#library(markovchain)

#create basic markov chains
markov1<-new("markovchain", states=c("a","b","c"), transitionMatrix=
               matrix(c(0.2,0.5,0.3,
                        0,1,0,
                        0.1,0.8,0.1),nrow=3, byrow=TRUE, dimnames=list(c("a","b","c"),
                                                                       c("a","b","c"))
               ))


mathematicaMatr <- markovchain:::zeros(5)
mathematicaMatr[1,] <- c(0, 1/3, 0, 2/3, 0)
mathematicaMatr[2,] <- c(1/2, 0, 0, 0, 1/2)
mathematicaMatr[3,] <- c(0, 0, 1/2, 1/2, 0)
mathematicaMatr[4,] <- c(0, 0, 1/2, 1/2, 0)
mathematicaMatr[5,] <- c(0, 0, 0, 0, 1)
statesNames <- letters[1:5]
mathematicaMc <- new("markovchain", transitionMatrix = mathematicaMatr,
                     name = "Mathematica MC", states = statesNames)

####end creating DTMC
context("Basic DTMC proprieties")

test_that("States are those that should be", {
  expect_equal(absorbingStates(markov1), "b")
  expect_equal(transientStates(markov1), c("a","c"))
  expect_equal(is.irreducible(mathematicaMc),FALSE)
  expect_equal(transientStates(mathematicaMc), c("a","b"))
  expect_equal(is.accessible(mathematicaMc, "a", "c"),TRUE)
  expect_equal(canonicForm(mathematicaMc)@transitionMatrix, markovchain:::.canonicFormRcpp(mathematicaMc)@transitionMatrix)
  expect_equal(recurrentClasses(mathematicaMc), list(c("c", "d"), c("e")))
  expect_equal(summary(mathematicaMc), list(closedClasses = list(c("c", "d"), c("e")), 
                                            recurrentClasses = list(c("c", "d"), c("e")),
                                            transientClasses = list(c("a", "b"))))
})

###testing proper conversion of objects
context("Conversion of objects")
provaMatr2Mc<-as(mathematicaMatr,"markovchain")

test_that("Conversion of objects", 
          {
            expect_equal(class(provaMatr2Mc)=="markovchain",TRUE)
          })


### Markovchain Fitting
sequence1 <- c("a", "b", "a", "a", NA, "a", "a", NA)
sequence2 <- c(NA, "a", "b", NA, "a", "a", "a", NA, "a", "b", "a", "b", "a", "b", "a", "a", "b", "b", "b", "a", NA)
mcFit <- markovchainFit(data = sequence1, byrow = FALSE, sanitize = TRUE)
mcFit2 <- markovchainFit(c("a","b","a","b"), sanitize = TRUE)

test_that("Fit should satisfy", {
  expect_equal((mcFit["logLikelihood"])[[1]], log(1/3) + 2*log(2/3))
  expect_equal(markovchainFit(data = sequence2, method = "bootstrap")["confidenceInterval"]
               [[1]]["confidenceLevel"][[1]], 0.95)
  expect_equal(mcFit2$upperEndpointMatrix, matrix(c(0,1,1,0), nrow = 2, byrow = TRUE,
                                                  dimnames = list(c("a", "b"), c("a", "b"))))
})


### Markovchain Fitting for bigger markov chain
bigseq <- rep(c("a", "b", "c"), 500000)
bigmcFit <- markovchainFit(bigseq)

test_that("MC Fit for large sequence 1", {
  expect_equal(bigmcFit$logLikelihood, 0)
  expect_equal(bigmcFit$confidenceLevel, 0.95)
  expect_equal(bigmcFit$estimate@transitionMatrix, bigmcFit$upperEndpointMatrix)
})

bigmcFit <- markovchainFit(bigseq, sanitize = TRUE)

test_that("MC Fit for large sequence 2", {
  expect_equal(bigmcFit$logLikelihood, 0)
  expect_equal(bigmcFit$confidenceLevel, 0.95)
  expect_equal(bigmcFit$estimate@transitionMatrix, bigmcFit$upperEndpointMatrix)
})


### Markovchain Fitting For dataframe or matrix as an input
matseq <- matrix(c("a", "b", "c", NA ,"b", "c"), nrow = 2, byrow = T)

