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聊聊storm client的nimbus.seeds参数

序本文主要研究一下storm client的nimbus.seeds参数 NIMBUS_SEEDS storm-core-1.1.0-sources.jar!...= "nimbus.seeds"; 可以看到这里废除了nimbus.host参数，而nimbus.seeds参数主要用于发现nimbus leader StormSubmitter storm-core...seeds = (List) conf.get(Config.NIMBUS_SEEDS); } for (String host...; } 这里仍然兼容NIMBUS_HOST参数，如果有NIMBUS_HOST参数则从中读取seeds，没有则从NIMBUS_SEEDS参数获取之后遍历seeds，根据每个seed创建NimbusClient...Did you specify a valid list of nimbus hosts for config nimbus.seeds?”)

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Opencv 图像超像素分割（SLIC、SEEDS、LSC）

目前常用的超像素分割算法有SLIC、SEEDS和LSC。下面来说说这些算法基于Opencv的Python实现。...img_slic = cv2.bitwise_and(img,img,mask = mask_inv_slic) #在原图上绘制超像素边界 mt.PIS(img_slic) pass SEEDS...,True) seeds.iterate(img,10) #输入图像大小必须与初始化形状相同，迭代次数为10 mask_seeds = seeds.getLabelContourMask() label_seeds...= seeds.getLabels() number_seeds = seeds.getNumberOfSuperpixels() mask_inv_seeds = cv2.bitwise_not(mask_seeds...) img_seeds = cv2.bitwise_and(img,img,mask = mask_inv_seeds) mt.PIS(img_seeds) pass LSC 利用opencv中

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Seeds Note：卡片记录灵感，画布连接思考的独特体验

今天，给大家介绍一款卡片笔记工具——Seeds Note。卡片记录灵感，随时随地捕捉想法 Seeds Note 最吸引我的地方之一就是它的卡片记录功能。...而且，Seeds 的卡片支持多种格式，包括文本、图片、链接，甚至代码块，这让我在记录时更加灵活。...此外，Seeds Note 还支持个性化定制。你可以根据自己的喜好调整界面风格，甚至自定义快捷键，让使用体验更加符合个人习惯。...无论你是想记录灵感，还是构建知识体系，Seeds Note 都能成为你强大的“第二大脑”。...如果你还在寻找一款高效、灵活且富有创意的笔记工具，不妨试试 Seeds Note，它可能会给你带来意想不到的惊喜！官网地址：Seeds Note - 卡片记录灵感，画布连接思考

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谷歌最新天气预报模型SEEDS登Science子刊

SEEDS基于去噪扩散概率模型，这是一种由Google Research部分开创的最先进的生成式人工智能方法。...SEEDS可以根据操作性数值天气预报系统中的一两个预报来生成大量的集合。...其中A是真实观测的代理，（Ca-Ch）是SEEDS模拟出来的8个样本，而（Da-Dh）是来自GEFS的预报。...虽然肉眼可能很难直接看出明显的区别，但SEEDS更能捕捉到交叉场和空间相关性，这会与真实的天气更加贴近。...而SEEDS生成的绿色点，却可以提供更好的统计覆盖，基于它精准的生成能力和高效的生成速度。天气预报新模式？

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论文笔记系列-Well Begun Is Half Done：Generating High-Quality Seeds for Automatic Image Dataset Constructio

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K-均值算法（一）

data=np.concatenate((list(zip(x1,y1)),list(zip(x2,y2))),axis= 0) return data def classfy(data, seeds...for j in range(1,K): distance = np.linalg.norm(point-seeds[j]) if distance <...= [seed0,seed1,seed2] #seeds = [seed0,seed1] distanceSum_log =[] #用于查看收敛与否 colors = ['g', 'b', 'orange...)): if seeds[j] is not None: plt.scatter(seeds[j][0], seeds[j][1], s=35,c="red",alpha..., seeds)) for j in range( len(seeds)): seeds[j] = centerPoint(groups[j]

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剥开比原看代码02：比原启动后去哪里连接别的节点

exist, add them to the address book and dial out if sm.config.P2P.Seeds !...= "" { // dial out seeds := strings.Split(sm.config.P2P.Seeds, ",") if err :=...sm.DialSeeds(seeds); err !...= nil { return err } } // ... } 其中的sm.config.P2P.Seeds就对应于config.toml中的seeds...(seeds []string) error { return sm.sw.DialSeeds(sm.addrBook, seeds) } 其实是是调用了sm.sw.DialSeeds，而sm.sw

