#! /usr/bin/env python
# -*- coding: utf-8 -*-
# Coursework in Python 
from DAPICourseworkLibrary import *
from numpy import *
#
# Coursework 1 begins here
#
# Function to compute the prior distribution of the variable root from the data set
def Prior(theData, root, noStates):
    prior = zeros((noStates[root]), float )
# Coursework 1 task 1 should be inserted here
    
# end of Coursework 1 task 1
    return prior
# Function to compute a CPT with parent node varP and xchild node varC from the data array
# it is assumed that the states are designated by consecutive integers starting with 0
def CPT(theData, varC, varP, noStates):
    cPT = zeros((noStates[varC], noStates[varP]), float )
# Coursework 1 task 2 should be inserte4d here
   
# end of coursework 1 task 2
    return cPT
# Function to calculate the joint probability table of two variables in the data set
def JPT(theData, varRow, varCol, noStates):
    jPT = zeros((noStates[varRow], noStates[varCol]), float )
#Coursework 1 task 3 should be inserted here 
    
# end of coursework 1 task 3
    return jPT
#
# Function to convert a joint probability table to a conditional probability table
def JPT2CPT(aJPT):
#Coursework 1 task 4 should be inserted here 
   
# coursework 1 taks 4 ends here
    return aJPT

#
# Function to query a naive Bayesian network
def Query(theQuery, naiveBayes): 
    rootPdf = zeros((naiveBayes[0].shape[0]), float)
# Coursework 1 task 5 should be inserted here
  

# end of coursework 1 task 5
    return rootPdf
#
# End of Coursework 1
#
# Coursework 2 begins here
#
# Calculate the mutual information from the joint probability table of two variables
def MutualInformation(jP):
    mi=0.0
# Coursework 2 task 1 should be inserted here
   

# end of coursework 2 task 1
    return mi
#
# construct a dependency matrix for all the variables
def DependencyMatrix(theData, noVariables, noStates):
    MIMatrix = zeros((noVariables,noVariables))
# Coursework 2 task 2 should be inserted here
    

# end of coursework 2 task 2
    return MIMatrix
# Function to compute an ordered list of dependencies 
def DependencyList(depMatrix):
    depList=[]
# Coursework 2 task 3 should be inserted here
    

# end of coursework 2 task 3
    return array(depList2)
#
# Functions implementing the spanning tree algorithm
# Coursework 2 task 4

def SpanningTreeAlgorithm(depList, noVariables):
    spanningTree = []
  
    return array(spanningTree)
#
# End of coursework 2
#

#
# main program part for Coursework 1
#
noVariables, noRoots, noStates, noDataPoints, datain = ReadFile("Neurones.txt")
theData = array(datain)
AppendString("results.txt","Coursework One Results by dfg")
AppendString("results.txt","") #blank line
AppendString("results.txt","The prior probability of node 0")
prior = Prior(theData, 0, noStates)
AppendList("results.txt", prior)
#
# continue as described
#
#