alexgmcm

use std::collections::HashMap;
//use std::sync::Mutex;
use std::sync::Arc;
use std::thread;

pub fn frequency(input: &[&str], worker_count: usize) -> HashMap<char, usize> {
    let input_len = input.len();
    let lines_per_thread = ((input_len as f64) / (worker_count as f64)).ceil() as usize;
    let mut thread_handles = vec![];

alexgmcm

function LeftRotate(i, r, bitSize) {
if (i.isNegative()){
  //take twos complement representation
  i=bigInt(1).shiftLeft(32).subtract(i.abs()).and(bigInt("00000000ffffffff",16));
  var mask=bigInt(1).shiftLeft(bitSize).subtract(1);
  var x=i.shiftLeft(r).or(i.shiftRight(bitSize-r)).and(mask);
  var x_arr=x.toArray(2)
  var neg=false
  if (x_arr['value'][0]==1){
    //convert back

alexgmcm

function p = predictOneVsAll(all_theta, X)
%PREDICT Predict the label for a trained one-vs-all classifier. The labels 
%are in the range 1..K, where K = size(all_theta, 1). 
%  p = PREDICTONEVSALL(all_theta, X) will return a vector of predictions
%  for each example in the matrix X. Note that X contains the examples in
%  rows. all_theta is a matrix where the i-th row is a trained logistic
%  regression theta vector for the i-th class. You should set p to a vector
%  of values from 1..K (e.g., p = [1; 3; 1; 2] predicts classes 1, 3, 1, 2
%  for 4 examples) 

alexgmcm

function [all_theta] = oneVsAll(X, y, num_labels, lambda)
%ONEVSALL trains multiple logistic regression classifiers and returns all
%the classifiers in a matrix all_theta, where the i-th row of all_theta 
%corresponds to the classifier for label i
%   [all_theta] = ONEVSALL(X, y, num_labels, lambda) trains num_labels
%   logisitc regression classifiers and returns each of these classifiers
%   in a matrix all_theta, where the i-th row of all_theta corresponds 
%   to the classifier for label i

% Some useful variables

alexgmcm

function [J, grad] = costFunctionReg(theta, X, y, lambda)
%COSTFUNCTIONREG Compute cost and gradient for logistic regression with regularization
%   J = COSTFUNCTIONREG(theta, X, y, lambda) computes the cost of using
%   theta as the parameter for regularized logistic regression and the
%   gradient of the cost w.r.t. to the parameters. 

% Initialize some useful values
m = length(y); % number of training examples

% You need to return the following variables correctly