function q = find_strategy_2normal(distribution1, distribution2)

% same function as in the previous assignment, only extreme
% values of priors are handled now
Am = distribution1.Mean;
As = distribution1.Sigma;
Ap = distribution1.Prior;
Cm = distribution2.Mean;
Cs = distribution2.Sigma;
Cp = distribution2.Prior;

if distribution1.Prior == 1
    q.decision = [1 1 1];
    q.t1 = -Inf;
    q.t2 = Inf;
    return
elseif distribution2.Prior == 1
    q.decision = [2 2 2];
    q.t1 = -Inf;
    q.t2 = Inf;
    return
end

% quadratic discriminative function
Coef = zeros(1,3);
Coef(1) = 1/(2*Cs^2) - 1/(2*As^2);    % a
Coef(2) = Am/(As^2) - Cm/(Cs^2);          % b
Coef(3) = log((Ap*Cs)/(As*Cp))+Cm^2/(2*Cs^2)-Am^2/(2*As^2); % c

% computing the polinomial roots
Ts = roots(Coef); % thresholds

is_convex = Coef(1) > 0;

% assign thresholds and decisions
if isreal(Ts)
    if Ts(1) ~= Ts(2)
          q.t1 = min(Ts);
          q.t2 = max(Ts);
        if is_convex
          q.decision = [1 2 1];  %not guess
        else
          q.decision = [2 1 2];  %not guess
        end
    else
      q.t1 = -Inf;
      q.t2 = Inf;
      if is_convex
        q.decision = [1 1 1];    %not guess
      else
        q.decision = [2 2 2];    %not guess
      end
    end
else
  q.t1 = -Inf;
  q.t2 = Inf;
  if is_convex
    q.decision = [1 1 1];   %not guess
  else
    q.decision = [2 2 2];   %not guess
  end
end