本文整理匯總了Python中sklearn.cluster.FeatureAgglomeration方法的典型用法代碼示例。如果您正苦於以下問題：Python cluster.FeatureAgglomeration方法的具體用法？Python cluster.FeatureAgglomeration怎麽用？Python cluster.FeatureAgglomeration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.cluster的用法示例。
在下文中一共展示了cluster.FeatureAgglomeration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_linkage_misc
# 需要導入模塊: from sklearn import cluster [as 別名]
# 或者: from sklearn.cluster import FeatureAgglomeration [as 別名]
def test_linkage_misc():
# Misc tests on linkage
rng = np.random.RandomState(42)
X = rng.normal(size=(5, 5))
assert_raises(ValueError, AgglomerativeClustering(linkage='foo').fit, X)
assert_raises(ValueError, linkage_tree, X, linkage='foo')
assert_raises(ValueError, linkage_tree, X, connectivity=np.ones((4, 4)))
# Smoke test FeatureAgglomeration
FeatureAgglomeration().fit(X)
# test hierarchical clustering on a precomputed distances matrix
dis = cosine_distances(X)
res = linkage_tree(dis, affinity="precomputed")
assert_array_equal(res[0], linkage_tree(X, affinity="cosine")[0])
# test hierarchical clustering on a precomputed distances matrix
res = linkage_tree(X, affinity=manhattan_distances)
assert_array_equal(res[0], linkage_tree(X, affinity="manhattan")[0]) 示例2: test_ward_agglomeration
# 需要導入模塊: from sklearn import cluster [as 別名]
# 或者: from sklearn.cluster import FeatureAgglomeration [as 別名]
def test_ward_agglomeration():
# Check that we obtain the correct solution in a simplistic case
rng = np.random.RandomState(0)
mask = np.ones([10, 10], dtype=np.bool)
X = rng.randn(50, 100)
connectivity = grid_to_graph(*mask.shape)
agglo = FeatureAgglomeration(n_clusters=5, connectivity=connectivity)
agglo.fit(X)
assert np.size(np.unique(agglo.labels_)) == 5
X_red = agglo.transform(X)
assert X_red.shape[1] == 5
X_full = agglo.inverse_transform(X_red)
assert np.unique(X_full[0]).size == 5
assert_array_almost_equal(agglo.transform(X_full), X_red)
# Check that fitting with no samples raises a ValueError
assert_raises(ValueError, agglo.fit, X[:0]) 示例3: test_objectmapper
# 需要導入模塊: from sklearn import cluster [as 別名]
# 或者: from sklearn.cluster import FeatureAgglomeration [as 別名]
def test_objectmapper(self):
df = pdml.ModelFrame([])
self.assertIs(df.cluster.AffinityPropagation, cluster.AffinityPropagation)
self.assertIs(df.cluster.AgglomerativeClustering, cluster.AgglomerativeClustering)
self.assertIs(df.cluster.Birch, cluster.Birch)
self.assertIs(df.cluster.DBSCAN, cluster.DBSCAN)
self.assertIs(df.cluster.FeatureAgglomeration, cluster.FeatureAgglomeration)
self.assertIs(df.cluster.KMeans, cluster.KMeans)
self.assertIs(df.cluster.MiniBatchKMeans, cluster.MiniBatchKMeans)
self.assertIs(df.cluster.MeanShift, cluster.MeanShift)
self.assertIs(df.cluster.SpectralClustering, cluster.SpectralClustering)
self.assertIs(df.cluster.bicluster.SpectralBiclustering,
cluster.bicluster.SpectralBiclustering)
self.assertIs(df.cluster.bicluster.SpectralCoclustering,
cluster.bicluster.SpectralCoclustering) 示例4: test_ward_agglomeration
# 需要導入模塊: from sklearn import cluster [as 別名]
# 或者: from sklearn.cluster import FeatureAgglomeration [as 別名]
def test_ward_agglomeration():
# Check that we obtain the correct solution in a simplistic case
rng = np.random.RandomState(0)
mask = np.ones([10, 10], dtype=np.bool)
X = rng.randn(50, 100)
connectivity = grid_to_graph(*mask.shape)
agglo = FeatureAgglomeration(n_clusters=5, connectivity=connectivity)
agglo.fit(X)
assert_true(np.size(np.unique(agglo.labels_)) == 5)
X_red = agglo.transform(X)
assert_true(X_red.shape[1] == 5)
X_full = agglo.inverse_transform(X_red)
assert_true(np.unique(X_full[0]).size == 5)
assert_array_almost_equal(agglo.transform(X_full), X_red)
# Check that fitting with no samples raises a ValueError
assert_raises(ValueError, agglo.fit, X[:0]) 示例5: test_feature_agglomeration
# 需要導入模塊: from sklearn import cluster [as 別名]
# 或者: from sklearn.cluster import FeatureAgglomeration [as 別名]
def test_feature_agglomeration():
n_clusters = 1
X = np.array([0, 0, 1]).reshape(1, 3) # (n_samples, n_features)
agglo_mean = FeatureAgglomeration(n_clusters=n_clusters,
pooling_func=np.mean)
agglo_median = FeatureAgglomeration(n_clusters=n_clusters,
pooling_func=np.median)
assert_no_warnings(agglo_mean.fit, X)
assert_no_warnings(agglo_median.fit, X)
assert np.size(np.unique(agglo_mean.labels_)) == n_clusters
assert np.size(np.unique(agglo_median.labels_)) == n_clusters
assert np.size(agglo_mean.labels_) == X.shape[1]
assert np.size(agglo_median.labels_) == X.shape[1]
# Test transform
Xt_mean = agglo_mean.transform(X)
Xt_median = agglo_median.transform(X)
assert Xt_mean.shape[1] == n_clusters
assert Xt_median.shape[1] == n_clusters
assert Xt_mean == np.array([1 / 3.])
assert Xt_median == np.array([0.])
# Test inverse transform
X_full_mean = agglo_mean.inverse_transform(Xt_mean)
X_full_median = agglo_median.inverse_transform(Xt_median)
assert np.unique(X_full_mean[0]).size == n_clusters
assert np.unique(X_full_median[0]).size == n_clusters
assert_array_almost_equal(agglo_mean.transform(X_full_mean),
Xt_mean)
assert_array_almost_equal(agglo_median.transform(X_full_median),
Xt_median)