arunreddy

StandardSGD <LogisticRegressionFunction<>> sgdOpt(lrf);
sgdOpt.MaxIterations() = maxIterations;
sgdOpt.Tolerance() = tolerance;
sgdOpt.StepSize() = stepSize;
Log::Info << "Training model with SGD optimizer." << endl;

// This will train the model.
model.Train(sgdOpt);

arunreddy

// Without policy class..
Optimize(arma::mat& iterate){
  
  arma::mat v = arma::zeros<arma::mat>(iterate.n_rows, iterate.n_cols);
  
  for(;;){
    
    v = momentum*v - stepSize * gradient;
    iterate += v;
  }

arunreddy

/**
 * Stochastic Gradient Descent is a technique for minimizing a function which
 * can be expressed as a sum of other functions.  That is, suppose we have
 *
 */
class SGDMomentum{
 public:

  /**
   * Constructor

arunreddy

Keybase proof

I hereby claim:

• I am arunreddy on github.
• I am arunreddy (https://keybase.io/arunreddy) on keybase.
• I have a public key whose fingerprint is 70DE 4849 57EB F197 1C6C B887 AB43 972A 33B0 3B5C

To claim this, I am signing this object:

arunreddy

import os
import random
import string
import tempfile
import subprocess

def random_id(length=8):
    return ''.join(random.sample(string.ascii_letters + string.digits, length))

TEMPLATE_SERIAL = """

arunreddy

using PyPlot;
using Distributions;

TOPIC_N = 5;
VOCABULARY_SIZE = 1000;
DOC_NUM = 100;
TERM_PER_DOC = 200;

X = zeros(DOC_NUM,VOCABULARY_SIZE);

arunreddy

# Install ARCH Linux with encrypted file-system and UEFI
# The official installation guide (https://wiki.archlinux.org/index.php/Installation_Guide) contains a more verbose description.

# Download the archiso image from https://www.archlinux.org/
# Copy to a usb-drive
dd if=archlinux.img of=/dev/sdX bs=16M && sync # on linux

# Boot from the usb. If the usb fails to boot, make sure that secure boot is disabled in the BIOS configuration.

# Set swedish keymap

arunreddy

a = [1 -2 ; -3 4];
aPos = (a.>0.0).*a;
aInd = (a.<0.0).*a*-1;

arunreddy

#include <stdio.h>
int main()
{
    int i, x, y, sizeX, sizeY, width, height, count, c;

    /* All five shape types */
    const int features = 5;
    const int feature[][2] = {{2,1}, {1,2}, {3,1}, {1,3}, {2,2}};
    const int frameSize = 24;

arunreddy

CmdUtils.executeCommand("SMILExtract -C /media/MEDIA02/msthesis/emobase2010.conf -I example_audio1.wav -O targetFile.arff  -label1 );


    public static String executeCommand(String command) {

        StringBuffer output = new StringBuffer();
        Process p;
        try {
            p = Runtime.getRuntime().exec(command);
            p.waitFor();