 0.62068966 0.62068966
0.62068966 0.62068966 0.62068966 0.62068966 0.62068966 0.62068966
0.62068966 0.62068966 0.62068966 0.62068966 0.62068966 0.62068966]
[0.65517241 0.65517241 0.65517241 0.65517241 0.65517241 0.65517241
0.65517241 0.65517241 0.65517241 0.65517241 0.65517241 0.65517241
0.65517241 0.65517241 0.65517241 0.65517241 0.65517241 0.65517241
0.65517241 0.65517241 0.65517241 0.65517241 0.65517241 0.65517241
0.65517241 0.65517241 0.65517241 0.65517241 0.65517241 0.65517241]
[0.68965517 0.68965517 0.68965517 0.68965517 0.68965517 0.68965517
0.68965517 0.68965517 0.68965517 0.68965517 0.68965517 0.68965517
0.68965517 0.68965517 0.68965517 0.68965517 0.68965517 0.68965517
0.68965517 0.68965517 0.68965517 0.68965517 0.68965517 0.68965517
0.68965517 0.68965517 0.68965517 0.68965517 0.68965517 0.68965517]
[0.72413793 0.72413793 0.72413793 0.72413793 0.72413793 0.72413793
0.72413793 0.72413793 0.72413793 0.72413793 0.72413793 0.72413793
0.72413793 0.72413793 0.72413793 0.72413793 0.72413793 0.72413793
0.72413793 0.72413793 0.72413793 0.72413793 0.72413793 0.72413793
0.72413793 0.72413793 0.72413793 0.72413793 0.72413793 0.72413793]
[0.75862069 0.75862069 0.75862069 0.75862069 0.75862069 0.75862069
0.75862069 0.75862069 0.75862069 0.75862069 0.75862069 0.75862069
0.75862069 0.75862069 0.75862069 0.75862069 0.75862069 0.75862069
0.75862069 0.75862069 0.75862069 0.75862069 0.75862069 0.75862069
0.75862069 0.75862069 0.75862069 0.75862069 0.75862069 0.75862069]
[0.79310345 0.79310345 0.79310345 0.79310345 0.79310345 0.79310345
0.79310345 0.79310345 0.79310345 0.79310345 0.79310345 0.79310345
0.79310345 0.79310345 0.79310345 0.79310345 0.79310345 0.79310345
0.79310345 0.79310345 0.79310345 0.79310345 0.79310345 0.79310345
0.79310345 0.79310345 0.79310345 0.79310345 0.79310345 0.79310345]
[0.82758621 0.82758621 0.82758621 0.82758621 0.82758621 0.82758621
0.82758621 0.82758621 0.82758621 0.82758621 0.82758621 0.82758621
0.82758621 0.82758621 0.82758621 0.82758621 0.82758621 0.82758621
0.82758621 0.82758621 0.82758621 0.82758621 0.82758621 0.82758621
0.82758621 0.82758621 0.82758621 0.82758621 0.82758621 0.82758621]
[0.86206897 0.86206897 0.86206897 0.86206897 0.86206897 0.86206897
0.86206897 0.86206897 0.86206897 0.86206897 0.86206897 0.86206897
0.86206897 0.86206897 0.86206897 0.86206897 0.86206897 0.86206897
0.86206897 0.86206897 0.86206897 0.86206897 0.86206897 0.86206897
0.86206897 0.86206897 0.86206897 0.86206897 0.86206897 0.86206897]
[0.89655172 0.89655172 0.89655172 0.89655172 0.89655172 0.89655172
0.89655172 0.89655172 0.89655172 0.89655172 0.89655172 0.89655172
0.89655172 0.89655172 0.89655172 0.89655172 0.89655172 0.89655172
0.89655172 0.89655172 0.89655172 0.89655172 0.89655172 0.89655172
0.89655172 0.89655172 0.89655172 0.89655172 0.89655172 0.89655172]
[0.93103448 0.93103448 0.93103448 0.93103448 0.93103448 0.93103448
0.93103448 0.93103448 0.93103448 0.93103448 0.93103448 0.93103448
0.93103448 0.93103448 0.93103448 0.93103448 0.93103448 0.93103448
0.93103448 0.93103448 0.93103448 0.93103448 0.93103448 0.93103448
0.93103448 0.93103448 0.93103448 0.93103448 0.93103448 0.93103448]
[0.96551724 0.96551724 0.96551724 0.96551724 0.96551724 0.96551724
0.96551724 0.96551724 0.96551724 0.96551724 0.96551724 0.96551724
0.96551724 0.96551724 0.96551724 0.96551724 0.96551724 0.96551724
0.96551724 0.96551724 0.96551724 0.96551724 0.96551724 0.96551724
0.96551724 0.96551724 0.96551724 0.96551724 0.96551724 0.96551724]
[1. 1. 1. 1. 1. 1.
