In [65]:
from sklearn import datasets
X, y = datasets.make_classification(n_samples=10000, n_features=2, n_informative=2,n_repeated=0,n_redundant=0, n_classes=2)
In [66]:
print X.shape, y.shape
(10000, 2) (10000,)
In [67]:
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05)
In [68]:
print X_train.shape, y_train.shape
(9500, 2) (9500,)
In [69]:
from sklearn.svm import SVC
clf = SVC(kernel='linear')
In [70]:
clf = clf.fit(X_train, y_train)
In [71]:
clf
Out[71]:
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape=None, degree=3, gamma='auto', kernel='linear',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
In [77]:
clf.coef_
fxy = X_test.T[0] * 0.05390999 + X_test.T[1]*1.44377091
In [74]:
y_pred = clf.predict(X_test)
In [75]:
print y_test
[0 0 0 0 1 1 1 1 0 1 0 1 1 0 0 0 1 1 1 0 1 0 1 1 0 0 0 0 1 0 0 1 0 0 0 1 1
1 1 0 0 0 1 1 0 0 1 0 1 1 1 0 1 0 1 0 1 0 0 0 1 1 1 0 1 0 1 0 0 0 0 1 1 0
1 1 1 1 0 0 0 0 0 1 1 1 0 0 0 0 0 1 0 1 0 1 0 1 0 0 0 1 0 1 0 1 0 1 1 0 1
0 0 1 1 1 1 1 0 1 0 1 1 0 1 0 0 0 1 1 1 0 1 0 1 0 1 0 0 1 1 0 0 0 0 0 0 1
1 0 1 0 0 0 0 1 1 0 0 0 1 0 1 0 0 1 0 1 0 1 1 1 0 0 1 0 0 0 1 1 0 1 1 0 1
1 1 0 0 1 1 0 0 1 1 0 1 0 1 1 1 1 0 1 1 0 1 1 0 1 1 1 0 1 1 1 0 1 1 0 0 0
0 0 1 1 1 0 0 0 0 0 0 1 0 1 1 1 1 0 0 0 1 1 0 1 0 1 1 0 1 1 0 1 1 0 1 1 0
0 0 1 1 0 1 1 1 0 1 1 1 1 0 1 0 0 1 1 1 1 0 1 0 1 1 0 1 1 1 1 1 0 1 1 1 1
1 0 0 1 1 1 1 1 0 1 0 0 0 1 1 1 0 0 1 1 1 0 1 0 0 0 1 0 1 1 0 0 1 1 1 1 1
0 1 0 0 1 1 0 1 0 1 1 1 0 0 0 0 0 0 1 0 0 1 1 0 0 0 1 1 1 1 0 1 0 0 1 1 1
1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 0 1 0 1 0 0 1 1 1 0 0 1 1 1 0 0 0 1 0 0 0
1 1 0 1 1 1 0 0 1 1 0 0 1 0 1 0 1 1 0 0 0 0 0 0 1 1 0 1 0 0 1 1 1 0 0 0 0
1 1 1 1 1 0 0 0 1 1 0 0 0 1 0 1 0 1 1 1 0 1 1 0 0 1 1 0 1 1 0 1 1 0 0 0 0
0 1 0 1 1 0 0 1 0 1 0 0 1 0 0 0 0 1 0]
In [83]:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure(figsize=(20,20))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(X_test.T[0][y_test == 1],X_test.T[1][y_test == 1],y_test[y_test == 1], c='b')
ax.scatter(X_test.T[0][y_test == 0],X_test.T[1][y_test == 0],y_test[y_test == 0], c='r')
Out[83]:
<mpl_toolkits.mplot3d.art3d.Patch3DCollection at 0x7f818762c5d0>
In [79]:
fxy
Out[79]:
array([-1.85772484, -2.03713246, -1.73799167, -0.56599725, 3.10887447,
0.09149489, 1.44314323, 0.93566345, -1.70386602, 2.34891265,
1.04193115, 1.99925874, 1.56944465, -1.68166295, -3.58463468,
-0.22337617, 0.22977519, 2.25597716, 2.75933594, -1.49768848,
0.74868966, -1.16893567, 2.74590296, 4.02776905, -2.69222678,
-1.4723831 , -1.53413688, -2.10740474, 1.25765434, -1.42720993,
-0.88944446, 0.49761041, -1.98676171, -1.22299303, -2.86276199,
-0.10933267, 0.32424519, -1.00350864, 1.07815676, -1.55925241,
-1.09402194, -1.68445853, 0.53614289, 0.73628945, -1.00540325,
0.34914406, -1.45183734, -0.54475339, 0.12284056, 4.57601638,
5.04772587, -0.79502459, -0.43429223, -1.25223572, 2.3182777 ,
