What's the efficient way to multiply two arrays and get sum of multiplied values in Ruby?

What's the efficient way to multiply two arrays and get sum of multiply values in Ruby? I have two arrays in Ruby:

array_A 开发者_C百科= [1, 2, 1, 4, 5, 3, 2, 6, 5, 8, 9]
array_B = [3, 2, 4, 2, 5, 1, 3, 3, 7, 5, 4]

My aim is to get the sum value of array_A * array_B, i.e., 1*3 + 2*2 + 1*4 + ... + 8*5 + 9*4.

Because I need to calculate them million times in my apps, what's the most efficient way to do such calculations?

It's just like a matrix calcuation: 1* N matrix * N*1 matrix or a vector dot product.

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Update

I've just updated benchmarks according to new comments. Following Joshua's comment, the inject method will gain a 25% speedup, see array walking without to_a in the table below.

However since speed is the primary goal for the OP we have a new winner for the contest which reduces runtime from .34 to .22 in my benchmarks.

I still prefer inject method because it's more ruby-ish, but if speed matters then the while loop seems to be the way.

New Answer

You can always benchmark all these answers, I did it for curiosity:

> ./matrix.rb 
Rehearsal --------------------------------------------------------------
matrix method                1.500000   0.000000   1.500000 (  1.510685)
array walking                0.470000   0.010000   0.480000 (  0.475307)
array walking without to_a   0.340000   0.000000   0.340000 (  0.337244)
array zip                    0.590000   0.000000   0.590000 (  0.594954)
array zip 2                  0.500000   0.000000   0.500000 (  0.509500)
while loop                   0.220000   0.000000   0.220000 (  0.219851)
----------------------------------------------------- total: 3.630000sec

                                 user     system      total        real
matrix method                1.500000   0.000000   1.500000 (  1.501340)
array walking                0.480000   0.000000   0.480000 (  0.480052)
array walking without to_a   0.340000   0.000000   0.340000 (  0.338614)
array zip                    0.610000   0.010000   0.620000 (  0.625805)
array zip 2                  0.510000   0.000000   0.510000 (  0.506430)
while loop                   0.220000   0.000000   0.220000 (  0.220873)

Simple array walking wins, Matrix method is worse because it includes object instantiation. I think that if you want to beat the inject while method (to beat here means an order of magnitude fastest) you need to implement a C extension and bind it in your ruby program.

Here it's the script I've used

#!/usr/bin/env ruby

require 'benchmark'
require 'matrix'

array_A = [1, 2, 1, 4, 5, 3, 2, 6, 5, 8, 9]
array_B = [3, 2, 4, 2, 5, 1, 3, 3, 7, 5, 4]

def matrix_method a1, a2
  (Matrix.row_vector(a1) * Matrix.column_vector(a2)).element(0,0)
end

n = 100000

Benchmark.bmbm do |b|
  b.report('matrix method') { n.times { matrix_method(array_A, array_B) } }
  b.report('array walking') { n.times { (0...array_A.count).to_a.inject(0) {|r, i| r + array_A[i]*array_B[i]} } }
  b.report('array walking without to_a') { n.times { (0...array_A.count).inject(0) {|r, i| r + array_A[i]*array_B[i]} } }
  b.report('array zip') { n.times { array_A.zip(array_B).map{|i,j| i*j }.inject(:+) } }  
  b.report('array zip 2') { n.times { array_A.zip(array_B).inject(0) {|r, (a, b)| r + (a * b)} } }
  b.report('while loop') do
    n.times do
      sum, i, size = 0, 0, array_A.size
      while i < size
        sum += array_A[i] * array_B[i]
        i += 1
      end
      sum
    end
  end
end

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Walking through each element should be a must

(0...array_A.count).inject(0) {|r, i| r + array_A[i]*array_B[i]}

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I would start simple and use the Ruby matrix class:

require 'matrix'

a = Matrix.row_vector( [1, 2, 1, 4, 5, 3, 2, 6, 5, 8, 9])
b = Matrix.column_vector([3, 2, 4, 2, 5, 1, 3, 3, 7, 5, 4])

result= a * b
puts result.element(0,0)

If this turns out to be too slow, then do the exact same method but with an external math library.

