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--- ece4580:module_surveillance:m2w2 [2017/04/03 10:42] – pvela
+++ ece4580:module_surveillance:m2w2 [2024/08/20 21:38] (current) – external edit 127.0.0.1
@@ Line 30: / Line 30: @@
 === Implementation ===
-  - Create a function that takes in a model and the (sequential) template data matrix, and returns the score. Call it ''scoreTarget''. Create a second function that takes in the template matrix data, plus the set of models, and returns both the index into the best scoring model (first argument) and its associated score (second argument). Call it ''matchTargetData.''  Since your model is a Gaussian mixture class instance, and you will have multiple such ones, they should be stored in an array (or at least you should do/have done something to that effect).
+  - Create a function that takes in a model and the (sequential) template data matrix, and returns the score. Call it ''scoreTarget''.
+  - Create a second function that takes in the template matrix data, plus the set of models, and returns both the index into the best scoring model (first argument) and its associated score (second argument). Call it ''matchTargetData.''  Since your model is a Gaussian mixture class instance, and you will have multiple such ones, they should be stored in an array (or at least you should do/have done something to that effect).
+  - Test out the ''matchTargetData'' function using the **exact same** data that generated the model. In practice you should get back the index to the model that the data was used to create. If you pass it in the same order as you created the models, then your ''matchTargetData'' output for the data should basically go from 1 to the number of models you have, in order.  Comment on whether this happened or not.  If some of the outputs were wrong, plot the cartoon visualization of the confused template models, plot the actual data being tested, and explain why the matching could have been mistaken.
+  - Test out the ''matchTargetData'' function using the **second template or paired data** for the person. Assess the accuracy of the output by computing (total correct) / (total test cases). Are the model assignment errors consistent with the mistakes made in the previous step when the exact same data was used? If different, is the mistake sensible or not? Support with visual and scoring evidence.
+  - Download someone else's data from the google drive location (hopefully there is more than one set of data there), and apply the same procedure to it. What this means is, use the first set of the data to train a set of Gaussian mixture models, then use the second set to test with. Report the accuracy, and answer the same questions as the previous step.
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