91 lines
2.6 KiB
C++
91 lines
2.6 KiB
C++
// Copyright 2008-2016 Conrad Sanderson (http://conradsanderson.id.au)
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// Copyright 2008-2016 National ICT Australia (NICTA)
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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// ------------------------------------------------------------------------
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//! \addtogroup op_stddev
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//! @{
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//! \brief
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//! For each row or for each column, find the standard deviation.
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//! The result is stored in a dense matrix that has either one column or one row.
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//! The dimension for which the standard deviations are found is set via the stddev() function.
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template<typename T1>
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inline
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void
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op_stddev::apply(Mat<typename T1::pod_type>& out, const mtOp<typename T1::pod_type, T1, op_stddev>& in)
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{
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arma_extra_debug_sigprint();
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typedef typename T1::elem_type in_eT;
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typedef typename T1::pod_type out_eT;
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const unwrap_check_mixed<T1> tmp(in.m, out);
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const Mat<in_eT>& X = tmp.M;
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const uword norm_type = in.aux_uword_a;
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const uword dim = in.aux_uword_b;
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arma_debug_check( (norm_type > 1), "stddev(): parameter 'norm_type' must be 0 or 1" );
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arma_debug_check( (dim > 1), "stddev(): parameter 'dim' must be 0 or 1" );
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const uword X_n_rows = X.n_rows;
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const uword X_n_cols = X.n_cols;
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if(dim == 0)
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{
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arma_extra_debug_print("op_stddev::apply(): dim = 0");
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out.set_size((X_n_rows > 0) ? 1 : 0, X_n_cols);
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if(X_n_rows > 0)
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{
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out_eT* out_mem = out.memptr();
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for(uword col=0; col<X_n_cols; ++col)
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{
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out_mem[col] = std::sqrt( op_var::direct_var( X.colptr(col), X_n_rows, norm_type ) );
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}
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}
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}
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else
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if(dim == 1)
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{
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arma_extra_debug_print("op_stddev::apply(): dim = 1");
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out.set_size(X_n_rows, (X_n_cols > 0) ? 1 : 0);
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if(X_n_cols > 0)
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{
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podarray<in_eT> dat(X_n_cols);
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in_eT* dat_mem = dat.memptr();
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out_eT* out_mem = out.memptr();
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for(uword row=0; row<X_n_rows; ++row)
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{
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dat.copy_row(X, row);
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out_mem[row] = std::sqrt( op_var::direct_var( dat_mem, X_n_cols, norm_type) );
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}
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}
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}
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}
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//! @}
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