1. 1. 1. 1. 1. 1.
1. 1. 1. 1. 1. 1.
1. 1. 1. 1. 1. 1.
1. 1. 1. 1. 1. 1. ]]
打印XX.ravel():
[0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0.03448276 0.03448276 0.03448276 0.03448276 0.03448276 0.03448276
0.03448276 0.03448276 0.03448276 0.03448276 0.03448276 0.03448276
0.03448276 0.03448276 0.03448276 0.03448276 0.03448276 0.03448276
0.03448276 0.03448276 0.03448276 0.03448276 0.03448276 0.03448276
0.03448276 0.03448276 0.03448276 0.03448276 0.03448276 0.03448276
0.06896552 0.06896552 0.06896552 0.06896552 0.06896552 0.06896552
0.06896552 0.06896552 0.06896552 0.06896552 0.06896552 0.06896552
0.06896552 0.06896552 0.06896552 0.06896552 0.06896552 0.06896552
0.06896552 0.06896552 0.06896552 0.06896552 0.06896552 0.06896552
0.06896552 0.06896552 0.06896552 0.06896552 0.06896552 0.06896552
0.10344828 0.10344828 0.10344828 0.10344828 0.10344828 0.10344828
0.10344828 0.10344828 0.10344828 0.10344828 0.10344828 0.10344828
0.10344828 0.10344828 0.10344828 0.10344828 0.10344828 0.10344828
0.10344828 0.10344828 0.10344828 0.10344828 0.10344828 0.10344828
0.10344828 0.10344828 0.10344828 0.10344828 0.10344828 0.10344828
0.13793103 0.13793103 0.13793103 0.13793103 0.13793103 0.13793103
0.13793103 0.13793103 0.13793103 0.13793103 0.13793103 0.13793103
0.13793103 0.13793103 0.13793103 0.13793103 0.13793103 0.13793103
0.13793103 0.13793103 0.13793103 0.13793103 0.13793103 0.13793103
0.13793103 0.13793103 0.13793103 0.13793103 0.13793103 0.13793103
0.17241379 0.17241379 0.17241379 0.17241379 0.17241379 0.17241379
0.17241379 0.17241379 0.17241379 0.17241379 0.17241379 0.17241379
0.17241379 0.17241379 0.17241379 0.17241379 0.17241379 0.17241379
0.17241379 0.17241379 0.17241379 0.17241379 0.17241379 0.17241379
0.17241379 0.17241379 0.17241379 0.17241379 0.17241379 0.17241379
0.20689655 0.20689655 0.20689655 0.20689655 0.20689655 0.20689655
0.20689655 0.20689655 0.20689655 0.20689655 0.20689655 0.20689655
0.20689655 0.20689655 0.20689655 0.20689655 0.20689655 0.20689655
0.20689655 0.20689655 0.20689655 0.20689655 0.20689655 0.20689655
0.20689655 0.20689655 0.20689655 0.20689655 0.20689655 0.20689655
0.24137931 0.24137931 0.24137931 0.24137931 0.24137931 0.24137931
0.24137931 0.24137931 0.24137931 0.24137931 0.24137931 0.24137931
0.24137931 0.24137931 0.24137931 0.24137931 0.24137931 0.24137931
0.24137931 0.24137931 0.24137931 0.24137931 0.24137931 0.24137931
0.24137931 0.24137931 0.24137931 0.24137931 0.24137931 0.24137931
0.27586207 0.27586207 0.27586207 0.27586207 0.27586207 0.27586207
0.27586207 0.27586207 0.27586207 0.27586207 0.27586207 0.27586207
0.27586207 0.27586207 0.27586207 0.27586207 0.27586207 0.27586207
0.27586207 0.27586207 0.27586207 0.27586207 0.27586207 0.27586207
0.27586207 0.27586207 0.27586207 0.27586207 0.27586207 0.27586207
0.31034483 0.31034483 0.31034483 0.31034483 0.31034483 0.31034483
0.31034483 0.31034483 0.31034483 0.31034483 0.31034483 0.31034483
0.31034483 0.31034483 0.31034483 0.31034483 0.31034483 0.31034483
0.31034483 0.31034483 0.31034483 0.31034483 0.31034483 0.31034483
0.31034483 0.31034483 0.31034483 0.31034483 0.31034483 0.31034483
0.34482759 0.34482759 0.34482759 0.34482759 0.34482759 0.34482759
