Github user njayaram2 commented on a diff in the pull request:
https://github.com/apache/madlib/pull/243#discussion_r175947887
--- Diff: src/modules/convex/algo/igd.hpp ---
@@ -90,20 +90,27 @@ IGD<State, ConstState, Task>::transition(state_type
&state,
for (int curr_epoch=0; curr_epoch < n_epochs; curr_epoch++) {
double loss = 0.0;
- for (int curr_batch=0, curr_batch_row_index=0; curr_batch <
n_batches;
- curr_batch++, curr_batch_row_index += batch_size) {
- Matrix X_batch;
- ColumnVector y_batch;
- if (curr_batch == n_batches-1) {
- // last batch
- X_batch =
tuple.indVar.bottomRows(n_rows-curr_batch_row_index);
- y_batch = tuple.depVar.tail(n_rows-curr_batch_row_index);
- } else {
- X_batch = tuple.indVar.block(curr_batch_row_index, 0,
batch_size, n_ind_cols);
- y_batch = tuple.depVar.segment(curr_batch_row_index,
batch_size);
- }
- loss += Task::getLossAndUpdateModel(
- state.task.model, X_batch, y_batch, state.task.stepsize);
+ int random_curr_batch[n_batches];
+ for(int i=0; i<n_batches; i++) {
+ random_curr_batch[i] = i;
+ }
+ int curr_batch_row_index = 0;
+ std::random_shuffle(&random_curr_batch[0],
&random_curr_batch[n_batches]);
+ for (int i=0; i < n_batches; i++) {
+ int curr_batch = random_curr_batch[i];
+ int curr_batch_row_index = curr_batch * batch_size;
+ Matrix X_batch;
+ Matrix Y_batch;
+ if (curr_batch == n_batches-1) {
+ // last batch
+ X_batch =
tuple.indVar.bottomRows(n_rows-curr_batch_row_index);
+ Y_batch =
tuple.depVar.bottomRows(n_rows-curr_batch_row_index);
+ } else {
+ X_batch = tuple.indVar.block(curr_batch_row_index, 0,
batch_size, n_ind_cols);
+ Y_batch = tuple.depVar.block(curr_batch_row_index, 0,
batch_size, n_ind_cols);
--- End diff --
Yes, that seems right.
---
