Scaling attribute values using weights

Here's a process that multiplies each value of an attribute within one example set by a constant in another example set. The constants are specific for each attribute and the process uses weights derived from the example set. In effect, a matrix multiplication is happening.

At a high level, the process works as follows.

1. The Iris data set is used with weights being produced using "Weight By Information Gain"
2. These weights are transformed into an example set and stored for later use inside a Loop operator
3. A subprocess is used to make sure everything works in the right order (this technique is also used inside the Loop).
4. A "Loop Attributes" operator iterates over all attributes and generates a new attribute based on multiplying the existing value by a weight. The attribute name is required to be contained in the weights example set. 
5. The weight for each example is calculated with a combination of filtering and macro extraction.