/*    Isolation forests and variations thereof, with adjustments for incorporation
*     of categorical variables and missing values.
*     Writen for C++11 standard and aimed at being used in R and Python.
*     
*     This library is based on the following works:
*     [1] Liu, Fei Tony, Kai Ming Ting, and Zhi-Hua Zhou.
*         "Isolation forest."
*         2008 Eighth IEEE International Conference on Data Mining. IEEE, 2008.
*     [2] Liu, Fei Tony, Kai Ming Ting, and Zhi-Hua Zhou.
*         "Isolation-based anomaly detection."
*         ACM Transactions on Knowledge Discovery from Data (TKDD) 6.1 (2012): 3.
*     [3] Hariri, Sahand, Matias Carrasco Kind, and Robert J. Brunner.
*         "Extended Isolation Forest."
*         arXiv preprint arXiv:1811.02141 (2018).
*     [4] Liu, Fei Tony, Kai Ming Ting, and Zhi-Hua Zhou.
*         "On detecting clustered anomalies using SCiForest."
*         Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Berlin, Heidelberg, 2010.
*     [5] https://sourceforge.net/projects/iforest/
*     [6] https://math.stackexchange.com/questions/3388518/expected-number-of-paths-required-to-separate-elements-in-a-binary-tree
*     [7] Quinlan, J. Ross. C4. 5: programs for machine learning. Elsevier, 2014.
*     [8] Cortes, David.
*         "Distance approximation using Isolation Forests."
*         arXiv preprint arXiv:1910.12362 (2019).
*     [9] Cortes, David.
*         "Imputing missing values with unsupervised random trees."
*         arXiv preprint arXiv:1911.06646 (2019).
*     [10] https://math.stackexchange.com/questions/3333220/expected-average-depth-in-random-binary-tree-constructed-top-to-bottom
*     [11] Cortes, David.
*          "Revisiting randomized choices in isolation forests."
*          arXiv preprint arXiv:2110.13402 (2021).
*     [12] Guha, Sudipto, et al.
*          "Robust random cut forest based anomaly detection on streams."
*          International conference on machine learning. PMLR, 2016.
*     [13] Cortes, David.
*          "Isolation forests: looking beyond tree depth."
*          arXiv preprint arXiv:2111.11639 (2021).
*     [14] Ting, Kai Ming, Yue Zhu, and Zhi-Hua Zhou.
*          "Isolation kernel and its effect on SVM"
*          Proceedings of the 24th ACM SIGKDD
*          International Conference on Knowledge Discovery & Data Mining. 2018.
* 
*     BSD 2-Clause License
*     Copyright (c) 2019-2022, David Cortes
*     All rights reserved.
*     Redistribution and use in source and binary forms, with or without
*     modification, are permitted provided that the following conditions are met:
*     * Redistributions of source code must retain the above copyright notice, this
*       list of conditions and the following disclaimer.
*     * Redistributions in binary form must reproduce the above copyright notice,
*       this list of conditions and the following disclaimer in the documentation
*       and/or other materials provided with the distribution.
*     THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
*     AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
*     IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
*     DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
*     FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
*     DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
*     SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
*     CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
*     OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
*     OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/

#ifdef _FOR_PYTHON

#include "isotree.hpp"

template <class T>
T deepcopy_obj(T obj)
{
    T res = obj;
    return res;
}

IsoForest get_IsoForest()
{
    return IsoForest();
}

ExtIsoForest get_ExtIsoForest()
{
    return ExtIsoForest();
}

Imputer get_Imputer()
{
    return Imputer();
}

TreesIndexer get_Indexer()
{
    return TreesIndexer();
}

bool get_has_openmp(void)
{
    #ifdef _OPENMP
    return true;
    #else
    return false;
    #endif
}

size_t py_strerrorlen_s()
{
    return strlen(strerror(errno));
}

void copy_errno_msg(char *inp)
{
    strcpy(inp, strerror(errno));
}

#endif