assignments:assignment3
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assignments:assignment3 [2015/10/02 09:48] asa |
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| important for its function, and are therefore useful in differentiating between classes of proteins. | important for its function, and are therefore useful in differentiating between classes of proteins. | ||
| A given protein will typically contain only a handful of motifs, and | A given protein will typically contain only a handful of motifs, and | ||
| - | so the data is very sparse. It is also very high dimensional, since | + | so the data is very sparse. |
| + | Therefore, only the non-zero elements of the data are represented. | ||
| + | Each line in the file describes a single example and has the format: | ||
| + | |||
| + | <code> | ||
| + | d1scta_,a.1.1.2 31417:1.0 32645:1.0 39208:1.0 42164:1.0 .... | ||
| + | </code> | ||
| + | The first column is the ID of the protein, the second is the class it belongs to (the values for the class variable are ''a.1.1.2'', which is the given class of proteins, and ''rest'' which is the negative class representing the rest of the database), and the rest of the elements are pairs of the form ''feature_id:value'' - an id of a feature and the value associated with it. | ||
| + | This is an extension of the format used by LibSVM, that scikit-learn can read. | ||
| + | See a discussion [[http://scikit-learn.org/stable/datasets/#datasets-in-svmlight-libsvm-format | here]]. | ||
| + | |||
| + | We note that the data is very high dimensional since | ||
| the number of conserved patterns in the space of all proteins is | the number of conserved patterns in the space of all proteins is | ||
| large. | large. | ||
assignments/assignment3.txt · Last modified: 2016/09/20 09:34 by asa
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