0.34482759 0.34482759 0.34482759 0.34482759 0.34482759 0.34482759
0.34482759 0.34482759 0.34482759 0.34482759 0.34482759 0.34482759
0.34482759 0.34482759 0.34482759 0.34482759 0.34482759 0.34482759
0.34482759 0.34482759 0.34482759 0.34482759 0.34482759 0.34482759
0.37931034 0.37931034 0.37931034 0.37931034 0.37931034 0.37931034
0.37931034 0.37931034 0.37931034 0.37931034 0.37931034 0.37931034
0.37931034 0.37931034 0.37931034 0.37931034 0.37931034 0.37931034
0.37931034 0.37931034 0.37931034 0.37931034 0.37931034 0.37931034
0.37931034 0.37931034 0.37931034 0.37931034 0.37931034 0.37931034
0.4137931 0.4137931 0.4137931 0.4137931 0.4137931 0.4137931
0.4137931 0.4137931 0.4137931 0.4137931 0.4137931 0.4137931
0.4137931 0.4137931 0.4137931 0.4137931 0.4137931 0.4137931
0.4137931 0.4137931 0.4137931 0.4137931 0.4137931 0.4137931
0.4137931 0.4137931 0.4137931 0.4137931 0.4137931 0.4137931
0.44827586 0.44827586 0.44827586 0.44827586 0.44827586 0.44827586
0.44827586 0.44827586 0.44827586 0.44827586 0.44827586 0.44827586
0.44827586 0.44827586 0.44827586 0.44827586 0.44827586 0.44827586
0.44827586 0.44827586 0.44827586 0.44827586 0.44827586 0.44827586
0.44827586 0.44827586 0.44827586 0.44827586 0.44827586 0.44827586
0.48275862 0.48275862 0.48275862 0.48275862 0.48275862 0.48275862
0.48275862 0.48275862 0.48275862 0.48275862 0.48275862 0.48275862
0.48275862 0.48275862 0.48275862 0.48275862 0.48275862 0.48275862
0.48275862 0.48275862 0.48275862 0.48275862 0.48275862 0.48275862
0.48275862 0.48275862 0.48275862 0.48275862 0.48275862 0.48275862
0.51724138 0.51724138 0.51724138 0.51724138 0.51724138 0.51724138
0.51724138 0.51724138 0.51724138 0.51724138 0.51724138 0.51724138
0.51724138 0.51724138 0.51724138 0.51724138 0.51724138 0.51724138
0.51724138 0.51724138 0.51724138 0.51724138 0.51724138 0.51724138
0.51724138 0.51724138 0.51724138 0.51724138 0.51724138 0.51724138
0.55172414 0.55172414 0.55172414 0.55172414 0.55172414 0.55172414
0.55172414 0.55172414 0.55172414 0.55172414 0.55172414 0.55172414
0.55172414 0.55172414 0.55172414 0.55172414 0.55172414 0.55172414
0.55172414 0.55172414 0.55172414 0.55172414 0.55172414 0.55172414
0.55172414 0.55172414 0.55172414 0.55172414 0.55172414 0.55172414
0.5862069 0.5862069 0.5862069 0.5862069 0.5862069 0.5862069
0.5862069 0.5862069 0.5862069 0.5862069 0.5862069 0.5862069
0.5862069 0.5862069 0.5862069 0.5862069 0.5862069 0.5862069
0.5862069 0.5862069 0.5862069 0.5862069 0.5862069 0.5862069
0.5862069 0.5862069 0.5862069 0.5862069 0.5862069 0.5862069
0.62068966 0.62068966 0.62068966 0.62068966 0.62068966 0.62068966
0.62068966 0.62068966 0.62068966 0.62068966 0.62068966 0.62068966
0.62068966 0.62068966 0.62068966 0.62068966 0.62068966 0.62068966
0.62068966 0.62068966 0.62068966 0.62068966 0.62068966 0.62068966
0.62068966 0.62068966 0.62068966 0.62068966 0.62068966 0.62068966
0.65517241 0.65517241 0.65517241 0.65517241 0.65517241 0.65517241
0.65517241 0.65517241 0.65517241 0.65517241 0.65517241 0.65517241
0.65517241 0.65517241 0.65517241 0.65517241 0.65517241 0.65517241
0.65517241 0.65517241 0.65517241 0.65517241 0.65517241 0.65517241
0.65517241 0.65517241 0.65517241 0.65517241 0.65517241 0.65517241
0.68965517 0.68965517 0.68965517 0.68965517 0.68965517 0.68965517
