kde | R Documentation |

An implementation of kernel density estimation with dual-tree algorithms. Given a set of reference points and query points and a kernel function, this can estimate the density function at the location of each query point using trees; trees that are built can be saved for later use.

```
kde(
abs_error = NA,
algorithm = NA,
bandwidth = NA,
initial_sample_size = NA,
input_model = NA,
kernel = NA,
mc_break_coef = NA,
mc_entry_coef = NA,
mc_probability = NA,
monte_carlo = FALSE,
query = NA,
reference = NA,
rel_error = NA,
tree = NA,
verbose = getOption("mlpack.verbose", FALSE)
)
```

`abs_error` |
Relative error tolerance for the prediction. Default value "0" (numeric). |

`algorithm` |
Algorithm to use for the prediction.('dual-tree', 'single-tree'). Default value "dual-tree" (character). |

`bandwidth` |
Bandwidth of the kernel. Default value "1" (numeric). |

`initial_sample_size` |
Initial sample size for Monte Carlo estimations. Default value "100" (integer). |

`input_model` |
Contains pre-trained KDE model (KDEModel). |

`kernel` |
Kernel to use for the prediction.('gaussian', 'epanechnikov', 'laplacian', 'spherical', 'triangular'). Default value "gaussian" (character). |

`mc_break_coef` |
Controls what fraction of the amount of node's descendants is the limit for the sample size before it recurses. Default value "0.4" (numeric). |

`mc_entry_coef` |
Controls how much larger does the amount of node descendants has to be compared to the initial sample size in order to be a candidate for Monte Carlo estimations. Default value "3" (numeric). |

`mc_probability` |
Probability of the estimation being bounded by relative error when using Monte Carlo estimations. Default value "0.95" (numeric). |

`monte_carlo` |
Whether to use Monte Carlo estimations when possible. Default value "FALSE" (logical). |

`query` |
Query dataset to KDE on (numeric matrix). |

`reference` |
Input reference dataset use for KDE (numeric matrix). |

`rel_error` |
Relative error tolerance for the prediction. Default value "0.05" (numeric). |

`tree` |
Tree to use for the prediction.('kd-tree', 'ball-tree', 'cover-tree', 'octree', 'r-tree'). Default value "kd-tree" (character). |

`verbose` |
Display informational messages and the full list of parameters and timers at the end of execution. Default value "getOption("mlpack.verbose", FALSE)" (logical). |

This program performs a Kernel Density Estimation. KDE is a non-parametric way of estimating probability density function. For each query point the program will estimate its probability density by applying a kernel function to each reference point. The computational complexity of this is O(N^2) where there are N query points and N reference points, but this implementation will typically see better performance as it uses an approximate dual or single tree algorithm for acceleration.

Dual or single tree optimization avoids many barely relevant calculations (as kernel function values decrease with distance), so it is an approximate computation. You can specify the maximum relative error tolerance for each query value with "rel_error" as well as the maximum absolute error tolerance with the parameter "abs_error". This program runs using an Euclidean metric. Kernel function can be selected using the "kernel" option. You can also choose what which type of tree to use for the dual-tree algorithm with "tree". It is also possible to select whether to use dual-tree algorithm or single-tree algorithm using the "algorithm" option.

Monte Carlo estimations can be used to accelerate the KDE estimate when the Gaussian Kernel is used. This provides a probabilistic guarantee on the the error of the resulting KDE instead of an absolute guarantee.To enable Monte Carlo estimations, the "monte_carlo" flag can be used, and success probability can be set with the "mc_probability" option. It is possible to set the initial sample size for the Monte Carlo estimation using "initial_sample_size". This implementation will only consider a node, as a candidate for the Monte Carlo estimation, if its number of descendant nodes is bigger than the initial sample size. This can be controlled using a coefficient that will multiply the initial sample size and can be set using "mc_entry_coef". To avoid using the same amount of computations an exact approach would take, this program recurses the tree whenever a fraction of the amount of the node's descendant points have already been computed. This fraction is set using "mc_break_coef".

A list with several components:

`output_model` |
If specified, the KDE model will be saved here (KDEModel). |

`predictions` |
Vector to store density predictions (numeric column). |

mlpack developers

```
# For example, the following will run KDE using the data in "ref_data" for
# training and the data in "qu_data" as query data. It will apply an
# Epanechnikov kernel with a 0.2 bandwidth to each reference point and use a
# KD-Tree for the dual-tree optimization. The returned predictions will be
# within 5% of the real KDE value for each query point.
## Not run:
output <- kde(reference=ref_data, query=qu_data, bandwidth=0.2,
kernel="epanechnikov", tree="kd-tree", rel_error=0.05)
out_data <- output$predictions
## End(Not run)
# the predicted density estimations will be stored in "out_data".
# If no "query" is provided, then KDE will be computed on the "reference"
# dataset.
# It is possible to select either a reference dataset or an input model but
# not both at the same time. If an input model is selected and parameter
# values are not set (e.g. "bandwidth") then default parameter values will be
# used.
#
# In addition to the last program call, it is also possible to activate Monte
# Carlo estimations if a Gaussian kernel is used. This can provide faster
# results, but the KDE will only have a probabilistic guarantee of meeting
# the desired error bound (instead of an absolute guarantee). The following
# example will run KDE using a Monte Carlo estimation when possible. The
# results will be within a 5% of the real KDE value with a 95% probability.
# Initial sample size for the Monte Carlo estimation will be 200 points and a
# node will be a candidate for the estimation only when it contains 700 (i.e.
# 3.5*200) points. If a node contains 700 points and 420 (i.e. 0.6*700) have
# already been sampled, then the algorithm will recurse instead of keep
# sampling.
## Not run:
output <- kde(reference=ref_data, query=qu_data, bandwidth=0.2,
kernel="gaussian", tree="kd-tree", rel_error=0.05, monte_carlo=,
mc_probability=0.95, initial_sample_size=200, mc_entry_coef=3.5,
mc_break_coef=0.6)
out_data <- output$predictions
## End(Not run)
```

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