from sklearn.preprocessing import QuantileTransformer qt= QuantileTransformer(n_quantiles=1000,random_state=420,output_distribution='normal')#实例化 #参数解释:n_quantiles,代表分布的数量 ,output_distribution代表要分布要转换成什么,默认是均匀分布。normal是正态分布 qt_data= qt.fit_transform(data) #对正...
x1,y1 = np.random.multivariate_normal(mean1,cov1,1000).T mean2 = [0,8] cov2 = [[0.3,0],[0,0.3]] x2,y2 = np.random.multivariate_normal(mean2,cov2,1000).T mean3 = [8,4] cov3 = [[1.5,0],[0,1]] x3,y3 = np.random.multivariate_normal(mean3,cov3,1000).T #绘制三...
import numpy as npimport matplotlib.pyplot as pltfrom sklearn.cluster import KMeans# 生成武侠风格的数据集np.random.seed(42)data_A = np.random.normal(loc=[1, 1], scale=0.2, size=(50, 2))data_B = np.random.normal(loc=[5, 5], scale=0.2, size=(50, 2))data_C = np.random....
前言 kmeans算法主要用来实现自动聚类,是一种非监督的机器学习算法,使用非常广泛。在opencv3.0中提供了这样一个函数,直接调用就能实现自动聚类,非常方便。 API介绍 double kmeans(InputArray data, int K, InputOutputArray bestLabels, TermCriteria criteria, int attempts, int flags, OutputArray centers=noArray() ...
X2 = np.random.multivariate_normal([2,50], [[1,0],[0,10]], n_points) X = np.concatenate([X1,X2]) y = np.array([0]*n_points + [1]*n_points) # KNN模型的训练过程 clfs = [] neighbors = [1,3,5,9,11,13,15,17,19]#训练多个K ...
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K-means算法MacQueen在1967年提出的,是最简单与最常见数据分类方法之一并且最为一种常见数据分析技术在机器学习、数据挖掘、模式识别、图像分析等领域都用应用。从机器学习的角度看,K-means属于一种无监督的机器学习方法。无监督学习(Unsupervised Learning)简单的说就是在不给定正确答案(标签)的情况下,在数据中找到一...
kwindrem/GuiModsPublic NotificationsYou must be signed in to change notification settings Fork29 Star227 main BranchesTags Code Repository files navigation README NOTE: starting with firmware v3.50, the "New UI" (aka gui-v2) is available for use For GuiMods, the following opitons must be con...
bool saturateRange = false // 只有当随机数产生方式为均匀分布时才有效,表示的是是否产生的数据要布满整个范围 ) 1. 2. 3. 4. 5. 6. 7. 将原数组(矩阵)打乱 : randShuffle( InputOutputArray dst, // 输入输出数组(一维) double iterFactor=1. , // 表示随机交换元素的数量的缩放因子,总的交换次数...