No checks that the training data is unweighted when the user has opted to output additial statistics.

git-svn-id: https://svn.cms.waikato.ac.nz/svn/weka@11128 e0a1b77d-ad91-4216-81b1-defd5f83fa92
No checks that the training data is unweighted when the user has opted to output additial statistics.

git-svn-id: https://svn.cms.waikato.ac.nz/svn/weka@11129 e0a1b77d-ad91-4216-81b1-defd5f83fa92
Now checks that the training data is unweighted when the user has opted to output additional statistics.

git-svn-id: https://svn.cms.waikato.ac.nz/svn/weka@11130 e0a1b77d-ad91-4216-81b1-defd5f83fa92
SimpleLinearRegression can now process attributes with missing values. Instances with missing values are separated out and the mean class value of those instances is predicted when a new instance has a missing value for that attribute (as in DecisionStump). Now checks that the training data is unweighted if the user has opted to output additional statistics

git-svn-id: https://svn.cms.waikato.ac.nz/svn/weka@11131 e0a1b77d-ad91-4216-81b1-defd5f83fa92