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29 changes: 15 additions & 14 deletions pwcca.py
Original file line number Diff line number Diff line change
Expand Up @@ -22,8 +22,9 @@

"""

import numpy as np
import cca_core
import numpy as np


def compute_pwcca(acts1, acts2, epsilon=0.):
""" Computes projection weighting for weighting CCA coefficients
Expand All @@ -36,24 +37,24 @@ def compute_pwcca(acts1, acts2, epsilon=0.):
Original cca coefficient mean and weighted mean

"""
sresults = cca_core.get_cca_similarity(acts1, acts2, epsilon=epsilon,
compute_dirns=False, compute_coefs=True, verbose=False)
sresults = cca_core.get_cca_similarity(acts1, acts2, epsilon=epsilon,
compute_dirns=False, compute_coefs=True, verbose=False)
if np.sum(sresults["x_idxs"]) <= np.sum(sresults["y_idxs"]):
dirns = np.dot(sresults["coef_x"],
(acts1[sresults["x_idxs"]] - \
sresults["neuron_means1"][sresults["x_idxs"]])) + sresults["neuron_means1"][sresults["x_idxs"]]
coefs = sresults["cca_coef1"]
acts = acts1
idxs = sresults["x_idxs"]
coefs_z = sresults["coef_x"]
neuron_means = sresults["neuron_means1"]
coefs = sresults["cca_coef1"]
else:
dirns = np.dot(sresults["coef_y"],
(acts1[sresults["y_idxs"]] - \
sresults["neuron_means2"][sresults["y_idxs"]])) + sresults["neuron_means2"][sresults["y_idxs"]]
coefs = sresults["cca_coef2"]
acts = acts2
idxs = sresults["y_idxs"]
coefs_z = sresults["coef_y"]
neuron_means = sresults["neuron_means2"]
coefs = sresults["cca_coef2"]

dirns = np.dot(coefs_z, (acts[idxs] - neuron_means[idxs])) + neuron_means[idxs]
P, _ = np.linalg.qr(dirns.T)
weights = np.sum(np.abs(np.dot(P.T, acts[idxs].T)), axis=1)
weights = weights/np.sum(weights)
return np.sum(weights*coefs), weights, coefs
weights = weights / np.sum(weights)

return np.sum(weights * coefs), weights, coefs