Lines Matching full:column
81 row and column position are to be stored. These variables should be of type
97 Partial reductions are reductions that can operate column- or row-wise on a Matrix or
98 Array, applying the reduction operation on each column or row and
99 returning a column or row-vector with the corresponding values. Partial reductions are applied
103 in each column in a given matrix, storing the result in a row-vector:
125 <b>Note that column-wise operations return a 'row-vector' while row-wise operations
126 return a 'column-vector'</b>
130 Here is another example that finds the column whose sum of elements is the maximum
131 within a matrix. With column-wise partial reductions this can be coded as:
142 The previous example applies the \link DenseBase::sum() sum() \endlink reduction on each column
159 to obtain the column index where the maximum sum is found,
160 which is the column index 2 (third column) in this case.
165 constructs an expression where a vector (column or row) is interpreted as a matrix by replicating it in
168 A simple example is to add a certain column-vector to each column in a matrix.
181 to every column of the matrix. Alternatively, it can be interpreted as repeating the vector \c v four times to
188 The operators <tt>-=</tt>, <tt>+</tt> and <tt>-</tt> can also be used column-wise and row-wise. On arrays, we
190 multiplication and division column-wise or row-wise. These operators are not available on matrices because it
191 is not clear what they would do. If you want multiply column 0 of a matrix \c mat with \c v(0), column 1 with
194 It is important to point out that the vector to be added column-wise or row-wise must be of type Vector,
235 - <tt>m.colwise() - v</tt> is a broadcasting operation, subtracting <tt>v</tt> from each column in <tt>m</tt>. The result of this operation
244 - <tt>(m.colwise() - v).colwise().squaredNorm()</tt> is a partial reduction, computing the squared norm column-wise. The result of
245 this operation is a row-vector where each coefficient is the squared Euclidean distance between each column in <tt>m</tt> and <tt>v</tt>: \f[
252 - Finally, <tt>minCoeff(&index)</tt> is used to obtain the index of the column in <tt>m</tt> that is closest to <tt>v</tt> in terms of Euclidean
