def Get_Best_Centroids(k, iterations):
print "Feature Analysis/Clustering Mode - feature selection from multiple k's"
feature_holder = featurevector.feature_holder(
filename=FEATURE_VECTOR_FILENAME)
mfccs = feature_holder.get_feature('mfcc')
j_measures = np.zeros(iterations)
max = 0
bestCentroids = 0
bestDistortion = 0
for i in range(iterations):
centroids, distortion = kmeans.scipy_kmeans(mfccs, k)
classes, dist = kmeans.scipy_vq(mfccs, centroids)
j_measures[i] = calcJ(mfccs, classes, centroids, k)
if j_measures[i] > max:
max = j_measures[i]
bestCentroids = centroids
bestDistortion = distortion
return bestCentroids, bestDistortion
def clustering(args):
''' run clustering on a single k'''
print "Feature Analysis/Clustering Mode: single k"
feature_holder = featurevector.feature_holder(
filename=FEATURE_VECTOR_FILENAME)
sones_holder = featurevector.feature_holder(filename=SONE_VECTOR_FILENAME)
k = args.k
print feature_holder
mfccs = feature_holder.get_feature('mfcc')
print sones_holder
sones = sones_holder.get_feature('sones')
centroids, distortion = Get_Best_Centroids(k, 1)
print "Distortion for this run: %0.3f" % (distortion)
classes, dist = kmeans.scipy_vq(mfccs, centroids)
# Get the inter class dist matrix
inter_class_dist_matrix = mir_utils.GetSquareDistanceMatrix(centroids)
eventBeginnings = feature_holder.get_event_start_indecies()
# write audio if given -w
if args.plot_segments:
PlotWaveformWClasses(k, feature_holder, classes)
if args.write_audio_results:
WriteAudioFromClasses(k, feature_holder, classes)
plot.plot(mfccs, sones, eventBeginnings, centroids,
inter_class_dist_matrix, classes)
def feature_selection(args):
''' run clustering on a range of k's'''
print "Feature Analysis/Clustering Mode - feature selection from multiple k's"
feature_holder = featurevector.feature_holder(
filename=FEATURE_VECTOR_FILENAME)
kMin = args.k_min
kMax = args.k_max
kHop = args.k_hop
mfccs = feature_holder.get_feature('mfcc')
nmfcc = len(mfccs)
print "N MFCCS:", nmfcc
results = []
for k in range(kMin, kMax, kHop):
print "Running k-Means with k=%d" % (k)
if k >= nmfcc:
print "WARNING! k is greater than the number of samples!"
centroids, distortion = kmeans.scipy_kmeans(mfccs, k)
classes, dist = kmeans.scipy_vq(mfccs, centroids)
J0 = calcJ(mfccs, classes, centroids, k)
results.append((k, distortion, dist, J0))
plot.plot_feature_selection(kMin, kMax, kHop, results)
def Get_Best_Centroids(k, iterations):
feature_holder = featurevector.feature_holder(
filename=FEATURE_VECTOR_FILENAME)
mfccs = feature_holder.get_feature('mfcc')
j_measures = np.zeros(iterations)
max = 0
bestCentroids = 0
bestDistortion = 0
for i in range(iterations):
centroids, distortion = kmeans.scipy_kmeans(mfccs, k)
classes, dist = kmeans.scipy_vq(mfccs, centroids)
j_measures[i] = calcJ(mfccs, classes, centroids, k)
if j_measures[i] > max:
max = j_measures[i]
bestCentroids = centroids
bestDistortion = distortion
'''
plt.close()
fig, (ax1) = plt.subplots(1)
ax1.plot(j_measures)
ax1.set_title("J measures over multiple iterations of k")
ax1.set_xlabel("iterations")
ax1.set_ylabel("J-measure values")
plt.show()
'''
return bestCentroids, bestDistortion
def Get_Best_Centroids(k, iterations):
feature_holder = featurevector.feature_holder(filename=FEATURE_VECTOR_FILENAME)
mfccs = feature_holder.get_feature('mfcc')
j_measures = np.zeros(iterations)
max = 0;
bestCentroids = 0
bestDistortion = 0
for i in range(iterations):
centroids, distortion = kmeans.scipy_kmeans(mfccs, k)
classes, dist = kmeans.scipy_vq(mfccs, centroids)
j_measures[i] = calcJ(mfccs, classes, centroids, k)
if j_measures[i] > max:
max = j_measures[i]
bestCentroids = centroids
bestDistortion = distortion
'''
plt.close()
fig, (ax1) = plt.subplots(1)
ax1.plot(j_measures)
ax1.set_title("J measures over multiple iterations of k")
ax1.set_xlabel("iterations")
ax1.set_ylabel("J-measure values")
plt.show()
'''
return bestCentroids, bestDistortion
def feature_selection(args):
''' run clustering on a range of k's'''
print "Feature Analysis/Clustering Mode - feature selection from multiple k's"
feature_holder = featurevector.feature_holder(filename=FEATURE_VECTOR_FILENAME)
kMin = args.k_min
kMax = args.k_max
kHop = args.k_hop
mfccs = feature_holder.get_feature('mfcc')
nmfcc = len(mfccs)
print "N MFCCS:", nmfcc
results = []
for k in range(kMin, kMax, kHop):
print "Running k-Means with k=%d" % (k)
if k >= nmfcc:
print "WARNING! k is greater than the number of samples!"
centroids, distortion = Get_Best_Centroids(k,20)
classes, dist = kmeans.scipy_vq(mfccs, centroids)
J0 = calcJ(mfccs, classes, centroids, k)
results.append( (k, distortion, dist, J0) )
plot.plot_feature_selection(kMin, kMax, kHop, results)
def Get_Best_Centroids(k, iterations):
print "Feature Analysis/Clustering Mode - feature selection from multiple k's"
feature_holder = featurevector.feature_holder(filename=FEATURE_VECTOR_FILENAME)
mfccs = feature_holder.get_feature('mfcc')
j_measures = np.zeros(iterations)
max = 0;
bestCentroids = 0
bestDistortion = 0
for i in range(iterations):
centroids, distortion = kmeans.scipy_kmeans(mfccs, k)
classes, dist = kmeans.scipy_vq(mfccs, centroids)
j_measures[i] = calcJ(mfccs, classes, centroids, k)
if j_measures[i] > max:
max = j_measures[i]
bestCentroids = centroids
bestDistortion = distortion
return bestCentroids, bestDistortion
def clustering(args):
''' run clustering on a single k'''
print "Feature Analysis/Clustering Mode: single k"
feature_holder = featurevector.feature_holder(filename=FEATURE_VECTOR_FILENAME)
sones_holder = featurevector.feature_holder(filename=SONE_VECTOR_FILENAME)
k = args.k
print feature_holder
mfccs = feature_holder.get_feature('mfcc')
print sones_holder
sones = sones_holder.get_feature('sones')
centroids, distortion = Get_Best_Centroids(k, 1)
print "Distortion for this run: %0.3f" % (distortion)
classes,dist = kmeans.scipy_vq(mfccs, centroids)
# Get the inter class dist matrix
inter_class_dist_matrix = mir_utils.GetSquareDistanceMatrix(centroids)
eventBeginnings = feature_holder.get_event_start_indecies()
# write audio if given -w
if args.plot_segments:
PlotWaveformWClasses(k, feature_holder,classes)
if args.write_audio_results:
WriteAudioFromClasses(k, feature_holder, classes)
plot.plot(mfccs, sones, eventBeginnings, centroids, inter_class_dist_matrix, classes)
