apachaves

## Algoritmo completo modularizado (dividido em pequenas funções para facilitar compreensão). ##

library('plyr')
library('dplyr')

compute_probabilities <- function(df, target_variable_name){

    feature_variables_names <- names(df[,!(names(df) %in% target_variable_name)])
    rules_probabilities <- as.data.frame(rbind(NULL, NULL))

apachaves

## Algoritmo completo modularizado (dividido em pequenas funções para facilitar compreensão). ##

import numpy as np
import pandas as pd
from dfply import *

def compute_probabilities(df, target):
    prob_table = pd.DataFrame()
    feature_matrix = df.drop(target, axis=1)
    feature_variables = feature_matrix.columns.values

apachaves

#Université Paris-Saclay - M2 Data & Knowledge
#Web Data Models - Silviu Maniu - Final Project
#Anderson Pinheiro de Araújo Chaves

def readFileByChunk(file_object):
    while True:
        data = file_object.readline()
        if not data:
            break
        #elif data != ' ' and data != '\n' and data != '0' and data != '1':

apachaves

import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;

apachaves

import java.io.IOException;
import java.util.ArrayList;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;

apachaves

public void addFact(HashSet<Variable> newfacts) {
    for(Variable var : newfacts) {
		    this.facts.add(var);
		}
	}

apachaves

public FactBase forwardChaining(Formalism ruleBase, Formalism factBase) {
	FactBase updatedFactBase = (FactBase) factBase;
	FactBase oldFactBase = (FactBase) factBase;
	HornRuleBase hornRuleBase = (HornRuleBase) ruleBase;
		
	do {
		oldFactBase = updatedFactBase;
		for (HornRule rule : hornRuleBase.getRules()) {
			if (this.eval(rule, updatedFactBase)) {
				updatedFactBase.addFact(rule.getConclusions());

apachaves

public boolean eval(HornRule rule, FactBase fb) {
	for (Variable condition : rule.getConditions()) {
	boolean conditionMatched = false;			
		for (Variable fact : fb.getFact()) {
			if (condition.equals(fact))
				conditionMatched = true;
		}
		if (!conditionMatched)
			return false;
	}