# for matrix as input

test_that("Markovchain Fit for matrix as input", {
  
  # for matrix as input    
  
  expect_equal(markovchainFit(matseq)$estimate@transitionMatrix, 
               matrix(c(0, 1, 0, 0, 0, 1, 0, 0, 0), nrow = 3, 
                      byrow = TRUE, dimnames = list(c("a", "b", "c"), c("a", "b", "c"))))
  
  expect_equal(markovchainFit(matseq, sanitize = TRUE)$estimate@transitionMatrix, 
               matrix(c(0, 1, 0, 0, 0, 1, 1/3, 1/3, 1/3), nrow = 3, 
                      byrow = TRUE, dimnames = list(c("a", "b", "c"), c("a", "b", "c"))))
  
  # for data frame as input
  expect_equal(markovchainFit(as.data.frame(matseq))$estimate@transitionMatrix, 
               matrix(c(0, 1, 0, 0, 0, 1, 0, 0, 0), nrow = 3, 
                      byrow = TRUE, dimnames = list(c("a", "b", "c"), c("a", "b", "c"))))
  
  expect_equal(markovchainFit(as.data.frame(matseq), sanitize = TRUE)$estimate@transitionMatrix, 
               matrix(c(0, 1, 0, 0, 0, 1, 1/3, 1/3, 1/3), nrow = 3, 
                      byrow = TRUE, dimnames = list(c("a", "b", "c"), c("a", "b", "c"))))
  
})



### Markovchain Fitting(mle) with sanitize parameter
mle_sequence <- c("a", "b", NA, "b", "b", "a", "a", "a", "b", "b", NA, "b", "b", "a", "a", "b", "a", "a", "b", "c")
mle_fit1 <- markovchainFit(mle_sequence)
mle_fit2 <- markovchainFit(mle_sequence, sanitize = TRUE)

test_that("MarkovchainFit MLE", {
  expect_equal(mle_fit1$estimate@transitionMatrix, 
               matrix(c(0.5, 0.5, 0, 3/7, 3/7, 1/7, 0, 0, 0), nrow = 3, 
                      byrow = TRUE, dimnames = list(c("a", "b", "c"), c("a", "b", "c"))))
  
  expect_equal(mle_fit2$estimate@transitionMatrix, 
               matrix(c(0.5, 0.5, 0, 3/7, 3/7, 1/7, 1/3, 1/3, 1/3), nrow = 3, 
                      byrow = TRUE, dimnames = list(c("a", "b", "c"), c("a", "b", "c"))))
  
  expect_equal(mle_fit1$logLikelihood, mle_fit2$logLikelihood)
  
  expect_equal(mle_fit1$confidenceInterval, mle_fit2$confidenceInterval)
  
  expect_equal(mle_fit2$standardError, mle_fit2$standardError)
})

### Markovchain Fitting(laplace) with sanitize parameter
lap_sequence <- c("a", "b", NA, "b", "b", "a", "a", "a", "b", "b", NA, "b", "b", "a", "a", "b", "a", "a", "b", "c")
lap_fit1 <- markovchainFit(lap_sequence, "laplace")
lap_fit2 <- markovchainFit(lap_sequence, "laplace", sanitize = TRUE)

test_that("Markovchain Laplace", {
  expect_equal(lap_fit1$estimate@transitionMatrix, 
               matrix(c(0.5, 0.5, 0, 3/7, 3/7, 1/7, 0, 0, 0), nrow = 3, 
                      byrow = TRUE, dimnames = list(c("a", "b", "c"), c("a", "b", "c"))))
  
  expect_equal(lap_fit2$estimate@transitionMatrix, 
               matrix(c(0.5, 0.5, 0, 3/7, 3/7, 1/7, 1/3, 1/3, 1/3), nrow = 3, 
                      byrow = TRUE, dimnames = list(c("a", "b", "c"), c("a", "b", "c"))))
  
  expect_equal(lap_fit1$logLikelihood, lap_fit2$logLikelihood)
})

