### Set c as a confusion matrix, previously calculated and stored
### (this confusion matrix has rows as predictions and columns as actuals):
c = as.matrix(as.data.frame.matrix(confusion_RF$table))
### Initialize length 16 MCC vector:
MCC = vector(mode = "numeric", length = 16)
### For each location:
for(k in 1:16){
  A = as.double(c[k,k]) ### A = #(true positives)
  B = as.double(sum(c[k,]) - c[k,k]) ### B = #(false positives)
  C = as.double(sum(c[,k]) - c[k,k]) ### C = #(false negatives)
  D = as.double(sum(c) - A - B - C) ### D = #(true negatives)
  if((((A + B)*(A + C)*(D + B)*(D + C))^.5) == 0){ ### if denominator is zero, arbitrarily set as one - MCC will come out to be zero
    MCC[k] = (A*D) - (B*C)
  }
  else{MCC[k] = ((A*D) - (B*C))/(((A + B)*(A + C)*(D + B)*(D + C))^.5)} ### MCC value by formula
  rm(A,B,C,D)
}
### Rounding, setting as column vector:
MCC = round(matrix(MCC, nrow = 16, ncol = 1), digits = 2)
### Set appropriate column name:
colnames(MCC) = "MCC"
### Save results to machine (to be appended to pre-existing statistics by class table):
write.table(MCC, file = "C:/Users/jdmunyon/Desktop/Dropbox/Senior_Project/data_and_code/results/50_50/RF_MCCs.csv", sep = ",", row.names = FALSE, col.names = TRUE)
rm(c, MCC)

c = as.matrix(as.data.frame.matrix(confusion_ada$table))
MCC = vector(mode = "numeric", length = 16)
for(k in 1:16){
  A = as.double(c[k,k])
  B = as.double(sum(c[k,]) - c[k,k])
  C = as.double(sum(c[,k]) - c[k,k])
  D = as.double(sum(c) - A - B - C)
  if((((A + B)*(A + C)*(D + B)*(D + C))^.5) == 0){ # if denominator is zero, arbitrarily set as one - MCC will come out to be zero
    MCC[k] = (A*D) - (B*C)
  }
  else{MCC[k] = ((A*D) - (B*C))/(((A + B)*(A + C)*(D + B)*(D + C))^.5)}
  rm(A,B,C,D)
}
MCC = round(matrix(MCC, nrow = 16, ncol = 1), digits = 2)
colnames(MCC) = "MCC"
write.table(MCC, file = "C:/Users/jdmunyon/Desktop/Dropbox/Senior_Project/data_and_code/results/50_50/ada_MCCs.csv", sep = ",", row.names = FALSE, col.names = TRUE)
rm(c, MCC)

c = as.matrix(as.data.frame.matrix(confusion_ada_cv$table))
MCC = vector(mode = "numeric", length = 16)
for(k in 1:16){
  A = as.double(c[k,k])
  B = as.double(sum(c[k,]) - c[k,k])
  C = as.double(sum(c[,k]) - c[k,k])
  D = as.double(sum(c) - A - B - C)
  if((((A + B)*(A + C)*(D + B)*(D + C))^.5) == 0){ # if denominator is zero, arbitrarily set as one - MCC will come out to be zero
    MCC[k] = (A*D) - (B*C)
  }
  else{MCC[k] = ((A*D) - (B*C))/(((A + B)*(A + C)*(D + B)*(D + C))^.5)}
  rm(A,B,C,D)
}
MCC = round(matrix(MCC, nrow = 16, ncol = 1), digits = 2)
colnames(MCC) = "MCC"
write.table(MCC, file = "C:/Users/jdmunyon/Desktop/Dropbox/Senior_Project/data_and_code/results/50_50/ada_cv_MCCs.csv", sep = ",", row.names = FALSE, col.names = TRUE)
rm(c, MCC)

c = as.matrix(as.data.frame.matrix(confusion_bagging$table))
MCC = vector(mode = "numeric", length = 16)
for(k in 1:16){
  A = as.double(c[k,k])
  B = as.double(sum(c[k,]) - c[k,k])
  C = as.double(sum(c[,k]) - c[k,k])
  D = as.double(sum(c) - A - B - C)
  if((((A + B)*(A + C)*(D + B)*(D + C))^.5) == 0){ # if denominator is zero, arbitrarily set as one - MCC will come out to be zero
    MCC[k] = (A*D) - (B*C)
  }
  else{MCC[k] = ((A*D) - (B*C))/(((A + B)*(A + C)*(D + B)*(D + C))^.5)}
  rm(A,B,C,D)
}
MCC = round(matrix(MCC, nrow = 16, ncol = 1), digits = 2)
colnames(MCC) = "MCC"
write.table(MCC, file = "C:/Users/jdmunyon/Desktop/Dropbox/Senior_Project/data_and_code/results/50_50/bagging_MCCs.csv", sep = ",", row.names = FALSE, col.names = TRUE)
rm(c, MCC)

