statsmodels.nonparametric.kernels_asymmetric.pdf_kernel_asym(x, sample, bw, kernel_type, weights=
Density estimate based on asymmetric kernel.
Points for which density is evaluated.
xcan be scalar or 1-dim.
Sample from which kernel estimate is computed.
Bandwidth parameter, there is currently no default value for it.
Kernel name or kernel function. Currently supported kernel names are “beta”, “beta2”, “gamma”, “gamma2”, “bs”, “invgamma”, “invgauss”, “lognorm”, “recipinvgauss” and “weibull”.
If weights is not None, then kernel for sample points are weighted by it. No weights corresponds to uniform weighting of each component with 1 / nobs, where nobs is the size of sample.
If x is an 1-dim array, then points can be evaluated in vectorized form. To limit the amount of memory, a loop can work in batches. The number of batches is determined so that the intermediate array sizes are limited by
np.size(batch) * len(sample) < batch_size * 1000.
Default is to have at most 10000 elements in intermediate arrays.