## digitalmars.D.learn - map reduce for functioneren with two parameters

- Martijn Pot (3/3) Aug 06 2014 I was wondering whether there is a way to use map reduce to
- H. S. Teoh via Digitalmars-d-learn (17/20) Aug 06 2014 You can probably do this if each element of the range contains the

I was wondering whether there is a way to use map reduce to calculate e.g. the weighted mean and weighted standard deviation. These would not only need the values, but also the weights.

Aug 06 2014

On Wed, Aug 06, 2014 at 06:44:44PM +0000, Martijn Pot via Digitalmars-d-learn wrote:I was wondering whether there is a way to use map reduce to calculate e.g. the weighted mean and weighted standard deviation. These would not only need the values, but also the weights.You can probably do this if each element of the range contains the weight. If it isn't already part of your structure, you can use zip to associate them together. Perhaps something like this: // Warning: untested code float[] values = ...; float[] weights = ...; assert(values.length == weights.length); auto weightedMean = reduce!( // b[0] == values[i]; b[1] == weights[i] (a,b) => a + b[0]*b[1] )(zip(values,weights)) / values.length; Of course, values and weights don't have to be arrays, zip works with arbitrary ranges. T -- People tell me that I'm skeptical, but I don't believe it.

Aug 06 2014