aiscool

import pandas as pd
from sklearn.metrics import accuracy_score
from sklearn.neural_network import MLPClassifier

df = pd.read_csv('Baseline.csv') #Baseline.csv dengan full.csv
maxi = 0
max_i = 0
max_j = 0
for i in range(10):
	data = df.sample(frac=0.7, random_state=i)

aiscool

#!/usr/bin/env python

"""
Original Script : https://gist.github.com/efaisal/4583986

Muat turun data waktu solat Jakim

Create database nama 'waktusolat' dalam MySQL

Lepas tu create table 'waktu_solat'

aiscool

import matplotlib.pyplot as plt
from sklearn import svm,metrics
import pandas as pd

df = pd.read_csv('training-test.csv')
df = df.iloc[:,1:]

train = df.sample(frac=0.9, random_state=255)
test = df.drop(train.index)

aiscool

package javaapplication1;

/**
 *
 * @author commander
 */

import java.io.BufferedReader;
import java.io.FileNotFoundException;

aiscool

//source : http://www.codebytes.in/2015/02/a-shortest-path-finding-algorithm.html
import java.util.*;

public class AStar {
    public static final int V_H_COST = 1;
    
    static class Cell{  
        int heuristicCost = 0; //Heuristic cost
        int finalCost = 0; //G+H
        int i, j;

aiscool

Port of this python code in Javascript.

LSH from Wikipedia:

Locality-sensitive hashing (LSH) reduces the dimensionality of high-dimensional data. LSH hashes input items so that similar items map to the same “buckets” with high probability (the number of buckets being much smaller than the universe of possible input items). LSH differs from conventional and cryptographic hash functions because it aims to maximize the probability of a “collision” for similar items. Locality-sensitive hashing has much in common with data clustering and nearest neighbor search.

More explanation:

• http://stackoverflow.com/questions/12952729/how-to-understand-locality-sensitive-hashing (Great simple explanation)
• https://en.wikipedia.org/wiki/Locality-sensitive_hashing
• http://infolab.stanford.edu/~ullman/mmds/ch3a.pdf