vpd-dials {tdarec} | R Documentation |
Tune Vectorizations of Persistent Homology
Description
These tuning functions govern the parameters of vectorizations implemented in TDAvec.
Usage
num_coef(range = c(1L, unknown()), trans = NULL)
poly_type(values = c("R", "S", "T"), trans = NULL)
img_sigma(range = c(unknown(), unknown()), trans = transform_log10())
num_levels(range = c(1L, unknown()), trans = NULL)
weight_func_pl(
values = c("triangle", "epanechnikov", "tricubic"),
trans = NULL
)
bandwidth(range = c(unknown(), unknown()), trans = transform_log10())
weight_power(range = c(1, 2), trans = NULL)
num_bars(range = c(1L, unknown()), trans = NULL)
num_bins(range = c(2L, 20L), trans = NULL)
tent_shift(range = c(unknown(), unknown()), trans = transform_log10())
Arguments
range |
A two-element vector holding the defaults for the smallest and largest possible values, respectively. If a transformation is specified, these values should be in the transformed units. |
trans |
A |
values |
A character string of possible values. |
Details
The parameter num_coef
is passed to m
in
TDAvec::computeComplexPolynomial()
.
The parameter poly_type
is passed to polyType
in
TDAvec::computeComplexPolynomial()
.
The parameter img_sigma
is passed to sigma
in
TDAvec::computePersistenceImage()
.
The parameter num_levels
is passed to k
in
TDAvec::computePersistenceLandscape()
.
The parameter weight_func_pl
is passed to kernel
in
TDAvec::computePersistenceLandscape()
.
The parameter bandwidth
is passed to h
in
TDAvec::computePersistenceLandscape()
.
The parameter weight_power
is passed to p
in
TDAvec::computePersistenceSilhouette()
.
The parameter num_bars
is passed to r
in
TDAvec::computeTropicalCoordinates()
.
The parameter num_bins
is passed to d
in
TDAvec::computeTemplateFunction()
.
The parameter tent_shift
is passed to epsilon
in
TDAvec::computeTemplateFunction()
.
Value
A param
object or list of param
objects.
Examples
data.frame(dist = I(list(eurodist, UScitiesD * 1.6))) %>%
transform(pd = I(lapply(dist, ripserr::vietoris_rips))) %>%
subset(select = c(pd)) %>%
print() -> pd_data
# `num_coef` for `step_vpn_complex_polynomial()`
(nc_man <- num_coef(range = c(1L, 3L)))
grid_regular(nc_man)
# `poly_type` for `step_vpn_complex_polynomial()`
(pt_man <- poly_type(values = c("R", "S")))
grid_regular(pt_man)
# `img_sigma` for `step_vpn_persistence_image()`
(is_man <- img_sigma(range = c(100, 400), trans = NULL))
grid_regular(is_man)
(is_dat <- img_sigma() %>% get_pers_max_frac(x = pd_data))
grid_regular(is_dat)
(is_hom <- img_sigma() %>% get_pers_max_frac(x = pd_data, hom_degrees = seq(2L)))
grid_regular(is_hom)
# `num_levels` for `step_vpn_persistence_landscape()`
(nl_man <- num_levels(range = c(1L, 6L)))
grid_regular(nl_man)
# `weight_func_pl` for `step_vpn_persistence_landscape()`
(wfp_man <- weight_func_pl(values = c("triangle", "tricubic")))
grid_regular(wfp_man)
# `bandwidth` for `step_vpn_persistence_landscape()`
(b_man <- bandwidth(range = c(500, 1500), trans = NULL))
grid_regular(b_man)
(b_dat <- bandwidth() %>% get_pers_max_frac(x = pd_data))
grid_regular(b_dat)
(b_hom <- bandwidth() %>% get_pers_max_frac(x = pd_data, hom_degrees = seq(2L)))
grid_regular(b_hom)
# `weight_power` for `step_vpn_persistence_silhouette()`
(wp_man <- weight_power(range = c(1, 3)))
grid_regular(wp_man)
# `num_bars` for `step_vpn_tropical_coordinates()`
(nb_man <- num_bars(range = c(1L, 3L)))
grid_regular(nb_man)
# `num_bins` for `step_vpn_tent_template_functions()`
(nb_man <- num_bins(range = c(5L, 10L)))
grid_regular(nb_man)
# `tent_shift` for `step_vpn_tent_template_functions()`
(ts_man <- tent_shift(range = c(100, 200), trans = NULL))
grid_regular(ts_man)
(ts_dat <- tent_shift() %>% get_pers_min_mult(x = pd_data))
grid_regular(ts_dat)
(ts_hom <- tent_shift() %>% get_pers_min_mult(x = pd_data, hom_degrees = seq(2L)))
grid_regular(ts_hom)