-2.6650258 , -0.93265312, -0.59899664, -1.61421621, -2.63449163,
-0.59808679, -0.74773957, 2.33795638, -0.87253117, -1.12802294,
-2.4595588 , 0.47484859, -1.58404545, -1.8881585 , -0.96542695,
-3.43951715, 0.45031694, 1.12996918, -2.25028025, 0.78676403,
-0.1521939 , 1.88761104, -0.93994878, 4.82707926, -1.58455994,
-1.47657497, -1.15351077, -1.07949264, -1.20473363, 2.87635306,
0.58155145, 1.02550226, -1.12473924, -2.06890276, -1.42545756,
-2.60353552, 1.53641514, -1.66146634, 1.37612882, -0.55530881,
3.40936725, -2.00652775, 3.42554883, -3.31797447, 0.47276628,
-1.97164571, -1.38592399, -1.19483907, 0.57952876, -1.74543787,
4.20414045, -2.31202568, 1.57316514, 1.31585646, -1.42839893,
2.82851915, -3.03713577, -1.64445726, 2.85401749, -0.2775073 ,
-1.89145124, 1.2992946 , 2.33607954, -1.18047593, 1.89564494,
-0.24636645, 4.40089836, 1.72617222, -1.87628783, 2.06185552,
-2.11168199, 0.53868581, -1.72492903, -1.44331931, 6.39120209,
0.62602149, -2.83614623, 3.11863773, -0.40443537, 0.06668322,
-1.21606371, 3.21523675, -0.22642744, -3.26562739, 3.05842815,
2.05547442, -0.58301627, -2.97759046, -1.21868905, -1.31449501,
-1.24447557, -1.39940831, 5.0204982 , 0.20858931, -1.61637243,
2.74467806, -1.21875028, -1.76422151, -0.67834323, -0.69613575,
-0.15562467, 2.86000216, -0.89383732, -1.8083195 , -2.4262491 ,
3.02030919, -1.58064472, 1.84882623, -1.49855569, 0.61145745,
1.27204883, -3.37529387, -0.2678598 , -1.10960648, 0.92410289,
2.09970544, 0.97915512, -0.22381797, -1.49579081, 1.06391968,
-2.57753093, -2.26150939, -0.25288734, 0.70183796, 1.670442 ,
-1.52782613, 1.76862967, 0.09646208, -2.63722739, 4.45541492,
0.82012787, 1.70407894, -1.60239762, -2.73017179, -1.31375103,
-2.88474476, -2.17063409, -0.23295872, 2.39558976, -0.16865225,
0.80507531, 0.19275393, -0.76137982, -5.21002415, -0.76934167,
1.7533606 , 0.58986527, -1.31790178, 2.80569164, 2.94864516,
-1.29308568, -0.8501434 , 0.25973608, -1.2370245 , 2.76582265,
0.2227845 , 0.7749762 , -2.34312691, 1.62022582, -0.73840987,
3.77754034, -1.8407381 , 1.28685566, -0.95772179, -0.08485133,
-1.24091482, -2.02053682, -0.61312761, -0.93802982, 1.27123709,
2.29359119, 1.37489575, -1.79160285, 0.54039744, -4.91889271,
1.49633883, -0.86348839, -1.21306386, 1.54275095, -1.95148648,
2.15745844, 1.11623574, 0.88507354, 2.95949746, -0.44647268,
-1.96005068, -0.93943816, 1.92107649, 3.62370687, -0.85532619,
0.65759629, -1.42602353, 2.6464037 , 1.92669762, -0.73849774,
-1.41071403, 2.29293581, -1.89355067, -0.1115986 , 2.77240614,
-3.32554007, -0.21371662, -0.39294308, -2.93254602, -0.25861252,
-0.32663506, 2.02276512, 0.69158404, -0.6864624 , -0.90508742,
2.95338446, -2.37397939, -1.05873094, 2.8068621 , 3.64397589,
0.47063088, 0.97882645, -2.14633472, 3.83945983, -0.53551677,
-0.85286642, 0.3457059 , 2.25735317, -0.48831907, 0.30079398,
