marcelcaraciolo

#-*-coding:utf-8 -*-


"""
Neste primeiro dojo, vamos tentar resolver um problema bem simples com o
 objetivo de ter um primeiro contato com as bibliotecas Numpy e Scipy.

Problema: Dada uma matriz de dimensão qualquer, encontrar o elemento de 
valor máximo e o elemento de valor mínimo.
   

marcelcaraciolo

#-*-coding:utf-8 -*-


"""
Neste primeiro dojo, vamos tentar resolver um problema bem simples com o
 objetivo de ter um primeiro contato com as bibliotecas Numpy e Scipy.

Problema: Dada uma matriz de dimensão qualquer, encontrar o elemento de 
valor máximo e o elemento de valor mínimo.
   

marcelcaraciolo

#-*-coding:utf-8 -*-


"""
Neste primeiro dojo, vamos tentar resolver um problema bem simples com o
 objetivo de ter um primeiro contato com as bibliotecas Numpy e Scipy.

Problema: Dada uma matriz de dimensão qualquer, encontrar o elemento de 
valor máximo e o elemento de valor mínimo.
   

marcelcaraciolo

import json

import urllib

API_KEY = "7084a6cb5d680c54df55fb3401ba042bfe9e5d55f61b65f0d8f027e0a0f486b2"

IP = '187.41.231.167'

url = "http://api.ipinfodb.com/v3/ip-city/?key=%s&ip=%s&format=json" % (API_KEY, IP)

marcelcaraciolo

from scipy import linalg
import numpy as np
from scipy.spatial.distance import cosine

#Let's define the matrix
user_ids = np.array(['Amanda', 'Anna', 'Bruno', 'Ricardo'])
item_ids = np.array(['Back to The Future', 'Conan',
                    'Lord of the Rings', 'Star Wars'])

matrix = np.matrix([

marcelcaraciolo

def spearman_coefficient(X, Y):
    """
    Considering the rows of X (and Y=X) as vectors, compute the
    distance matrix between each pair of vectors.

    Like  Pearson Coefficient , but compares relative ranking of preference
    values instead of preference values themselves. That is, each user's
    preferences are sorted and then assign a rank as their preference value,
    with 1 being assigned to the least preferred item.

marcelcaraciolo

import datetime
import sys
import random


def _rank_dists(ranks1, ranks2):
    """Finds the difference between the values in ranks1 and ranks2 for keys
    present in both dicts. If the arguments are not dicts, they are converted
    from (key, rank) sequences.
    """

marcelcaraciolo

# BM25F Model

def bm25(idf, tf, fl, avgfl, B, K1):
    # idf - inverse document frequency
    # tf - term frequency in the current document
    # fl - field length in the current document
    # avgfl - average field length across documents in collection
    # B, K1 - free paramters

    return idf * ((tf * (K1 + 1)) / (tf + K1 * (1 - B + B * (fl / avgfl))))

marcelcaraciolo

# setup.py
from distutils.core import setup
from distutils.extension import Extension
from Cython.Distutils import build_ext
# for notes on compiler flags see:
# http://docs.python.org/install/index.html
setup(
cmdclass = {’build_ext’: build_ext},
ext_modules = [Extension("calculate_z", ["calculate_z.pyx"])]
)

marcelcaraciolo

#include <stdio.h>

int fatorial(int n){

	if (n == 1) {
		
		return 1;
	
	}