Examples of org.apache.mahout.cf.taste.impl.model.GenericDataModel

Package org.apache.mahout.cf.taste.impl.model

Examples of org.apache.mahout.cf.taste.impl.model.GenericDataModel

org.apache.mahout.cf.taste.impl.model.GenericDataModel

A simple {@link DataModel} which uses a given {@link List} of users as its data source. This implementationis mostly useful for small experiments and is not recommended for contexts where performance is important.

                           Long.toString(itemID),
                           Float.toString(preferenceValue));
        }
      }
    }
    return new GenericDataModel(rawData);
  }

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                                                                  new GenericPreference(3L, 4L, 3.0f))));


    userData.put(4L, new GenericUserPreferenceArray(Arrays.asList(new GenericPreference(4L, 1L, 3.0f),
                                                                  new GenericPreference(4L, 4L, 5.0f))));


    dataModel = new GenericDataModel(userData);
    factorizer = new ALSWRFactorizer(dataModel, 3, 0.065, 10);
  }

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      }


      userData.put(userIndex, new GenericUserPreferenceArray(row));
    }


    dataModel = new GenericDataModel(userData);
  }

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    userData.put(3L, new GenericUserPreferenceArray(Arrays.asList(new GenericPreference(3L, 2L, 5.0f),
        new GenericPreference(3L, 4L, 3.0f))));


    userData.put(4L, new GenericUserPreferenceArray(Arrays.asList(new GenericPreference(4L, 1L, 3.0f),
        new GenericPreference(4L, 4L, 5.0f))));
    dataModel = new GenericDataModel(userData);
  }

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      if (random.nextDouble() < evaluationPercentage) {
        splitOneUsersPrefs(trainingPercentage, trainingPrefs, testPrefs, userID, dataModel);
      }
    }
    
    DataModel trainingModel = dataModelBuilder == null ? new GenericDataModel(trainingPrefs)
        : dataModelBuilder.buildDataModel(trainingPrefs);
    
    Recommender recommender = recommenderBuilder.buildRecommender(trainingModel);
    
    double result = getEvaluation(testPrefs, recommender);

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      LongPrimitiveIterator it2 = dataModel.getUserIDs();
      while (it2.hasNext()) {
        dataSplitter.processOtherUser(userID, relevantItemIDs, trainingUsers, it2.nextLong(), dataModel);
      }


      DataModel trainingModel = dataModelBuilder == null ? new GenericDataModel(trainingUsers)
          : dataModelBuilder.buildDataModel(trainingUsers);
      try {
        trainingModel.getPreferencesFromUser(userID);
      } catch (NoSuchUserException nsee) {
        continue; // Oops we excluded all prefs for the user -- just move on

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        for (File updateFile : findUpdateFilesAfter(newLastModified)) {
          processFile(new FileLineIterator(updateFile, false), data, timestamps, false);
        }


        return new GenericDataModel(GenericDataModel.toDataMap(data, true), timestamps);


      } else {


        FastByIDMap<PreferenceArray> rawData = ((GenericDataModel) delegate).getRawUserData();


        for (File updateFile : findUpdateFilesAfter(Math.max(oldLastUpdateFileModifieid, newLastModified))) {
          processFile(new FileLineIterator(updateFile, false), rawData, timestamps, true);
        }


        return new GenericDataModel(rawData, timestamps);


      }


    } else {

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      }
      if (!prefsList.isEmpty()) {
        result.put(userIDs[i], new GenericUserPreferenceArray(prefsList));
      }
    }
    return new GenericDataModel(result);
  }

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            mongoTimestamp = getDate(user.get("created_at"));
          }
        }
      }
    }
    delegate = new GenericDataModel(GenericDataModel.toDataMap(userIDPrefMap, true));
  }

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            removeMongoUserItem(Long.toString(userID), Long.toString(itemID));
          }
        }
      }
    }
    return new GenericDataModel(rawData);
  }

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