本文整理匯總了Python中sklearn.kernel_approximation.AdditiveChi2Sampler方法的典型用法代碼示例。如果您正苦於以下問題：Python kernel_approximation.AdditiveChi2Sampler方法的具體用法？Python kernel_approximation.AdditiveChi2Sampler怎麽用？Python kernel_approximation.AdditiveChi2Sampler使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類sklearn.kernel_approximation的用法示例。
在下文中一共展示了kernel_approximation.AdditiveChi2Sampler方法的5個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_input_validation
# 需要導入模塊: from sklearn import kernel_approximation [as 別名]
# 或者: from sklearn.kernel_approximation import AdditiveChi2Sampler [as 別名]
def test_input_validation():
# Regression test: kernel approx. transformers should work on lists
# No assertions; the old versions would simply crash
X = [[1, 2], [3, 4], [5, 6]]
AdditiveChi2Sampler().fit(X).transform(X)
SkewedChi2Sampler().fit(X).transform(X)
RBFSampler().fit(X).transform(X)
X = csr_matrix(X)
RBFSampler().fit(X).transform(X) 示例2: __init__
# 需要導入模塊: from sklearn import kernel_approximation [as 別名]
# 或者: from sklearn.kernel_approximation import AdditiveChi2Sampler [as 別名]
def __init__(self, sample_steps=2, sample_interval=None):
self._hyperparams = {
'sample_steps': sample_steps,
'sample_interval': sample_interval}
self._wrapped_model = Op(**self._hyperparams) 示例3: chi_squared_projection
# 需要導入模塊: from sklearn import kernel_approximation [as 別名]
# 或者: from sklearn.kernel_approximation import AdditiveChi2Sampler [as 別名]
def chi_squared_projection(features):
chi2_feature = AdditiveChi2Sampler()
X_transformed = chi2_feature.fit_transform(features)
X_transformed = X_transformed.tocsr()
return X_transformed 示例4: test_objectmapper
# 需要導入模塊: from sklearn import kernel_approximation [as 別名]
# 或者: from sklearn.kernel_approximation import AdditiveChi2Sampler [as 別名]
def test_objectmapper(self):
df = pdml.ModelFrame([])
self.assertIs(df.kernel_approximation.AdditiveChi2Sampler,
ka.AdditiveChi2Sampler)
self.assertIs(df.kernel_approximation.Nystroem, ka.Nystroem)
self.assertIs(df.kernel_approximation.RBFSampler, ka.RBFSampler)
self.assertIs(df.kernel_approximation.SkewedChi2Sampler,
ka.SkewedChi2Sampler) 示例5: test_additive_chi2_sampler
# 需要導入模塊: from sklearn import kernel_approximation [as 別名]
# 或者: from sklearn.kernel_approximation import AdditiveChi2Sampler [as 別名]
def test_additive_chi2_sampler():
# test that AdditiveChi2Sampler approximates kernel on random data
# compute exact kernel
# abbreviations for easier formula
X_ = X[:, np.newaxis, :]
Y_ = Y[np.newaxis, :, :]
large_kernel = 2 * X_ * Y_ / (X_ + Y_)
# reduce to n_samples_x x n_samples_y by summing over features
kernel = (large_kernel.sum(axis=2))
# approximate kernel mapping
transform = AdditiveChi2Sampler(sample_steps=3)
X_trans = transform.fit_transform(X)
Y_trans = transform.transform(Y)
kernel_approx = np.dot(X_trans, Y_trans.T)
assert_array_almost_equal(kernel, kernel_approx, 1)
X_sp_trans = transform.fit_transform(csr_matrix(X))
Y_sp_trans = transform.transform(csr_matrix(Y))
assert_array_equal(X_trans, X_sp_trans.A)
assert_array_equal(Y_trans, Y_sp_trans.A)
# test error is raised on negative input
Y_neg = Y.copy()
Y_neg[0, 0] = -1
assert_raises(ValueError, transform.transform, Y_neg)
# test error on invalid sample_steps
transform = AdditiveChi2Sampler(sample_steps=4)
assert_raises(ValueError, transform.fit, X)
# test that the sample interval is set correctly
sample_steps_available = [1, 2, 3]
for sample_steps in sample_steps_available:
# test that the sample_interval is initialized correctly
transform = AdditiveChi2Sampler(sample_steps=sample_steps)
assert_equal(transform.sample_interval, None)
# test that the sample_interval is changed in the fit method
transform.fit(X)
assert_not_equal(transform.sample_interval_, None)
# test that the sample_interval is set correctly
sample_interval = 0.3
transform = AdditiveChi2Sampler(sample_steps=4,
sample_interval=sample_interval)
assert_equal(transform.sample_interval, sample_interval)
transform.fit(X)
assert_equal(transform.sample_interval_, sample_interval)