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R语言模拟:Bias Variance Decomposition

subset中的k值 # num：模拟次数 # sigma:随机误差的标准差 # test_id 用于计算偏差误差的训练集样本编号，1-80中任一整数 # regtype:knn或best sub # seeds...:随机数种子 # 返回方差偏差误差等值 getError seeds,n_test){ set.seed(seeds) testset...:随机数种子 n_test <- 100 modeltype <- 'reg' num <- 100 seeds <- 1 result seeds...,n_test) result seeds,n_test)) result seeds,n_test)) result seeds,n_test)) for (i in seq(10,50,10

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python生成随机四位数和AttributeError: module random has no attribute sample

import string import random # 方法一 seeds = "0123456789" random_str = [] for i in range(4): random_str.append...(random.choice(seeds)) print("".join(random_str)) # 方法二 # seeds = string.digits random_str = random.choices...(seeds, k=4) print("".join(random_str)) # 方法三 # seeds = string.digits random_str = random.sample(seeds

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情人节程序员用HTML网页表白【华丽满屏烟花-生日快乐】 HTML5七夕情人节表白网页源码 HTML+CSS+JavaScript

playerVY=playerVZ=pitch=yaw=pitchV=yawV=0;scale=600;seedTimer=0;seedInterval=5,seedLife=100;gravity=.02;seeds....1-Math.random()*.2;seed.vy=-1.5;//*(1+Math.random()/2);seed.vz=.1-Math.random()*.2;seed.born=frames;seeds.push...;++i){seeds[i].vy+=gravity;seeds[i].x+=seeds[i].vx;seeds[i].y+=seeds[i].vy;seeds[i].z+=seeds[i].vz;if...(frames-seeds[i].born>seedLife){splode(seeds[i].x,seeds[i].y,seeds[i].z);seeds.splice(i,1);}}for(i=0;...;++i){point=rasterizePoint(seeds[i].x,seeds[i].y,seeds[i].z);if(point.d!

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布隆过滤器的java实现

Bloomfilter { private static final int DEFAULT_SIZE = 2 << 29;//布隆过滤器的比特长度 private static final int[] seeds...能很好的降低错误率 private BitSet bits = new BitSet(DEFAULT_SIZE); private SimpleHash[] func = new SimpleHash[seeds.length...]; public Bloomfilter() { for (int i = 0; i seeds.length; i++) { func[i] = new SimpleHash...(DEFAULT_SIZE, seeds[i]); } } private void addValue(String value) { for(SimpleHash...; i++) { // func[i] = new SimpleHash(DEFAULT_SIZE, seeds[i]); // } //// BufferedReader reader

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使用IsolationForest 与Meanshift算法进行异常检测

class ‘sklearn.cluster.mean_shift_.MeanShift’> 中显示不能分类的数据其类别标签就是-1 def mean_shift(X, bandwidth=None, seeds...seeds : array-like, shape=[n_seeds, n_features] or None Point used as initial kernel locations...Setting this option to True will speed up the algorithm because fewer seeds will be initialized...Ignored if seeds argument is not None....is None: if bin_seeding: seeds = get_bin_seeds(X, bandwidth, min_bin_freq)

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明月机器学习系列016：OPTICS聚类算法详解

self.unprocessed = [p for p in self.points] self.ordered = [] # 处理过的样本点 seeds...: # 优先从seeds选择一个点 if seeds: seeds.sort(key=lambda n: n.rd)...point = seeds.pop(0) else: point = self.unprocessed[0] # mark..._update(point_neighbors, point, seeds) # when all points have been processed # return...the ordered list return self.ordered def _update(self, neighbors, point, seeds)

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CentOS7 cassandra安装与测试

start_rpc: true$g' /opt/apache-cassandra-3.11.7/conf/cassandra.yaml #节点分配:192.168.6.117,192.168.6.118分配为seeds...，三节点配置完全一样 sed -i 's$seeds: "127.0.0.1"$seeds: "192.168.6.117,192.168.6.118"$g' /opt/apache-cassandra...: localhost$rpc_address: 192.168.6.117$g' /opt/apache-cassandra-3.11.7/conf/cassandra.yaml 启动 #先启动seeds...节点（192.168.6.117,192.168.6.118）再启动非seeds节点 /opt/apache-cassandra-3.11.7/bin/cassandra -R #查看集群状态 /opt

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Java项目实战篇：用Redis快速实现BloomFilter！

