本文整理汇总了Python中Orange.data.Table.shuffle方法的典型用法代码示例。如果您正苦于以下问题：Python Table.shuffle方法的具体用法？Python Table.shuffle怎么用？Python Table.shuffle使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Orange.data.Table的用法示例。
在下文中一共展示了Table.shuffle方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: SVMTest
# 需要导入模块: from Orange.data import Table [as 别名]
# 或者: from Orange.data.Table import shuffle [as 别名]
class SVMTest(unittest.TestCase):
def setUp(self):
self.data = Table('ionosphere')
self.data.shuffle()
def test_SVM(self):
learn = SVMLearner()
res = CrossValidation(self.data, [learn], k=2)
self.assertGreater(CA(res)[0], 0.9)
def test_LinearSVM(self):
learn = LinearSVMLearner()
res = CrossValidation(self.data, [learn], k=2)
self.assertTrue(0.8 < CA(res)[0] < 0.9)
def test_NuSVM(self):
learn = NuSVMLearner(nu=0.01)
res = CrossValidation(self.data, [learn], k=2)
self.assertGreater(CA(res)[0], 0.9)
def test_SVR(self):
nrows, ncols = 200, 5
X = np.random.rand(nrows, ncols)
y = X.dot(np.random.rand(ncols))
data = Table(X, y)
learn = SVRLearner(kernel='rbf', gamma=0.1)
res = CrossValidation(data, [learn], k=2)
self.assertLess(RMSE(res)[0], 0.15)
def test_NuSVR(self):
nrows, ncols = 200, 5
X = np.random.rand(nrows, ncols)
y = X.dot(np.random.rand(ncols))
data = Table(X, y)
learn = NuSVRLearner(kernel='rbf', gamma=0.1)
res = CrossValidation(data, [learn], k=2)
self.assertLess(RMSE(res)[0], 0.1)
def test_OneClassSVM(self):
np.random.seed(42)
domain = Domain((ContinuousVariable("c1"), ContinuousVariable("c2")))
X_in = 0.3 * np.random.randn(40, 2)
X_out = np.random.uniform(low=-4, high=4, size=(20, 2))
X_all = Table(domain, np.r_[X_in + 2, X_in - 2, X_out])
n_true_in = len(X_in) * 2
n_true_out = len(X_out)
nu = 0.2
learner = OneClassSVMLearner(nu=nu)
cls = learner(X_all)
y_pred = cls(X_all)
n_pred_out_all = np.sum(y_pred == -1)
n_pred_in_true_in = np.sum(y_pred[:n_true_in] == 1)
n_pred_out_true_out = np.sum(y_pred[- n_true_out:] == -1)
self.assertTrue(all(np.absolute(y_pred) == 1))
self.assertTrue(n_pred_out_all <= len(X_all) * nu)
self.assertTrue(np.absolute(n_pred_out_all - n_true_out) < 2)
self.assertTrue(np.absolute(n_pred_in_true_in - n_true_in) < 4)
self.assertTrue(np.absolute(n_pred_out_true_out - n_true_out) < 3)