#include <stdio.h>
#include "support.hh"

// Function to calculate the gradient.  I recommend you write the code
// this way so that next week, when the gradient is not as trivial to
// write your code will not need to change much.
vector gradient(vector x, double y, vector w)
{
  vector grad(w.dimension());

  // Calculate gradient for single point and put into 'grad' vector

  return grad;
}

main()
{
  // Read data into matrix 'x'
  matrix x(read_data("set1"));

  // # of rows of x is # of data points
  int points = x.rows();
  vector y(points);

  // Put y's into separate vector, augment x vectors with -1
  // (see problem set, problem #1)
  for (int i = 0; i < points; i++) {
    y[i] = x[i][2];
    x[i][2] = -1;
  }

  // Put steepest descent loop here.  Go through entire data set,
  // keeping cumulative sum of gradients at each point, then divide by
  // number of data points, multiply by learning rate and update
  // weights.  Repeat until magnitude of change of weights drops below
  // some minimum value.

  // To calculate magnitude of some vector, use v.magnitude();

  // Example of how to access data
  int trial = 10;
  cout << "Tenth data triplet: " << "x = {" << x[trial - 1][0] 
       << ", " << x[trial - 1][1] << "}, y = " << y[trial - 1] << "\n";

  // Print ten random numbers
  // random_number() returns a random # between -1 and 1
  // Use it to initialize initial weight vector
  cout << "\nTen random numbers:\n";
  for (i = 0; i < 10; i++)
    cout << random_number() << "\n";
}