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- def trainingDataGeneratorForAI(ball, ballDirX, paddle1):
- paddle1yInc = 0
- # TODO 2 - part A - write the logic for your training program
- # Leverage conventionalPlayer code OR leverage your own manual play
- # Call recordData(???, ???, ???, ...) to record the training data in a csv file
- if ball.x < WINDOWWIDTH/2:
- if ballDirX == -1:
- if paddle1.centery < ball.centery:
- paddle1yInc = 1
- else:
- paddle1yInc = -1
- recordData(ballDirX, ball.centery, paddle1.centery, paddle1yInc)
- paddle1.y += paddle1yInc
- return paddle1
- # pong playing DNN
- def neuralNetworkAI(ball, ballDirX, paddle1):
- paddle1yInc = 0
- thisFeatureInstance = np.array([
- [ballDirX, ball.centery, paddle1.centery]
- ])
- labelMatrix = model.predict(thisFeatureInstance)
- paddle1yInc = labelMatrix[0][0]
- if paddle1yInc > 0.6:
- paddle1yInc = 1
- elif paddle1yInc < -0.6:
- paddle1yInc = -1
- paddle1.y += paddle1yInc
- return paddle1
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