paulhbenoit

import sys
from sklearn.datasets import fetch_mldata
from sklearn import neighbors

options = [['2', 'uniform'], ['2', 'distance'], ['3', 'uniform'], ['3', 'distance'] ]
index = int(sys.argv[1])

# Fetch MNIST database
mnist = fetch_mldata('MNIST original')

paulhbenoit

import sys
from sklearn.datasets import fetch_mldata
from sklearn import neighbors

# Fetch MNIST database
mnist = fetch_mldata('MNIST original')

# crop to keep only 0's, 1's and 2's and normalize greyscale to 1.0
L = 18622
X, y = mnist.data[:L] / 255., mnist.target[:L]

paulhbenoit

from sklearn.datasets import fetch_mldata

# Fetch MNIST database
mnist = fetch_mldata('MNIST original')
X, y = mnist.data / 255., mnist.target

paulhbenoit

smith@wesson:~$ python qPiMc.py 
 3> 3.168
 1> 3.112
 2> 3.128
 9> 3.14
 6> 3.12
10> 3.104
 7> 3.152
 5> 3.128
 8> 3.192

paulhbenoit

#!/usr/bin/env python

import qarnot

conn = qarnot.connection.Connection(client_token='<YOUR_API_TOKEN>')
task = conn.create_task('pi Monte Carlo', 'docker-batch', '1-10')

d = conn.create_disk('disk')
d.add_file('piMc.py')
task.resources.append(d)

paulhbenoit

smith@wesson:~$ python piMc.py 1 1000
3.112
smith@wesson:~$ python piMc.py 2 1000
3.128

paulhbenoit

import random, sys

seed = int(sys.argv[1])
samples = int(sys.argv[2])
 
def in_circle(point):
    x = point[0]
    y = point[1]
    return x**2 + y**2 < 1
 

paulhbenoit

>>> import math
>>> abs(piMc2(1000,100) – math.pi) # 1000*100 samples
0.0033126535897927134
>>> abs(piMc2(10000,100) – math.pi) # 10000*100 samples
0.002076653589794475
>>> abs(piMc2(1000,1000) – math.pi) # 1000*1000 samples
0.00252734641020691
>>> abs(piMc2(10000,1000) – math.pi) # 10000*1000 samples
0.00011454641020680612
>>> abs(piMc2(1000,10000) – math.pi) # 1000*10000 samples

paulhbenoit

def piMc(seed, samples):
    random.seed(seed)
    count = inside_count = 0
    i = samples
    while i>0:
        point = random.random(),random.random()
        if in_circle(point):
            inside_count += 1
        count += 1
        i = i-1

paulhbenoit

import random
 
def in_circle(point):
    x = point[0]
    y = point[1]
    return x**2 + y**2 < 1
 
def piMc(samples):
    count = inside_count = 0
    i = samples