tastyminerals

MultiDataSetIterator iterator = new RecordReaderMultiDataSetIterator.Builder(batchSize)
        .addReader("csvInput", featuresReader)
        .addReader("csvLabels", labelsReader)
        .addInput("csvInput") //Input: all columns from input reader
        .addOutput("csvLabels", 0, 3) //Output 1: columns 0 to 3 inclusive
        .addOutputOneHot("csvLabels", 4, numClasses)   //Output 2: column 4 -> convert to one-hot for classification
        .build();

tastyminerals

  // init DL4J seq readers
  val seqWordsReader = new CSVSequenceRecordReader()
  seqWordsReader.initialize(new FileSplit(new File(wordsFileSavePath)))

  val seqFeatsReader = new CSVSequenceRecordReader()
  seqFeatsReader.initialize(new FileSplit(new File(featsFileSavePath)))

  val seqLabelsReader = new CSVSequenceRecordReader()
  seqLabelsReader.initialize(new FileSplit(new File(labelsFileSavePath)))

tastyminerals

  // init DL4J seq readers
  val seqWordsReader = new CSVSequenceRecordReader()
  seqWordsReader.initialize(new FileSplit(new File(wordsFileSavePath)))

  val seqFeatsReader = new CSVSequenceRecordReader()
  seqFeatsReader.initialize(new FileSplit(new File(featsFileSavePath)))

  val seqLabelsReader = new CSVSequenceRecordReader()
  seqLabelsReader.initialize(new FileSplit(new File(labelsFileSavePath)))

tastyminerals

class NetworkInit(vocabSize: Int) {
  private val embeddingWidth = DatasetTools.getTomlConfTable("romain").getLong("inputsize").toInt
  private val hiddenSize = DatasetTools.getTomlConfTable("romain").getLong("hiddensize").toInt
  private val numberOfFeats = DatasetTools.getTomlConfTable("romain").getLong("feats").toInt
  private val numberOfClasses = DatasetTools.getTomlConfTable("romain").getLong("classes").toInt

  val config: ComputationGraphConfiguration = new NeuralNetConfiguration.Builder()
    .learningRate(DatasetTools.getTomlConfTable("romain").getDouble("minlr"))
    .graphBuilder()
    .addInputs("wordIndeces")

tastyminerals

class NetworkInit(vocabSize: Int) {
  private val embeddingWidth = 100
  private val hiddenSize = 200
  private val numberOfFeats = 9
  private val numberOfClasses = 1

  val config: ComputationGraphConfiguration = new NeuralNetConfiguration.Builder()
    .learningRate(DatasetTools.getTomlConfTable("romain").getDouble("minlr"))
    .graphBuilder()
    .addInputs("wordIndeces")

tastyminerals

#!/usr/bin/rdmd
import std.algorithm : cartesianProduct;
import std.array;
import std.container.rbtree : redBlackTree;
import std.stdio;


int minimum_coins(int target, in int[] denominations)
{
    auto origSet = redBlackTree(target);

tastyminerals

Description	NumPy (MKL) (sec.)	Julia (sec.)
Dot (scalar) product of two 300000 arrays (float64), (1000 loops)	0.03528142820068751	0.027905 (x1/1.3)
Element-wise sum of two 100x100 matrices (int), (1000 loops)	0.0037877704002312385	0.0061 (x1.6)
Element-wise multiplication of two 100x100 matrices (float64), (1000 loops)	0.004193491550176986	0.032161 (x7.7)
L2 norm of 500x600 matrix (float64), (1000 loops)	0.023907507749936486	0.096 (x4)
Matrix product of 500x600 and 600x500 matrices (float64)	0.0018566828504845035	0.01988 (x10.7)
Sort of 500x600 matrix (float64)	**0.0103262

tastyminerals

using BenchmarkTools
using Random
using LinearAlgebra


BenchmarkTools.DEFAULT_PARAMETERS.evals = 20

# define arrays and matrices
rows, cols = 500, 600
reduceRows, reduceCols = Int(rows / 5), Int(cols / 6)

tastyminerals

import argparse
from collections import defaultdict as dd
from time import perf_counter as timer

import numpy as np


def functions(nruns=1):
    rows, cols = 500, 600
    reduceRows, reduceCols = rows / 5, cols / 6