### Markovchain Fitting when some states are not present in the given sequence
mix_seq <- c("a", "b", NA, "b", "b", "a", "a", "a", "b", "b", NA, "b", "b", "a", "a", "b", "a", "a", "b", "c")

mix_fit1 <- markovchainFit(mix_seq, "mle", sanitize = TRUE, possibleStates = c("d"))
mix_fit2 <- markovchainFit(mix_seq, "laplace", sanitize = TRUE, possibleStates = c("d")) 
mix_fit3 <- markovchainFit(mix_seq, "map", sanitize = TRUE, possibleStates = c("d")) 


test_that("Mixture of Markovchain Fitting", {
  expect_equal(mix_fit2$estimate@transitionMatrix, 
               matrix(c(.5, .5, 0, 0, 3/7, 3/7, 1/7, 0,
                        1/4, 1/4, 1/4, 1/4, 1/4, 1/4, 1/4, 1/4), nrow = 4, byrow = TRUE,
                      dimnames = list(c("a", "b", "c", "d"), c("a", "b", "c", "d"))
               )
  )
  
  expect_equal(mix_fit1$estimate@transitionMatrix, 
               matrix(c(.5, .5, 0, 0, 3/7, 3/7, 1/7, 0,
                        1/4, 1/4, 1/4, 1/4, 1/4, 1/4, 1/4, 1/4), nrow = 4, byrow = TRUE,
                      dimnames = list(c("a", "b", "c", "d"), c("a", "b", "c", "d"))
               )
  )
  
  expect_equal(mix_fit3$estimate@transitionMatrix, 
               matrix(c(.5, .5, 0, 0, 3/7, 3/7, 1/7, 0,
                        1/4, 1/4, 1/4, 1/4, 1/4, 1/4, 1/4, 1/4), nrow = 4, byrow = TRUE,
                      dimnames = list(c("a", "b", "c", "d"), c("a", "b", "c", "d"))
               )
  )
  
})


### Test for createSequenceMatrix
rsequence <- c("a", "b", NA, "b", "b", "a", "a", "a", "b", "b", NA, "b", "b", "a", "a", "b", "a", "a", "b", "c")

test_that("createSequenceMatrix : Permutation of parameters",{
  expect_equal(createSequenceMatrix(rsequence, FALSE, FALSE), 
                   matrix(c(4, 4, 0, 3, 3, 1, 0, 0, 0), nrow = 3, 
                          byrow = TRUE, dimnames = list(c("a", "b", "c"), c("a", "b", "c"))))
  
  expect_equal(createSequenceMatrix(rsequence, FALSE, TRUE), 
               matrix(c(4, 4, 0, 3, 3, 1, 1, 1, 1), nrow = 3, 
                      byrow = TRUE, dimnames = list(c("a", "b", "c"), c("a", "b", "c"))))
  
  expect_equal(createSequenceMatrix(rsequence, TRUE, FALSE), 
               matrix(c(4/8, 4/8, 0, 3/7, 3/7, 1/7, 0, 0, 0), nrow = 3, 
                      byrow = TRUE, dimnames = list(c("a", "b", "c"), c("a", "b", "c"))))
  
  expect_equal(createSequenceMatrix(rsequence, TRUE, TRUE), 
               matrix(c(4/8, 4/8, 0, 3/7, 3/7, 1/7, 1/3, 1/3, 1/3), nrow = 3, 
                      byrow = TRUE, dimnames = list(c("a", "b", "c"), c("a", "b", "c"))))
})

### Test for createSequenceMatrix : input nx2 matrix
data <- matrix(c("a", "a", "b", "a", "b", "a", "b", "a", NA, "a", "a", "a", "a", "b", NA, "b"), ncol = 2,
               byrow = TRUE)

test_that("createSequenceMatrix : input as matrix",{
  expect_equal(createSequenceMatrix(data),
               matrix(c(2, 1, 3, 0), nrow = 2, byrow = TRUE, 
                      dimnames = list(c("a", "b"), c("a", "b"))))
  
  expect_equal(createSequenceMatrix(data, toRowProbs = TRUE),
               matrix(c(2/3, 1/3, 3/3, 0), nrow = 2, byrow = TRUE, 
                      dimnames = list(c("a", "b"), c("a", "b"))))
  
  expect_equal(createSequenceMatrix(data, toRowProbs = TRUE, possibleStates = "d",
                                    sanitize = TRUE),
               matrix(c(2/3, 1/3, 0, 1, 0, 0, 1/3, 1/3, 1/3), nrow = 3, 
                      byrow = TRUE, dimnames = list(c("a", "b", "d"), c("a", "b", "d"))))
})

