fromsklearn import svm fromsklearn.datasets import make_blobs, make_moons fromsklearn.covariance import EllipticEnvelope fromsklearn.ensemble import IsolationForest fromsklearn.neighbors import LocalOutlierFactor matplotlib.rcParams['contour.negative_linestyle'] ='solid' # Example settings n_samples = 300...
开发者ID:allefpablo,项目名称:scikit-learn,代码行数:18,代码来源:test_iforest.py 注:本文中的sklearn.ensemble.IsolationForest类示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。
需要一些机器学习常用的工具库 pandas,numpy,sklearn,matplotlib,scipy 如果你是初学者,第一次使用python尝试机器学习,不知道如何下载一些工具库。 我推荐你学会使用python自带的pip安装。 在linux命令行输入(windows则用cmd操作,当然还有其他方,我建议自行了解,并采用适合自己的方式解决) example: $pip install pandas ...
import matplotlib.pyplot as plt from sklearn.ensemble import IsolationForest rng=np.random.RandomState(42) # 生成训练数据 X=0.3*rng.randn(100,2) # 100条二维数据 X_train=np.r_[X+2,X-2] # 200条数据(X+2,X-2)拼接而成 X = 0.3 * rng.randn(20, 2) X_test = np.r_[X + 2, X...
from sklearn.ensemble import IsolationForest import matplotlib.pyplot as plt Data Preparation We’ll be using make_blob() function to create a dataset with random data points. 1 2 random.seed(3) X, _ = make_blobs(n_samples=300, centers=1, cluster_std=.3, center_box=(20, 5)) Let...
import numpy as np import pandas as pd from sklearn.ensemble import IsolationForest def _validate_input(X): if isinstance(X, pd.DataFrame): if X.columns.dtype == np.object_: raise ValueError("X cannot have string feature names.") elif X.columns.nunique() != len(X.columns): raise Va...
<class ‘sklearn.cluster.mean_shift_.MeanShift’> 中显示不能分类的数据其类别标签就是-1 代码语言:javascript 复制 def mean_shift(X, bandwidth=None, seeds=None, bin_seeding=False, min_bin_freq=1, cluster_all=True, max_iter=300, n_jobs=None): """Perform mean shift clustering of data usi...
fromsklearn.neighbors import LocalOutlierFactor matplotlib.rcParams['contour.negative_linestyle'] ='solid' # Example settings n_samples = 300 outliers_fraction = 0.15 n_outliers =int(outliers_fraction * n_samples) n_inliers = n_samples - n_outliers ...
from sklearn.neighbors import LocalOutlierFactor matplotlib.rcParams['contour.negative_linestyle'] ='solid' # Example settings n_samples = 300 ...
from sklearn.neighbors import LocalOutlierFactor matplotlib.rcParams['contour.negative_linestyle'] = 'solid' # Example settings n_samples = 300 outliers_fraction = 0.15 n_outliers = int(outliers_fraction * n_samples) n_inliers = n_samples - n_outliers # define outlier/ anomaly detection methods...