The year is 1983, the strategy of D´etente (de-escalation via verbal agreement) has failed. Both the United States of America and the Soviet Union have been stockpiling nuclear weapons and recent political events have brought the world on the cusp of total annihilation. As an operator for a nuclear early warning device you receive an unknown signal...
In this coursework, you will be given example points that follow an unknown signal. You will be required to reconstruct the signal (i.e. estimate a model given the data) and give the resulting error. Your solutions will be auto- marked with a variety of different inputs in order to test both the correctness and robustness of your system. You also need to write a report presenting and discussing your results.
Your final solution will use the linear least squares regression code that you have created in the worksheets which will need to be extended in order to cope with non-linear regression. Your solution should:
Read in a file containing a number of different line segments (each made up of 20 points)Determine the function type of line segment, e.g. linear/polynomial/unknown function (there is one unknown function), using least squares regression
• Produce the total reconstruction error/residual
Produce a figure showing the reconstructed line from the points if an optional argument is given.Note: All Code written for the least squares regression sections must be written by yourself. Any calls to Scipy’s regression solver or any other similar library are forbidden and will be treated as not implemented.
Each solution will go through 50 tests in order to test its correctness and robustness. These test files will be similar in nature to the train files that you have been provided. Each test will be worth 2 points broken down as the following:
• 0: Solution gave incorrect output format and/or didn’t finish execution.
• 1: Solution gave correct output format but a large error difference.
• 2: Solution gave correct output format and a small error difference.
Note: The ‘error difference’ will be the difference in error between your solution and a model solution we have coded. This will be different for every test and more information will be provided when the marks are released.
In addition, you are asked to write a short report discussing and showing your results/solution (more details below).
4.1 Solution [0 − 60%]
Your mark will depend on the quality of your solution according to (of in- creasing difficulty):
1. Solutions which have implemented the linear least squares regression.
2. Solutions with an extended least squares regression to handle polyno- mial inputs with few errors.
3. Solutions with an extended least squares regression fine-tuned to the output signal, but that fail on edge cases.
4. A very good solution that reconstructs every signal with minimum error.
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