### Test for markovchainSequence and rmarkovchain
statesNames <- c("a", "b", "c")
mcB <- new("markovchain", states = statesNames, 
           transitionMatrix = matrix(c(0.2, 0.5, 0.3, 0, 0.2, 0.8, 0.1, 0.8, 0.1), 
           nrow = 3, byrow = TRUE, dimnames = list(statesNames, statesNames)))

s1 <- markovchainSequence(10, mcB)
s2 <- markovchainSequence(10, mcB, include.t0 = TRUE)
s3 <- markovchainSequence(10, mcB, t0 = "b", include.t0 = TRUE)

s4 <- markovchainSequence(10, mcB, useRCpp = FALSE)
s5 <- markovchainSequence(10, mcB, include.t0 = TRUE, useRCpp = FALSE)
s6 <- markovchainSequence(10, mcB, t0 = "b", include.t0 = TRUE, useRCpp = FALSE)

test_that("Output format of markovchainSequence", {
  expect_equal(length(s1), 10)
  expect_equal(length(s2), 11)
  expect_equal(length(s3), 11)
  expect_equal(s3[1], "b")
  expect_equal(length(s4), 10)
  expect_equal(length(s5), 11)
  expect_equal(length(s6), 11)
  expect_equal(s6[1], "b")
})

statesNames <- c("a", "b", "c")
mcA <- new("markovchain", states = statesNames, transitionMatrix = 
             matrix(c(0.2, 0.5, 0.3, 0, 0.2, 0.8, 0.1, 0.8, 0.1), nrow = 3, 
                    byrow = TRUE, dimnames = list(statesNames, statesNames)))
mcB <- new("markovchain", states = statesNames, transitionMatrix = 
             matrix(c(0.2, 0.5, 0.3, 0, 0.2, 0.8, 0.1, 0.8, 0.1), nrow = 3, 
                    byrow = TRUE, dimnames = list(statesNames, statesNames)))
mcC <- new("markovchain", states = statesNames, transitionMatrix = 
             matrix(c(0.2, 0.5, 0.3, 0, 0.2, 0.8, 0.1, 0.8, 0.1), nrow = 3, 
                    byrow = TRUE, dimnames = list(statesNames, statesNames)))
mclist <- new("markovchainList", markovchains = list(mcA, mcB, mcC))

o1 <- rmarkovchain(15, mclist, "list")
o2 <- rmarkovchain(15, mclist, "matrix")
o3 <- rmarkovchain(15, mclist, "data.frame")

o4 <- rmarkovchain(15, mclist, "list", t0 = "a", include.t0 = TRUE)
o5 <- rmarkovchain(15, mclist, "matrix", t0 = "a", include.t0 = TRUE)
o6 <- rmarkovchain(15, mclist, "data.frame", t0 = "a", include.t0 = TRUE)

test_that("Output format of rmarkovchain", {
  expect_equal(length(o1), 15)
  expect_equal(length(o1[[1]]), 3)
  expect_equal(all(dim(o2) == c(15, 3)), TRUE)
  expect_equal(all(dim(o3) == c(45, 2)), TRUE)
  
  expect_equal(length(o4), 15)
  expect_equal(length(o4[[1]]), 4)
  expect_equal(o4[[1]][1], "a")
  expect_equal(all(dim(o5) == c(15, 4)), TRUE)
  expect_equal(all(o5[, 1] == "a"), TRUE)
  expect_equal(all(dim(o6) == c(60, 2)), TRUE)
})


### MAP fit function tests
data1 <- c("a", "b", "a", "c", "a", "b", "a", "b", "c", "b", "b", "a", "b")
data2 <- c("c", "a", "b")

test_that("MAP fits must satisfy", {
  expect_identical(markovchainFit(data1, method = "map")$estimate@transitionMatrix, 
                   markovchainFit(data1, method = "mle")$estimate@transitionMatrix)
  
  expect_identical(markovchainFit(data1, method = "map")$estimate@transitionMatrix, 
                   matrix(c(0.0, 0.6, 0.5, 
                           0.8, 0.2, 0.5, 
                           0.2, 0.2, 0.0), nrow = 3, 
                          dimnames = list(c("a", "b", "c"), c("a", "b", "c"))))
  
  expect_identical(markovchainFit(data1, method = "map", hyperparam = 
                                    matrix(c(2, 1, 3, 
                                             4, 5, 2, 
                                             2, 2, 1), nrow = 3, 
                                           dimnames = list(c("a", "b", "c"), c("a", "b", "c"))))$estimate@transitionMatrix, 
                   matrix(c(1/10, 3/10, 3/5, 
                            7/10, 5/10, 2/5, 
                            2/10, 2/10, 0), nrow = 3, 
                          dimnames = list(c("a", "b", "c"), c("a", "b", "c"))))
})

