Derivative¶
-
class
gtda.curves.
Derivative
(order=1, n_jobs=None)[source]¶ Derivatives of multi-channel curves.
A multi-channel (integer sampled) curve is a 2D array of shape
(n_channels, n_bins)
, where each row represents the y-values in one of the channels. This transformer computes the n-th order derivative of each channel in each multi-channel curve in a collection, by discrete differences. The output is another collection of multi-channel curves.- Parameters
order (int, optional, default:
1
) – Order of the derivative to be taken.n_jobs (int or None, optional, default:
None
) – The number of jobs to use for the computation.None
means 1 unless in ajoblib.parallel_backend
context.-1
means using all processors.
-
__init__
(order=1, n_jobs=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.
-
fit
(X, y=None)[source]¶ Compute
n_channels_
. Then, return the estimator.This function is here to implement the usual scikit-learn API and hence work in pipelines.
- Parameters
X (ndarray of shape (n_samples, n_channels, n_bins)) – Input data. Collection of multi-channel curves.
y (None) – There is no need for a target in a transformer, yet the pipeline API requires this parameter.
- Returns
self
- Return type
object
-
fit_transform
(X, y=None, **fit_params)¶ Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
- Parameters
X (ndarray of shape (n_samples, n_channels, n_bins)) – Input data. Collection of multi-channel curves.
y (None) – There is no need for a target in a transformer, yet the pipeline API requires this parameter.
- Returns
Xt – Output collection of multi-channel curves given by taking discrete differences of order order in each channel in the curves in X.
- Return type
ndarray of shape (n_samples, n_channels, n_bins - order)
-
fit_transform_plot
(X, y=None, sample=0, **plot_params)¶ Fit to data, then apply
transform_plot
.- Parameters
X (ndarray of shape (n_samples, ..)) – Input data.
y (ndarray of shape (n_samples,) or None) – Target values for supervised problems.
sample (int) – Sample to be plotted.
**plot_params – Optional plotting parameters.
- Returns
Xt – Transformed one-sample slice from the input.
- Return type
ndarray of shape (1, ..)
-
get_params
(deep=True)¶ Get parameters for this estimator.
- Parameters
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params – Parameter names mapped to their values.
- Return type
mapping of string to any
-
plot
(Xt, sample=0, channels=None, plotly_params=None)[source]¶ Plot a sample from a collection of derivatives of multi-channel curves arranged as in the output of
transform
.- Parameters
Xt (ndarray of shape (n_samples, n_channels, n_bins)) – Collection of multi-channel curves, such as returned by
transform
.sample (int, optional, default:
0
) – Index of the sample in Xt to be plotted.channels (list, tuple or None, optional, default:
None
) – Which channels to include in the plot.None
means plotting the firstn_channels_
channels.plotly_params (dict or None, optional, default:
None
) – Custom parameters to configure the plotly figure. Allowed keys are"traces"
and"layout"
, and the corresponding values should be dictionaries containing keyword arguments as would be fed to theupdate_traces
andupdate_layout
methods ofplotly.graph_objects.Figure
.
- Returns
fig – Plotly figure.
- Return type
plotly.graph_objects.Figure
object
-
set_params
(**params)¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
object
-
transform
(X, y=None)[source]¶ Compute derivatives of multi-channel curves.
- Parameters
X (ndarray of shape (n_samples, n_channels, n_bins)) – Input collection of multi-channel curves.
y (None) – There is no need for a target in a transformer, yet the pipeline API requires this parameter.
- Returns
Xt – Output collection of multi-channel curves given by taking discrete differences of order order in each channel in the curves in X.
- Return type
ndarray of shape (n_samples, n_channels, n_bins - order)
-
transform_plot
(X, sample=0, **plot_params)¶ Take a one-sample slice from the input collection and transform it. Before returning the transformed object, plot the transformed sample.
- Parameters
X (ndarray of shape (n_samples, ..)) – Input data.
sample (int) – Sample to be plotted.
**plot_params – Optional plotting parameters.
- Returns
Xt – Transformed one-sample slice from the input.
- Return type
ndarray of shape (1, ..)