CCN comparison in RNN example

MARBLE/preprocessing.py

@@ -117,7 +117,7 @@ def _compute_geometric_objects(
     n_geodesic_nb=2.0,
     var_explained=0.9,
     return_spectrum=True,
-    local_gauges=True,
+    local_gauges=False,
     compute_laplacian=False,
     compute_connection_laplacian=False,
     dim_man=None,

examples/RNN/RNN_scripts/helpers.py

@@ -35,14 +35,13 @@ def load_network(f):
     return z, net
 
 
-def sample_network(net, f):
+def sample_network(net, f, seed=0):
 
     if os.path.exists(f):
         print('Network found with same name. Loading...')
         return torch.load(open(f, "rb"))
 
     n_pops = 2
-    seed = 0
     z, _ = clustering.gmm_fit(net, n_pops, algo="bayes", random_state=seed)
     net_sampled = clustering.to_support_net(net, z)
 
@@ -68,7 +67,7 @@ def sample_network(net, f):
 
 
 def generate_trajectories(
-    net, input=None, epochs=None, n_traj=None, fname="./outputs/RNN_trajectories.pkl"
+    net, input=None, epochs=None, n_traj=None, fname="./data/RNN_trajectories.pkl"
 ):
 
     if fname is not None:
@@ -455,7 +454,7 @@ def plot_experiment(net, input, traj, epochs, rect=(-8, 8, -6, 6), traj_to_show=
     fig.subplots_adjust(hspace=0.1, wspace=0.1)
 
 
-def aggregate_data(traj, epochs, transient=10, only_stim=False):
+def aggregate_data(traj, epochs, transient=10, only_stim=False, pca=True):
 
     n_conds = len(traj)
     n_epochs = len(epochs) - 1
@@ -468,9 +467,10 @@ def aggregate_data(traj, epochs, transient=10, only_stim=False):
             for j in range(n_traj):  # trajectories
                 pos.append(traj[i][j][k][transient:])
 
-    pca = PCA(n_components=3)
-    pca.fit(np.vstack(pos))
-    print("Explained variance: ", pca.explained_variance_ratio_)
+    if pca:
+        pca = PCA(n_components=3)
+        pca.fit(np.vstack(pos))
+        print("Explained variance: ", pca.explained_variance_ratio_)
 
     # aggregate data under baseline condition (no input)
     pos, vel = [], []
@@ -480,7 +480,8 @@ def aggregate_data(traj, epochs, transient=10, only_stim=False):
             for k in [0, 2, 4]:
                 for j in range(n_traj):  # trajectories
                     pos_proj = traj[i][j][k][transient:]
-                    pos_proj = pca.transform(pos_proj)
+                    if pca:
+                        pos_proj = pca.transform(pos_proj)
                     pos_.append(pos_proj[:-1])  # stack trajectories
                     vel_.append(np.diff(pos_proj, axis=0))  # compute differences
 
@@ -494,7 +495,8 @@ def aggregate_data(traj, epochs, transient=10, only_stim=False):
         for k in [1, 3]:
             for j in range(n_traj):  # trajectories
                 pos_proj = traj[i][j][k][transient:]
-                pos_proj = pca.transform(pos_proj)
+                if pca:
+                    pos_proj = pca.transform(pos_proj)
                 pos_.append(pos_proj[:-1])
                 vel_.append(np.diff(pos_proj, axis=0))