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xgboost/cub/test/test_device_histogram.cu  view on Meta::CPAN

        typeid(CounterT).name(),
        NUM_ACTIVE_CHANNELS,
        NUM_CHANNELS);
    std::cout << CoutCast(max_level) << "\n";
    for (int channel = 0; channel < NUM_ACTIVE_CHANNELS; ++channel)
        std::cout << "\n\tChannel " << channel << ": " << num_levels[channel] - 1 << " bins [" << lower_level[channel] << ", " << upper_level[channel] << ")\n";
    fflush(stdout);

    // Allocate and initialize host and device data

    typedef SampleT Foo;        // rename type to quelch gcc warnings (bug?)
    SampleT*                    h_samples = new Foo[total_samples];
    CounterT*                   h_histogram[NUM_ACTIVE_CHANNELS];
    ScaleTransform<LevelT>      transform_op[NUM_ACTIVE_CHANNELS];

    for (int channel = 0; channel < NUM_ACTIVE_CHANNELS; ++channel)
    {
        int bins = num_levels[channel] - 1;
        h_histogram[channel] = new CounterT[bins];

        transform_op[channel].Init(

xgboost/cub/test/test_device_histogram.cu  view on Meta::CPAN

    {
        printf("Channel %d: %d bins [", channel, num_levels[channel] - 1);
        std::cout << levels[channel][0];
        for (int level = 1; level < num_levels[channel]; ++level)
            std::cout << ", " << levels[channel][level];
        printf("]\n");
    }
    fflush(stdout);

    // Allocate and initialize host and device data
    typedef SampleT Foo;        // rename type to quelch gcc warnings (bug?)
    SampleT*                    h_samples = new Foo[total_samples];
    CounterT*                   h_histogram[NUM_ACTIVE_CHANNELS];
    SearchTransform<LevelT>     transform_op[NUM_ACTIVE_CHANNELS];

    for (int channel = 0; channel < NUM_ACTIVE_CHANNELS; ++channel)
    {
        transform_op[channel].levels = levels[channel];
        transform_op[channel].num_levels = num_levels[channel];

        int bins = num_levels[channel] - 1;

xgboost/demo/binary_classification/README.md  view on Meta::CPAN

#### Dump Model
This is a preliminary feature, so far only tree model support text dump. XGBoost can display the tree models in text files and we can scan the model in an easy way:
```
../../xgboost mushroom.conf task=dump model_in=0002.model name_dump=dump.raw.txt
../../xgboost mushroom.conf task=dump model_in=0002.model fmap=featmap.txt name_dump=dump.nice.txt
```

In this demo, the tree boosters obtained will be printed in dump.raw.txt and dump.nice.txt, and the latter one is easier to understand because of usage of feature mapping featmap.txt

Format of ```featmap.txt: <featureid> <featurename> <q or i or int>\n ```:
  - Feature id must be from 0 to number of features, in sorted order.
  - i means this feature is binary indicator feature
  - q means this feature is a quantitative value, such as age, time, can be missing
  - int means this feature is integer value (when int is hinted, the decision boundary will be integer)

#### Monitoring Progress
When you run training we can find there are messages displayed on screen
```
tree train end, 1 roots, 12 extra nodes, 0 pruned nodes ,max_depth=3
[0]  test-error:0.016139