Merge pull request #652 from tink2123/fix_download_link

modified the UCI_housing_data download link

01.fit_a_line/README.cn.md

@@ -13,7 +13,7 @@ $$y_i = \omega_1x_{i1} + \omega_2x_{i2} + \ldots + \omega_dx_{id} + b,  i=1,\ldo
 初看起来，这个假设实在过于简单了，变量间的真实关系很难是线性的。但由于线性回归模型有形式简单和易于建模分析的优点，它在实际问题中得到了大量的应用。很多经典的统计学习、机器学习书籍\[[2,3,4](#参考文献)\]也选择对线性模型独立成章重点讲解。
 
 ## 效果展示
-我们使用从[UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing)获得的波士顿房价数据集进行模型的训练和预测。下面的散点图展示了使用模型对部分房屋价格进行的预测。其中，每个点的横坐标表示同一类房屋真实价格的中位数，纵坐标表示线性回归模型根据特征预测的结果，当二者值完全相等的时候就会落在虚线上。所以模型预测得越准确，则点离虚线越近。
+我们使用从[UCI Housing Data Set](http://paddlemodels.bj.bcebos.com/uci_housing/housing.data)获得的波士顿房价数据集进行模型的训练和预测。下面的散点图展示了使用模型对部分房屋价格进行的预测。其中，每个点的横坐标表示同一类房屋真实价格的中位数，纵坐标表示线性回归模型根据特征预测的结果，当二者值完全相等的时候就会落在虚线上。所以模型预测得越准确，则点离虚线越近。
 <p align="center">
     <img src = "https://github.com/PaddlePaddle/book/blob/develop/01.fit_a_line/image/predictions.png?raw=true" width=400><br/>
     图1. 预测值 V.S. 真实值
@@ -108,7 +108,7 @@ import sys
 from __future__ import print_function
 ```
 
-我们通过uci_housing模块引入了数据集合[UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing)
+我们通过uci_housing模块引入了数据集合[UCI Housing Data Set](http://paddlemodels.bj.bcebos.com/uci_housing/housing.data)
 
 其中，在uci_housing模块中封装了：
 

01.fit_a_line/README.md

@@ -17,7 +17,7 @@ $$y_i = \omega_1x_{i,1} + \omega_2x_{i,2} + \ldots + \omega_dx_{i,d} + b,  i=1,\
 where $\vec{\omega}$ and $b$ are the model parameters we want to estimate. Once they are learned, we will be able to predict the price of a home, given the attributes associated with it. We call this model **Linear Regression**. In other words, we want to regress a value against several values linearly. In practice, a linear model is often too simplistic to capture the real relationships between the variables. Yet, because Linear Regression is easy to train and analyze, it has been applied to a large number of real problems. As a result, it is an important topic in many classic Statistical Learning and Machine Learning textbooks \[[2,3,4](#References)\].
 
 ## Results Demonstration
-We first show the result of our model. The dataset [UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing) is used to train a linear model to predict the home prices in Boston. The figure below shows the predictions the model makes for some home prices. The $X$-axis represents the median value of the prices of similar homes within a bin, while the $Y$-axis represents the home value our linear model predicts. The dotted line represents points where $X=Y$. When reading the diagram, the closer the point is to the dotted line, better the model's prediction.
+We first show the result of our model. The dataset [UCI Housing Data Set](http://paddlemodels.bj.bcebos.com/uci_housing/housing.data) is used to train a linear model to predict the home prices in Boston. The figure below shows the predictions the model makes for some home prices. The $X$-axis represents the median value of the prices of similar homes within a bin, while the $Y$-axis represents the home value our linear model predicts. The dotted line represents points where $X=Y$. When reading the diagram, the closer the point is to the dotted line, better the model's prediction.
 <p align="center">
     <img src = "image/predictions_en.png" width=400><br/>
     Figure 1. Predicted Value V.S. Actual Value
@@ -127,7 +127,7 @@ except ImportError:
 
 ```
 
-We encapsulated the [UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing) in our Python module `uci_housing`.  This module can
+We encapsulated the [UCI Housing Data Set](http://paddlemodels.bj.bcebos.com/uci_housing/housing.data) in our Python module `uci_housing`.  This module can
 
 1. download the dataset to `~/.cache/paddle/dataset/uci_housing/housing.data`, if you haven't yet, and
 2.  [preprocess](#preprocessing) the dataset.

