str(patchsize))
print(" ")
############################################################################
# load classifiers, set thresholds, then sigmoid
clfs = []
for clfnametup in csortme:
clffile = clfnametup[1]
assert clffile.endswith('_LDAresult.hdf5')
clf = QuadraticDiscriminantAnalysis(store_covariances=False)
clf.classes_ = np.array([0, 1], dtype=np.int64)
h5file = tables.open_file(clffile, mode='r')
clf.means_ = np.array(h5file.root.means[:])
clf.priors_ = np.array(h5file.root.priors[:])
clf.rotations_ = np.array(h5file.root.rotations[:])
clf.scalings_ = np.array(h5file.root.scalings[:])
h5file.close()
assert clf.priors_.shape == (2, ), str(clf.priors_)
assert np.sum(np.fabs(clf.priors_ - np.array([0.5, 0.5]))) < 1e-9, str(
clf.priors_)
clf.priors_ = np.array([1. - fraction_manip, fraction_manip])
clfs.append(clf)
#clfthreshfile = clffile[:-len('result.hdf5')]+'highestROC.pkl'
#highestroc = cPickle.load(open(clfthreshfile,'rb'))
#distsfromdiag = (highestroc[3] - highestroc[2]) / np.sqrt(2.)
store_covariances=COMPUTE_COVARIANCES)
clf.fit(tr_X, tr_Y)
afttime = time.time()
print("AFTER: tr_X.shape " + str(tr_X.shape) + ", dtype " +
str(tr_X.dtype))
print("fitting took " + str(afttime - beftime) + " sec")
fraction_manip = 0.50
# delete training set, since it is no longer needed
del tr_X
del tr_Y
gc.collect()
clf.means_ = np.array(clf.means_)
clf.priors_ = np.array([1. - fraction_manip, fraction_manip]) #
clf.rotations_ = np.array(clf.rotations_)
clf.scalings_ = np.array(clf.scalings_)
print("clf.priors_ == " + str(clf.priors_))
# re-load validation set
tr_X, tr_Y = load_hdf5_train_set(validfilename + '.hdf5',
fraction_manip=fraction_manip)
describe("VALIDATION: BEFORE MEAN SUBTR: tr_X", tr_X)
#tr_X -= tr_mean_vec
#tr_X /= tr_scale_vec
#describe("VALIDATION: AFTER MEAN SUBTR: tr_X", tr_X)