0.68965517 0.68965517 0.68965517 0.68965517 0.68965517 0.68965517
0.68965517 0.68965517 0.68965517 0.68965517 0.68965517 0.68965517
0.68965517 0.68965517 0.68965517 0.68965517 0.68965517 0.68965517
0.68965517 0.68965517 0.68965517 0.68965517 0.68965517 0.68965517
0.72413793 0.72413793 0.72413793 0.72413793 0.72413793 0.72413793
0.72413793 0.72413793 0.72413793 0.72413793 0.72413793 0.72413793
0.72413793 0.72413793 0.72413793 0.72413793 0.72413793 0.72413793
0.72413793 0.72413793 0.72413793 0.72413793 0.72413793 0.72413793
0.72413793 0.72413793 0.72413793 0.72413793 0.72413793 0.72413793
0.75862069 0.75862069 0.75862069 0.75862069 0.75862069 0.75862069
0.75862069 0.75862069 0.75862069 0.75862069 0.75862069 0.75862069
0.75862069 0.75862069 0.75862069 0.75862069 0.75862069 0.75862069
0.75862069 0.75862069 0.75862069 0.75862069 0.75862069 0.75862069
0.75862069 0.75862069 0.75862069 0.75862069 0.75862069 0.75862069
0.79310345 0.79310345 0.79310345 0.79310345 0.79310345 0.79310345
0.79310345 0.79310345 0.79310345 0.79310345 0.79310345 0.79310345
0.79310345 0.79310345 0.79310345 0.79310345 0.79310345 0.79310345
0.79310345 0.79310345 0.79310345 0.79310345 0.79310345 0.79310345
0.79310345 0.79310345 0.79310345 0.79310345 0.79310345 0.79310345
0.82758621 0.82758621 0.82758621 0.82758621 0.82758621 0.82758621
0.82758621 0.82758621 0.82758621 0.82758621 0.82758621 0.82758621
0.82758621 0.82758621 0.82758621 0.82758621 0.82758621 0.82758621
0.82758621 0.82758621 0.82758621 0.82758621 0.82758621 0.82758621
0.82758621 0.82758621 0.82758621 0.82758621 0.82758621 0.82758621
0.86206897 0.86206897 0.86206897 0.86206897 0.86206897 0.86206897
0.86206897 0.86206897 0.86206897 0.86206897 0.86206897 0.86206897
0.86206897 0.86206897 0.86206897 0.86206897 0.86206897 0.86206897
0.86206897 0.86206897 0.86206897 0.86206897 0.86206897 0.86206897
0.86206897 0.86206897 0.86206897 0.86206897 0.86206897 0.86206897
0.89655172 0.89655172 0.89655172 0.89655172 0.89655172 0.89655172
0.89655172 0.89655172 0.89655172 0.89655172 0.89655172 0.89655172
0.89655172 0.89655172 0.89655172 0.89655172 0.89655172 0.89655172
0.89655172 0.89655172 0.89655172 0.89655172 0.89655172 0.89655172
0.89655172 0.89655172 0.89655172 0.89655172 0.89655172 0.89655172
0.93103448 0.93103448 0.93103448 0.93103448 0.93103448 0.93103448
0.93103448 0.93103448 0.93103448 0.93103448 0.93103448 0.93103448
0.93103448 0.93103448 0.93103448 0.93103448 0.93103448 0.93103448
0.93103448 0.93103448 0.93103448 0.93103448 0.93103448 0.93103448
0.93103448 0.93103448 0.93103448 0.93103448 0.93103448 0.93103448
0.96551724 0.96551724 0.96551724 0.96551724 0.96551724 0.96551724
0.96551724 0.96551724 0.96551724 0.96551724 0.96551724 0.96551724
0.96551724 0.96551724 0.96551724 0.96551724 0.96551724 0.96551724
0.96551724 0.96551724 0.96551724 0.96551724 0.96551724 0.96551724
0.96551724 0.96551724 0.96551724 0.96551724 0.96551724 0.96551724
1. 1. 1. 1. 1. 1.
1. 1. 1. 1. 1. 1.
1. 1. 1. 1. 1. 1.
1. 1. 1. 1. 1. 1.
1. 1. 1. 1. 1. 1. ]
打印np.vstack([XX.ravel(),YY.ravel()])
[[0. 0. 0. ... 1. 1. 1. ]
[0. 0.03448276 0.06896552 ... 0.93103448 0.96551724 1. ]]
打印xy:
[[0. 0. ]
[0. 0.03448276]
[0. 0.06896552]
...
