#include "isotree.hpp" 


/*    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.
* 
*     BSD 2-Clause License
*     Copyright (c) 2019, 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.
*/


/* Calculate distance or similarity between data points
* 
* Parameters
* ==========
* - numeric_data[nrows * ncols_numeric]
*       Pointer to numeric data for which to make calculations. Must be ordered by columns like Fortran,
*       not ordered by rows like C (i.e. entries 1..n contain column 0, n+1..2n column 1, etc.),
*       and the column order must be the same as in the data that was used to fit the model.
*       Pass NULL if there are no dense numeric columns.
*       Can only pass one of 'numeric_data' or 'Xc' + 'Xc_ind' + 'Xc_indptr'.
* - categ_data[nrows * ncols_categ]
*       Pointer to categorical data for which to make calculations. Must be ordered by columns like Fortran,
*       not ordered by rows like C (i.e. entries 1..n contain column 0, n+1..2n column 1, etc.),
*       and the column order must be the same as in the data that was used to fit the model.
*       Pass NULL if there are no categorical columns.
*       Each category should be represented as an integer, and these integers must start at zero and
*       be in consecutive order - i.e. if category '3' is present, category '2' must also be present
*       (note that they are not treated as being ordinal, this is just an encoding). Missing values
*       should be encoded as negative numbers such as (-1). The encoding must be the same as was used
*       in the data to which the model was fit.
* - Xc[nnz]
*       Pointer to numeric data in sparse numeric matrix in CSC format (column-compressed).
*       Pass NULL if there are no sparse numeric columns.
*       Can only pass one of 'numeric_data' or 'Xc' + 'Xc_ind' + 'Xc_indptr'.
* - Xc_ind[nnz]
*       Pointer to row indices to which each non-zero entry in 'Xc' corresponds.
*       Pass NULL if there are no sparse numeric columns in CSC format.
* - Xc_indptr[ncols_categ + 1]
*       Pointer to column index pointers that tell at entry [col] where does column 'col'
*       start and at entry [col + 1] where does column 'col' end.
*       Pass NULL if there are no sparse numeric columns in CSC format.
* - nrows
*       Number of rows in 'numeric_data', 'Xc', 'Xr, 'categ_data'.
* - nthreads
*       Number of parallel threads to use. Note that, the more threads, the more memory will be
*       allocated, even if the thread does not end up being used. Ignored when not building with
*       OpenMP support.
* - assume_full_distr
*       Whether to assume that the fitted model represents a full population distribution (will use a
*       standardizing criterion assuming infinite sample, and the results of the similarity between two points
*       at prediction time will not depend on the prescence of any third point that is similar to them, but will
*       differ more compared to the pairwise distances between points from which the model was fit). If passing
*       'false', will calculate pairwise distances as if the new observations at prediction time were added to
*       the sample to which each tree was fit, which will make the distances between two points potentially vary
*       according to other newly introduced points.
* - standardize_dist
*       Whether to standardize the resulting average separation depths between rows according
*       to the expected average separation depth in a similar way as when predicting outlierness,
*       in order to obtain a standardized distance. If passing 'false', will output the average
*       separation depth instead.
* - model_outputs
*       Pointer to fitted single-variable model object from function 'fit_iforest'. Pass NULL
*       if the calculations are to be made from an extended model. Can only pass one of
*       'model_outputs' and 'model_outputs_ext'.
* - model_outputs_ext
*       Pointer to fitted extended model object from function 'fit_iforest'. Pass NULL
*       if the calculations are to be made from a single-variable model. Can only pass one of
*       'model_outputs' and 'model_outputs_ext'.
* - tmat[nrows * (nrows - 1) / 2] (out)
*       Pointer to array where the resulting pairwise distances or average separation depths will
*       be written into. As the output is a symmetric matrix, this function will only fill in the
*       upper-triangular part, in which entry 0 <= i < j < n will be located at position
*           p(i,j) = (i * (n - (i+1)/2) + j - i - 1).
*       Can be converted to a dense square matrix through function 'tmat_to_dense'.
*       The array must already be initialized to zeros.
*/
void calc_similarity(double numeric_data[], int categ_data[],
					 double Xc[], sparse_ix Xc_ind[], sparse_ix Xc_indptr[],
					 size_t nrows, int nthreads, bool assume_full_distr, bool standardize_dist,
					 IsoForest *model_outputs, ExtIsoForest *model_outputs_ext, double tmat[])
{
	PredictionData prediction_data = {numeric_data, categ_data, nrows,
									  Xc, Xc_ind, Xc_indptr,
									  NULL, NULL, NULL};

