line = f.readline()print(data)print(data.keys())# ^ 去重后转化为 numpy 数组forkeyindata: data[key] = np.array(list(set(tuple(t)fortindata[key])))print(data)# ^ 获取Iris-virginica品种数据a = data['Iris-virginica']# ^ 拆分成四个数
import scikit_posthocs as spdf = sa.datasets.get_rdataset('iris').datadf.columns = df.columns.str.replace('.', '')lm = sfa.ols('SepalWidth ~ C(Species)', data=df).fit()anova = sa.stats.anova_lm(lm)print(anova) 获得了 ANOVA 测试结果,但不确定哪个变量类对结果的影响最大,可以使...
load_dataset('iris') df_tips = sns.load_dataset('tips') # 创建matplotlib的fig对象和子图对象ax fig, ax = plt.subplots(1,3, figsize=(12,4)) # 多个数值变量的箱线图 sns.boxplot(data=df.loc[:, ['sepal_length', 'sepal_width']], ax=ax[0]) ax[0].set_title('多个数值变量') #...
importscikit_posthocsassp df = sa.datasets.get_rdataset('iris').data df.columns = df.columns.str.replace('.','') lm = sfa.ols('SepalWidth ~ C(Species)', data=df).fit() anova = sa.stats.anova_lm(lm) print(anova) df sum_sq mean_...
df=sns.load_dataset('iris')df_tips=sns.load_dataset('tips')# 创建matplotlib的fig对象和子图对象ax fig,ax=plt.subplots(1,3,figsize=(12,4))# 多个数值变量的箱线图 sns.boxplot(data=df.loc[:,['sepal_length','sepal_width']],ax=ax[0])ax[0].set_title('多个数值变量')# 一个数值变量...
y=np.array(Data.get("iris")) #UsethedatatotrainC-SupportVectorClassifier svc_clf=SVC(gamma='auto') svc_clf.fit(X,y) end 要测试pystacked是否适用于您的系统,请在 Stata 中运行以下测试代码: clearall usehttps://statalasso.github.io/dta/cal_housing.dta,clear ...
DataType.ConfigName; Parameter SETTINGS = "Target:Basic"; Parameter ADAPTER = "Ens.InboundAdapter"; Method OnProcessInput(pInput As %RegisteredObject, Output pOutput As %RegisteredObject, ByRef pHint As %String) As %Status [ Language = python ] { import iris import random fruits = ["apple...
1、自动化office，包括对excel、word、ppt、email、pdf等常用办公场景的操作，python都有对应的工具库，...
def AgglomerativeClustering_for_iris():myutil = util()X,y = datasets.load_iris().data,datasets.load_iris().targetAC = AgglomerativeClustering(n_clusters=3)AC.fit(X)result = AC.fit_predict(X)title = "鸢尾花"myutil.draw_scatter_for_Clustering(X,y,result,title,"凝聚算法") ...
def bernoulliNB_for_iris():myutil = util()X,y = datasets.load_iris().data,datasets.load_iris().targetX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=8)nb = BernoulliNB()nb.fit(X,y)title = "贝努利贝叶斯 鸢尾花"myutil.print_scores(nb,X_train,y_train...