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int | init (const MedFeatures &medf, TQRF_Params ¶ms) |
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| virtual int | version () const |
| | Relevant for serializations.
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| virtual string | my_class_name () const |
| | For better handling of serializations it is highly recommended that each SerializableObject inheriting class will implement the next method.
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| virtual void | serialized_fields_name (vector< string > &field_names) const |
| | The names of the serialized fields.
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| virtual void * | new_polymorphic (string derived_name) |
| | for polymorphic classes that want to be able to serialize/deserialize a pointer * to the derived class given its type one needs to implement this function to return a new to the derived class given its type (as in my_type)
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virtual void | pre_serialization () |
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virtual void | post_deserialization () |
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| virtual size_t | get_size () |
| | Gets bytes sizes for serializations.
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| virtual size_t | serialize (unsigned char *blob) |
| | Serialiazing object to blob memory. return number ob bytes wrote to memory.
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| virtual size_t | deserialize (unsigned char *blob) |
| | Deserialiazing blob to object. returns number of bytes read.
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size_t | serialize_vec (vector< unsigned char > &blob) |
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size_t | deserialize_vec (vector< unsigned char > &blob) |
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virtual size_t | serialize (vector< unsigned char > &blob) |
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virtual size_t | deserialize (vector< unsigned char > &blob) |
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virtual int | read_from_file (const string &fname) |
| | read and deserialize model
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virtual int | write_to_file (const string &fname) |
| | serialize model and write to file
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virtual int | read_from_file_unsafe (const string &fname) |
| | read and deserialize model without checking version number - unsafe read
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int | init_from_string (string init_string) |
| | Init from string.
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int | init_params_from_file (string init_file) |
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int | init_param_from_file (string file_str, string ¶m) |
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| virtual int | init (map< string, string > &map) |
| | Virtual to init object from parsed fields.
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int | update_from_string (const string &init_string) |
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| virtual int | update (map< string, string > &map) |
| | Virtual to update object from parsed fields.
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virtual string | object_json () const |
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vector< vector< short > > | qx |
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vector< vector< float > > | q_to_val |
| | a vector of features that mimics the input x_in features matrix, but coded into quantized values
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const MedFeatures * | orig_medf |
| | from a q value to float value : q=0 is reserved for missing value the range for q>0 is : [q_to_val[q], q_to_val[q+1])
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int | nfeat = 0 |
| | pointer to the original MedFeatures
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vector< string > | feature_names |
| | just an easy helper that = qx.size()
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int | ncateg = 0 |
| | useful for debugging
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vector< const vector< float > * > | orig_data |
| | ncateg 0 is regression, otherwise categories are assumed to be 0 ... ncateg-1
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vector< string > | feat_names |
| | pointers to the original data given
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vector< float > | y |
| | as given in train
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vector< int > | y_i |
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vector< int > | last_time_slice |
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int | n_time_slices |
| | when there's more than 1 time slice there may be censoring involved and the last_time_slice is the last uncensored one.
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vector< int > | slice_counts [2] |
| | 1 time slice is simply the regular case of a label for the whole future
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vector< vector< int > > | lists |
| | counts of elements in slices (in case of non regression trees). slices with no variability are not interesting.
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vector< int > | is_categorial_feat |
| | lists[0] is always the lines used for training the trees in round 1 the others can be used for later stages, for example : lists[1] could be used for early stopping measurements or for estimating weights for trees/nodes
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vector< vector< vector< int > > > | time_categ_pids |
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vector< vector< vector< int > > > | time_categ_idx |
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vector< vector< int > > | categ_pids |
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vector< vector< int > > | categ_idx |
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unordered_map< int, vector< vector< vector< int > > > > | pid2time_categ_idx |
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vector< float > | wgts |
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vector< float > | orig_wgts |
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vector< float > | probs |
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vector< float > | w_to_sum |
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vector< vector< float > > | sum_over_trees |
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float | alpha0 |
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