test_that("predictiveDistribution must satisfy", {
  expect_equal(predictiveDistribution(data1, character()), 0)
  
  expect_equal(predictiveDistribution(data1, data2, hyperparam = 
                                            matrix(c(2, 1, 3, 
                                                     4, 5, 2, 
                                                     2, 2, 1), nrow = 3, 
                                                   dimnames = list(c("a", "b", "c"), c("a", "b", "c")))), 
                   log(4 / 13))
})

test_that("inferHyperparam must satisfy", {
  expect_identical(inferHyperparam(data = data1)$dataInference, 
                   matrix(c(1, 4, 2, 
                            5, 2, 2, 
                            2, 2, 1), nrow = 3, 
                          dimnames = list(c("a", "b", "c"), c("a", "b", "c"))))
  
  expect_identical(inferHyperparam(transMatr = 
                                     matrix(c(0.0, 0.6, 0.5, 
                                              0.8, 0.2, 0.5, 
                                              0.2, 0.2, 0.0), nrow = 3, 
                                            dimnames = list(c("a", "b", "c"), c("a", "b", "c"))),
                                   scale = c(10, 10, 10))$scaledInference, 
                   matrix(c(0, 6, 5, 
                            8, 2, 5, 
                            2, 2, 0), nrow = 3, 
                          dimnames = list(c("a", "b", "c"), c("a", "b", "c"))))
})

pDRes <- c(log(3/2), log(3/2))
names(pDRes) <- c("a", "b")
test_that("priorDistribution must sastisfy", {
  expect_equal(priorDistribution(matrix(c(0.5, 0.5, 0.5, 0.5), 
                                            nrow = 2, 
                                            dimnames = list(c("a", "b"), c("a", "b"))), 
                                     matrix(c(2, 2, 2, 2), 
                                            nrow = 2, 
                                            dimnames = list(c("a", "b"), c("a", "b")))), 
                   pDRes)
})

energyStates <- c("sigma", "sigma_star")
byRow <- TRUE
gen <- matrix(data = c(-3, 3,
                       1, -1), nrow = 2,
              byrow = byRow, dimnames = list(energyStates, energyStates))
molecularCTMC <- new("ctmc", states = energyStates, 
                     byrow = byRow, generator = gen, 
                     name = "Molecular Transition Model")      
test_that("steadyStates must satisfy", {
  expect_identical(steadyStates(molecularCTMC), 
                   matrix(c(1/4, 3/4), nrow = 1, dimnames = list(c(), energyStates)))
})




### Tests for expectedRewards function

### Examples taken from Stochastic Processes: Theory for Applications, Robert G. Gallager,Cambridge University Press

transMatr<-matrix(c(0.99,0.01,0.01,0.99),nrow=2,byrow=TRUE)
simpleMc<-new("markovchain", states=c("a","b"),
              transitionMatrix=transMatr)

test_that("expectedRewards must satisfy", {
  expect_equal(expectedRewards(simpleMc,1,c(0,1)),c(0.01,1.99))
  expect_equal(expectedRewards(simpleMc,2,c(0,1)),c(0.0298,2.9702))
})


### Tests for committorAB function

transMatr <- matrix(c(0,0,0,1,0.5,
                      0.5,0,0,0,0,
                      0.5,0,0,0,0,
                      0,0.2,0.4,0,0,
                      0,0.8,0.6,0,0.5),nrow = 5)
object <- new("markovchain", states=c("a","b","c","d","e"),transitionMatrix=transMatr, name="simpleMc")
answer <- c(0.444,0.889,0.000,0.444,1.000)
names <- c("a","b","c","d","e")
names(answer) <- names
test_that("committorAB must satisfy", {
  expect_equal(round(committorAB(object,c(5),c(3)),3),answer)
})


### Tests for firstPassageMultiple function


statesNames <- c("a", "b", "c")
testmarkov <- new("markovchain", states = statesNames, transitionMatrix =
                    matrix(c(0.2, 0.5, 0.3,
                             0.5, 0.1, 0.4,
                             0.1, 0.8, 0.1), nrow = 3, byrow = TRUE,
                           dimnames = list(statesNames, statesNames)
                    ))
answer <- matrix(c(.8000,
                   0.6000,
                   0.2540
                   ),nrow = 3,dimnames = list(c("1","2","3"),"set"))
test_that("firstPassageMultiple function satisfies", {
  expect_equal(firstPassageMultiple(testmarkov,"a",c("b","c"),3),answer)
})