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| | GHybridNonlinearPCA (size_t intrinsicDims) |
| | General-purpose constructor. More...
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| virtual | ~GHybridNonlinearPCA () |
| | Destructor. More...
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| virtual double | predict (size_t user, size_t item) |
| | See the comment for GCollaborativeFilter::predict. More...
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| void | setItemAttributes (GMatrix &itemAttrs) |
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| virtual void | train (GMatrix &data) |
| | See the comment for GCollaborativeFilter::train. More...
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| | GNonlinearPCA (size_t intrinsicDims) |
| | General-purpose constructor. More...
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| | GNonlinearPCA (GDomNode *pNode, GLearnerLoader &ll) |
| | Deserialization constructor. More...
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| virtual | ~GNonlinearPCA () |
| | Destructor. More...
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| void | clampItemElement (size_t item, size_t attr, double val) |
| | Specify that a certain attribute of a certain item profile has a fixed value. (Values for attr are from 0 to m_pModel->outputLayer().inputs()-1. No mechanism is provided to clamp the item bias.) More...
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| void | clampItems (const GMatrix &data, size_t offset=0) |
| | Assumes that column 0 of data is an item ID, and all other columns specify profile values to clamp beginning at the specifed profile offset. More...
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| void | clampUserElement (size_t user, size_t attr, double val) |
| | Specify that a certain attribute of a certain user profile has a fixed value. (Values for attr are from 0 to m_intrinsicDims-2. No mechanism is provided to clamp the input bias.) More...
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| void | clampUsers (const GMatrix &data, size_t offset=0) |
| | Assumes that column 0 of data is a user ID, and all other columns specify profile values to clamp beginning at the specifed profile offset. More...
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| virtual void | impute (double *pVec, size_t dims) |
| | See the comment for GCollaborativeFilter::impute. More...
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| GNeuralNet * | model () |
| | Returns a pointer to the neural net that is used to model the recommendation space. You may want to use this method to add layers to the network. (At least one layer is necessary). You may also use it to set the learning rate, or change activation functions before the model is trained. More...
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| void | noInputBias () |
| | Specify to use no bias value with the inputs. More...
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| void | noThreePass () |
| | Specify not to use three-pass training. (It will just use one pass instead.) More...
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| virtual GDomNode * | serialize (GDom *pDoc) const |
| | See the comment for GCollaborativeFilter::serialize. More...
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| void | setDecayRate (double d) |
| | Set the rate to decay the learning rate. More...
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| void | setMinIters (size_t i) |
| | Sset the min number of iterations to train. More...
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| void | setRegularizer (double d) |
| | Set the regularization value. More...
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| GMatrix * | users () |
| | Returns a pointer to the matrix of user preference vectors. More...
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| | GCollaborativeFilter () |
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| | GCollaborativeFilter (GDomNode *pNode, GLearnerLoader &ll) |
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| virtual | ~GCollaborativeFilter () |
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| void | basicTest (double minMSE) |
| | Performs a basic unit test on this collaborative filter. More...
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| double | crossValidate (GMatrix &data, size_t folds, double *pOutMAE=NULL) |
| | This randomly assigns each rating to one of the folds. Then, for each fold, it calls train with a dataset that contains everything except for the ratings in that fold. It predicts values for the items in the fold, and returns the mean-squared difference between the predictions and the actual ratings. If pOutMAE is non-NULL, it will be set to the mean-absolute error. More...
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| GMatrix * | precisionRecall (GMatrix &data, bool ideal=false) |
| | This divides the data into two equal-size parts. It trains on one part, and then measures the precision/recall using the other part. It returns a three-column data set with recall scores in column 0 and corresponding precision scores in column 1. The false-positive rate is in column 2. (So, if you want a precision-recall plot, just drop column 2. If you want an ROC curve, drop column 1 and swap the remaining two columns.) This method assumes the ratings range from 0 to 1, so be sure to scale the ratings to fit that range before calling this method. If ideal is true, then it will ignore your model and report the ideal results as if your model always predicted the correct rating. (This is useful because it shows the best possible results.) More...
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| GRand & | rand () |
| | Returns a reference to the pseudo-random number generator associated with this object. More...
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| double | trainAndTest (GMatrix &train, GMatrix &test, double *pOutMAE=NULL) |
| | This trains on the training set, and then tests on the test set. Returns the mean-squared difference between actual and target predictions. More...
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| void | trainDenseMatrix (const GMatrix &data, const GMatrix *pLabels=NULL) |
| | Train from an m-by-n dense matrix, where m is the number of users and n is the number of items. All attributes must be continuous. Missing values are indicated with UNKNOWN_REAL_VALUE. If pLabels is non-NULL, then the labels will be appended as additional items. More...
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A collaborative filtering algorithm invented by Mike Smith.
Public Member Functions inherited from GClasses::GNonlinearPCA