These tuning functions govern the parameters of vectorizations implemented in TDAvec.
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())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.
A trans object from the scales package, such as
scales::transform_log10() or scales::transform_reciprocal(). If not provided,
the default is used which matches the units used in range. If no
transformation, NULL.
A character string of possible values.
A param object or list of param objects.
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().
data.frame(dist = I(list(eurodist, UScitiesD * 1.6))) %>%
transform(pd = I(lapply(dist, ripserr::vietoris_rips))) %>%
subset(select = c(pd)) %>%
print() -> pd_data
#> pd
#> 1 c(0, 0, ....
#> 2 c(0, 0, ....
# `num_coef` for `step_vpn_complex_polynomial()`
(nc_man <- num_coef(range = c(1L, 3L)))
#> # Polynomial coefficients (quantitative)
#> Range: [1, 3]
grid_regular(nc_man)
#> # A tibble: 3 × 1
#> num_coef
#> <int>
#> 1 1
#> 2 2
#> 3 3
# `poly_type` for `step_vpn_complex_polynomial()`
(pt_man <- poly_type(values = c("R", "S")))
#> Type of polynomial (qualitative)
#> 2 possible values include:
#> 'R' and 'S'
grid_regular(pt_man)
#> # A tibble: 2 × 1
#> poly_type
#> <chr>
#> 1 R
#> 2 S
# `img_sigma` for `step_vpn_persistence_image()`
(is_man <- img_sigma(range = c(100, 400), trans = NULL))
#> Convolved Gaussian standard deviation (quantitative)
#> Range: [100, 400]
grid_regular(is_man)
#> # A tibble: 3 × 1
#> img_sigma
#> <dbl>
#> 1 100
#> 2 250
#> 3 400
(is_dat <- img_sigma() %>% get_pers_max_frac(x = pd_data))
#> Convolved Gaussian standard deviation (quantitative)
#> Transformer: log-10 [1e-100, Inf]
#> Range (transformed scale): [1.15, 3.15]
grid_regular(is_dat)
#> # A tibble: 3 × 1
#> img_sigma
#> <dbl>
#> 1 14.1
#> 2 141.
#> 3 1406.
(is_hom <- img_sigma() %>% get_pers_max_frac(x = pd_data, hom_degrees = seq(2L)))
#> Convolved Gaussian standard deviation (quantitative)
#> Transformer: log-10 [1e-100, Inf]
#> Range (transformed scale): [1.15, 3.15]
grid_regular(is_hom)
#> # A tibble: 3 × 1
#> img_sigma
#> <dbl>
#> 1 14.1
#> 2 141.
#> 3 1406.
# `num_levels` for `step_vpn_persistence_landscape()`
(nl_man <- num_levels(range = c(1L, 6L)))
#> # Levels or envelopes (quantitative)
#> Range: [1, 6]
grid_regular(nl_man)
#> # A tibble: 3 × 1
#> num_levels
#> <int>
#> 1 1
#> 2 3
#> 3 6
# `weight_func_pl` for `step_vpn_persistence_landscape()`
(wfp_man <- weight_func_pl(values = c("triangle", "tricubic")))
#> Kernel distance weight function (qualitative)
#> 2 possible values include:
#> 'triangle' and 'tricubic'
grid_regular(wfp_man)
#> # A tibble: 2 × 1
#> weight_func_pl
#> <chr>
#> 1 triangle
#> 2 tricubic
# `bandwidth` for `step_vpn_persistence_landscape()`
(b_man <- bandwidth(range = c(500, 1500), trans = NULL))
#> Kernel bandwidth (quantitative)
#> Range: [500, 1500]
grid_regular(b_man)
#> # A tibble: 3 × 1
#> bandwidth
#> <dbl>
#> 1 500
#> 2 1000
#> 3 1500
(b_dat <- bandwidth() %>% get_pers_max_frac(x = pd_data))
#> Kernel bandwidth (quantitative)
#> Transformer: log-10 [1e-100, Inf]
#> Range (transformed scale): [1.15, 3.15]
grid_regular(b_dat)
#> # A tibble: 3 × 1
#> bandwidth
#> <dbl>
#> 1 14.1
#> 2 141.
#> 3 1406.
(b_hom <- bandwidth() %>% get_pers_max_frac(x = pd_data, hom_degrees = seq(2L)))
#> Kernel bandwidth (quantitative)
#> Transformer: log-10 [1e-100, Inf]
#> Range (transformed scale): [1.15, 3.15]
grid_regular(b_hom)
#> # A tibble: 3 × 1
#> bandwidth
#> <dbl>
#> 1 14.1
#> 2 141.
#> 3 1406.
# `weight_power` for `step_vpn_persistence_silhouette()`
(wp_man <- weight_power(range = c(1, 3)))
#> Exponent weight (quantitative)
#> Range: [1, 3]
grid_regular(wp_man)
#> # A tibble: 3 × 1
#> weight_power
#> <dbl>
#> 1 1
#> 2 2
#> 3 3
# `num_bars` for `step_vpn_tropical_coordinates()`
(nb_man <- num_bars(range = c(1L, 3L)))
#> # Bars (persistence pairs) (quantitative)
#> Range: [1, 3]
grid_regular(nb_man)
#> # A tibble: 3 × 1
#> num_bars
#> <int>
#> 1 1
#> 2 2
#> 3 3
# `num_bins` for `step_vpn_tent_template_functions()`
(nb_man <- num_bins(range = c(5L, 10L)))
#> Discretization grid bins (quantitative)
#> Range: [5, 10]
grid_regular(nb_man)
#> # A tibble: 3 × 1
#> num_bins
#> <int>
#> 1 5
#> 2 7
#> 3 10
# `tent_shift` for `step_vpn_tent_template_functions()`
(ts_man <- tent_shift(range = c(100, 200), trans = NULL))
#> Discretization grid shift (quantitative)
#> Range: [100, 200]
grid_regular(ts_man)
#> # A tibble: 3 × 1
#> tent_shift
#> <dbl>
#> 1 100
#> 2 150
#> 3 200
(ts_dat <- tent_shift() %>% get_pers_min_mult(x = pd_data))
#> Discretization grid shift (quantitative)
#> Transformer: log-10 [1e-100, Inf]
#> Range (transformed scale): [1.51, 3.51]
grid_regular(ts_dat)
#> # A tibble: 3 × 1
#> tent_shift
#> <dbl>
#> 1 32
#> 2 320
#> 3 3200
(ts_hom <- tent_shift() %>% get_pers_min_mult(x = pd_data, hom_degrees = seq(2L)))
#> Discretization grid shift (quantitative)
#> Transformer: log-10 [1e-100, Inf]
#> Range (transformed scale): [1.51, 3.51]
grid_regular(ts_hom)
#> # A tibble: 3 × 1
#> tent_shift
#> <dbl>
#> 1 32
#> 2 320
#> 3 3200