本文整理汇总了Python中sklearn.metrics.pairwise.rbf_kernel方法的典型用法代码示例。如果您正苦于以下问题:Python pairwise.rbf_kernel方法的具体用法?Python pairwise.rbf_kernel怎么用?Python pairwise.rbf_kernel使用的例子?那么, 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sklearn.metrics.pairwise
的用法示例。
在下文中一共展示了pairwise.rbf_kernel方法的15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: __kernel_definition__
# 需要导入模块: from sklearn.metrics import pairwise [as 别名]
# 或者: from sklearn.metrics.pairwise import rbf_kernel [as 别名]
def __kernel_definition__(self):
"""Select the kernel function
Returns
-------
kernel : a callable relative to selected kernel
"""
if hasattr(self.kernel, '__call__'):
return self.kernel
if self.kernel == 'rbf' or self.kernel == None:
return lambda X,Y : rbf_kernel(X,Y,self.rbf_gamma)
if self.kernel == 'poly':
return lambda X,Y : polynomial_kernel(X, Y, degree=self.degree, gamma=self.rbf_gamma, coef0=self.coef0)
if self.kernel == 'linear':
return lambda X,Y : linear_kernel(X,Y)
if self.kernel == 'precomputed':
return lambda X,Y : X
示例2: test_fastfood
# 需要导入模块: from sklearn.metrics import pairwise [as 别名]
# 或者: from sklearn.metrics.pairwise import rbf_kernel [as 别名]
def test_fastfood():
"""test that Fastfood fast approximates kernel on random data"""
# compute exact kernel
gamma = 10.0
kernel = rbf_kernel(X, Y, gamma=gamma)
sigma = np.sqrt(1 / (2 * gamma))
# approximate kernel mapping
ff_transform = Fastfood(sigma, n_components=1000, random_state=42)
pars = ff_transform.fit(X)
X_trans = pars.transform(X)
Y_trans = ff_transform.transform(Y)
kernel_approx = np.dot(X_trans, Y_trans.T)
print("approximation:", kernel_approx[:5, :5])
print("true kernel:", kernel[:5, :5])
assert_array_almost_equal(kernel, kernel_approx, decimal=1)
示例3: _build_kernel
# 需要导入模块: from sklearn.metrics import pairwise [as 别名]
# 或者: from sklearn.metrics.pairwise import rbf_kernel [as 别名]
def _build_kernel(x, kernel, gamma=None):
if kernel in {'pearson', 'spearman'}:
if kernel == 'spearman':
x = np.apply_along_axis(rankdata, 1, x)
return np.corrcoef(x)
if kernel in {'cosine', 'normalized_angle'}:
x = 1 - squareform(pdist(x, metric='cosine'))
if kernel == 'normalized_angle':
x = 1 - np.arccos(x, x)/np.pi
return x
if kernel == 'gaussian':
if gamma is None:
gamma = 1 / x.shape[1]
return rbf_kernel(x, gamma=gamma)
if callable(kernel):
return kernel(x)
raise ValueError("Unknown kernel '{0}'.".format(kernel))
示例4: test_pairwise_kernels_callable
# 需要导入模块: from sklearn.metrics import pairwise [as 别名]
# 或者: from sklearn.metrics.pairwise import rbf_kernel [as 别名]
def test_pairwise_kernels_callable():