	#ifdef _OPENMP
	std::vector<WorkerForSimilarity> worker_memory(nthreads);
	#else
	std::vector<WorkerForSimilarity> worker_memory(1);
	#endif

	size_t ntrees = (model_outputs != NULL)? model_outputs->trees.size() : model_outputs_ext->hplanes.size();

	if (model_outputs != NULL)
	{
		#pragma omp parallel for schedule(dynamic) num_threads(nthreads) shared(ntrees, worker_memory, prediction_data, model_outputs)
		for (size_t_for tree = 0; tree < ntrees; tree++)
		{
			initialize_worker_for_sim(worker_memory[omp_get_thread_num()], prediction_data, model_outputs, NULL, assume_full_distr);
			traverse_tree_sim(worker_memory[omp_get_thread_num()],
							  prediction_data,
							  *model_outputs,
							  model_outputs->trees[tree],
							  (size_t)0);
		}
	}

	else
	{
		#pragma omp parallel for schedule(dynamic) num_threads(nthreads) shared(ntrees, worker_memory, prediction_data, model_outputs_ext)
		for (size_t_for hplane = 0; hplane < ntrees; hplane++)
		{
			initialize_worker_for_sim(worker_memory[omp_get_thread_num()], prediction_data, NULL, model_outputs_ext, assume_full_distr);
			traverse_hplane_sim(worker_memory[omp_get_thread_num()],
								prediction_data,
								*model_outputs_ext,
								model_outputs_ext->hplanes[hplane],
								(size_t)0);
		}	
	}
	
	/* gather and transform the results */
	gather_sim_result(&worker_memory, NULL,
					  &prediction_data, NULL,
					  model_outputs, model_outputs_ext,
					  tmat, ntrees, assume_full_distr,
					  standardize_dist, nthreads);
}

void traverse_tree_sim(WorkerForSimilarity   &workspace,
					   PredictionData        &prediction_data,
					   IsoForest             &model_outputs,
					   std::vector<IsoTree>  &trees,
					   size_t                curr_tree)
{
	if (workspace.st == workspace.end)
		return;

	if (trees[curr_tree].score > 0)
	{
		long double rem = (long double) trees[curr_tree].remainder;
		if (!workspace.weights_arr.size())
		{
			rem += (long double)(workspace.end - workspace.st + 1);
			increase_comb_counter(workspace.ix_arr.data(), workspace.st, workspace.end,
								  prediction_data.nrows, workspace.tmat_sep.data(),
								  workspace.assume_full_distr? 4 : expected_separation_depth(rem) + 1);
		}

		else
		{
			if (!workspace.assume_full_distr)
			{
				rem += std::accumulate(workspace.ix_arr.begin() + workspace.st,
									   workspace.ix_arr.begin() + workspace.end,
									   (long double) 0,
									   [&workspace](long double curr, size_t ix)
									   			   {return curr + (long double)workspace.weights_arr[ix];}
									   	);
			}

			increase_comb_counter(workspace.ix_arr.data(), workspace.st, workspace.end,
								  prediction_data.nrows, workspace.tmat_sep.data(),
								  workspace.weights_arr.data(),
								  workspace.assume_full_distr? 4 : expected_separation_depth(rem) + 1);
		}
		return;
	}

	else
	{
		if (!workspace.weights_arr.size())
			increase_comb_counter(workspace.ix_arr.data(), workspace.st, workspace.end,
								  prediction_data.nrows, workspace.tmat_sep.data(), -1);
		else
			increase_comb_counter(workspace.ix_arr.data(), workspace.st, workspace.end,
								  prediction_data.nrows, workspace.tmat_sep.data(),
								  workspace.weights_arr.data(), -1);
	}