-1.30843036, -0.94167553, -1.86676346, -0.02862307, 1.47313307,
-0.78621902, 1.56593206, -0.03377011, -1.13222443, -0.1318761 ,
3.21506725, -2.31042064, -2.61603028, 0.97945539, 1.55636999,
0.46589031, 2.38947997, -1.25376223, -2.23896699, 2.62674634,
4.19954415, 4.49760973, 3.21228381, -1.17085031, -0.51144268,
3.39349146, 1.76813596, -2.31391672, -0.34232039, 3.33122398,
-0.09886334, 0.39707806, -0.47581757, -0.98714956, 0.91237716,
2.32919302, 6.01322563, -1.14565412, 1.53372861, -1.55879044,
-3.93797478, -2.24367166, -0.03066717, -0.69589382, 3.46962572,
0.72101249, 0.46691175, -1.61180143, -3.12098672, 0.29778506,
2.31416043, 1.64281611, 1.88496202, -1.8026261 , 2.12676533,
-1.0694828 , 0.27751427, 0.71515606, 1.04780665, -2.64990971,
1.84813697, -2.21932261, 0.43339759, -2.63090899, -1.29350394,
-1.43114921, -0.78173038, -2.14303199, -1.64645785, 0.24904247,
-2.4248746 , 2.33262519, -1.34688634, -1.69939559, 3.31147793,
-1.36369582, -1.11798945, -0.9048655 , 0.76954512, -1.16761336,
1.67959289, -0.16306159, 1.82307797, -1.48594496, 2.61357661,
-0.58000957, -0.57960492, 6.42357477, 3.28833769, 2.06292065,
1.77221102, -1.42693154, -3.0729614 , -0.28936754, -2.14997408,
-1.97792842, 1.92359895, 0.04467625, 3.75891919, -1.75494531,
-1.15201557, -1.5108533 , -2.37321671, 0.85974086, 3.84372976,
1.33557361, -1.05066731, -1.28150454, -1.55800651, 1.79418431,
-1.07777829, -4.08470455, 0.50160843, 6.45689304, -1.24512086,
-1.26554071, -1.14151767, 3.37442597, 1.44098385, 0.86187106,
-1.96187534, -1.91455802, -0.37273998, 1.91355974, -0.80787749,
-2.95091124, 0.05054755, 0.91791209, -1.9359218 , -0.85861161,
0.4509615 , 1.83682795, 2.92384962, -0.84270308, -1.94285545,
2.62879542, -1.65076018, -0.79267282, -1.24629263, 0.68027092,
-3.06025831, 0.55567321, -3.56006253, -1.34027435, 1.29537026,
-1.74828098, -0.09455522, -2.4379866 , -1.26257814, -1.3402734 ,
-1.41392547, 2.37944899, 1.79943738, -0.57089872, 3.66886207,
0.47959041, -2.87184999, 2.71137446, 2.64913204, 3.15649799,
-4.69919197, -1.99218698, -0.30695872, -1.38554218, 0.222889 ,
1.9780409 , 2.98326426, 4.61864188, 3.00200989, -0.11381509,
0.16653171, -1.6281692 , -1.8103118 , 4.71697609, -1.83975745,
-0.32228099, -1.57131459, 1.23865188, -2.21248596, 2.62240799,
-1.21282965, 3.44377869, 2.96748761, 2.23098606, -1.56840026,
-1.0819231 , -1.31299778, -0.82306442, 0.23527903, 2.2357239 ,
3.87059688, -2.67284265, 0.86074926, 4.03997315, -1.8308677 ,
4.38261951, 0.7872619 , -2.15517288, -2.0856926 , 1.42045692,
-0.85904114, -0.54618229, 2.59064947, -0.41331548, -1.76865851,
-0.2659286 , -0.6395213 , -2.83233188, -1.01937742, -1.90964638,
0.35094178, -1.2905772 , -0.16194998, -1.18416797, -2.09521854,
-2.54989741, -1.06646015, -0.58638132, 1.64808504, -1.86296994])
In [ ]:
Content source: zhongdai/learn-scikit
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