Created
February 11, 2014 01:53
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| import org.apache.commons.math3.linear._ | |
| import collection.mutable.PriorityQueue | |
| val dim = 100 | |
| val K = 100 | |
| val rand = new scala.util.Random() | |
| val data_ary = (0 until 5000).map{i => (i, (0 until dim).map{j => rand.nextGaussian}.toArray)} | |
| val data_vec = data_ary.map{t => (t._1, MatrixUtils.createRealVector(t._2))} | |
| def dot_ary(u : Array[Double], v : Array[Double]) : Double = { | |
| var i = 0 | |
| var res = 0.0 | |
| while(i < dim){ | |
| res += u(i) * v(i) | |
| i += 1 | |
| } | |
| res | |
| } | |
| def dot_vec(u : RealVector, v : RealVector) : Double = u.dotProduct(v) | |
| def max_dot_dumb[V](data : Iterable[(Int, V)], dotfn : (V, V) => Double) : Iterable[(Int, Iterable[(Int, Double)])] = { | |
| data.map{t => (t._1, data.map{s => (s._1, dotfn(t._2, s._2))}.toList.sortBy{-_._2}.take(K))} | |
| } | |
| def max_dot_pq[V](data : Iterable[(Int, V)], dotfn : (V, V) => Double) : Iterable[(Int, Iterable[(Int, Double)])] = { | |
| data.map{t => | |
| val q = new PriorityQueue[(Int, Double)]()(Ordering.by[(Int, Double), Double](-_._2)) | |
| var worst = 0.0 | |
| var size = 0 | |
| data.foreach{s => | |
| val dot = dotfn(t._2, s._2) | |
| if(size < K || dot > worst) { | |
| size += 1 | |
| q.enqueue((s._1, dot)) | |
| if(size > K) { | |
| q.dequeue | |
| size -= 1 | |
| } | |
| worst = q.head._2 | |
| } | |
| } | |
| (t._1, q.toList.sortBy{-_._2}) | |
| } | |
| } | |
| def time[V](fn: (Iterable[(Int, V)], (V, V) => Double) => Iterable[(Int, Iterable[(Int, Double)])], a : Iterable[(Int, V)], b : (V, V) => Double) : Double = { | |
| val startTime = System.nanoTime | |
| val res = fn(a, b) | |
| (System.nanoTime - startTime) / 1000000.0 | |
| } | |
| val trials = 5 | |
| val names = "dumb_vec,dumb_ary,pq_vec,pq_ary".split(",") | |
| val res = names.map{s => (s, new Array[Double](trials))}.toMap | |
| (0 until trials).foreach{t => | |
| names.foreach{m => | |
| val timems = m match{ | |
| case "dumb_vec" => time[RealVector](max_dot_dumb, data_vec, dot_vec) | |
| case "dumb_ary" => time[Array[Double]](max_dot_dumb, data_ary, dot_ary) | |
| case "pq_vec" => time[RealVector](max_dot_pq, data_vec, dot_vec) | |
| case "pq_ary" => time[Array[Double]](max_dot_pq, data_ary, dot_ary) | |
| } | |
| res(m)(t) = timems | |
| } | |
| } | |
| res.foreach{kv => | |
| val mean = kv._2.sum / trials | |
| val sd = math.sqrt(kv._2.map{v => (v - mean) * (v - mean)}.sum / trials) | |
| println(kv._1 + ": " + mean + " (" + sd +")") | |
| } |
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