	/* divide according to tree */
	if (prediction_data.Xc != NULL)
		std::sort(workspace.ix_arr.begin() + workspace.st, workspace.ix_arr.begin() + workspace.end + 1);
	size_t st_NA, end_NA, split_ix;
	switch(trees[curr_tree].col_type)
	{
		case Numeric:
		{
			if (prediction_data.Xc == NULL)
				divide_subset_split(workspace.ix_arr.data(),
									prediction_data.numeric_data + prediction_data.nrows * trees[curr_tree].col_num,
									workspace.st, workspace.end, trees[curr_tree].num_split,
									model_outputs.missing_action, st_NA, end_NA, split_ix);
			else
				divide_subset_split(workspace.ix_arr.data(), workspace.st, workspace.end, trees[curr_tree].col_num,
									prediction_data.Xc, prediction_data.Xc_ind, prediction_data.Xc_indptr,
									trees[curr_tree].num_split, model_outputs.missing_action,
									st_NA, end_NA, split_ix);

			break;
		}

		case Categorical:
		{
			switch(model_outputs.cat_split_type)
			{
				case SingleCateg:
				{
					divide_subset_split(workspace.ix_arr.data(),
										prediction_data.categ_data + prediction_data.nrows * trees[curr_tree].col_num,
										workspace.st, workspace.end, trees[curr_tree].chosen_cat,
						 				model_outputs.missing_action, st_NA, end_NA, split_ix);
					break;
				}

				case SubSet:
				{
					if (!trees[curr_tree].cat_split.size())
						divide_subset_split(workspace.ix_arr.data(),
											prediction_data.categ_data + prediction_data.nrows * trees[curr_tree].col_num,
											workspace.st, workspace.end,
											model_outputs.missing_action, model_outputs.new_cat_action,
											trees[curr_tree].pct_tree_left < .5, st_NA, end_NA, split_ix);
					else
						divide_subset_split(workspace.ix_arr.data(),
											prediction_data.categ_data + prediction_data.nrows * trees[curr_tree].col_num,
											workspace.st, workspace.end, trees[curr_tree].cat_split.data(),
											(int) trees[curr_tree].cat_split.size(),
											model_outputs.missing_action, model_outputs.new_cat_action,
											(bool)(trees[curr_tree].pct_tree_left < .5), st_NA, end_NA, split_ix);
					break;
				}
			}
			break;
		}
	}


	/* continue splitting recursively */
	size_t orig_end = workspace.end;
	switch(model_outputs.missing_action)
	{
		case Impute:
		{
			split_ix = (trees.back().pct_tree_left >= .5)? end_NA : st_NA;
		}

		case Fail:
		{
			if (split_ix > workspace.st)
			{
				workspace.end = split_ix - 1;
				traverse_tree_sim(workspace,
								  prediction_data,
								  model_outputs,
								  trees,
								  trees[curr_tree].tree_left);
			}


			if (split_ix < orig_end)
			{
				workspace.st  = split_ix;
				workspace.end = orig_end;
				traverse_tree_sim(workspace,
								  prediction_data,
								  model_outputs,
								  trees,
								  trees[curr_tree].tree_right);
			}
			break;
		}

		case Divide: /* new_cat_action = 'Weighted' will also fall here */
		{
			std::vector<double> weights_arr;
			std::vector<size_t> ix_arr;
			if (end_NA > workspace.st)
			{
				weights_arr.assign(workspace.weights_arr.begin(),
								   workspace.weights_arr.begin() + (end_NA - 1));
				ix_arr.assign(workspace.ix_arr.begin(),
							  workspace.ix_arr.begin() + (end_NA - 1));
			}

