title: How to use multiple fuzzy input sets / relationships in scikit-fuzzy?
body: <p>I'm making a fuzzy agent with scikit-fuzzy which determines how good a basketball player is based on length and motivation. Obviously this is a learning project and not a realistic implementation.</p>
  
  <p>In a simple example with only a relationship between length and quality, I have the following fuzzy rules:</p>
  
  <pre><code>R1 = fuzz.relation_product(l_tall, q_good)
  R2 = fuzz.relation_product(l_moderate, q_moderate)
  R3 = fuzz.relation_product(l_short, q_bad)
  R_combined = np.fmax(R1, np.fmax(R2, R3))
  fuzz.defuzz(quality, R_combined[length == 180], 'centroid')
  </code></pre>
  
  <p>This returns a crisp value, as expected.</p>
  
  <p>However, when I work with two kinds of relationships:</p>
  
  <pre><code>#Relationships between motivation and quality of player
  R1 = fuzz.relation_product(m_very, q_good)
  R2 = fuzz.relation_product(m_barely, q_bad)
  
  #Relationships between length and quality of player
  
  R3 = fuzz.relation_product(l_tall, q_good)
  R4 = fuzz.relation_product(l_short, q_bad)
  </code></pre>
  
  <p>I don't know what to do next to make it work. Do I combine all the rules, or only the similar rules, and how do I use the defuzzify function afterwards? I need to be able to have two parameters (length and motivation), so the approach I used above cannot work.</p>