def setUp(self):
self.rand = np.random.RandomState(1968486074)
self.n_factors = 9
self.f = funcsfa.SFA()
self.n_samples = 221
self.n_features = 37
self.X_a = self.rand.normal(0, 1, (self.n_samples, 30))
self.X_b = self.rand.normal(0, 1, (self.n_samples, 7))
self.data_one = funcsfa.DataMatrix(self.X_a)
self.data_two = funcsfa.StackedDataMatrix(
[funcsfa.DataMatrix(self.X_a),
funcsfa.DataMatrix(self.X_b)])
def setUp(self):
self.n_samples = 400
self.n_features = [2000, 180]
self.n_factors = 10
self.rand = np.random.RandomState(856273263)
self.B_a = self.rand.normal(0, 0.9,
(self.n_features[0], self.n_factors))
self.B_b = self.rand.normal(0, 1.1,
(self.n_features[1], self.n_factors))
Zvar = np.linspace(10, 1, self.n_factors)
Zvar = Zvar / np.mean(Zvar)
self.Z = self.rand.normal(0, np.sqrt(Zvar),
(self.n_samples, self.n_factors))
self.data = funcsfa.StackedDataMatrix([
funcsfa.DataMatrix(np.dot(self.Z, self.B_a.T)),
funcsfa.DataMatrix(np.dot(self.Z, self.B_b.T))
])
self.f = funcsfa.SFA()
def setUp(self):
self.rand = np.random.RandomState(1273641113)
self.n_factors = 9
self.f = funcsfa.SFA()
self.n_samples = 514
samples = [f'n{i}' for i in range(self.n_samples)]
self.n_features = 107 + 49
f1 = [f'f1_{i}' for i in range(107)]
f2 = [f'f2_{i}' for i in range(49)]
self.X_a = self.rand.normal(0, 1, (self.n_samples, 107))
self.Xw_a = self.rand.normal(0, 1, (self.n_samples, 107))
self.X_b = self.rand.normal(0, 1, (self.n_samples, 49))
self.Xw_b = self.rand.normal(0, 1, (self.n_samples, 49))
self.data = funcsfa.StackedDataMatrix(
[
funcsfa.DataMatrix(self.X_a, samples, f1, self.Xw_a),
funcsfa.DataMatrix(self.X_b, samples, f2, self.Xw_b)
],
['a', 'b'],
)
self.data.to_netcdf('test_roundtrip.nc')
self.data2 = funcsfa.StackedDataMatrix.from_netcdf('test_roundtrip.nc')