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This is how I would do it

array_A.zip(array_B).map{|i,j| i*j }.inject(:+)

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This is another way:

array_A.zip(array_B).inject(0) {|r, (a, b)| r + (a * b)}

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Since speed is our primary criterion, I'm going to submit this method as it's fastest according to Peter's benchmarks.

sum, i, size = 0, 0, a1.size
while i < size
  sum += a1[i] * a2[i]
  i += 1
end
sum

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Try the NMatrix gem. It is a numerical computation library. I think it uses the same C and C++ libraries that Octave and Matlab uses.

You would be able to do the matrix multiplication like this:

require 'nmatrix'

array_A = [1, 2, 1, 4, 5, 3, 2, 6, 5, 8, 9]
array_B = [3, 2, 4, 2, 5, 1, 3, 3, 7, 5, 4]

vec_a = array_A.to_nm([1,array_A.length])    # create an NMatrix
vec_b = array_B.to_nm([1,array_B.length])

sum = vec_a.dot(vec_b.transpose)

I am not sure how the speed will compare using pure Ruby but I imagine it to be faster, especially for large and sparse vectors.

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array1.zip(array2).map{|x| x.inject(&:*)}.sum

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EDIT: Vector is not fastest (Marc Bollinger is totally right).

Here is the modified code with vector and n-times:

require 'benchmark'
require 'matrix'

array_A = [1, 2, 1, 4, 5, 3, 2, 6, 5, 8, 9]
array_B = [3, 2, 4, 2, 5, 1, 3, 3, 7, 5, 4]

vector_A = Vector[*array_A]
vector_B = Vector[*array_B]

def matrix_method a1, a2
  (Matrix.row_vector(a1) * Matrix.column_vector(a2)).element(0,0)
end

def vector_method a1, a2
  a1.inner_product(a2)
end

n = 100000

Benchmark.bmbm do |b|
  b.report('matrix method') { n.times { matrix_method(array_A, array_B) } }
  b.report('array walking') { n.times { (0...array_A.count).to_a.inject(0) {|r, i| r + array_A[i]*array_B[i]} } }
  b.report('array walking without to_a') { n.times { (0...array_A.count).inject(0) {|r, i| r + array_A[i]*array_B[i]} } }
  b.report('array zip') { n.times { array_A.zip(array_B).map{|i,j| i*j }.inject(:+) } }
  b.report('array zip 2') { n.times { array_A.zip(array_B).inject(0) {|r, (a, b)| r + (a * b)} } }
  b.report('while loop') do
    n.times do
      sum, i, size = 0, 0, array_A.size
      while i < size
        sum += array_A[i] * array_B[i]
        i += 1
      end
      sum
    end
  end
  b.report('vector') { n.times { vector_method(vector_A, vector_B) } }
end

And the results:

Rehearsal --------------------------------------------------------------
matrix method                0.860000   0.010000   0.870000 (  0.911755)
array walking                0.290000   0.000000   0.290000 (  0.294779)
array walking without to_a   0.190000   0.000000   0.190000 (  0.215780)
array zip                    0.420000   0.010000   0.430000 (  0.441830)
array zip 2                  0.340000   0.000000   0.340000 (  0.352058)
while loop                   0.080000   0.000000   0.080000 (  0.085314)
vector                       0.310000   0.000000   0.310000 (  0.325498)
----------------------------------------------------- total: 2.510000sec

                                 user     system      total        real
matrix method                0.870000   0.020000   0.890000 (  0.952630)
array walking                0.290000   0.000000   0.290000 (  0.340443)
array walking without to_a   0.220000   0.000000   0.220000 (  0.240651)
array zip                    0.400000   0.010000   0.410000 (  0.441829)
array zip 2                  0.330000   0.000000   0.330000 (  0.359365)
while loop                   0.080000   0.000000   0.080000 (  0.090099)
vector                       0.300000   0.010000   0.310000 (  0.325903)
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Too bad. :(