title: H2O.GeneralizedLowRankModel objective is NA when passing loss by column
body: <p>I am working with <code>h2o</code> <code>glrm</code> function. When I am trying to pass <code>loss_by_col</code> argument in order to specify different loss function for each column in my DataFrame (I have normal, poisson and binomial variables, so I am passing "Quadratic", "Poisson" and "Logistic" loss), the objective is not getting computed. The <code>testmodel@model$objective</code> returns <code>NaN</code>. But at the same time summary shows that there was few iterations made and objective was NA for all of them. The quality of model is very bad, but the archetypes are somehow computed. So I am confused. How should pass different loss for every variable in my dataset? Here is a (i hope) reproducible example:</p>
  
  <pre><code>df &lt;- data.frame(p1 = rpois(100, 5), n1 = rnorm(100), b1 = rbinom(100, 1, 0.5))
  df$b1 &lt;- factor(df$b1)
  h2df &lt;- as.h2o(df)
  
  testmodel &lt;- h2o.glrm(h2df,
           k=3,
           loss_by_col=c("Poisson", "Quadratic", "Logistic"),
           transform="STANDARDIZE")
  testmodel@model$objective
  summary(testmodel)
  plot(testmodel)
  </code></pre>