from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs
from yellowbrick.cluster import KElbowVisualizer
# Generate synthetic dataset with 8 random clusters# 建立具有8个随机点簇中心的数据
X, y = make_blobs(n_samples=1000, n_features=12, centers=8, random_state=42)# Instantiate the clustering model and visualizer
model = KMeans()# 可视化
visualizer = KElbowVisualizer(model, k=(4,12))
visualizer.fit(X)# Fit the data to the visualizer
visualizer.show();# Finalize and render the figure
from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs
from yellowbrick.cluster import KElbowVisualizer
# Generate synthetic dataset with 8 random clusters
X, y = make_blobs(n_samples=1000, n_features=12, centers=8, random_state=42)# Instantiate the clustering model and visualizer
model = KMeans()
visualizer = KElbowVisualizer(
model, k=(4,12), metric='calinski_harabasz', timings=False)
visualizer.fit(X)# Fit the data to the visualizer
visualizer.show();# Finalize and render the figure
默认情况下，参数locate_elbow设置为True，使用“elbow检测算法”自动找到可能与k的最佳值相对应的“肘”。但是，用户可以通过设置关闭功能locate_elbow=False。您可以在 Kevin Arvai的Knee point detection in Python中阅读有关此算法的实现的信息。
from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs
from yellowbrick.cluster import KElbowVisualizer
# Generate synthetic dataset with 8 random clusters
X, y = make_blobs(n_samples=1000, n_features=12, centers=8, random_state=42)# Instantiate the clustering model and visualizer
model = KMeans()
visualizer = KElbowVisualizer(
model, k=(4,12), metric='calinski_harabasz', timings=False, locate_elbow=False)
visualizer.fit(X)# Fit the data to the visualizer
visualizer.show();# Finalize and render the figure
from sklearn.cluster import KMeans
from yellowbrick.cluster.elbow import kelbow_visualizer
from yellowbrick.datasets.loaders import load_nfl
X, y = load_nfl()# Use the quick method and immediately show the figure
kelbow_visualizer(KMeans(random_state=4), X, k=(2,10));
from sklearn.cluster import KMeans
from yellowbrick.cluster import SilhouetteVisualizer
from yellowbrick.datasets import load_nfl
# Load a clustering dataset
X, y = load_nfl()# Specify the features to use for clustering# 指定某五个特征
features =['Rec','Yds','TD','Fmb','Ctch_Rate']# 挑选这些特征重tgb>20的数据
X = X.query('Tgt >= 20')[features]# Instantiate the clustering model and visualizer
model = KMeans(5, random_state=42)
visualizer = SilhouetteVisualizer(model, colors='yellowbrick')
visualizer.fit(X)# Fit the data to the visualizer
visualizer.show();# Finalize and render the figure
from sklearn.cluster import KMeans
from yellowbrick.cluster import silhouette_visualizer
from yellowbrick.datasets import load_credit
# Load a clustering dataset
X, y = load_credit()# Specify rows to cluster: under 40 y/o and have either graduate or university education
X = X[(X['age']<=40)&(X['edu'].isin([1,2]))]# Use the quick method and immediately show the figure
silhouette_visualizer(KMeans(5, random_state=42), X, colors='yellowbrick');
from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs
from yellowbrick.cluster import InterclusterDistance
# Generate synthetic dataset with 12 random clusters
X, y = make_blobs(n_samples=1000, n_features=12, centers=12, random_state=42)# Instantiate the clustering model and visualizer# 六个簇类
model = KMeans(6)
visualizer = InterclusterDistance(model)
visualizer.fit(X)# Fit the data to the visualizer|
visualizer.show();# Finalize and render the figure
androi中提到了布尔数组;
布尔数组默认的是false, 并且只会打印false或者是true
布尔数组的例子; 根据字符数组创建布尔数组
char[] c = {'p','u','b','l','i','c'};
//根据字符数组的长度创建布尔数组的个数
boolean[] b = new bool
文章摘自：http://blog.csdn.net/yangwawa19870921/article/details/7553181
在编写HQL时，可能会出现这种代码：
select a.name,b.age from TableA a left join TableB b on a.id=b.id
如果这是HQL，那么这段代码就是错误的，因为HQL不支持
1. 简单的for循环
public static void main(String[] args) {
for (int i = 1, y = i + 10; i < 5 && y < 12; i++, y = i * 2) {
System.err.println("i=" + i + " y="
异常信息本地化
Spring Security支持将展现给终端用户看的异常信息本地化，这些信息包括认证失败、访问被拒绝等。而对于展现给开发者看的异常信息和日志信息（如配置错误）则是不能够进行本地化的，它们是以英文硬编码在Spring Security的代码中的。在Spring-Security-core-x
近来工作中遇到这样的两个需求
1. 给个Date对象，找出该时间所在月的第一天和最后一天
2. 给个Date对象，找出该时间所在周的第一天和最后一天
需求1中的找月第一天很简单，我记得api中有setDate方法可以使用
使用setDate方法前，先看看getDate
var date = new Date();
console.log(date);
// Sat J
MyBatis的update元素的用法与insert元素基本相同，因此本篇不打算重复了。本篇仅记录批量update操作的
sql语句，懂得SQL语句，那么MyBatis部分的操作就简单了。 注意：下列批量更新语句都是作为一个事务整体执行，要不全部成功，要不全部回滚。
MSSQL的SQL语句
WITH R AS（
SELECT 'John' as name, 18 as
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