protected void initialize()
{
int num3;
RandomWrapper wrapper = RandomUtils.getRandom();
this.userVectors = new double[this.dataModel.getNumUsers()][];
this.itemVectors = new double[this.dataModel.getNumItems()][];
double num = this.getAveragePreference();
for (int i = 0; i < this.userVectors.Length; i++)
{
this.userVectors[i] = new double[this.rank];
this.userVectors[i][0] = num;
this.userVectors[i][USER_BIAS_INDEX] = 0.0;
this.userVectors[i][ITEM_BIAS_INDEX] = 1.0;
num3 = FEATURE_OFFSET;
while (num3 < this.rank)
{
this.userVectors[i][num3] = wrapper.nextGaussian() * NOISE;
num3++;
}
}
for (int j = 0; j < this.itemVectors.Length; j++)
{
this.itemVectors[j] = new double[this.rank];
this.itemVectors[j][0] = 1.0;
this.itemVectors[j][USER_BIAS_INDEX] = 1.0;
this.itemVectors[j][ITEM_BIAS_INDEX] = 0.0;
for (num3 = FEATURE_OFFSET; num3 < this.rank; num3++)
{
this.itemVectors[j][num3] = wrapper.nextGaussian() * NOISE;
}
}
}
protected void initialize()
{
RandomWrapper random = RandomUtils.getRandom();
userVectors = new double[dataModel.GetNumUsers()][];
itemVectors = new double[dataModel.GetNumItems()][];
double globalAverage = getAveragePreference();
for (int userIndex = 0; userIndex < userVectors.Length; userIndex++)
{
userVectors[userIndex] = new double[rank];
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] = new double[rank];
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;
}
}
}
protected override void prepareTraining()
{
int num;
int num2;
base.prepareTraining();
RandomWrapper wrapper = RandomUtils.getRandom();
this.p = new double[base.dataModel.getNumUsers()][];
for (num = 0; num < this.p.Length; num++)
{
this.p[num] = new double[base.numFeatures];
num2 = 0;
while (num2 < RatingSGDFactorizer.FEATURE_OFFSET)
{
this.p[num][num2] = 0.0;
num2++;
}
num2 = RatingSGDFactorizer.FEATURE_OFFSET;
while (num2 < base.numFeatures)
{
this.p[num][num2] = wrapper.nextGaussian() * base.randomNoise;
num2++;
}
}
this.y = new double[base.dataModel.getNumItems()][];
for (num = 0; num < this.y.Length; num++)
{
this.y[num] = new double[base.numFeatures];
num2 = 0;
while (num2 < RatingSGDFactorizer.FEATURE_OFFSET)
{
this.y[num][num2] = 0.0;
num2++;
}
for (num2 = RatingSGDFactorizer.FEATURE_OFFSET; num2 < base.numFeatures; num2++)
{
this.y[num][num2] = wrapper.nextGaussian() * base.randomNoise;
}
}
this.itemsByUser = new Dictionary <int, List <int> >();
IEnumerator <long> enumerator = base.dataModel.getUserIDs();
while (enumerator.MoveNext())
{
long current = enumerator.Current;
int num4 = base.userIndex(current);
FastIDSet set = base.dataModel.getItemIDsFromUser(current);
List <int> list = new List <int>(set.size());
this.itemsByUser[num4] = list;
foreach (long num5 in set)
{
int item = base.itemIndex(num5);
list.Add(item);
}
}
}
protected virtual void prepareTraining()
{
RandomWrapper random = RandomUtils.getRandom();
userVectors = new double[dataModel.GetNumUsers()][]; //numFeatures
itemVectors = new double[dataModel.GetNumItems()][];
double globalAverage = getAveragePreference();
for (int userIndex = 0; userIndex < userVectors.Length; userIndex++)
{
userVectors[userIndex] = new double[numFeatures];
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] = new double[numFeatures];
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();
}
protected virtual void prepareTraining()
{
int num3;
RandomWrapper wrapper = RandomUtils.getRandom();
this.userVectors = new double[this.dataModel.getNumUsers()][];
this.itemVectors = new double[this.dataModel.getNumItems()][];
double num = this.getAveragePreference();
for (int i = 0; i < this.userVectors.Length; i++)
{
this.userVectors[i] = new double[this.numFeatures];
this.userVectors[i][0] = num;
this.userVectors[i][USER_BIAS_INDEX] = 0.0;
this.userVectors[i][ITEM_BIAS_INDEX] = 1.0;
num3 = FEATURE_OFFSET;
while (num3 < this.numFeatures)
{
this.userVectors[i][num3] = wrapper.nextGaussian() * this.randomNoise;
num3++;
}
}
for (int j = 0; j < this.itemVectors.Length; j++)
{
this.itemVectors[j] = new double[this.numFeatures];
this.itemVectors[j][0] = 1.0;
this.itemVectors[j][USER_BIAS_INDEX] = 1.0;
this.itemVectors[j][ITEM_BIAS_INDEX] = 0.0;
for (num3 = FEATURE_OFFSET; num3 < this.numFeatures; num3++)
{
this.itemVectors[j][num3] = wrapper.nextGaussian() * this.randomNoise;
}
}
this.cachePreferences();
this.shufflePreferences();
}