# Test the pairwise_kernels helper function
# with a callable function, with given keywords.
rng = np.random.RandomState(0)
X = rng.random_sample((5, 4))
Y = rng.random_sample((2, 4))
metric = callable_rbf_kernel
kwds = {'gamma': 0.1}
K1 = pairwise_kernels(X, Y=Y, metric=metric, **kwds)
K2 = rbf_kernel(X, Y=Y, **kwds)
assert_array_almost_equal(K1, K2)
# callable function, X=Y
K1 = pairwise_kernels(X, Y=X, metric=metric, **kwds)
K2 = rbf_kernel(X, Y=X, **kwds)
assert_array_almost_equal(K1, K2)
示例5: test_spectral_embedding_unnormalized
# 需要导入模块: from sklearn.metrics import pairwise [as 别名]
# 或者: from sklearn.metrics.pairwise import rbf_kernel [as 别名]
def test_spectral_embedding_unnormalized():
# Test that spectral_embedding is also processing unnormalized laplacian
# correctly
random_state = np.random.RandomState(36)
data = random_state.randn(10, 30)
sims = rbf_kernel(data)
n_components = 8
embedding_1 = spectral_embedding(sims,
norm_laplacian=False,
n_components=n_components,
drop_first=False)
# Verify using manual computation with dense eigh
laplacian, dd = csgraph.laplacian(sims, normed=False,
return_diag=True)
_, diffusion_map = eigh(laplacian)
embedding_2 = diffusion_map.T[:n_components]
embedding_2 = _deterministic_vector_sign_flip(embedding_2).T
assert_array_almost_equal(embedding_1, embedding_2)
示例6: test_spectral_embedding_first_eigen_vector
# 需要导入模块: from sklearn.metrics import pairwise [as 别名]
# 或者: from sklearn.metrics.pairwise import rbf_kernel [as 别名]
def test_spectral_embedding_first_eigen_vector():
# Test that the first eigenvector of spectral_embedding
# is constant and that the second is not (for a connected graph)
random_state = np.random.RandomState(36)
data = random_state.randn(10, 30)
sims = rbf_kernel(data)
n_components = 2
for seed in range(10):
embedding = spectral_embedding(sims,
norm_laplacian=False,
n_components=n_components,
drop_first=False,
random_state=seed)
assert np.std(embedding[:, 0]) == pytest.approx(0)
assert np.std(embedding[:, 1]) > 1e-3
示例7: test_svr_predict
# 需要导入模块: from sklearn.metrics import pairwise [as 别名]
# 或者: from sklearn.metrics.pairwise import rbf_kernel [as 别名]
def test_svr_predict():
# Test SVR's decision_function
# Sanity check, test that predict implemented in python
# returns the same as the one in libsvm
X = iris.data
y = iris.target
# linear kernel
reg = svm.SVR(kernel='linear', C=0.1).fit(X, y)
dec = np.dot(X, reg.coef_.T) + reg.intercept_
assert_array_almost_equal(dec.ravel(), reg.predict(X).ravel())
# rbf kernel
reg = svm.SVR(kernel='rbf', gamma=1).fit(X, y)
rbfs = rbf_kernel(X, reg.support_vectors_, gamma=reg.gamma)
dec = np.dot(rbfs, reg.dual_coef_.T) + reg.intercept_
assert_array_almost_equal(dec.ravel(), reg.predict(X).ravel())
示例8: test_nystroem_default_parameters
# 需要导入模块: from sklearn.metrics import pairwise [as 别名]
# 或者: from sklearn.metrics.pairwise import rbf_kernel [as 别名]
def test_nystroem_default_parameters():
rnd = np.random.RandomState(42)
X = rnd.uniform(size=(10, 4))
# rbf kernel should behave as gamma=None by default
# aka gamma = 1 / n_features
nystroem = Nystroem(n_components=10)
X_transformed = nystroem.fit_transform(X)
K = rbf_kernel(X, gamma=None)
K2 = np.dot(X_transformed, X_transformed.T)
assert_array_almost_equal(K, K2)
# chi2 kernel should behave as gamma=1 by default
nystroem = Nystroem(kernel='chi2', n_components=10)
X_transformed = nystroem.fit_transform(X)
K = chi2_kernel(X, gamma=1)
K2 = np.dot(X_transformed, X_transformed.T)
assert_array_almost_equal(K, K2)
示例9: test_gridsearch_pipeline_precomputed
# 需要导入模块: from sklearn.metrics import pairwise [as 别名]
# 或者: from sklearn.metrics.pairwise import rbf_kernel [as 别名]
def test_gridsearch_pipeline_precomputed():
# Test if we can do a grid-search to find parameters to separate
# circles with a perceptron model using a precomputed kernel.
X, y = make_circles(n_samples=400, factor=.3, noise=.05,
random_state=0)
kpca = KernelPCA(kernel="precomputed", n_components=2)
pipeline = Pipeline([("kernel_pca", kpca),
("Perceptron", Perceptron(max_iter=5))])
param_grid = dict(Perceptron__max_iter=np.arange(1, 5))
grid_search = GridSearchCV(pipeline, cv=3, param_grid=param_grid)
X_kernel = rbf_kernel(X, gamma=2.)
grid_search.fit(X_kernel, y)
assert_equal(grid_search.best_score_, 1)
# 0.23. warning about tol not having its correct default value.
示例10: _fit
# 需要导入模块: from sklearn.metrics import pairwise [as 别名]
# 或者: from sklearn.metrics.pairwise import rbf_kernel [as 别名]
def _fit(self, X):
self.estimator_ = OneClassSVM(
cache_size = self.cache_size,
gamma = self.gamma,
max_iter = self.max_iter,
nu = self.nu,
shrinking = self.shrinking,
tol = self.tol
).fit(X)
l, = self.support_.shape
self.nu_l_ = self.nu * l
Q = rbf_kernel(
self.support_vectors_, gamma=self.estimator_._gamma
)
c2 = (self.dual_coef_ @ Q @ self.dual_coef_.T)[0, 0]
self.R2_ = c2 + 2. * self.intercept_[0] + 1.