c = as.matrix(as.data.frame.matrix(confusion_bagging_cv$table))
MCC = vector(mode = "numeric", length = 16)
for(k in 1:16){
  A = as.double(c[k,k])
  B = as.double(sum(c[k,]) - c[k,k])
  C = as.double(sum(c[,k]) - c[k,k])
  D = as.double(sum(c) - A - B - C)
  if((((A + B)*(A + C)*(D + B)*(D + C))^.5) == 0){ # if denominator is zero, arbitrarily set as one - MCC will come out to be zero
    MCC[k] = (A*D) - (B*C)
  }
  else{MCC[k] = ((A*D) - (B*C))/(((A + B)*(A + C)*(D + B)*(D + C))^.5)}
  rm(A,B,C,D)
}
MCC = round(matrix(MCC, nrow = 16, ncol = 1), digits = 2)
colnames(MCC) = "MCC"
write.table(MCC, file = "C:/Users/jdmunyon/Desktop/Dropbox/Senior_Project/data_and_code/results/50_50/bagging_cv_MCCs.csv", sep = ",", row.names = FALSE, col.names = TRUE)
rm(c, MCC)

c = as.matrix(as.data.frame.matrix(confusion_samme$table))
MCC = vector(mode = "numeric", length = 16)
for(k in 1:16){
  A = as.double(c[k,k])
  B = as.double(sum(c[k,]) - c[k,k])
  C = as.double(sum(c[,k]) - c[k,k])
  D = as.double(sum(c) - A - B - C)
  if((((A + B)*(A + C)*(D + B)*(D + C))^.5) == 0){ # if denominator is zero, arbitrarily set as one - MCC will come out to be zero
    MCC[k] = (A*D) - (B*C)
  }
  else{MCC[k] = ((A*D) - (B*C))/(((A + B)*(A + C)*(D + B)*(D + C))^.5)}
  rm(A,B,C,D)
}
MCC = round(matrix(MCC, nrow = 16, ncol = 1), digits = 2)
colnames(MCC) = "MCC"
write.table(MCC, file = "C:/Users/jdmunyon/Desktop/Dropbox/Senior_Project/data_and_code/results/50_50/samme_MCCs.csv", sep = ",", row.names = FALSE, col.names = TRUE)
rm(c, MCC)

c = as.matrix(as.data.frame.matrix(confusion_svms$table))
MCC = vector(mode = "numeric", length = 16)
for(k in 1:16){
  A = as.double(c[k,k])
  B = as.double(sum(c[k,]) - c[k,k])
  C = as.double(sum(c[,k]) - c[k,k])
  D = as.double(sum(c) - A - B - C)
  if((((A + B)*(A + C)*(D + B)*(D + C))^.5) == 0){ # if denominator is zero, arbitrarily set as one - MCC will come out to be zero
    MCC[k] = (A*D) - (B*C)
  }
  else{MCC[k] = ((A*D) - (B*C))/(((A + B)*(A + C)*(D + B)*(D + C))^.5)}
  rm(A,B,C,D)
}
MCC = round(matrix(MCC, nrow = 16, ncol = 1), digits = 2)
colnames(MCC) = "MCC"
write.table(MCC, file = "C:/Users/jdmunyon/Desktop/Dropbox/Senior_Project/data_and_code/results/50_50/svms_MCCs.csv", sep = ",", row.names = FALSE, col.names = TRUE)
rm(c, MCC)

c = as.matrix(as.data.frame.matrix(confusion_CDA$table))
MCC = vector(mode = "numeric", length = 16)
for(k in 1:16){
  A = as.double(c[k,k])
  B = as.double(sum(c[k,]) - c[k,k])
  C = as.double(sum(c[,k]) - c[k,k])
  D = as.double(sum(c) - A - B - C)
  if((((A + B)*(A + C)*(D + B)*(D + C))^.5) == 0){ # if denominator is zero, arbitrarily set as one - MCC will come out to be zero
    MCC[k] = (A*D) - (B*C)
  }
  else{MCC[k] = ((A*D) - (B*C))/(((A + B)*(A + C)*(D + B)*(D + C))^.5)}
  rm(A,B,C,D)
}
MCC = round(matrix(MCC, nrow = 16, ncol = 1), digits = 2)
colnames(MCC) = "MCC"
write.table(MCC, file = "C:/Users/jdmunyon/Desktop/Dropbox/Senior_Project/data_and_code/results/50_50/CDA_MCCs.csv", sep = ",", row.names = FALSE, col.names = TRUE)
rm(c, MCC)