This repository is implementation of Generalized Canonical Correlation Analysis(GCCA). CCA can use only 2 data but GCCA can use more than 2 data.
CCA is the method to transform 2 data to one joint space. See example graph:
CCA inplementation contains PCCA (Probablistic Canonical Correlation Analysis) transformation that is assumed that there is latent space in 2 data.
GCCA is the method to transform multiple data to one joint space. See example graph:
You can give GCCA any number of data.
You can use 'git clone' command to install
You have to install python dependent libraries in advance as follow:
numpy==1.9.1
scipy==0.14.1
matplotlib==1.4.2
h5py==2.4.0
fromccaimportCCAimportloggingimportnumpyasnp# set log levellogging.root.setLevel(level=logging.INFO)
# create data in advancea=np.random.rand(50, 50)
b=np.random.rand(50, 60)
# create instance of CCAcca=CCA()
# calculate CCAcca.fit(a, b)
# transformcca.transform(a, b)
# transform by PCCAcca.ptransform(a, b)
# savecca.save_params("save/cca.h5")
# loadcca.load_params("save/cca.h5")
# plotcca.plot_pcca_result()fromgccaimportGCCAimportloggingimportnumpyasnp# set log levellogging.root.setLevel(level=logging.INFO)
# create data in advancea=np.random.rand(50, 50)
b=np.random.rand(50, 60)
c=np.random.rand(50, 70)
d=np.random.rand(50, 80)
e=np.random.rand(50, 90)
f=np.random.rand(50, 100)
g=np.random.rand(50, 110)
h=np.random.rand(50, 120)
i=np.random.rand(50, 130)
j=np.random.rand(50, 140)
k=np.random.rand(50, 150)
# create instance of GCCAgcca=GCCA()
# calculate GCCAgcca.fit(a, b, c, d, e, f, g, h, i, j, k)
# transformgcca.transform(a, b, c, d, e, f, g, h, i, j, k)
# savegcca.save_params("save/gcca.h5")
# loadgcca.load_params("save/gcca.h5")
# plotgcca.plot_gcca_result()That's it!