add distance between clusters

params.py

@@ -28,4 +28,5 @@ cluster_nb_max = 5 #maximum nb of clusters in 1 sec.
 #plots
 plot_simi = True
 plot_struct = True
+plot_dist = True
 timestamps = True

spectral_clustering_audio.py

@@ -461,33 +461,48 @@ def extract_time_boundaries(cluster_ids, onsets, nb_frames, sr):
 	return bound_times, bound_labels
 
 
-def compute_distance(M, type_dist, plot_opt):
-	frame0 = np.mean(M, axis=1)
-	distance = []
-	for i in range(M.shape[1]):
-	#distance_eucl0.append( np.sum( (Msync_normed[:,i] - frame0)**2) )
-	#if i > 0: distance_cosine.append( scipy.spatial.distance.cosine(Msync_normed[:,i], Msync_normed[:,i-1]) )
-		if type_dist == 'cos':
-			distance.append( scipy.spatial.distance.cosine(M[:,i], frame0))
-		elif type_dist == 'eucl':
-			distance.append( np.sum( (M[:,i] - frame0)**2) )
-
-	if plot_opt:
-		plt.plot(distance)
-		plt.tight_layout()
-	
-	return distance
 
 ##################################
-def extract_cosine_distance_clusters(center_clusters, distance_ref):
+def extract_cosine_distance_clusters(center_clusters, distance_ref, type_dist = 'cos'):
 	distance = []
 
 	for center in center_clusters:
-		distance.append( scipy.spatial.distance.cosine( center, distance_ref) )
+		if type_dist == 'cos':
+			distance.append( scipy.spatial.distance.cosine( center, distance_ref) )
+		elif type_dist == 'eucl':
+			distance.append(np.sqrt( np.sum( (center - distance_ref)**2) ))
 
 	return distance
 
 
+def extract_distance_between_clusters(center_clusters, type_dist = 'cos'):
+	distance = np.zeros((center_clusters.shape))
+	
+	for i, center_i in enumerate(center_clusters):
+		for j, center_j in enumerate(center_clusters):
+			if type_dist == 'cos':
+				distance[i,j] = scipy.spatial.distance.cosine( center_i, center_j)
+			elif type_dist == 'eucl':
+				distance[i,j] = np.sqrt( np.sum( (center_i - center_j)**2) )
+	
+	x = range(i+1)
+	y = range(j+1)
+	xloc = [c + 0.5 for c in x]
+	cx = [str(c) for c in x]
+	print(cx)
+	fig_d, ax_d = plt.subplots(figsize=(5, 4))
+	p_d = ax_d.pcolor(distance, cmap = 'inferno_r')
+	cb = fig_d.colorbar(p_d)
+	ax_d.xaxis.set_ticks(xloc)
+	ax_d.xaxis.set_ticklabels(cx)
+	ax_d.yaxis.set_ticks(xloc)
+	ax_d.yaxis.set_ticklabels(cx)
+	ax_d.set_title('Distance between clusters')
+	ax_d.set_xlabel('clusters numbers')
+	plt.tight_layout()
+	
+	return distance
+	
 
 
 def main():
@@ -573,6 +588,10 @@ def main():
 			distance_cosine_cluster = extract_cosine_distance_clusters( KM.cluster_centers_, np.mean(X[:10*NFFT,:], axis=0))
 		else:
 			distance_cosine_cluster = None
+			
+		if params.plot_dist:
+			distance_between_clusters = extract_distance_between_clusters( KM.cluster_centers_ )
+				
 
 		# and plot the resulting structure representation
 		if params.plot_struct: plot_structure(Rf, X, seg_ids, k, onset_times)