ninjakx

#include <iostream>
#include <vector>
#include<map>
#include<queue>
#include<math.h>
#include<vector>
#include <cassert>
#include <algorithm>
using namespace std;

ninjakx

%%time 
a = nearest_ngbr_raw(arr)

ninjakx

%%time 
a = nearest_ngbr_raw(arr)

ninjakx

import numpy as np
%load_ext line_profiler

ninjakx

%%writefile simulation.py
import numpy as np
def comp_inner_raw(i, x):
    res = np.zeros(x.shape[0], dtype=np.float64)
    for j in range(x.shape[0]):
        res[j] = np.sqrt(np.sum((i-x[j])**2))
    return res
  
def nearest_ngbr_raw(x):
    dist = {}

ninjakx

from simulation import nearest_ngbr_raw
arr = np.random.rand(1000,800)
%lprun -T sim_result -f nearest_ngbr_raw nearest_ngbr_raw(arr)
print(open('sim_result', 'r').read())

ninjakx

from numba import jit, njit
@jit('float64[:](float64[:], float64[:, :])')
def comp_inner_numba(i, x):
    res = np.zeros(x.shape[0], dtype=np.float64)
    for j in range(x.shape[0]):
        res[j] = np.sqrt(np.sum((i-x[j])**2))
    return res

def nearest_ngbr_numba(x):
    dist = {}

ninjakx

%%time 
b = nearest_ngbr_numba(arr)

ninjakx

#include <cstdlib>
#include <algorithm>
#include <numeric>
#include <vector>
#include <stdio.h>
#include <iostream>
#include <map>
#include <cmath>
#include <math.h>

ninjakx

%module myknn
#define SWIGPYTHON_BUILTIN

%{
  #include "numpy/arrayobject.h"
  #define SWIG_FILE_WITH_INIT  /* To import_array() below */
  #include "myknn.h"
%}

%include "std_map.i"