01.fit_a_line/index.cn.html

@@ -55,7 +55,7 @@ $$y_i = \omega_1x_{i1} + \omega_2x_{i2} + \ldots + \omega_dx_{id} + b,  i=1,\ldo
 初看起来，这个假设实在过于简单了，变量间的真实关系很难是线性的。但由于线性回归模型有形式简单和易于建模分析的优点，它在实际问题中得到了大量的应用。很多经典的统计学习、机器学习书籍\[[2,3,4](#参考文献)\]也选择对线性模型独立成章重点讲解。
 
 ## 效果展示
-我们使用从[UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing)获得的波士顿房价数据集进行模型的训练和预测。下面的散点图展示了使用模型对部分房屋价格进行的预测。其中，每个点的横坐标表示同一类房屋真实价格的中位数，纵坐标表示线性回归模型根据特征预测的结果，当二者值完全相等的时候就会落在虚线上。所以模型预测得越准确，则点离虚线越近。
+我们使用从[UCI Housing Data Set](http://paddlemodels.bj.bcebos.com/uci_housing/housing.data)获得的波士顿房价数据集进行模型的训练和预测。下面的散点图展示了使用模型对部分房屋价格进行的预测。其中，每个点的横坐标表示同一类房屋真实价格的中位数，纵坐标表示线性回归模型根据特征预测的结果，当二者值完全相等的时候就会落在虚线上。所以模型预测得越准确，则点离虚线越近。
 <p align="center">
     <img src = "https://github.com/PaddlePaddle/book/blob/develop/01.fit_a_line/image/predictions.png?raw=true" width=400><br/>
     图1. 预测值 V.S. 真实值
@@ -150,7 +150,7 @@ import sys
 from __future__ import print_function
 ```
 
-我们通过uci_housing模块引入了数据集合[UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing)
+我们通过uci_housing模块引入了数据集合[UCI Housing Data Set](http://paddlemodels.bj.bcebos.com/uci_housing/housing.data)
 
 其中，在uci_housing模块中封装了：
 

01.fit_a_line/index.html

@@ -59,7 +59,7 @@ $$y_i = \omega_1x_{i,1} + \omega_2x_{i,2} + \ldots + \omega_dx_{i,d} + b,  i=1,\
 where $\vec{\omega}$ and $b$ are the model parameters we want to estimate. Once they are learned, we will be able to predict the price of a home, given the attributes associated with it. We call this model **Linear Regression**. In other words, we want to regress a value against several values linearly. In practice, a linear model is often too simplistic to capture the real relationships between the variables. Yet, because Linear Regression is easy to train and analyze, it has been applied to a large number of real problems. As a result, it is an important topic in many classic Statistical Learning and Machine Learning textbooks \[[2,3,4](#References)\].
 
 ## Results Demonstration
-We first show the result of our model. The dataset [UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing) is used to train a linear model to predict the home prices in Boston. The figure below shows the predictions the model makes for some home prices. The $X$-axis represents the median value of the prices of similar homes within a bin, while the $Y$-axis represents the home value our linear model predicts. The dotted line represents points where $X=Y$. When reading the diagram, the closer the point is to the dotted line, better the model's prediction.
+We first show the result of our model. The dataset [UCI Housing Data Set](http://paddlemodels.bj.bcebos.com/uci_housing/housing.data) is used to train a linear model to predict the home prices in Boston. The figure below shows the predictions the model makes for some home prices. The $X$-axis represents the median value of the prices of similar homes within a bin, while the $Y$-axis represents the home value our linear model predicts. The dotted line represents points where $X=Y$. When reading the diagram, the closer the point is to the dotted line, better the model's prediction.
 <p align="center">
     <img src = "image/predictions_en.png" width=400><br/>
     Figure 1. Predicted Value V.S. Actual Value
@@ -169,7 +169,7 @@ except ImportError:
 
 ```
 
-We encapsulated the [UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing) in our Python module `uci_housing`.  This module can
+We encapsulated the [UCI Housing Data Set](http://paddlemodels.bj.bcebos.com/uci_housing/housing.data) in our Python module `uci_housing`.  This module can
 
 1. download the dataset to `~/.cache/paddle/dataset/uci_housing/housing.data`, if you haven't yet, and
 2.  [preprocess](#preprocessing) the dataset.