Merge pull request #57 from JesseLivezey/analysis_update

fixed analysis funcs from dca update

Author: JesseLivezey

dca/analysis.py

@@ -3,7 +3,6 @@
 from sklearn.decomposition import PCA
 from scipy.stats import special_ortho_group as sog
 
-from .cov_util import calc_cross_cov_mats_from_data
 from .data_util import CrossValidate, form_lag_matrix
 from .methods_comparison import SlowFeatureAnalysis as SFA
 from .dca import DynamicalComponentsAnalysis
@@ -114,15 +113,14 @@ def run_analysis(X, Y, T_pi_vals, dim_vals, offset_vals, num_cv_folds, decoding_
         Y_test_ctd = Y_test - Y_mean
 
         # compute cross-cov mats for DCA
-        T_max = 2 * np.max(T_pi_vals)
-        cross_cov_mats = calc_cross_cov_mats_from_data(X_train_ctd, T_max)
+        dca_model = DynamicalComponentsAnalysis(T=np.max(T_pi_vals))
+        dca_model.estimate_data_statistics(X_train_ctd)
 
         # do PCA/SFA
         pca_model = PCA(svd_solver='full').fit(np.concatenate(X_train_ctd))
         sfa_model = SFA(1).fit(X_train_ctd)
 
         # make DCA object
-        opt = DynamicalComponentsAnalysis(d=1, T=1)
 
         # loop over dimensionalities
         for dim_idx in range(len(dim_vals)):
@@ -147,9 +145,8 @@ def run_analysis(X, Y, T_pi_vals, dim_vals, offset_vals, num_cv_folds, decoding_
             # loop over T_pi vals
             for T_pi_idx in range(len(T_pi_vals)):
                 T_pi = T_pi_vals[T_pi_idx]
-                opt.cross_covs = cross_cov_mats[:2 * T_pi]
-                opt.fit_projection(d=dim, n_init=n_init)
-                V_dca = opt.coef_
+                dca_model.fit_projection(d=dim, T=T_pi, n_init=n_init)
+                V_dca = dca_model.coef_
 
                 # compute DCA R2 over offsets
                 X_train_dca = [np.dot(Xi, V_dca) for Xi in X_train_ctd]
@@ -221,21 +218,19 @@ def run_dim_analysis_dca(X, Y, T_pi, dim_vals, offset, num_cv_folds, decoding_wi
         Y_train_ctd = [Yi - Y_mean for Yi in Y_train]
         Y_test_ctd = Y_test - Y_mean
 
+        # make DCA object
         # compute cross-cov mats for DCA
-        cross_cov_mats = calc_cross_cov_mats_from_data(X_train_ctd, 2 * T_pi)
-
-        # make DCA object with arb parameters
-        opt = DynamicalComponentsAnalysis(d=1, T=1)
+        dca_model = DynamicalComponentsAnalysis(T=T_pi)
+        dca_model.estimate_data_statistics(X_train_ctd)
 
         # loop over dimensionalities
         for dim_idx in range(len(dim_vals)):
             dim = dim_vals[dim_idx]
             if verbose:
                 print("dim", dim_idx + 1, "of", len(dim_vals))
 
-            opt.cross_covs = cross_cov_mats
-            opt.fit_projection(d=dim, n_init=n_init)
-            V_dca = opt.coef_
+            dca_model.fit_projection(d=dim, n_init=n_init)
+            V_dca = dca_model.coef_
 
             # compute DCA R2 over offsets
             X_train_dca = [np.dot(Xi, V_dca) for Xi in X_train_ctd]

dca/dca.py

@@ -292,17 +292,17 @@ def make_cepts2(X, T_pi):
 
     # Compute the power spectral density
     window = torch.Tensor(hann(Y.shape[-1])[np.newaxis, np.newaxis]).type(Y.dtype)
-    Yf = torch.rfft(Y * window, 1, onesided=True)
-    spect = Yf[:, :, :, 0]**2 + Yf[:, :, :, 1]**2
+    Yf = torch.fft.rfft(Y * window, dim=1)
+    spect = abs(Yf)**2
     spect = spect.mean(dim=1)
     spect = torch.cat([torch.flip(spect[:, 1:], dims=(1,)), spect], dim=1)
 
     # Log of the DFT of the autocorrelation
     logspect = torch.log(spect) - np.log(float(Y.shape[-1]))
 
     # Compute squared cepstral coefs (b_k^2)
-    cepts = torch.rfft(logspect, 1, onesided=True) / float(Y.shape[-1])
-    cepts = torch.sqrt(cepts[:, :, 0]**2 + cepts[:, :, 1]**2)
+    cepts = torch.fft.rfft(logspect, dim=1) / float(Y.shape[-1])
+    cepts = abs(cepts)
     return cepts**2