def __call__(self):
# z-scoring
if self.treedata is None:
raise SortingError("No examples for similarity measure available!")
zs = ZScore(self.data, replace_inf=True)
data_zs = zs.normalize(self.data)
# z-scored mean value of pca procjected treedata
mu = zs.normalize(self.treedata)
data_zs, mu = filter_nans(data_zs, mu)
mu = mu.mean(axis=0)
distsq = [np.power((x - mu), 2).sum() for x in data_zs]
return np.sqrt(distsq)
def __call__(self):
# z-scoring
if self.treedata is None:
raise SortingError("No examples for similarity measure available!")
zs = ZScore(self.data, replace_inf=True)
data_zs = zs.normalize(self.data)
# z-scored mean value of pca procjected treedata
mu = zs.normalize(self.treedata)
data_zs, mu = filter_nans(data_zs, mu)
mu = mu.mean(axis=0)
distsq = [np.power((x - mu), 2).sum() for x in data_zs]
return np.sqrt(distsq)
def __call__(self):
# z-scoring
if self.treedata is None:
raise SortingError("No examples for similarity measure available!")
if self.treedata.shape[1] < 2:
raise SortingError(("CosineSimilarity needs at least 2 "
"features for sorting"))
zs = ZScore(self.data, replace_inf=True)
data_zs = zs.normalize(self.data)
# z-scored mean value of pca procjected treedata
mu = zs.normalize(self.treedata)
data_zs, mu = filter_nans(data_zs, mu)
mu = mu.mean(axis=0)
denom = np.sqrt((mu**2).sum()) * np.sqrt((data_zs**2).sum(axis=1))
s_cos = np.sum(mu * data_zs, axis=1) / denom
return -1.0 * s_cos
def __call__(self):
# z-scoring
if self.treedata is None:
raise SortingError("No examples for similarity measure available!")
if self.treedata.shape[1] < 2:
raise SortingError(("CosineSimilarity needs at least 2 "
"features for sorting"))
zs = ZScore(self.data, replace_inf=True)
data_zs = zs.normalize(self.data)
# z-scored mean value of pca procjected treedata
mu = zs.normalize(self.treedata)
data_zs, mu = filter_nans(data_zs, mu)
mu = mu.mean(axis=0)
denom = np.sqrt((mu**2).sum())*np.sqrt((data_zs**2).sum(axis=1))
s_cos = np.sum(mu*data_zs, axis=1)/denom
return -1.0*s_cos