			if (end_NA > workspace.st)
			{
				workspace.end = end_NA - 1;
				for (size_t row = st_NA; row < end_NA; row++)
					workspace.weights_arr[workspace.ix_arr[row]] *= trees[curr_tree].pct_tree_left;
				traverse_tree_sim(workspace,
								  prediction_data,
								  model_outputs,
								  trees,
								  trees[curr_tree].tree_left);
			}

			if (st_NA < orig_end)
			{
				workspace.st = st_NA;
				workspace.end = orig_end;
				if (weights_arr.size())
				{
					std::copy(weights_arr.begin(),
							  weights_arr.begin() + (end_NA - 1),
							  workspace.weights_arr.begin());
					std::copy(ix_arr.begin(),
							  ix_arr.begin() + (end_NA - 1),
							  workspace.ix_arr.begin());
					weights_arr.clear();
					weights_arr.shrink_to_fit();
					ix_arr.clear();
					ix_arr.shrink_to_fit();
				}

				for (size_t row = st_NA; row < end_NA; row++)
					workspace.weights_arr[workspace.ix_arr[row]] *= (1 - trees[curr_tree].pct_tree_left);
				traverse_tree_sim(workspace,
								  prediction_data,
								  model_outputs,
								  trees,
								  trees[curr_tree].tree_right);
			}
			break;
		}
	}
}

void traverse_hplane_sim(WorkerForSimilarity     &workspace,
						 PredictionData          &prediction_data,
						 ExtIsoForest            &model_outputs,
						 std::vector<IsoHPlane>  &hplanes,
						 size_t                  curr_tree)
{
	if (workspace.st == workspace.end)
		return;

	if (hplanes[curr_tree].score > 0)
	{
		increase_comb_counter(workspace.ix_arr.data(), workspace.st, workspace.end,
							  prediction_data.nrows, workspace.tmat_sep.data(),
							  workspace.assume_full_distr? 4 : 
							  expected_separation_depth((long double) hplanes[curr_tree].remainder
							  							+ (long double)(workspace.end - workspace.st + 1)
							  							+ (long double)1)
							  );
		return;
	}

	else
	{
		increase_comb_counter(workspace.ix_arr.data(), workspace.st, workspace.end,
							  prediction_data.nrows, workspace.tmat_sep.data(), -1);
	}

	if (prediction_data.Xc != NULL)
		std::sort(workspace.ix_arr.begin() + workspace.st, workspace.ix_arr.begin() + workspace.end + 1);

	/* reconstruct linear combination */
	size_t ncols_numeric = 0;
	size_t ncols_categ   = 0;
	std::fill(workspace.comb_val.begin(), workspace.comb_val.begin() + (workspace.end - workspace.st + 1), 0);
	if (prediction_data.categ_data != NULL || prediction_data.Xc != NULL)
	{
		for (size_t col = 0; col < hplanes[curr_tree].col_num.size(); col++)
		{
			switch(hplanes[curr_tree].col_type[col])
			{
				case Numeric:
				{
					if (prediction_data.Xc == NULL)
						add_linear_comb(workspace.ix_arr.data(), workspace.st, workspace.end, workspace.comb_val.data(),
										prediction_data.numeric_data + prediction_data.nrows * hplanes[curr_tree].col_num[col],
										hplanes[curr_tree].coef[ncols_numeric], (double)0,
										(model_outputs.missing_action == Fail)?  workspace.comb_val[0] : hplanes[curr_tree].fill_val[col],
										model_outputs.missing_action, NULL, NULL, false);
					else
						add_linear_comb(workspace.ix_arr.data(), workspace.st, workspace.end,
										hplanes[curr_tree].col_num[col], workspace.comb_val.data(),
										prediction_data.Xc, prediction_data.Xc_ind, prediction_data.Xc_indptr,
										hplanes[curr_tree].coef[ncols_numeric], (double)0,
										(model_outputs.missing_action == Fail)?  workspace.comb_val[0] : hplanes[curr_tree].fill_val[col],
										model_outputs.missing_action, NULL, NULL, false);
					ncols_numeric++;
					break;
				}

