Creating a DML cookbook sounds very useful ... especially for new data scientists starting to pick up DML. It shows vectorization avoiding loops, and people are familiar with the semantics. There may be more nuggets like that through-out the DML algorithms. And the list will grow in the course of time.
Regards, Berthold Reinwald IBM Almaden Research Center office: (408) 927 2208; T/L: 457 2208 e-mail: [email protected] From: Shirish Tatikonda <[email protected]> To: [email protected] Date: 12/16/2015 10:03 PM Subject: Re: DML example on main SystemML website Deron, Along with such a complete algorithm, we could also include one/two common and useful DML snippets. We could also create a "DML Cookbook" with such snippets and keep adding more over time. Some example snippets are below -- note that I created them quite a while back, and they may need revision and testing. *Classifier Performance* # Confusion matrix cm = table(truthLabels, predictedLabels) TP = as.scalar(cm[1,1]) TN = as.scalar(cm[2,2]) FP = as.scalar(cm[1,2]) FN = as.scalar(cm[2,1]) accuracy = (TP+TN)/nrow(truthLabels) precision = TP / (TP+FP) recall = TP / (TP+FN) print("Accuracy = " + accuracy + ", Precision = " + precision + ", Recall = " + recall); *Covariance matrix* A = read("input.mtx"); N = nrow(A); # column means mu = colSums(A)/N; # Covarianace matrix C = (t(A) %*% A)/(N-1) - (N/(N-1))*t(mu) %*% mu; *Select rows satisfying a predicate* ind = diag(ppred(A[,1], thresh, ">")); ind = removeEmpty(target=ind, margin="rows"); result = ind %*% A; *Center and Scale columns* A = read("input.mtx"); cm = colMeans(A); cvars = (colSums (A^2)); cvars = (cvars - N*(cm^2))/(N-1); Ascaled = (A-cm)/sqrt(cvars); *Random shuffling of rows* N = nrow(A); s = sample(N, N, replace=FALSE); tab = table(seq(1:N), s); result = tab %*% A; On Wed, Dec 16, 2015 at 5:59 PM, Deron Eriksson <[email protected]> wrote: > That example is perfect. Concise and powerful. Thank you Fred. > > Deron > > > On Wed, Dec 16, 2015 at 4:16 PM, Frederick R Reiss <[email protected]> > wrote: > > > We can use the Poisson nonnegative matrix factorization example from last > > week's webcast: > > > > i = 0 > > while(i < max_iterations) { > > H = (H * (t(W) %*% (V/(W%*%H + epsilon)))) / t(colSums(W)) > > W = (W * ((V/(W%*%H) + epsilon) %*% t(H))) / t(rowSums(H)) > > i = i + 1; > > } > > > > > > Sound ok to everyone? > > > > Fred > > > > > > [image: Inactive hide details for Deron Eriksson ---12/16/2015 04:02:26 > > PM---Hi, I think the main SystemML website at http://systemml.i]Deron > > Eriksson ---12/16/2015 04:02:26 PM---Hi, I think the main SystemML > website > > at http://systemml.incubator.apache.org/ > > > > From: Deron Eriksson <[email protected]> > > To: [email protected] > > Date: 12/16/2015 04:02 PM > > Subject: DML example on main SystemML website > > ------------------------------ > > > > > > > > Hi, > > > > I think the main SystemML website at > http://systemml.incubator.apache.org/ > > needs to be updated so that the DML example is an actual algorithm or at > > least a fragment of an algorithm. > > > > Does anyone have a recommendation for a short, concise example that shows > > the power of DML? > > > > Thanks! > > Deron > > > > > > >
