bsfit.nodes.cirpy.utils.get_circ_conv¶

bsfit.nodes.cirpy.utils.get_circ_conv(X_1: numpy.ndarray, X_2: numpy.ndarray)[source]¶

convolve circular data

Parameters

X_1 (np.ndarray) – a column vector or a matrix
X_2 (np.ndarray) – a column vector or a matrix

Usage:

import numpy as np
from bsfit.nodes.cirpy.utils import get_circ_conv
impulse = np.zeros([10,1])
impulse[5] = 1
convolved = get_circ_conv(np.random.rand(10,1), impulse)

Out:

Returns: convolved matrix
Return type: (np.array)

Notes

Convolution is applied column-wise between columns i of X_1 and i of X_2 The probability that value i in vector 2 would be combined with at least one value from vector 1 vector 1 and 2 are col vectors (vertical)