[1. 0.93103448]
[1. 0.96551724]
[1. 1. ]]
ax
.
contour
(
XX
,
YY
,
Z
,
colors
=
'k'
,
levels
=
[
-
1
,
0
,
1
]
,
alpha
=
0.5
,
linestyles
=
[
'--'
,
'-'
,
'--'
]
)
ax
.
scatter
(
clf
.
support_vectors_
[
:
,
0
]
,
clf
.
support_vectors_
[
:
,
1
]
,
s
=
100
,
linewidths
=
1
,
facecolors
=
'none'
)
plt
.
show
(
)
c:\users\huawei\appdata\local\programs\python\python36\lib\site-packages\ipykernel_launcher.py:2: UserWarning: No contour levels were found within the data range.
print
(
'xx.shape:'
,
xx
.
shape
,
'\n'
)
print
(
'yy.shape:'
,
yy
.
shape
,
'\n'
)
print
(
'XX.shape:'
,
XX
.
shape
,
'\n'
)
print
(
'YY.shape:'
,
YY
.
shape
,
'\n'
)
print
(
'XX.ravel().shape:'
,
XX
.
ravel
(
)
.
shape
,
'\n'
)
print
(
'np.vstack([XX.ravel(),YY.ravel()]).shape:'
,
np
.
vstack
(
[
XX
.
ravel
(
)
,
YY
.
ravel
(
)
]
)
.
shape
,
'\n'
)
print
(
'np.vstack([XX.ravel(),YY.ravel()]).T.shape:'
,
np
.
vstack
(
[
XX
.
ravel
(
)
,
YY
.
ravel
(
)
]
)
.
T
.
shape
,
'\n'
)
print
(
'clf.decision_function(xy).shape:'
,
clf
.
decision_function
(
xy
)
.
shape
,
'\n'
)
xx.shape: (30,)
yy.shape: (30,)
XX.shape: (30, 30)
YY.shape: (30, 30)
XX.ravel().shape: (900,)
np.vstack([XX.ravel(),YY.ravel()]).shape: (2, 900)
np.vstack([XX.ravel(),YY.ravel()]).T.shape: (900, 2)
clf.decision_function(xy).shape: (900,)
print
(
'Z:\n'
,
Z
,
'\n'
)
Z:
[[-3.2115599 -3.24045912 -3.26935835 -3.29825757 -3.3271568 -3.35605602
-3.38495524 -3.41385447 -3.44275369 -3.47165292 -3.50055214 -3.52945137
-3.55835059 -3.58724982 -3.61614904 -3.64504827 -3.67394749 -3.70284672
-3.73174594 -3.76064516 -3.78954439 -3.81844361 -3.84734284 -3.87624206
-3.90514129 -3.93404051 -3.96293974 -3.99183896 -4.02073819 -4.04963741]
[-3.22031683 -3.24921606 -3.27811528 -3.30701451 -3.33591373 -3.36481296
-3.39371218 -3.4226114 -3.45151063 -3.48040985 -3.50930908 -3.5382083
-3.56710753 -3.59600675 -3.62490598 -3.6538052 -3.68270443 -3.71160365
-3.74050288 -3.7694021 -3.79830132 -3.82720055 -3.85609977 -3.884999
-3.91389822 -3.94279745 -3.97169667 -4.0005959 -4.02949512 -4.05839435]
[-3.22907377 -3.25797299 -3.28687222 -3.31577144 -3.34467067 -3.37356989
-3.40246912 -3.43136834 -3.46026756 -3.48916679 -3.51806601 -3.54696524
-3.57586446 -3.60476369 -3.63366291 -3.66256214 -3.69146136 -3.72036059
-3.74925981 -3.77815904 -3.80705826 -3.83595748 -3.86485671 -3.89375593
-3.92265516 -3.95155438 -3.98045361 -4.00935283 -4.03825206 -4.06715128]
[-3.2378307 -3.26672993 -3.29562915 -3.32452838 -3.3534276 -3.38232683
-3.41122605 -3.44012528 -3.4690245 -3.49792372 -3.52682295 -3.55572217
-3.5846214 -3.61352062 -3.64241985 -3.67131907 -3.7002183 -3.72911752
-3.75801675 -3.78691597 -3.8158152 -3.84471442 -3.87361364 -3.90251287
-3.93141209 -3.96031132 -3.98921054 -4.01810977 -4.04700899 -4.07590822]
[-3.24658764 -3.27548686 -3.30438609 -3.33328531 -3.36218454 -3.39108376
-3.41998299 -3.44888221 -3.47778144 -3.50668066 -3.53557988 -3.56447911
-3.59337833 -3.62227756 -3.65117678 -3.68007601 -3.70897523 -3.73787446
-3.76677368 -3.79567291 -3.82457213 -3.85347136 -3.88237058 -3.9112698
-3.94016903 -3.96906825 -3.99796748 -4.0268667 -4.05576593 -4.08466515]
