def read_data(self, path):
conn = sql.connect(path)
self.V = pd.read_sql('select * from data;', conn).values
self.S = data.euclidean_to_simplex(self.V)
self.Yl = data.angular_to_euclidean(data.euclidean_to_angular(self.V))
self.A = data.euclidean_to_angular(self.Yl)
self.Vi = self.cast_to_cube(self.A)
self.pVi = self.probit(self.Vi)
conn.close()
return
def read_data(self, path):
self.Z = pd.read_csv(path).values
self.R = self.Z.max(axis=1)
self.V = (self.Z.T / self.R).T
self.S = data.euclidean_to_simplex(self.V)
self.A = data.euclidean_to_angular(self.V)
self.Yl = data.angular_to_euclidean(self.A)
self.Vi = self.cast_to_cube(self.A)
self.pVi = self.probit(self.Vi)
self.I = (np.arange(self.Z.shape[0]), )
return
def generate_posterior_predictive(self, n_per_sample = 10):
""" Generates posterior prediction, projects to hypercube, then
casts to angular space """
hyp = self.generate_posterior_predictive_hypercube(n_per_sample)
return dm.euclidean_to_angular(hyp)
def generate_posterior_predictive_angular(self, n_per_sample = 1):
hypercube = self.generate_posterior_predictive_hypercube(n_per_sample)
return euclidean_to_angular(hypercube)
