Package org.apache.mahout.common

Examples of org.apache.mahout.common.RandomWrapper.nextGaussian()

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    for (int userIndex = 0; userIndex < userVectors.length; userIndex++) {
      userVectors[userIndex][0] = globalAverage;
      userVectors[userIndex][USER_BIAS_INDEX] = 0; // will store user bias
      userVectors[userIndex][ITEM_BIAS_INDEX] = 1; // corresponding item feature contains item bias
      for (int feature = FEATURE_OFFSET; feature < numFeatures; feature++) {
        userVectors[userIndex][feature] = random.nextGaussian() * randomNoise;
      }
    }
    for (int itemIndex = 0; itemIndex < itemVectors.length; itemIndex++) {
      itemVectors[itemIndex][0] = 1; // corresponding user feature contains global average
      itemVectors[itemIndex][USER_BIAS_INDEX] = 1; // corresponding user feature contains user bias
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    for (int itemIndex = 0; itemIndex < itemVectors.length; itemIndex++) {
      itemVectors[itemIndex][0] = 1; // corresponding user feature contains global average
      itemVectors[itemIndex][USER_BIAS_INDEX] = 1; // corresponding user feature contains user bias
      itemVectors[itemIndex][ITEM_BIAS_INDEX] = 0; // will store item bias
      for (int feature = FEATURE_OFFSET; feature < numFeatures; feature++) {
        itemVectors[itemIndex][feature] = random.nextGaussian() * randomNoise;
      }
    }

    cachePreferences();
    shufflePreferences();
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    for (int userIndex = 0; userIndex < userVectors.length; userIndex++) {
      userVectors[userIndex][0] = globalAverage;
      userVectors[userIndex][USER_BIAS_INDEX] = 0; // will store user bias
      userVectors[userIndex][ITEM_BIAS_INDEX] = 1; // corresponding item feature contains item bias
      for (int feature = FEATURE_OFFSET; feature < rank; feature++) {
        userVectors[userIndex][feature] = random.nextGaussian() * NOISE;
      }
    }
    for (int itemIndex = 0; itemIndex < itemVectors.length; itemIndex++) {
      itemVectors[itemIndex][0] = 1; // corresponding user feature contains global average
      itemVectors[itemIndex][USER_BIAS_INDEX] = 1; // corresponding user feature contains user bias
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    for (int itemIndex = 0; itemIndex < itemVectors.length; itemIndex++) {
      itemVectors[itemIndex][0] = 1; // corresponding user feature contains global average
      itemVectors[itemIndex][USER_BIAS_INDEX] = 1; // corresponding user feature contains user bias
      itemVectors[itemIndex][ITEM_BIAS_INDEX] = 0; // will store item bias
      for (int feature = FEATURE_OFFSET; feature < rank; feature++) {
        itemVectors[itemIndex][feature] = random.nextGaussian() * NOISE;
      }
    }
  }

  //TODO: needs optimization
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    for (int userIndex = 0; userIndex < userVectors.length; userIndex++) {
      userVectors[userIndex][0] = globalAverage;
      userVectors[userIndex][USER_BIAS_INDEX] = 0; // will store user bias
      userVectors[userIndex][ITEM_BIAS_INDEX] = 1; // corresponding item feature contains item bias
      for (int feature = FEATURE_OFFSET; feature < rank; feature++) {
        userVectors[userIndex][feature] = random.nextGaussian() * NOISE;
      }
    }
    for (int itemIndex = 0; itemIndex < itemVectors.length; itemIndex++) {
      itemVectors[itemIndex][0] = 1; // corresponding user feature contains global average
      itemVectors[itemIndex][USER_BIAS_INDEX] = 1; // corresponding user feature contains user bias
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    for (int itemIndex = 0; itemIndex < itemVectors.length; itemIndex++) {
      itemVectors[itemIndex][0] = 1; // corresponding user feature contains global average
      itemVectors[itemIndex][USER_BIAS_INDEX] = 1; // corresponding user feature contains user bias
      itemVectors[itemIndex][ITEM_BIAS_INDEX] = 0; // will store item bias
      for (int feature = FEATURE_OFFSET; feature < rank; feature++) {
        itemVectors[itemIndex][feature] = random.nextGaussian() * NOISE;
      }
    }
  }

  //TODO: needs optimization
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