[-3.25534457 -3.2842438 -3.31314302 -3.34204225 -3.37094147 -3.3998407
-3.42873992 -3.45763915 -3.48653837 -3.5154376 -3.54433682 -3.57323604
-3.60213527 -3.63103449 -3.65993372 -3.68883294 -3.71773217 -3.74663139
-3.77553062 -3.80442984 -3.83332907 -3.86222829 -3.89112752 -3.92002674
-3.94892596 -3.97782519 -4.00672441 -4.03562364 -4.06452286 -4.09342209]
[-3.26410151 -3.29300073 -3.32189996 -3.35079918 -3.37969841 -3.40859763
-3.43749686 -3.46639608 -3.49529531 -3.52419453 -3.55309376 -3.58199298
-3.6108922 -3.63979143 -3.66869065 -3.69758988 -3.7264891 -3.75538833
-3.78428755 -3.81318678 -3.842086 -3.87098523 -3.89988445 -3.92878368
-3.9576829 -3.98658212 -4.01548135 -4.04438057 -4.0732798 -4.10217902]
[-3.27285845 -3.30175767 -3.33065689 -3.35955612 -3.38845534 -3.41735457
-3.44625379 -3.47515302 -3.50405224 -3.53295147 -3.56185069 -3.59074992
-3.61964914 -3.64854836 -3.67744759 -3.70634681 -3.73524604 -3.76414526
-3.79304449 -3.82194371 -3.85084294 -3.87974216 -3.90864139 -3.93754061
-3.96643984 -3.99533906 -4.02423828 -4.05313751 -4.08203673 -4.11093596]
[-3.28161538 -3.31051461 -3.33941383 -3.36831305 -3.39721228 -3.4261115
-3.45501073 -3.48390995 -3.51280918 -3.5417084 -3.57060763 -3.59950685
-3.62840608 -3.6573053 -3.68620452 -3.71510375 -3.74400297 -3.7729022
-3.80180142 -3.83070065 -3.85959987 -3.8884991 -3.91739832 -3.94629755
-3.97519677 -4.004096 -4.03299522 -4.06189444 -4.09079367 -4.11969289]
[-3.29037232 -3.31927154 -3.34817077 -3.37706999 -3.40596921 -3.43486844
-3.46376766 -3.49266689 -3.52156611 -3.55046534 -3.57936456 -3.60826379
-3.63716301 -3.66606224 -3.69496146 -3.72386068 -3.75275991 -3.78165913
-3.81055836 -3.83945758 -3.86835681 -3.89725603 -3.92615526 -3.95505448
-3.98395371 -4.01285293 -4.04175216 -4.07065138 -4.0995506 -4.12844983]
[-3.29912925 -3.32802848 -3.3569277 -3.38582693 -3.41472615 -3.44362537
-3.4725246 -3.50142382 -3.53032305 -3.55922227 -3.5881215 -3.61702072
-3.64591995 -3.67481917 -3.7037184 -3.73261762 -3.76151684 -3.79041607
-3.81931529 -3.84821452 -3.87711374 -3.90601297 -3.93491219 -3.96381142
-3.99271064 -4.02160987 -4.05050909 -4.07940832 -4.10830754 -4.13720676]
[-3.30788619 -3.33678541 -3.36568464 -3.39458386 -3.42348309 -3.45238231
-3.48128153 -3.51018076 -3.53907998 -3.56797921 -3.59687843 -3.62577766
-3.65467688 -3.68357611 -3.71247533 -3.74137456 -3.77027378 -3.799173
-3.82807223 -3.85697145 -3.88587068 -3.9147699 -3.94366913 -3.97256835
-4.00146758 -4.0303668 -4.05926603 -4.08816525 -4.11706448 -4.1459637 ]
[-3.31664312 -3.34554235 -3.37444157 -3.4033408 -3.43224002 -3.46113925
-3.49003847 -3.51893769 -3.54783692 -3.57673614 -3.60563537 -3.63453459
-3.66343382 -3.69233304 -3.72123227 -3.75013149 -3.77903072 -3.80792994
-3.83682916 -3.86572839 -3.89462761 -3.92352684 -3.95242606 -3.98132529
-4.01022451 -4.03912374 -4.06802296 -4.09692219 -4.12582141 -4.15472064]
[-3.32540006 -3.35429928 -3.38319851 -3.41209773 -3.44099696 -3.46989618
-3.49879541 -3.52769463 -3.55659385 -3.58549308 -3.6143923 -3.64329153
-3.67219075 -3.70108998 -3.7299892 -3.75888843 -3.78778765 -3.81668688