				case Categorical:
				{
					switch(model_outputs.cat_split_type)
					{
						case SingleCateg:
						{
							add_linear_comb(workspace.ix_arr.data(), workspace.st, workspace.end, workspace.comb_val.data(),
											prediction_data.categ_data + prediction_data.nrows * hplanes[curr_tree].col_num[col],
											(int)0, NULL, hplanes[curr_tree].fill_new[ncols_categ],
											hplanes[curr_tree].chosen_cat[ncols_categ],
											(model_outputs.missing_action == Fail)?  workspace.comb_val[0] : hplanes[curr_tree].fill_val[col],
											workspace.comb_val[0], NULL, NULL, model_outputs.new_cat_action,
											model_outputs.missing_action, SingleCateg, false);
							break;
						}

						case SubSet:
						{
							add_linear_comb(workspace.ix_arr.data(), workspace.st, workspace.end, workspace.comb_val.data(),
											prediction_data.categ_data + prediction_data.nrows * hplanes[curr_tree].col_num[col],
											(int) hplanes[curr_tree].cat_coef[ncols_categ].size(),
											hplanes[curr_tree].cat_coef[ncols_categ].data(), (double) 0, (int) 0,
											(model_outputs.missing_action == Fail)? workspace.comb_val[0] : hplanes[curr_tree].fill_val[col],
											hplanes[curr_tree].fill_new[ncols_categ], NULL, NULL,
											model_outputs.new_cat_action, model_outputs.missing_action, SubSet, false);
							break;
						}
					}
					ncols_categ++;
					break;
				}
			} 
		}
	}

	
	else /* faster version for numerical-only */
	{
		for (size_t col = 0; col < hplanes[curr_tree].col_num.size(); col++)
			add_linear_comb(workspace.ix_arr.data(), workspace.st, workspace.end, workspace.comb_val.data(),
							prediction_data.numeric_data + prediction_data.nrows * hplanes[curr_tree].col_num[col],
							hplanes[curr_tree].coef[col], (double)0,
							(model_outputs.missing_action == Fail)?  workspace.comb_val[0] : hplanes[curr_tree].fill_val[col],
							model_outputs.missing_action, NULL, NULL, false);
	}

	/* divide data */
	size_t split_ix = divide_subset_split(workspace.ix_arr.data(), workspace.comb_val.data(),
										  workspace.st, workspace.end, hplanes[curr_tree].split_point);

	/* continue splitting recursively */
	size_t orig_end = workspace.end;
	if (split_ix > workspace.st)
	{
		workspace.end = split_ix - 1;
		traverse_hplane_sim(workspace,
							prediction_data,
							model_outputs,
							hplanes,
							hplanes[curr_tree].hplane_left);
	}

	if (split_ix < orig_end)
	{
		workspace.st  = split_ix;
		workspace.end = orig_end;
		traverse_hplane_sim(workspace,
							prediction_data,
							model_outputs,
							hplanes,
							hplanes[curr_tree].hplane_right);
	}

}

void gather_sim_result(std::vector<WorkerForSimilarity> *worker_memory,
					   std::vector<WorkerMemory> *worker_memory_m,
					   PredictionData *prediction_data, InputData *input_data,
					   IsoForest *model_outputs, ExtIsoForest *model_outputs_ext,
					   double *restrict tmat, size_t ntrees, bool assume_full_distr,
					   bool standardize_dist, int nthreads)
{
	size_t ncomb = (prediction_data != NULL)?
					(prediction_data->nrows * (prediction_data->nrows - 1)) / 2
						:
					(input_data->nrows * (input_data->nrows - 1)) / 2;

