• Ed (37/37) Mar 06 2013 I'm new to D and am trying to implement simple sparse vectors
• jerro (5/7) Mar 07 2013 Errors of this magnitude are to be expected. the value of accum
• Rene Zwanenburg (5/10) Mar 07 2013 This.
• Nick B (5/42) Mar 10 2013 Hi Ed
• Danny Arends (8/8) Mar 10 2013 You could also try to use a Kahan Accumulator to 'fix' this

"Ed" <steveb microsoft.com> writes:

I'm new to D and am trying to implement simple sparse vectors 
using associative arrays, but I'm getting fairly large floating 
point errors. Example code for sparse dot product:

import std.stdio;
import std.math;
import std.random;
static import std.datetime;

int main(string[] args) {
   double[int] v1;
   double[int] v2;
   Random gen;
   gen.seed(cast(uint)std.datetime.Clock.currTime().stdTime());

   double accum = 0;
   double val;
   foreach(i;1 .. 1000) {
     val = uniform(-1000.0,1000.0,gen);
     accum += val * val;
     v1[i] = val;
     v2[i] = val;
   }

   double accum2 = 0;
   double v2Val;
   foreach(k;v1.byKey()) {
     v2Val= v2.get(k,0);
     if(v2Val != 0) {
       accum2 += v1.get(k,0) * v2Val;
     }
   }

   writefln("accum - accum2 = %e", accum - accum2);

   return 0;
}

This outputs values such as:
accum - accum2 = -4.172325e-07
accum - accum2 = 2.384186e-07
accum - accum2 = 4.172325e-07

Are errors of this magnitude to be expected using doubles, or is 
this a compiler bug?

"jerro" <a a.com> writes:

 Are errors of this magnitude to be expected using doubles, or 
 is this a compiler bug?

Errors of this magnitude are to be expected. the value of accum 
in your example is somewhere around 3e+08, so the relative error 
is around 1e-15, and double.epsilon is 2.22045e-16.

By the way, you can use unpredictableSeed to get an unpredictable 
seed.

"Rene Zwanenburg" <renezwanenburg gmail.com> writes:

On Thursday, 7 March 2013 at 09:43:21 UTC, jerro wrote:
 Are errors of this magnitude to be expected using doubles, or 
 is this a compiler bug?

 Errors of this magnitude are to be expected. the value of accum 
 in your example is somewhere around 3e+08, so the relative 
 error is around 1e-15, and double.epsilon is 2.22045e-16.

This.

You can use reals to store the intermediary results. A real has 
the largest hardware supported size, which is 80 bits for x87. 
It's not a silver bullet but can be useful in cases like this.

"Nick B" <nick.barbalich gmail.com> writes:

On Thursday, 7 March 2013 at 07:03:04 UTC, Ed wrote:
 I'm new to D and am trying to implement simple sparse vectors 
 using associative arrays, but I'm getting fairly large floating 
 point errors. Example code for sparse dot product:

 import std.stdio;
 import std.math;
 import std.random;
 static import std.datetime;

 int main(string[] args) {
   double[int] v1;
   double[int] v2;
   Random gen;
   gen.seed(cast(uint)std.datetime.Clock.currTime().stdTime());

   double accum = 0;
   double val;
   foreach(i;1 .. 1000) {
     val = uniform(-1000.0,1000.0,gen);
     accum += val * val;
     v1[i] = val;
     v2[i] = val;
   }

   double accum2 = 0;
   double v2Val;
   foreach(k;v1.byKey()) {
     v2Val= v2.get(k,0);
     if(v2Val != 0) {
       accum2 += v1.get(k,0) * v2Val;
     }
   }

   writefln("accum - accum2 = %e", accum - accum2);

   return 0;
 }

 This outputs values such as:
 accum - accum2 = -4.172325e-07
 accum - accum2 = 2.384186e-07
 accum - accum2 = 4.172325e-07

 Are errors of this magnitude to be expected using doubles, or 
 is this a compiler bug?

Hi Ed

I also interested in simple sparse vectors.  Any chance this code 
could be published or put in a library ?

Nick

"Danny Arends" <Danny.Arends gmail.com> writes:

You could also try to use a Kahan Accumulator to 'fix' this 
problem.

See wikipedia: 
http://en.wikipedia.org/wiki/Kahan_summation_algorithm

Its pretty straight forward to implement.

Gr,
Danny Arends
http://www.dannyarends.nl