cmp.run

# OTDM Lab 3: run file
# Marcel, Mengxue
# Autumn 2021

# To run this file:
#	$> ampl: include cmp.run;


#########
# SETUP #
#########

reset;
model cmp.mod;
data "./data/moons.dat"; #s1 #spiral #worms #moons

option solver cplex; #gurobi

#display A;
display k,m,n;


#################
# PREPROCESSING #
#################

# Calculate Euclidean distances matrix
#for {i in {1..m}} {
#	for {j in {1..m}} {
#		let D[i,j] := sqrt((A[j,1] - A[i,1])^2 + (A[j,2] - A[i,2])^2);
#	}
#}

# Calculate n-dimensional Euclidean distances matrix D
param partsum default 0;
for {i in {1..m}} {
	for {j in {1..m}} {
		for {dim in {1..n}} {
			let partsum := partsum + (A[j,dim] - A[i,dim])^2
		}
		let D[i,j] := sqrt(partsum);
		let partsum := 0;
	}
}

#display D;


#########
# SOLVE #
#########

problem CMP: x, objf, c1, c2, c3;
solve CMP;
#display x;
#display x > './out/x.txt';


###################
# POST-PROCESSING #
###################

var clusters{i in 1..m};

for{i in 1..m} {
	for{j in 1..m} {
		let clusters[i] := clusters[i] + x[j,i];
	}
}

print "";
for{i in 1..m} {
	if clusters[i] > 0 then {
		print "Cluster", i, "has", clusters[i], "points";
	}
}

print "";
for{i in 1..m} {
	for{j in 1..m} {
		if x[i,j] > 0 then {
			print "Point", i, "belongs to cluster", j;
		}
	}
}

display _total_solve_elapsed_time;

#display clusters;
#display clusters > './out/clusters.txt';



#########
# TRAIN #
#########

# Solve the primal
#reset;
#print "SVM_PRIMAL:";

#model svm-primal.mod;
#data "./data/size2000-seed75421.dat"; #spambase #size100-seed66407 #size2000-seed75421

#option solver cplex; #gurobi

#problem SVM_PRIMAL: w, gamma, s, primal, c1, c2;
#solve SVM_PRIMAL;
#display w, gamma, s;


# Compute w, gamma from the dual solution
#param w {1..n};
#let {j in {1..n}} w[j] := sum{i in {1..m}} lambda[i]*y_train[i]*A_train[i,j];
#display w;

#param gamma;
#for {i in {1..m}} {
#	if lambda[i] > 0.01 and lambda[i] < nu*0.99 then {
#		# A support vector point was found
#		let gamma := 1/y_train[i] - sum{j in {1..n}} w[j]*A_train[i,j];
#		break;
#	}
#}
#display gamma;




########
# TEST #
########

# Predict values with the test dataset
#param y_pred {1..m};
#let {i in {1..m}} y_pred[i] := gamma + sum{j in {1..n}}w[j]*A_test[i,j];
#let {i in {1..m}} y_pred[i] := if y_pred[i] <= 0 then -1 else 1;
#display y_pred;

# Check misclassifications
#param misclassifications default 0;
#for {i in {1..m}} {
#	if y_pred[i] != y_test[i] then
#		let misclassifications := misclassifications + 1;
#}

#display misclassifications;

#param accuracy = (m - misclassifications) / m;
#display accuracy;