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Commit cd59d98b authored by ShufanHuang's avatar ShufanHuang Committed by Yan Ni
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Fix broken link (#787) 

fix broken link for curfitting assessor doc
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docs/en_US/curvefittingAssessor.md
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...@@ -6,18 +6,16 @@ Curve Fitting Assessor is a LPA(learning, predicting, assessing) algorithm. It s ...@@ -6,18 +6,16 @@ Curve Fitting Assessor is a LPA(learning, predicting, assessing) algorithm. It s
In this algorithm, we use 12 curves to fit the learning curve, the large set of parametric curve models are chosen from [reference paper][1]. The learning curves' shape coincides with our prior knowlwdge about the form of learning curves: They are typically increasing, saturating functions. In this algorithm, we use 12 curves to fit the learning curve, the large set of parametric curve models are chosen from [reference paper][1]. The learning curves' shape coincides with our prior knowlwdge about the form of learning curves: They are typically increasing, saturating functions.
<p align="center"> ![](../img/curvefitting_learning_curve.PNG)
<img src="./learning_curve.PNG" alt="drawing"/>
</p>
We combine all learning curve models into a single, more powerful model. This combined model is given by a weighted linear combination: We combine all learning curve models into a single, more powerful model. This combined model is given by a weighted linear combination:
<p align="center">
<img src="./f_comb.gif" alt="drawing"/> ![](../img/curvefitting_f_comb.gif)
</p>
where the new combined parameter vector where the new combined parameter vector
<p align="center">
<img src="./expression_xi.gif" alt="drawing"/> ![](../img/curvefitting_expression_xi.gif)
</p>
Assuming additive a Gaussian noise and the noise parameter is initialized to its maximum likelihood estimate. Assuming additive a Gaussian noise and the noise parameter is initialized to its maximum likelihood estimate.
We determine the maximum probability value of the new combined parameter vector by learing the historical data. Use such value to predict the future trial performance, and stop the inadequate experiments to save computing resource. We determine the maximum probability value of the new combined parameter vector by learing the historical data. Use such value to predict the future trial performance, and stop the inadequate experiments to save computing resource.
...@@ -32,9 +30,7 @@ Concretely,this algorithm goes through three stages of learning, predicting and ...@@ -32,9 +30,7 @@ Concretely,this algorithm goes through three stages of learning, predicting and
The figure below is the result of our algorithm on MNIST trial history data, where the green point represents the data obtained by Assessor, the blue point represents the future but unknown data, and the red line is the Curve predicted by the Curve fitting assessor. The figure below is the result of our algorithm on MNIST trial history data, where the green point represents the data obtained by Assessor, the blue point represents the future but unknown data, and the red line is the Curve predicted by the Curve fitting assessor.
<p align="center"> ![](../img/curvefitting_example.PNG)
<img src="./example_of_curve_fitting.PNG" alt="drawing"/>
</p>
## 2. Usage ## 2. Usage
To use Curve Fitting Assessor, you should add the following spec in your experiment's YAML config file: To use Curve Fitting Assessor, you should add the following spec in your experiment's YAML config file:
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