matplotlib可视化安斯库姆四重奏 importnumpyasnpimportmatplotlib.pyplotaspltfromscipyimportstatsfromscipy.optimizeimportcurve_fit# 维基百科版本的安斯库姆四重奏数据dataset_I=[(10.0,8.04),(8.0,6.95),(13.0,7.58),(9.0,8.81),(11.0,8.33),(14.0,9.96),(6.0,7.24),(4.0,4.26),(12.0,10.84),(7.0,4.82),(...
The target statistics can be supplied directly or calculated based on a user-supplied dataset.The'datasauRus'package <https://cran.r-project.org/package=datasauRus>provides further examples of datasets that have markedly different scatter plots but share many sample summary statistics.Imports graphics,...
dataset I 11.0 II 11.0 III 11.0 IV 11.0 Name: x, dtype: float64 dataset I 4.127269 II 4.127629 III 4.122620 IV 4.123249 Name: y, dtype: float64 1 2 3 4 5 6 7 8 9 10 11 12 计算相关系数 X1 = anascombe.x[0:10].values X2 = anascombe.x[11:21].values X3 = anascombe.x[...
dataset = ['I','II','III','IV'] X = {} Y = {} foriinrange(0,4): xarray = anscombe.x[i *11:i *11+10].values X[dataset[i]] = xarray yarray = anscombe.y[i *11:i *11+10].values Y[dataset[i]] = yarray returnX, Y defgetMandV(array): mean = np.mean(array) va...
sns.set(style="ticks")#Load the example dataset for Anscombe's quartetdf = sns.load_dataset("anscombe")#Show the results of a linear regression within each datasetsns.lmplot(x="x", y="y", col="dataset", hue="dataset", data=df, ...
划分训练集和验证集_按照_MMPreTrain_CustomDataset_格式组织训练集和验证集__ 2025-03-09 14:50:04 积分:1 这是对于simclr的tracin探究,其中simclr_train是训练单独完整的simclr需要 2025-03-09 14:35:47 积分:1 201700072016__1.我们称选中奖品为事件_A,_选不中奖品为事件_B,_更改选择为_ ...
划分训练集和验证集_按照_MMPreTrain_CustomDataset_格式组织训练集和验证集__ 2025-03-09 14:50:04 积分:1 这是对于simclr的tracin探究,其中simclr_train是训练单独完整的simclr需要 2025-03-09 14:35:47 积分:1 201700072016__1.我们称选中奖品为事件_A,_选不中奖品为事件_B,_更改选择为_ 2025-03...