arcan3 commited on
Commit
8894eb2
·
1 Parent(s): fa2a5c2

som names changed

Browse files
Files changed (2) hide show
  1. app.py +2 -2
  2. funcs/som.py +4 -4
app.py CHANGED
@@ -309,8 +309,8 @@ with gr.Blocks(title='Cabasus') as cabasus_sensor:
309
  plot_box_leg = gr.Plot(label="Filtered Signal Plot")
310
  slice_slider = gr.Slider(minimum=1, maximum=300, label='Slice select', step=1)
311
 
312
- som_create = gr.Button('generate som')
313
- som_figures = gr.Plot(label="som activations")
314
 
315
  with gr.Row():
316
  slice_size_slider = gr.Slider(minimum=16, maximum=512, step=1, value=64, label="Slice Size", visible=False)
 
309
  plot_box_leg = gr.Plot(label="Filtered Signal Plot")
310
  slice_slider = gr.Slider(minimum=1, maximum=300, label='Slice select', step=1)
311
 
312
+ som_create = gr.Button('generate activation maps')
313
+ som_figures = gr.Plot(label="activations maps")
314
 
315
  with gr.Row():
316
  slice_size_slider = gr.Slider(minimum=16, maximum=512, step=1, value=64, label="Slice Size", visible=False)
funcs/som.py CHANGED
@@ -145,7 +145,7 @@ class ClusterSOM:
145
  fig, axes = self.rearrange_subplots(len(self.som_models))
146
 
147
  # fig, axes = plt.subplots(1, len(self.som_models), figsize=(20, 5), sharex=True, sharey=True)
148
- fig.suptitle(f"Activation map for SOM {prediction[0]}, node {prediction[1]}", fontsize=16)
149
 
150
  for idx, (som_key, som) in enumerate(self.som_models.items()):
151
  ax = axes[idx]
@@ -229,7 +229,7 @@ class ClusterSOM:
229
  prediction = self.predict([data[int(slice_select)-2]])[0]
230
 
231
  fig, axes = plt.subplots(1, len(self.som_models), figsize=(20, 5), sharex=True, sharey=True)
232
- fig.suptitle(f"Activation map for SOM {prediction[0]}, node {prediction[1]}", fontsize=16)
233
 
234
  for idx, (som_key, som) in enumerate(self.som_models.items()):
235
  ax = axes[idx]
@@ -244,13 +244,13 @@ class ClusterSOM:
244
  if som_key == prediction[0]: # Active SOM
245
  im_active = ax.imshow(activation_map, cmap='viridis', origin='lower', interpolation='none')
246
  ax.plot(winner[1], winner[0], 'r+') # Mark the BMU with a red plus sign
247
- ax.set_title(f"SOM {som_key}", color='blue', fontweight='bold')
248
  if hasattr(self, 'label_centroids'):
249
  label_idx = self.label_encodings.inverse_transform([som_key - 1])[0]
250
  ax.set_xlabel(f"Label: {label_idx}", fontsize=12)
251
  else: # Inactive SOM
252
  im_inactive = ax.imshow(activation_map, cmap='gray', origin='lower', interpolation='none')
253
- ax.set_title(f"SOM {som_key}")
254
 
255
  ax.set_xticks(range(activation_map.shape[1]))
256
  ax.set_yticks(range(activation_map.shape[0]))
 
145
  fig, axes = self.rearrange_subplots(len(self.som_models))
146
 
147
  # fig, axes = plt.subplots(1, len(self.som_models), figsize=(20, 5), sharex=True, sharey=True)
148
+ fig.suptitle(f"Activation map for A {prediction[0]}, node {prediction[1]}", fontsize=16)
149
 
150
  for idx, (som_key, som) in enumerate(self.som_models.items()):
151
  ax = axes[idx]
 
229
  prediction = self.predict([data[int(slice_select)-2]])[0]
230
 
231
  fig, axes = plt.subplots(1, len(self.som_models), figsize=(20, 5), sharex=True, sharey=True)
232
+ fig.suptitle(f"Activation map for A {prediction[0]}, node {prediction[1]}", fontsize=16)
233
 
234
  for idx, (som_key, som) in enumerate(self.som_models.items()):
235
  ax = axes[idx]
 
244
  if som_key == prediction[0]: # Active SOM
245
  im_active = ax.imshow(activation_map, cmap='viridis', origin='lower', interpolation='none')
246
  ax.plot(winner[1], winner[0], 'r+') # Mark the BMU with a red plus sign
247
+ ax.set_title(f"A {som_key}", color='blue', fontweight='bold')
248
  if hasattr(self, 'label_centroids'):
249
  label_idx = self.label_encodings.inverse_transform([som_key - 1])[0]
250
  ax.set_xlabel(f"Label: {label_idx}", fontsize=12)
251
  else: # Inactive SOM
252
  im_inactive = ax.imshow(activation_map, cmap='gray', origin='lower', interpolation='none')
253
+ ax.set_title(f"A {som_key}")
254
 
255
  ax.set_xticks(range(activation_map.shape[1]))
256
  ax.set_yticks(range(activation_map.shape[0]))