return self
示例11: GP
# 需要导入模块: from sklearn.metrics import pairwise [as 别名]
# 或者: from sklearn.metrics.pairwise import rbf_kernel [as 别名]
def GP(seq_length=30, num_samples=28*5*100, num_signals=1, scale=0.1, kernel='rbf', **kwargs):
# the shape of the samples is num_samples x seq_length x num_signals
samples = np.empty(shape=(num_samples, seq_length, num_signals))
#T = np.arange(seq_length)/seq_length # note, between 0 and 1
T = np.arange(seq_length) # note, not between 0 and 1
if kernel == 'periodic':
cov = periodic_kernel(T)
elif kernel =='rbf':
cov = rbf_kernel(T.reshape(-1, 1), gamma=scale)
else:
raise NotImplementedError
# scale the covariance
cov *= 0.2
# define the distribution
mu = np.zeros(seq_length)
print(np.linalg.det(cov))
distribution = multivariate_normal(mean=np.zeros(cov.shape[0]), cov=cov)
pdf = distribution.logpdf
# now generate samples
for i in range(num_signals):
samples[:, :, i] = distribution.rvs(size=num_samples)
return samples, pdf
示例12: gaussian
# 需要导入模块: from sklearn.metrics import pairwise [as 别名]
# 或者: from sklearn.metrics.pairwise import rbf_kernel [as 别名]
def gaussian(x, workers=None):
"""Default medial gaussian kernel similarity calculation"""
l1 = pairwise_distances(X=x, metric="l1", n_jobs=workers)
n = l1.shape[0]
med = np.median(
np.lib.stride_tricks.as_strided(
l1, (n - 1, n + 1), (l1.itemsize * (n + 1), l1.itemsize)
)[:, 1:]
)
# prevents division by zero when used on label vectors
med = med if med else 1
gamma = 1.0 / (2 * (med ** 2))
return rbf_kernel(x, gamma=gamma)
# p-value computation
示例13: test_affinity_mat_rbf
# 需要导入模块: from sklearn.metrics import pairwise [as 别名]
# 或者: from sklearn.metrics.pairwise import rbf_kernel [as 别名]
def test_affinity_mat_rbf(data):
v1_data = data['fit_data'][0]
spectral = data['spectral']
distances = cdist(v1_data, v1_data)
gamma = 1 / (2 * np.median(distances) ** 2)
true_kernel = rbf_kernel(v1_data, gamma=gamma)
g_kernel = spectral._affinity_mat(v1_data)
assert(g_kernel.shape[0] == data['n_fit'])
assert(g_kernel.shape[1] == data['n_fit'])
for ind1 in range(g_kernel.shape[0]):
for ind2 in range(g_kernel.shape[1]):
assert np.abs(true_kernel[ind1][ind2]
- g_kernel[ind1][ind2]) < 0.000001
示例14: test_affinity_mat_rbf2
# 需要导入模块: from sklearn.metrics import pairwise [as 别名]
# 或者: from sklearn.metrics.pairwise import rbf_kernel [as 别名]
def test_affinity_mat_rbf2(data):
v1_data = data['fit_data'][0]
gamma = 1
spectral = MultiviewSpectralClustering(random_state=RANDOM_STATE,
gamma=gamma)
distances = cdist(v1_data, v1_data)
gamma = 1 / (2 * np.median(distances) ** 2)
true_kernel = rbf_kernel(v1_data, gamma=1)
g_kernel = spectral._affinity_mat(v1_data)
assert(g_kernel.shape[0] == data['n_fit'])
assert(g_kernel.shape[1] == data['n_fit'])
for ind1 in range(g_kernel.shape[0]):
for ind2 in range(g_kernel.shape[1]):
assert np.abs(true_kernel[ind1][ind2]
- g_kernel[ind1][ind2]) < 0.000001
示例15: test_compute_eigs
# 需要导入模块: from sklearn.metrics import pairwise [as 别名]
# 或者: from sklearn.metrics.pairwise import rbf_kernel [as 别名]
def test_compute_eigs(data):
v1_data = data['fit_data'][0]
g_kernel = rbf_kernel(v1_data, v1_data)
n_clusts = data['n_clusters']
n_fit = data['n_fit']
spectral = data['spectral']
eigs = spectral._compute_eigs(g_kernel)
assert(eigs.shape[0] == n_fit)
assert(eigs.shape[1] == n_clusts)
col_mags = np.linalg.norm(eigs, axis=0)
for val in col_mags:
assert(np.abs(val - 1) < 0.000001)