以下是稍加修改后的hash函数： //总的bitmap大小 64Mprivatestaticfinalintcap=1<<29;/* * 不同哈希函数的种子，一般取质数 * seeds...数组共有8个值，则代表采用8种不同的哈希函数 */privateint[]seeds=newint[]{3,5,7,11,13,31,37,61};privateinthash(String...+){result=seed*result+value.charAt(i);}return(cap-1)&result;}Click to copy 剩下的事情便很简单了，对每个词典A中的单词，依次调seeds...=null){for(intseed:seeds){inthash=hash(word,seed);totalSet.add((long)hash);}long[]offsets=newlong[totalSet.size...];for(inti=0;iseeds.length;i++){inthash=hash(mobile,seeds[i]);offsets[i]=hash;}Listresults=

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免费 2025跨年倒计时代码HTML（流星雨+烟花+粒子特效）

; ++i) { seeds[i].vy += gravity; seeds[i].x += seeds[i].vx; seeds[i].y += seeds...splode(seeds[i].x, seeds[i].y, seeds[i].z); seeds.splice(i, 1); } } for...; ++i) { point = rasterizePoint(seeds[i].x, seeds[i].y, seeds[i].z); if (point.d !...; ++i) { seeds[i].vy += gravity; seeds[i].x += seeds[i].vx; seeds[i].y += seeds[...(seeds[i].x, seeds[i].y, seeds[i].z); seeds.splice(i, 1); } } for (i = 0; i

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html+css+js实现的新年烟花

pitch=yaw=pitchV=yawV=0; scale=600; seedTimer=0;seedInterval=5,seedLife=100;gravity=.02; seeds...;++i){ seeds[i].vy+=gravity; seeds[i].x+=seeds[i].vx; seeds[i].y+=seeds[i].vy...; seeds[i].z+=seeds[i].vz; if(frames-seeds[i].born>seedLife){ splode(seeds...[i].x,seeds[i].y,seeds[i].z); seeds.splice(i,1); } } for(i=0;i<sparks.length...;++i){ point=rasterizePoint(seeds[i].x,seeds[i].y,seeds[i].z); if(point.d!

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种子区域生长图像分割方法及Python实例

connects = [ Point(0, -1), Point(1, 0),Point(0, 1), Point(-1, 0)] return connects def regionGrow(img,seeds...height, weight = img.shape seedMark = np.zeros(img.shape) seedList = [] for seed in seeds...下面我们采用区域生长法只保留中间的白色圆圈 image_copy = image.copy()//255 seeds = [Point(256//2,256//2)] binaryImg = regionGrow...(image_copy,seeds,1) cv2.imwrite('test1.png', 255 * binaryImg) 区域生长法需要设定种子点，我们将种子点设为图像的中心点，即白色圆圈的中心点，

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Java实现多种方式的http数据抓取

@author Administrator -> junhong * * 2016年12月27日 */ public class HttpFetchUtilTest { String seeds...); } @Test public void testGetResponseCode() throws Exception{ for(String seed:seeds...} } @Test public void testJDKFetch() throws Exception{ for(String seed:seeds...} } @Test public void testURLFetch() throws Exception{ for(String seed:seeds...} } @Test public void testJsoupFetch() throws Exception{ for(String seed:seeds

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html_js

seedTimer = 0; seedInterval = 5, seedLife = 100; gravity = .02; seeds...; ++i) { seeds[i].vy += gravity; seeds[i].x += seeds[i].vx;...seeds[i].y += seeds[i].vy; seeds[i].z += seeds[i].vz; if (frames - seeds...[i].born > seedLife) { splode(seeds[i].x, seeds[i].y, seeds[i].z);...; ++i) { point = rasterizePoint(seeds[i].x, seeds[i].y, seeds[i].z);

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点击加载更多

聊聊storm client的nimbus.seeds参数

Opencv 图像超像素分割（SLIC、SEEDS、LSC）

Seeds Note：卡片记录灵感，画布连接思考的独特体验

谷歌最新天气预报模型SEEDS登Science子刊

论文笔记系列-Well Begun Is Half Done：Generating High-Quality Seeds for Automatic Image Dataset Constructio

K-均值算法（一）

剥开比原看代码02：比原启动后去哪里连接别的节点

R语言模拟:Bias Variance Decomposition

python生成随机四位数和AttributeError: module random has no attribute sample

情人节程序员用HTML网页表白【华丽满屏烟花-生日快乐】 HTML5七夕情人节表白网页源码 HTML+CSS+JavaScript

布隆过滤器的java实现

使用IsolationForest 与Meanshift算法进行异常检测

明月机器学习系列016：OPTICS聚类算法详解

CentOS7 cassandra安装与测试

Java项目实战篇：用Redis快速实现BloomFilter！

免费 2025跨年倒计时代码HTML（流星雨+烟花+粒子特效）

html+css+js实现的新年烟花

种子区域生长图像分割方法及Python实例

Java实现多种方式的http数据抓取

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