-3.8455861 -3.87448532 -3.90338455 -3.93228377 -3.961183 -3.99008222
-4.01898145 -4.04788067 -4.0767799 -4.10567912 -4.13457835 -4.16347757]
[-3.33415699 -3.36305622 -3.39195544 -3.42085467 -3.44975389 -3.47865312
-3.50755234 -3.53645157 -3.56535079 -3.59425001 -3.62314924 -3.65204846
-3.68094769 -3.70984691 -3.73874614 -3.76764536 -3.79654459 -3.82544381
-3.85434304 -3.88324226 -3.91214148 -3.94104071 -3.96993993 -3.99883916
-4.02773838 -4.05663761 -4.08553683 -4.11443606 -4.14333528 -4.17223451]
[-3.34291393 -3.37181315 -3.40071238 -3.4296116 -3.45851083 -3.48741005
-3.51630928 -3.5452085 -3.57410773 -3.60300695 -3.63190617 -3.6608054
-3.68970462 -3.71860385 -3.74750307 -3.7764023 -3.80530152 -3.83420075
-3.86309997 -3.8919992 -3.92089842 -3.94979764 -3.97869687 -4.00759609
-4.03649532 -4.06539454 -4.09429377 -4.12319299 -4.15209222 -4.18099144]
[-3.35167086 -3.38057009 -3.40946931 -3.43836854 -3.46726776 -3.49616699
-3.52506621 -3.55396544 -3.58286466 -3.61176389 -3.64066311 -3.66956233
-3.69846156 -3.72736078 -3.75626001 -3.78515923 -3.81405846 -3.84295768
-3.87185691 -3.90075613 -3.92965536 -3.95855458 -3.9874538 -4.01635303
-4.04525225 -4.07415148 -4.1030507 -4.13194993 -4.16084915 -4.18974838]
[-3.3604278 -3.38932702 -3.41822625 -3.44712547 -3.4760247 -3.50492392
-3.53382315 -3.56272237 -3.5916216 -3.62052082 -3.64942005 -3.67831927
-3.70721849 -3.73611772 -3.76501694 -3.79391617 -3.82281539 -3.85171462
-3.88061384 -3.90951307 -3.93841229 -3.96731152 -3.99621074 -4.02510996
-4.05400919 -4.08290841 -4.11180764 -4.14070686 -4.16960609 -4.19850531]
[-3.36918473 -3.39808396 -3.42698318 -3.45588241 -3.48478163 -3.51368086
-3.54258008 -3.57147931 -3.60037853 -3.62927776 -3.65817698 -3.68707621
-3.71597543 -3.74487465 -3.77377388 -3.8026731 -3.83157233 -3.86047155
-3.88937078 -3.91827 -3.94716923 -3.97606845 -4.00496768 -4.0338669
-4.06276612 -4.09166535 -4.12056457 -4.1494638 -4.17836302 -4.20726225]
[-3.37794167 -3.40684089 -3.43574012 -3.46463934 -3.49353857 -3.52243779
-3.55133702 -3.58023624 -3.60913547 -3.63803469 -3.66693392 -3.69583314
-3.72473237 -3.75363159 -3.78253081 -3.81143004 -3.84032926 -3.86922849
-3.89812771 -3.92702694 -3.95592616 -3.98482539 -4.01372461 -4.04262384
-4.07152306 -4.10042228 -4.12932151 -4.15822073 -4.18711996 -4.21601918]
[-3.38669861 -3.41559783 -3.44449705 -3.47339628 -3.5022955 -3.53119473
-3.56009395 -3.58899318 -3.6178924 -3.64679163 -3.67569085 -3.70459008
-3.7334893 -3.76238853 -3.79128775 -3.82018697 -3.8490862 -3.87798542
-3.90688465 -3.93578387 -3.9646831 -3.99358232 -4.02248155 -4.05138077
-4.08028 -4.10917922 -4.13807844 -4.16697767 -4.19587689 -4.22477612]
[-3.39545554 -3.42435477 -3.45325399 -3.48215321 -3.51105244 -3.53995166
-3.56885089 -3.59775011 -3.62664934 -3.65554856 -3.68444779 -3.71334701
-3.74224624 -3.77114546 -3.80004469 -3.82894391 -3.85784313 -3.88674236
-3.91564158 -3.94454081 -3.97344003 -4.00233926 -4.03123848 -4.06013771
-4.08903693 -4.11793616 -4.14683538 -4.1757346 -4.20463383 -4.23353305]
[-3.40421248 -3.4331117 -3.46201093 -3.49091015 -3.51980937 -3.5487086
-3.57760782 -3.60650705 -3.63540627 -3.6643055 -3.69320472 -3.72210395
