本文整理汇总了Python中sklearn.cluster.dbscan_.DBSCAN.fit方法的典型用法代码示例。如果您正苦于以下问题：Python DBSCAN.fit方法的具体用法？Python DBSCAN.fit怎么用？Python DBSCAN.fit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sklearn.cluster.dbscan_.DBSCAN的用法示例。
在下文中一共展示了DBSCAN.fit方法的4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: test_dbscan_balltree
# 需要导入模块: from sklearn.cluster.dbscan_ import DBSCAN [as 别名]
# 或者: from sklearn.cluster.dbscan_.DBSCAN import fit [as 别名]
def test_dbscan_balltree():
# Tests the DBSCAN algorithm with balltree for neighbor calculation.
eps = 0.8
min_samples = 10
D = pairwise_distances(X)
core_samples, labels = dbscan(D, metric="precomputed", eps=eps, min_samples=min_samples)
# number of clusters, ignoring noise if present
n_clusters_1 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_1, n_clusters)
db = DBSCAN(p=2.0, eps=eps, min_samples=min_samples, algorithm="ball_tree")
labels = db.fit(X).labels_
n_clusters_2 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_2, n_clusters)
db = DBSCAN(p=2.0, eps=eps, min_samples=min_samples, algorithm="kd_tree")
labels = db.fit(X).labels_
n_clusters_3 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_3, n_clusters)
db = DBSCAN(p=1.0, eps=eps, min_samples=min_samples, algorithm="ball_tree")
labels = db.fit(X).labels_
n_clusters_4 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_4, n_clusters)
db = DBSCAN(leaf_size=20, eps=eps, min_samples=min_samples, algorithm="ball_tree")
labels = db.fit(X).labels_
n_clusters_5 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_5, n_clusters)示例2: test_dbscan_feature
# 需要导入模块: from sklearn.cluster.dbscan_ import DBSCAN [as 别名]
# 或者: from sklearn.cluster.dbscan_.DBSCAN import fit [as 别名]
def test_dbscan_feature():
# Tests the DBSCAN algorithm with a feature vector array.
# Parameters chosen specifically for this task.
# Different eps to other test, because distance is not normalised.
eps = 0.8
min_samples = 10
metric = "euclidean"
# Compute DBSCAN
# parameters chosen for task
core_samples, labels = dbscan(X, metric=metric, eps=eps, min_samples=min_samples)
# number of clusters, ignoring noise if present
n_clusters_1 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_1, n_clusters)
db = DBSCAN(metric=metric, eps=eps, min_samples=min_samples)
labels = db.fit(X).labels_
n_clusters_2 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_2, n_clusters)示例3: test_dbscan_similarity
# 需要导入模块: from sklearn.cluster.dbscan_ import DBSCAN [as 别名]
# 或者: from sklearn.cluster.dbscan_.DBSCAN import fit [as 别名]
def test_dbscan_similarity():
# Tests the DBSCAN algorithm with a similarity array.
# Parameters chosen specifically for this task.
eps = 0.15
min_samples = 10
# Compute similarities
D = distance.squareform(distance.pdist(X))
D /= np.max(D)
# Compute DBSCAN
core_samples, labels = dbscan(D, metric="precomputed", eps=eps, min_samples=min_samples)
# number of clusters, ignoring noise if present
n_clusters_1 = len(set(labels)) - (1 if -1 in labels else 0)
assert_equal(n_clusters_1, n_clusters)
db = DBSCAN(metric="precomputed", eps=eps, min_samples=min_samples)
labels = db.fit(D).labels_
n_clusters_2 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_2, n_clusters)示例4: test_dbscan_callable
# 需要导入模块: from sklearn.cluster.dbscan_ import DBSCAN [as 别名]
# 或者: from sklearn.cluster.dbscan_.DBSCAN import fit [as 别名]
def test_dbscan_callable():
# Tests the DBSCAN algorithm with a callable metric.
# Parameters chosen specifically for this task.
# Different eps to other test, because distance is not normalised.
eps = 0.8
min_samples = 10
# metric is the function reference, not the string key.
metric = distance.euclidean
# Compute DBSCAN
# parameters chosen for task
core_samples, labels = dbscan(X, metric=metric, eps=eps, min_samples=min_samples, algorithm="ball_tree")
# number of clusters, ignoring noise if present
n_clusters_1 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_1, n_clusters)
db = DBSCAN(metric=metric, eps=eps, min_samples=min_samples, algorithm="ball_tree")
labels = db.fit(X).labels_
n_clusters_2 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_2, n_clusters)