Example #1
0
 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)])
Example #2
0
 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()
Example #3
0
 def setUp(self):
     self.n_samples = 400
     self.n_features = 2000
     self.n_factors = 10
     self.rand = np.random.RandomState(1968486074)
     self.B = self.rand.normal(0, 1, (self.n_features, 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.X = np.dot(self.Z, self.B.T)
     self.data = funcsfa.DataMatrix(self.X)
     self.f = funcsfa.SFA()
Example #4
0
 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')