marcusking01

package RandomizedOptimization;

import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics;
import shared.Trainer;

/**
 * A momentum convergence trainer trains a network
 * until convergence based on a sliding window of statistical variance on the error, using another trainer
 * @author Marcus King
 * @version 1.0

marcusking01

from __future__ import print_function
import csv
import re
from time import time

from sklearn.feature_extraction import DictVectorizer
from sklearn.cluster import KMeans, MiniBatchKMeans
from numpy import random

marcusking01

#!/usr/bin/ruby                                                                                                                                                                                                                      
 
require 'json'
require 'emr/common'
require 'digest'
require 'socket'
 
def run(cmd)
  if ! system(cmd) then
    raise "Command failed: #{cmd}"