3 3、创建一个函数，用来压缩输入图片： def compress_image(img，num_clusters): # 将输入的图片转换成(样本量,特征量)数组，以运行k-means聚类算法 X = img.reshape((-1，1)) kmeans = cluster.KMeans(n_clusters=num_clusters，n_init=4， random_state=5) kmeans.fit(X) cent...
n_clusters : int The number of clusters to form as well as the number of centroids to generate. sample_weight : array-like of shape (n_samples,), default=None The weights for each observation in X. If None, all observations are assigned equal weight. init : {'k-means++', 'random'...
public static int[] Cluster(double[][] rawData, int numClusters, int seed) { double[][] data = Normalized(rawData); bool changed = true; bool success = true; double[][] means = InitMeans(numClusters, data, seed); int[] clustering = new int[data.Length]; int maxCount = data.Le...
3536Console.WriteLine("Final clustering in internal form:\n");37ShowVector(clustering,true);3839Console.WriteLine("Raw data by cluster:\n");40Console.WriteLine("ID Height Weight");41ShowClustered(rawData, clustering, numClusters,1);4243Console.WriteLine("\nEnd k-means clustering demo\n");44C...
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val data =sc.textFile(args(0)) val parsedData =data.map(s => Vectors.dense(s.split(' ').map(_.trim.toDouble))).cache() //设置簇的个数为3 val numClusters =3 //迭代20次 val numIterations= 20 //运行10次,选出最优解 val runs=10 ...
2.可以使用k-Means算法实现对给定样本集的聚类。 内容 1.采用k-Means算法,对给出的15个样本数据进行聚类,聚类簇数可自由调整,最后输出簇数为2、3、5的聚类结果。 k-Means初识 k-Means算法是一种常用的聚类算法,用于将一组数据点划分为k个不同的簇(clusters)。每个簇都由其内部的数据点相似性特征而定义,而...
TextCluster instance (MiniBatchKMeans(n_clusters=2, n_init=10), <pytextclassifier.utils.tokenizer.Tokenizer object at 0x7f80bd4682b0>, TfidfVectorizer(ngram_range=(1, 2))) [1 1 1 1 1 1 1 1 1 1 1 8 1 1 1 1 1 1 1 1 1 1 9 1 1 8 1 1 9 1] clustering plot image: ...
km= MiniBatchKMeans(n_clusters=true_k, init='k-means++', n_init=1, init_size=1000, batch_size=1000, verbose=opts.verbose)else: km= KMeans(n_clusters=true_k, init='k-means++', max_iter=100, n_init=1, verbose=opts.verbose)print("Clustering sparse data with %s"%km) ...
static int[] Cluster(double[][] rawData, int numClusters, int numAttributes, int maxCount) { bool changed = true; int ct = 0; int numTuples = rawData.Length; int[] clustering = InitClustering(numTuples, numClusters, 0); double[][] means = Allocate(numClusters, numAttributes); dou...