-3.75100317 -3.7799024 -3.80880162 -3.83770085 -3.86660007 -3.89549929
-3.92439852 -3.95329774 -3.98219697 -4.01109619 -4.03999542 -4.06889464
-4.09779387 -4.12669309 -4.15559232 -4.18449154 -4.21339076 -4.24228999]
[-3.41296941 -3.44186864 -3.47076786 -3.49966709 -3.52856631 -3.55746553
-3.58636476 -3.61526398 -3.64416321 -3.67306243 -3.70196166 -3.73086088
-3.75976011 -3.78865933 -3.81755856 -3.84645778 -3.87535701 -3.90425623
-3.93315545 -3.96205468 -3.9909539 -4.01985313 -4.04875235 -4.07765158
-4.1065508 -4.13545003 -4.16434925 -4.19324848 -4.2221477 -4.25104692]
[-3.42172635 -3.45062557 -3.4795248 -3.50842402 -3.53732325 -3.56622247
-3.59512169 -3.62402092 -3.65292014 -3.68181937 -3.71071859 -3.73961782
-3.76851704 -3.79741627 -3.82631549 -3.85521472 -3.88411394 -3.91301317
-3.94191239 -3.97081161 -3.99971084 -4.02861006 -4.05750929 -4.08640851
-4.11530774 -4.14420696 -4.17310619 -4.20200541 -4.23090464 -4.25980386]
[-3.43048328 -3.45938251 -3.48828173 -3.51718096 -3.54608018 -3.57497941
-3.60387863 -3.63277785 -3.66167708 -3.6905763 -3.71947553 -3.74837475
-3.77727398 -3.8061732 -3.83507243 -3.86397165 -3.89287088 -3.9217701
-3.95066933 -3.97956855 -4.00846777 -4.037367 -4.06626622 -4.09516545
-4.12406467 -4.1529639 -4.18186312 -4.21076235 -4.23966157 -4.2685608 ]
[-3.43924022 -3.46813944 -3.49703867 -3.52593789 -3.55483712 -3.58373634
-3.61263557 -3.64153479 -3.67043401 -3.69933324 -3.72823246 -3.75713169
-3.78603091 -3.81493014 -3.84382936 -3.87272859 -3.90162781 -3.93052704
-3.95942626 -3.98832549 -4.01722471 -4.04612393 -4.07502316 -4.10392238
-4.13282161 -4.16172083 -4.19062006 -4.21951928 -4.24841851 -4.27731773]
[-3.44799715 -3.47689638 -3.5057956 -3.53469483 -3.56359405 -3.59249328
-3.6213925 -3.65029173 -3.67919095 -3.70809017 -3.7369894 -3.76588862
-3.79478785 -3.82368707 -3.8525863 -3.88148552 -3.91038475 -3.93928397
-3.9681832 -3.99708242 -4.02598165 -4.05488087 -4.08378009 -4.11267932
-4.14157854 -4.17047777 -4.19937699 -4.22827622 -4.25717544 -4.28607467]
[-3.45675409 -3.48565331 -3.51455254 -3.54345176 -3.57235099 -3.60125021
-3.63014944 -3.65904866 -3.68794789 -3.71684711 -3.74574633 -3.77464556
-3.80354478 -3.83244401 -3.86134323 -3.89024246 -3.91914168 -3.94804091
-3.97694013 -4.00583936 -4.03473858 -4.06363781 -4.09253703 -4.12143625
-4.15033548 -4.1792347 -4.20813393 -4.23703315 -4.26593238 -4.2948316 ]
[-3.46551102 -3.49441025 -3.52330947 -3.5522087 -3.58110792 -3.61000715
-3.63890637 -3.6678056 -3.69670482 -3.72560405 -3.75450327 -3.78340249
-3.81230172 -3.84120094 -3.87010017 -3.89899939 -3.92789862 -3.95679784
-3.98569707 -4.01459629 -4.04349552 -4.07239474 -4.10129397 -4.13019319
-4.15909241 -4.18799164 -4.21689086 -4.24579009 -4.27468931 -4.30358854]]
plt
.
scatter
(
XX
,
YY
,
c
=
Z
,
s
=
30
,
cmap
=
plt
.
cm
.
Paired
)
plt
.
show
(
)
# 测试
pp
=
np
.
linspace
(
0
,
10
,
900
)
tt
=
np
.
linspace
(
0
,
10
,
900
)
uu
=
np
.
vstack
(
[
pp
,
tt
]
)
.
T
Z_
=
clf
.
decision_function
(
uu
)
.
reshape
(
XX
.
shape
)
plt
.
scatter
(
XX
,
YY
,
c
=
Z_
,
s
=
30
,
cmap
=
plt
.
cm
.
Paired
)
plt
.
show
(
)
