Update QuickSampling.md

algorithms/QuickSampling.md

@@ -23,7 +23,6 @@ Outputs: sim = simulation, index = index of the simulated values in the flattene
 | `-k` | Number of best candidates to consider ∈ [1 ∞]. | ✔ |
 | `-n` | N closest neighbors to consider. If multiple variables: use a single N value if identical for all variables or use an array of N values if each variable has a different N. | ✔ |
 | `-ki` | Image of the weighting kernel. Although the use of a kernel is optional, it is highly recommended to specify one. The size of the kernel is also the size of the search neighborhood. The weights in the kernel can be used to normalize the variables. If multiple variables: use a single kernel value if identical for all variables or if each variable has a different kernel, stack all kernels along the last dimension. The number of kernels should then match the size of the array given for the parameter dt. Possibility to use different kernels for different parts of the simulation with the "-kii" parameter explained below. | |
-| `-f` | f=1/k equivalent to f of DS with a threshold to 0. | |
 | `-j` | To run in parallel (default is single core). To use as follows '-j', N1, N2, N3 (all three are optional but N3 needs N2, which in turn needs N1). Use integer values to specify a number of threads (or logical cores). Use decimal values ∈]0,1[ to indicate fraction of the maximum number of available logical cores (e.g., 0.5=50% of all available logical cores). N1 threads used to parallelize the path (path-level) Default: the maximum number of threads available. N2 threads used to parallelize over training images (if many TIs are available, each is scanned on a different core). Default: 1. N3 threads used to parallelize FFTs (node-level). Default: 1. N1 and N2 are recommended over N3. N1 is usually more efficient than N2, but requires more memory. | |
 | `-sp` | Simulation path, array of the size of the simulation grid containing values that specify the simulation path (from low to high). Default is a random path. Equal values are accepted but will always be ordered in the same way (i.e. not random). -∞ values are not simulated. In case of multiple variables, a vector simulation is default (same path for all variables) and the simulation path should be one dimension less than the number of variables. If you prefer a full simulation, provide an array containing the path for each variable and use the "-fs" flag below. | |
 | `-s` | Random seed value. | |
@@ -43,6 +42,7 @@ Outputs: sim = simulation, index = index of the simulated values in the flattene
 | `-csim` | With this flag QS will make sure to create a periodic (aka circular or cyclic) simulation over each dimension. | |
 | `-adsim` | Augmented dimentionality simulation: allows for 3D simulation using 2D training image, only for categories (Coming maybe some day!). | |
 | `-fs` | Full simulation: follows a different simulation path for each variable (as opposed to vector simulation, where the same simulation path is used for all variables). | |
+| `-f` | f=1/k equivalent to f of DS with a threshold to 0. | |
 | `-nV` | No Verbatim, i.e. prohibits neighbors in the training image to be neighbors in the simulation. (experimental). | |
 | `--forceSimulation` | Restimulate a value even if already existing. | |