dhruvbird

# Columns:
# log(N) N Time/Iter(usec)[std::lower_bound] N Time/Iter(usec)[l2search]
23 8388608 0.460176 8388608 0.269376
24 16777216 0.621821 16777216 0.435599
25 33554432 0.735565 33554432 0.615605
26 67108864 0.856156 67108864 0.598811
27 134217728 0.959821 134217728 0.636344
28 268435456 1.552199 268435456 0.771024
29 536870912 1.158228 536870912 0.818436
30 1073741824 1.936374 1073741824 0.905859

dhruvbird

/* Compile and run:
 *
 * g++ -std=c++0x InPlaceMergeNsqrtN.cpp && ./a.out 23
 */

/* Merges 2 sorted arrays of size N and sqrt(N) in Theta(N) time
 *
 */
#include <iostream>
#include <vector>

dhruvbird

def unionRanges(intervals):
    intervals = sorted(intervals) # Sort by elem[0]
    ranges = [ ]
    if len(intervals) == 0:
        return [ ]
    start = intervals[0][0]
    end = intervals[0][1]
    for i in range(1, len(intervals)):
        if intervals[i][0] < end:
            end = max(end, intervals[i][1])

dhruvbird

#include <iostream>
#include <stack>
using namespace std;

// Counts numbers from 0 to n-1.
struct Counter {
	int i = 0;
	int n;
	bool first = true; // Tracks if this is the first invocation of the next() function
	Counter(int _n): n(_n) {}

dhruvbird

#include <iostream>
using namespace std;

// Counts numbers from 0 to n-1.
struct Counter {
    int i = 0;
    int n;
    bool first = true; // Tracks if this is the first invocation of the next() function
    Counter(int _n): n(_n) {}
    int* next() {

dhruvbird

#include <iostream>
#include <stack>
using namespace std;

struct TreeNode {
    int val;
    TreeNode *left, *right;
    TreeNode(int v): val(v), left(nullptr), right(nullptr) {}
};

dhruvbird

import torch

class AddTensorsModel(torch.nn.Module):
  def __init__(self):
    super().__init__();
    self.t1 = torch.Tensor([0, 5, 10])

  def helper(self, x, y):
    z = self.t1
    z = z + x + y

dhruvbird

m = AddtensorsModel()
res = m(torch.Tensor([1, 2, 3]), torch.Tensor([3, 4, 5]))
print(res)

dhruvbird

scripted = torch.jit.script(m)

dhruvbird

from torch.utils.mobile_optimizer import optimize_for_mobile
optimized_model = optimize_for_mobile(scripted)