fix to use only 3 x weights in symmetry

Author: jdermont

.gitignore

@@ -30,3 +30,7 @@
 *.exe
 *.out
 *.app
+
+Makefile
+.qmake.stash
+tictactoe-ntuple

ntuple.h

@@ -45,23 +45,23 @@ class NTupleNetwork {
         score += weightsSym[0][tuples[0]];
         score += weightsSym[1][tuples[1]];
         score += weightsSym[0][tuples[2]];
-        score += weightsSym[2][tuples[3]];
-        score += weightsSym[3][tuples[4]];
-        score += weightsSym[2][tuples[5]];
-        score += weightsSym[4][tuples[6]];
-        score += weightsSym[4][tuples[7]];
+        score += weightsSym[0][tuples[3]];
+        score += weightsSym[1][tuples[4]];
+        score += weightsSym[0][tuples[5]];
+        score += weightsSym[2][tuples[6]];
+        score += weightsSym[2][tuples[7]];
         score = tanh(score+biasSym);
 
         float error = target - score;
         float delta = error * tanh_prime(score);
         weightsSym[0][tuples[0]] += learningRate * delta;
         weightsSym[1][tuples[1]] += learningRate * delta;
         weightsSym[0][tuples[2]] += learningRate * delta;
-        weightsSym[2][tuples[3]] += learningRate * delta;
-        weightsSym[3][tuples[4]] += learningRate * delta;
-        weightsSym[2][tuples[5]] += learningRate * delta;
-        weightsSym[4][tuples[6]] += learningRate * delta;
-        weightsSym[4][tuples[7]] += learningRate * delta;
+        weightsSym[0][tuples[3]] += learningRate * delta;
+        weightsSym[1][tuples[4]] += learningRate * delta;
+        weightsSym[0][tuples[5]] += learningRate * delta;
+        weightsSym[2][tuples[6]] += learningRate * delta;
+        weightsSym[2][tuples[7]] += learningRate * delta;
         biasSym += learningRate * delta;
     }
 
@@ -70,19 +70,19 @@ class NTupleNetwork {
         output += weightsSym[0][tuples[0]];
         output += weightsSym[1][tuples[1]];
         output += weightsSym[0][tuples[2]];
-        output += weightsSym[2][tuples[3]];
-        output += weightsSym[3][tuples[4]];
-        output += weightsSym[2][tuples[5]];
-        output += weightsSym[4][tuples[6]];
-        output += weightsSym[4][tuples[7]];
+        output += weightsSym[0][tuples[3]];
+        output += weightsSym[1][tuples[4]];
+        output += weightsSym[0][tuples[5]];
+        output += weightsSym[2][tuples[6]];
+        output += weightsSym[2][tuples[7]];
         return tanh(output+biasSym);
     }
 
 private:
     float weights[8][54] = {};
     float bias = 0;
 
-    float weightsSym[5][54] = {};
+    float weightsSym[3][54] = {};
     float biasSym = 0;
 
     float tanh_prime(float x) { // x already tanhed