	#ifdef _OPENMP
	if (nthreads > 1)
	{
		if (worker_memory != NULL)
		{
			for (WorkerForSimilarity &w : *worker_memory)
				if (w.tmat_sep.size())
					for (size_t ix = 0; ix < ncomb; ix++)
						tmat[ix] += w.tmat_sep[ix];
		}

		else
		{
			for (WorkerMemory &w : *worker_memory_m)
				if (w.tmat_sep.size())
					for (size_t ix = 0; ix < ncomb; ix++)
						tmat[ix] += w.tmat_sep[ix];
		}
	}
	
	else
	#endif
	{
		if (worker_memory != NULL)
			std::copy((*worker_memory)[0].tmat_sep.begin(), (*worker_memory)[0].tmat_sep.end(), tmat);
		else
			std::copy((*worker_memory_m)[0].tmat_sep.begin(), (*worker_memory_m)[0].tmat_sep.end(), tmat);
	}

	double ntrees_dbl = (double) ntrees;
	if (standardize_dist)
	{
		double div_trees = ntrees_dbl;
		if (assume_full_distr)
		{
			div_trees *= 2;
		}

		else if (input_data != NULL)
		{
			div_trees *= (expected_separation_depth(input_data->nrows) - 1);
		}

		else
		{
			div_trees *= ((
						   	(model_outputs != NULL)?
						   	 expected_separation_depth_hotstart(model_outputs->exp_avg_sep,
						   	 									model_outputs->orig_sample_size,
						   	 									model_outputs->orig_sample_size + prediction_data->nrows)
						   	 	:
						   	 expected_separation_depth_hotstart(model_outputs_ext->exp_avg_sep,
						   	 									model_outputs_ext->orig_sample_size,
						   	 									model_outputs_ext->orig_sample_size + prediction_data->nrows)
						  ) - 1);
		}

		
		#pragma omp parallel for schedule(static) num_threads(nthreads) shared(ncomb, tmat, ntrees_dbl, div_trees)
		for (size_t_for ix = 0; ix < ncomb; ix++)
			tmat[ix] = exp2( - (tmat[ix] - ntrees_dbl) / div_trees); /* from each tree should get (-1) */
	}
	
	else
	{
		#pragma omp parallel for schedule(static) num_threads(nthreads) shared(ncomb, tmat, ntrees_dbl)
		for (size_t_for ix = 0; ix < ncomb; ix++)
			tmat[ix] /= ntrees_dbl;
	}
}

void initialize_worker_for_sim(WorkerForSimilarity  &workspace,
							   PredictionData       &prediction_data,
							   IsoForest            *model_outputs,
							   ExtIsoForest         *model_outputs_ext,
							   bool                  assume_full_distr)
{
	workspace.st  = 0;
	workspace.end = prediction_data.nrows - 1;
	workspace.assume_full_distr = assume_full_distr; /* doesn't need to have one copy per worker */

	if (!workspace.ix_arr.size())
	{
		workspace.ix_arr.resize(prediction_data.nrows);
		std::iota(workspace.ix_arr.begin(), workspace.ix_arr.end(), (size_t)0);
		workspace.tmat_sep.resize((prediction_data.nrows * (prediction_data.nrows - 1)) / 2, 0);
	}

	if (model_outputs != NULL && (model_outputs->missing_action == Divide || model_outputs->new_cat_action == Weighted))
	{
		if (!workspace.weights_arr.size())
			workspace.weights_arr.resize(prediction_data.nrows, 1);
		else
			std::fill(workspace.weights_arr.begin(), workspace.weights_arr.end(), 1);
	}

	if (model_outputs_ext != NULL)
	{
		if (!workspace.comb_val.size())
			workspace.comb_val.resize(prediction_data.nrows, 0);
		else
			std::fill(workspace.comb_val.begin(), workspace.comb_val.end(), 0);
	}
}

