Description
Gaussian Process
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Gaussian Process
In this section, you are going to implement the Gaussian Process and visualize the result.
● Training data
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input.data is a 34×2 matrix. Every row corresponds to a 2D data point
(Xi,Yi).
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Yi = f(Xi) + i is a noisy observation, where i ~ N(∙|0, β-1). You can use β = 5 in this implementation.
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What you are going to do
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Part1: Apply Gaussian Process Regression to predict the distribution of f and visualize the result. Please use a rational quadratic kernel to compute similarities between different points.
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Details of the visualization:
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Show all training data points.
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Draw a line to represent the mean of f in range [-60,60].
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Mark the 95% confidence interval of f.
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(You can use matplotlib.pyplot to visualize the result, e.g. use matplotlib.pyplot.fill_between to mark the 95% confidence interval, or you can use any other package you like.)
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Part2: Optimize the kernel parameters by minimizing negative marginal log-likelihood, and visualize the result again. (You can use scipy.optimize.minimize to optimize the parameters.)
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Report
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Submit a report in pdf format. The report should be written in English.
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Report format:
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1. code with detailed explanations (20%)
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For example, show the formula of rational quadratic kernel and the process you optimize the kernel parameters
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Note that if you don’t explain your code, you cannot get any points in section 2 and 3 either.
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Part1 (10%)
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Part2 (10%)
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2. experiments settings and results (20%)
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Show the figures and the hyperparameters we asked you to show
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Note that if you don’t explain your code in the above section, you cannot get any points in this section either.
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Part1 (10%)
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Part2 (10%)
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3. observations and discussion (10%)
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Anything you want to discuss, such as comparing the performance when using different hyperparameters.
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Turn in
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Report (.pdf)
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Source code
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You should zip source code and report in one file and name it like ML_HW5-1_yourstudentID_name.zip, e.g. ML_HW5-1_0856XXX_王小明.zip.
P.S. If the zip file name has format error or the report is not in pdf format, there will be a penalty (-10). Please submit your homework before the deadline, late submission is not allowed.
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Packages allowed in this assignment:
You are only allowed to use numpy, scipy.optimize, scipy.spatial.distance, and package for visualizing results. Official introductions can be found online.
Important: scikit-learn is not allowed.