| () //set up each data item and encode data in key-value pairs |
| () class mapper: |
| () setup(): |
| () initialize input element |
| () map(key,element): |
| () flag data source to reducer |
| () identify data type |
| () normalize data element |
| () cleanup(): |
| () emit (key,aligned element) |
| () //process key-value pairs and update local model |
| () class reducer: |
| () setup(): |
| () initialize aligned element |
| () initialize local situation representations |
| () reduce(key,elements): |
| () for element in elements: |
| () if element meets hypothesis: |
| () update local situation representation per flag |
| () cleanup(): |
| () emit (key,local situation representations) |
| () //adjust global model and perform predictions |
| () class outputter: |
| () setup(): |
| () initialize input elements |
| () initialize local situation representations |
| () initialize global situation representation |
| () output(key,elements,representations): |
| () for element in input elements: |
| () if element has proper local situation representation: |
| () update global situation |
| () train neural network |
| () estimate error |
| () update neural network weights |
| () else: |
| () perform neural network prediction |
| () perform regression prediction |
| () generate report |
| () cleanup(): |
| () emit report |
