basinhopper
BasinHopper
Bases: RegressorMixin, BaseEstimator
SKlearn wrapper around the scipy basinhopper algorithm.
Can fit a model of the form f(X, *params) given the parameters ranges and default values.
fit(X, y)
Fit the model to the available observations.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
X |
ArrayLike
|
2D Array of shape (n_samples, n_features). Daily mean temperatures for each unique site/year (n_samples) and for each DOY (n_features). The first feature should correspond to the first DOY, and so forth up to (max) 366. |
required |
y |
ArrayLike
|
1D Array of length n_samples Observed DOY of the spring onset for each unique site/year. |
required |
Returns:
| Type | Description |
|---|---|
|
Fitted model |
predict(X)
Predict values of y given new predictors
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
X |
ArrayLike
|
array-like, shape (n_samples, n_features). Daily mean temperatures for each unique site/year (n_samples) and for each DOY (n_features). The first feature should correspond to the first DOY, and so forth up to (max) 366. |
required |
Returns:
| Name | Type | Description |
|---|---|---|
y |
array-like, shape (n_samples,) Predicted DOY of the spring onset for each sample in X. |