/**
  * @desc creates array of input data for every sample
  * @param json data - complete json that contains wine quality data
  * @return array of input data
*/
function extractInputs(data)
{
  let inputs = []
  inputs = data.map(d => [d.fixed_acidity, d.volatile_acidity, d.citric_acid, d.residual_sugar, d.chlorides, d.free_sulfur_dioxide, d.total_sulfur_dioxide, d.density, d.pH, d.sulphates, d.alcohol])
	return inputs;
}

/**
  * @desc converts data from json format to tensors
  * @param json data - complete json that contains wine quality data
  * @return tuple of converted data that can be used for training model
*/
function prepareDataFunction(data) {

  return tf.tidy(() => {
    tf.util.shuffle(data);

    const inputs = extractInputs(data);
    const outputs = data.map(d => d.quality);

    const inputTensor = tf.tensor2d(inputs, [inputs.length, inputs[0].length]);
    const outputTensor = tf.oneHot(tf.tensor1d(outputs, 'int32'), 10);

    const inputMax = inputTensor.max();
    const inputMin = inputTensor.min();
    const outputMax = outputTensor.max();
    const outputMin = outputTensor.min();

    const normalizedInputs = inputTensor.sub(inputMin).div(inputMax.sub(inputMin));
    const normalizedoutputs = outputTensor.sub(outputMin).div(outputMax.sub(outputMin));

    return {
      inputs: normalizedInputs,
      outputs: normalizedoutputs,
      inputMax,
      inputMin,
      outputMax,
      outputMin,
    }
  });
}