From 586faf5c3613773f0eb2a0f834e2bc9b4094b9b8 Mon Sep 17 00:00:00 2001 From: loopyme Date: Fri, 14 Jun 2019 18:58:02 +0800 Subject: [PATCH] =?UTF-8?q?=E8=B0=83=E6=95=B42.8=E7=9A=84=E5=8F=82?= =?UTF-8?q?=E8=80=83=E8=B5=84=E6=96=99=E6=A0=87=E6=B3=A8?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/27.md | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/docs/27.md b/docs/27.md index ff12a75..fa2c1e9 100644 --- a/docs/27.md +++ b/docs/27.md @@ -88,8 +88,7 @@ array([-0.41075698, -0.41075698, -0.41076071, -0.41075698, -0.41075698, “新”数据由输入数据线性组合而成，其权重根据 KDE 模型按概率给出。 -示例: - -* [Simple 1D Kernel Density Estimation](https://scikit-learn.org/stable/auto_examples/neighbors/plot_kde_1d.html#sphx-glr-auto-examples-neighbors-plot-kde-1d-py): 一维简单核密度估计的计算。 -* [Kernel Density Estimation](https://scikit-learn.org/stable/auto_examples/neighbors/plot_digits_kde_sampling.html#sphx-glr-auto-examples-neighbors-plot-digits-kde-sampling-py): 使用核密度估计来学习手写数字数据生成模型，以及使用该模型绘制新样本的示例 -* [Kernel Density Estimate of Species Distributions](https://scikit-learn.org/stable/auto_examples/neighbors/plot_species_kde.html#sphx-glr-auto-examples-neighbors-plot-species-kde-py): 使用Haversine距离度量来显示地理空间数据的核密度估计示例. +>示例: +>* [Simple 1D Kernel Density Estimation](https://scikit-learn.org/stable/auto_examples/neighbors/plot_kde_1d.html#sphx-glr-auto-examples-neighbors-plot-kde-1d-py): 一维简单核密度估计的计算。 +>* [Kernel Density Estimation](https://scikit-learn.org/stable/auto_examples/neighbors/plot_digits_kde_sampling.html#sphx-glr-auto-examples-neighbors-plot-digits-kde-sampling-py): 使用核密度估计来学习手写数字数据生成模型，以及使用该模型绘制新样本的示例 +>* [Kernel Density Estimate of Species Distributions](https://scikit-learn.org/stable/auto_examples/neighbors/plot_species_kde.html#sphx-glr-auto-examples-neighbors-plot-species-kde-py): 使用Haversine距离度量来显示地理空间数据的核密度估计示例. -- GitLab