kmodel=KMeans(n_clusters=k,n_jobs=-1,random_state=888)kmodel.fit(pred_images)kpredictions=kmodel.predict(pred_images)print(kpredictions)# 预测的类别 #0:dog1:cat 将分类后的图像保存到不同文件夹下 代码语言:javascript 复制 foriin["cat","dog"]:os.mkdir(r"C:\Users\Administrator\DeepLearnin...
Kmeans(n_clusters=4 # 对于指定聚类的簇数,无默认值 ,init="random" # 表示从数据集中随机挑选K个样本点作为初始簇中心 ,n_init=10 # 用于指定该算法运行次数,每次运行时都会选择不同的初始促中心,目的是防止算法收敛于局部最优,默认10 ,max_iter=300 # 表示单次运行的迭代次数,默认300 ,tol=0.0001 # ...
clusters_swiss_roll = KMeans(n_clusters=100,random_state=1).fit_predict(X) fig2 = plt.figure() ax = fig2.add_subplot(111,projection='3d') ax.scatter(X[:,0],X[:,1],X[:,2],c = clusters_swiss_roll,cmap = 'Spectral') plt.show()...
class sklearn.cluster.KMeans(n_clusters=8, init='k-means++', n_init=10, max_iter=300, tol=0.0001, precompute_distances='auto', verbose=0, random_state=None, copy_x=True, n_jobs=1, algorithm='auto') 1. 其中,n_clusters是KMeans中的k,表示模型需要分几个类别。 简单代码实现 import ma...
(0,):#0行矩阵#从data中随机生成0-data行数的6个整数作为索引值#random.randint(a,b,c)方法随机生成一个整数,从a到b,生成c个#data.shape[0]为data行数,生成self.n_clusters个即6个self.centroids=data[np.random.randint(0,data.shape[0],self.n_clusters),:]#开始迭代foriinrange(self.max_iter)...
0 : 1; yield return new DataPoint { Label = (uint)label, // Create random features with two clusters. // The first half has feature values centered around 0.6, while // the second half has values centered around 0.4. Features = Enumerable.Repeat(label, 50) .Select(index => label =...
The first initial mean is selected at random from all data items. The initial means are existing data items and they are sometimes called medoids.Next, a for loop is constructed to select the remaining k-1 means:C# Copy for (int k = 1; k < numClusters; ++k) { double[] dSquared ...
returncenters,clustersdefpredict(p_data,centers):# 预测新样本点所在的类# 计算p_data 到每个聚类中心的距离,然后返回距离最小所在的聚类。distances=[distance(p_data,centers[c])forcincenters]returnnp.argmin(distances) 测试 随机生成200个样本点,维度为2,聚为3类;...
static int[] InitClustering(int numTuples, int numClusters, int randomSeed) { Random random = new Random(randomSeed); int[] clustering = new int[numTuples]; for (int i = 0; i<numClusters; ++i)clustering[i] =i;for(inti=numClusters;i<clustering.Length; ++i)clustering[i] =random....
km =KMeans(n_clusters=3, init=bad_centers, n_init=1, max_iter=10, random_state=1)forthis_Xin(X, sp.coo_matrix(X)): km.fit(this_X) this_labels = km.labels_# Reorder the labels so that the first instance is in cluster 0,# the second in cluster 1, ...this_labels = np.un...