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## Demo
set.seed(12321)
W_all_test1 = list(matrix(rnorm(1, 0, 0.5), nc = 1), matrix(10, nc = 1))
b_all_test1 = list(matrix(rnorm(1, 0, 0.5), nc = 1), matrix(-5, nc = 1))
X = matrix(rnorm(200, 0, 2), ncol = 1)
Y = apply(X, 1, function(x){forward_propagation(x, W_all_test1, b_all_test1)})
X.train = X[1:150,]
Y.train = Y[1:150]
X.test = X[151:200,]
Y.test = Y[151:200]

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cppFunction('
  std::vector<int> c_which(NumericVector x, Function f, List args){
    std::vector<int> ind = as< std::vector<int> >(f(x, args));
    std::vector<int> out(ind);
    std::vector<int>::iterator it;
    int j = 0;
    it = std::find(ind.begin(), ind.end(), 1);
    while(it++ != ind.end()){
      out[j++] = it - ind.begin();
      it = std::find(it, ind.end(), 1);

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set.seed(1001)
x = rbind(cbind(rnorm(50, 1, 0.3), rnorm(50, 2, 0.3)), cbind(rnorm(50, 2, 0.3), rnorm(50, 1, 0.3)))
y = c(rep(1, 50), rep(-1, 50))

h <- function(param, C = 1){
  b = param[length(param)]
  w = param[-length(param)]
  g = y * (x %*% w + b)
  0.5 * w %*% w + C * sum(1 - g[g < 1])
}

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library(data.table)
library(rbenchmark)
library(doMC)
registerDoMC(4)

set.seed(42)
nrows = 1e5
nsearch = 1e3
d = data.frame(a = sample(letters, nrows, rep = T), b = sample(1:nrows, rep = T), c = runif(nrows))
d.dt = data.table(d, key = 'a,b')

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##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Hang Zhang, Rutgers University, Email: [email protected]
## Modified by Thomas Wolf, HuggingFace Inc., Email: [email protected]
## Copyright (c) 2017-2018
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

"""Encoding Data Parallel"""