提交 f0567602 编写于 作者: H Haonan

Also translate the chinese in the figures

上级 f510f1d4
...@@ -14,7 +14,7 @@ where $\omega_{d}$ and $b$ are the model parameters we want to estimate. Once th ...@@ -14,7 +14,7 @@ where $\omega_{d}$ and $b$ are the model parameters we want to estimate. Once th
We first show the training result of our model. We use the [UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing) to train a linear model and predict the house prices in Boston. The figure below shows the predictions the model makes for some house prices. The $X$ coordinate of each point represents the median value of the prices of a certain type of houses, while the $Y$ coordinate represents the predicted value by our linear model. When $X=Y$, the point lies exactly on the dotted line. In other words, the more precise the model predicts, the closer the point is to the dotted line. We first show the training result of our model. We use the [UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing) to train a linear model and predict the house prices in Boston. The figure below shows the predictions the model makes for some house prices. The $X$ coordinate of each point represents the median value of the prices of a certain type of houses, while the $Y$ coordinate represents the predicted value by our linear model. When $X=Y$, the point lies exactly on the dotted line. In other words, the more precise the model predicts, the closer the point is to the dotted line.
<p align="center"> <p align="center">
<img src = "image/predictions.png" width=400><br/> <img src = "image/predictions.png" width=400><br/>
Figure 1. Predicted Value V.S. Real Value Figure 1. Predicted Value V.S. Actual Value (波士顿房价预测->Prediction of Boston house prices; 预测价格->Predicted prices; 单位->Units; 实际价格->Actual prices)
</p> </p>
## Model Overview ## Model Overview
...@@ -25,9 +25,9 @@ In the UCI Housing Data Set, there are 13 house properties $x_{i,d}$ that are re ...@@ -25,9 +25,9 @@ In the UCI Housing Data Set, there are 13 house properties $x_{i,d}$ that are re
$$\hat{Y} = \omega_1X_{1} + \omega_2X_{2} + \ldots + \omega_{13}X_{13} + b$$ $$\hat{Y} = \omega_1X_{1} + \omega_2X_{2} + \ldots + \omega_{13}X_{13} + b$$
where $\hat{Y}$ is the predicted value used to differentiate from the real value $Y$. The model parameters to be learned are: $\omega_1, \ldots, \omega_{13}, b$, where $\omega$ are called the weights and $b$ is called the bias. where $\hat{Y}$ is the predicted value used to differentiate from the actual value $Y$. The model parameters to be learned are: $\omega_1, \ldots, \omega_{13}, b$, where $\omega$ are called the weights and $b$ is called the bias.
Now we need an optimization goal, so that with the learned parameters, $\hat{Y}$ is close to $Y$ as much as possible. Here we introduce the concept of [Loss Function (Cost Function)](https://en.wikipedia.org/wiki/Loss_function). The Loss Function has such property: given any pair of the real value $y_i$ and the predicted value $\hat{y_i}$, its output is always non-negative. This non-negative value reflects the model error. Now we need an optimization goal, so that with the learned parameters, $\hat{Y}$ is close to $Y$ as much as possible. Here we introduce the concept of [Loss Function (Cost Function)](https://en.wikipedia.org/wiki/Loss_function). The Loss Function has such property: given any pair of the actual value $y_i$ and the predicted value $\hat{y_i}$, its output is always non-negative. This non-negative value reflects the model error.
For Linear Regression, the most common Loss Function is [Mean Square Error (MSE)](https://en.wikipedia.org/wiki/Mean_squared_error) which has the following form: For Linear Regression, the most common Loss Function is [Mean Square Error (MSE)](https://en.wikipedia.org/wiki/Mean_squared_error) which has the following form:
...@@ -86,7 +86,7 @@ There are at least three reasons for [Feature Normalization](https://en.wikipedi ...@@ -86,7 +86,7 @@ There are at least three reasons for [Feature Normalization](https://en.wikipedi
<p align="center"> <p align="center">
<img src = "image/ranges.png" width=550><br/> <img src = "image/ranges.png" width=550><br/>
Figure 2. The value ranges of the features Figure 2. The value ranges of the features (特征尺度->Feature value range)
</p> </p>
#### Prepare Training and Test Sets #### Prepare Training and Test Sets
...@@ -169,7 +169,7 @@ Now we can use the trained model to do prediction. ...@@ -169,7 +169,7 @@ Now we can use the trained model to do prediction.
```bash ```bash
python predict.py python predict.py
``` ```
Here by default we use the model in `output/pass-00029` for prediction, and compare the real house price with the predicted one. The result is shown in `predictions.png`. Here by default we use the model in `output/pass-00029` for prediction, and compare the actual house price with the predicted one. The result is shown in `predictions.png`.
If you want to use another model or test on other data, you can pass in a new model path or data path: If you want to use another model or test on other data, you can pass in a new model path or data path:
```bash ```bash
python predict.py -m output/pass-00020 -t data/housing.test.npy python predict.py -m output/pass-00020 -t data/housing.test.npy
......
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