10@descriptions: K-means Algorithm implementation. 11@filename: Filename of input data. 12@knums: Clusters number. 13''' 14def__init__(self, filename, knums): 15self._filename = filename; 16self._knums = knums 17self._dimension = 0 18"""self._samples := [(seqx, x1, x2, ...
train方法:根据设置的KMeans聚类参数,构建KMeans聚类,并执行run方法进行训练 run方法:主要调用runAlgorithm方法进行聚类中心点等的核心计算,返回KMeansModel initialModel:可以直接设置KMeansModel作为初始化聚类中心选择,也支持随机和k-means || 生成中心点
* Implementation of K-Means algorithm. */privatedefrunAlgorithm(data:RDD[VectorWithNorm],instr:Option[Instrumentation]):KMeansModel={val sc=data.sparkContext val initStartTime=System.nanoTime()val distanceMeasureInstance=DistanceMeasure.decodeFromString(this.distanceMeasure)val centers=initialModel match{...
In this study, an architecture is designed on the FPGA for the K-means algorithm and the accuracy and efficiency of the generated architecture are compared with the software applied in the standard processor and the performance is tested. When the results are examined, it is seen that the ...
k-means algorithm算法是一种得到最广泛使用的基于划分的聚类算法,把n个对象分为k个簇,以使簇内具有较高的相似度。相似度的计算根据一个簇中对象的平均值来进行。它与处理混合正态分布的最大期望算法很相似,因为他们都试图找到数据中自然聚类的中心。
) } model } // runAlgorithm方法,KMeans实现方法。 /** * Implementation of K-Means algorithm. */ privatedef runAlgorithm(data: RDD[VectorWithNorm]): KMeansModel = { val sc = data.sparkContext val initStartTime = System.nanoTime() val centers =if (initializationMode == KMeans.RANDOM) ...
train方法:根据设置的KMeans聚类参数,构建KMeans聚类,并执行run方法进行训练 run方法:主要调用runAlgorithm方法进行聚类中心点等的核心计算,返回KMeansModel initialModel:可以直接设置KMeansModel作为初始化聚类中心选择,也支持随机和k-means || 生成中心点
kmeans_test.go README MIT license kmeans k-means clustering algorithm implementation written in Go What It Does k-means clusteringpartitions a multi-dimensional data set intokclusters, where each data point belongs to the cluster with the nearest mean, serving as a prototype of the cluster. ...
首先看一下,sklearn.cluster.k_means模块下的函数k_means方法: def k_means(X, n_clusters, init='k-means++', precompute_distances='auto', n_init=10, max_iter=300, verbose=False, tol=1e-4, random_state=None, copy_x=True, n_jobs=1, algorithm="auto", return_n_iter=False): 首先,我...
因为小数据在CPU上的计算也足够快,在GPU上执行反而会在数据传输上花费大量时间 参考: 深入解读:如何使用GPU/CUDA实现Kmeans聚类算法-百度开发者中心 GitHub - serban/kmeans: A CUDA implementation of the k-means clustering algorithm GitHub - krulis-martin/cuda-kmeans: A novell, highly-optimized CUDA impl...