pykelihood.kernels.

polynomial_regression

polynomial_regression(x, degree=2, **constraints)

Polynomial regression in the columns of the data.

\[y = \sum_{i=1}^{n} \sum_{d=1}^{D_i} \beta_{i,d} x_i^d\]

Parameters:

• x (array-like or int) – The number of dimensions (int) or the data the kernel will be computed on. There will be one parameter for each column.

• degree (int or Sequence) – The degree of the polynomial for each covariate. If an integer, the same degree is used for all.

• constraints (dict, optional) – Fixed values for the parameters of the regression. The constraints are given as beta_i_d=value, where i is the index of the column starting with 1 and d is the degree. If x is provided as a dataframe and the second column is named cname, the following constraints are equivalent: beta_2_2=2, beta_cname_2=2, cname_2=2.

Returns:

The polynomial regression computed from the input data.

Return type:

float